Technical Programmes CCDC 2021

Saturday, 22 May, 2021

SatA02 Room02 Modeling, Control and Simulations of Biological Systems (I) 13:30-16:50 Chair: Chong Jiang Nanjing Sport Inst.CO-Chair: Yingchun Zhong Guangdong Univ. of Tech.

13:30-13:50 SatA02-1 64 Behavioral Intention of College Students’ Participating in Sports During Public Health Emergency: An Extended Model of Goal-directed Behavior Chong Jiang Nanjing Sport Inst.Dexin Zou Nanjing Sport Inst.This study examined the influencing factors on college students' participation intention in sport during the COVID-19 public health emergency applying an extended model of goal-directed behavior in China. Two new variables, cognition of public health emergency and frequency of past behavior, were added to improve the model’s predictive ability. Using 558 college students in China as research samples, the results have shown that attitude, perceived behavior control, positive anticipated emotion and negative anticipated emotion had a positive effect toward desire for sports participation, which, in turn, influenced their intentionsports participation. At the same time, cognition of public health emergency and frequency of past behavior had positive effects toward desire and intention-sports participation. In addition, the results also show that the complex correlation coefficient of the desire to participate in physical exercise is 0.44 and complex correlation coefficient of participation intention in physical exercise is 0.61. The findings of this study will shed light on a better understanding of the decision-making processes of Chinese college students when public health emergency is incorporated.

13:50-14:10 SatA02-2 173 Algorithms to Automatically Measure Number of Splitting and Merging of Fascicular Groups in Peripheral Nerve from MicroCT images Xiaohong Yi Guangdong Univ. of Tech.Yingchun Zhong Guangdong Univ. of Tech.Jian Qi The First Affiliated Hospital of Sun Yat-sen Univ.Shuang Zhu Zhujiang Hospital of Southern Medical Univ.It is very significant to mining the rules of splitting and merging of fascicular groups in the peripheral nerve during its extension in space through measuring the number of splitting and merging of them. Presently, the number of splitting and merging is counted manually, which is deficient and easy to make mistakes. So, this paper proposed two algorithms to count the number of splitting and merging from MicroCT images automatically. The Algorithm No.1 counts the number of splitting and merging through calculating the difference between two sequential images. The Algorithm No.2 counts the number of splitting and merging through tracking the centroid of each nerve bundle. Experiments show that, the Algorithm No.2 can not only correctly count the times while the nerve bundles split and merge on the same image, but also accurately count the times when a nerve bundle splits into more than two, and three or more fascicular groups merge into a same nerve bundle. The precision of counting of Algorithm No.2 reaches 100%. This investigation is one of the important basic for exploring the rules of splitting and merging.

14:10-14:30 SatA02-3 231 Optic-fiber Optical Force Acting on Micro-sized Particle: Ray-Tracing Approximation versus Electromagnetic Wave Approaches Hai-peng Li Northeastern Univ. at QinhuangdaoSheng Hu Northeastern Univ. at QinhuangdaoYong Zhao Northeastern Univ. at QinhuangdaoOptic-fiber optical tweezers (OFOTs) have become the most convenient approach and low-cost implementation of micro-particle manipulation. Over the past decade, this technique has seen considerable development in experimental and theoretical fields. Especially for theoretical research, simulated results were in a good agreement with experimental observation in particle trajectory and elastic deformation. This could provide a valuable insight relevant to motion behavior on micro-particle exerted by optical force. Nevertheless, optical force can be analyzed by either geometric or electromagnetic model at present. In order to compare with the two approaches, both ray-tracing and electromagnetic models are built and analyzed in this paper. Analyzing particle size and its refractive index, there results indicate that the curves of trapping efficiencies in transverse and longitudinal direction are of high similarity by using the two approaches. And yet theoretical calculation of such trapping efficiencies still exists error. When micro-particle is gradually closed to the position of maximum light intensity, this leads to the greater error between geometric and electromagnetic model.

14:30-14:50 SatA02-4 705 Prediction and Analysis of COVID-19 Epidemic Situation via Modified SEIR Model with Asymptomatic Infection

Yi-Xuan Guo China Jiliang Univ.Meng Yuan China Jiliang Univ.Yi-Kang Wang China Jiliang Univ.Xue-Yi Liu China Jiliang Univ.Bao-Lin Zhang Qingdao Univ. of Science & Tech.This paper deals with the prediction and analysis of COVID-19 epidemic situation based on a modified SEIR model with asymptomatic infection. First, by considering the self-isolation and asymptomatic infection, a modified SEIR model is proposed to predict and evaluate the epidemic situation of COVID-19 in Hubei Province, China. Then, based on the daily data reported by the Health Commission of Hubei Province, the modified SEIR model is solved numerically, and the parameters of the modified model are inverted by the least square method. Third, based on the modified model, the epidemic situation of COVID-19 in Hubei Province is predicted and verified. The simulation results show that the modified SEIR model is significant and reliable to describe the spread property of the COVID-19, thereby providing a potential theoretical support for the decision-making of epidemic prevention and control in the future.

14:50-15:10 SatA02-5 893 Stability Analysis and Turing Instability of A SIR Model with Reaction-Diffusion Mingyue Zhang Nanjing Univ. of Posts and TelecommunicationsMin Xiao Nanjing Univ. of Posts and TelecommunicationsRong Qian Nanjing Univ. of Posts and TelecommunicationsRong Fang Nanjing Univ. of Posts and TelecommunicationsJian Li Nanjing Univ. of Posts and TelecommunicationsThe spatio-temporal dynamic of diffusion-driven epidemic model is an original, cutting-edge and practical research. In order to improve the accuracy of epidemic modelling, a new SIR epidemic model with diffusion is developed in this paper. We determine the position of all equilibria and discuss the existence of the positive equilibrium in the reaction-diffusion epidemic model. The theory of partial differential equations is adopted to establish the occurrence conditions of Turing instability. Numerical simulation examples show the accuracy of theoretical derivation.

15:10-15:30 SatA02-6 1003 Turing Stability Analysis in a Reaction-Diffusion Predator-Prey System with Fear Effect Gong Chen Nanjing Univ. of Posts and TelecommunicationsMin Xiao Nanjing Univ. of Posts and TelecommunicationsShi Chen Nanjing Univ. of Posts and TelecommunicationsShuai Zhou Nanjing Univ. of Posts and TelecommunicationsYunxiang Lu Nanjing Univ. of Posts and TelecommunicationsRuitao Xing Nanjing Univ. of Posts and TelecommunicationsIn this paper, a Leslie-Gower predator-prey system with diffusion under homogeneous Neumann boundary conditions is investigated. By analyzing the corresponding characteristic equation, the necessary and sufficient conditions for locally asymptotically stable and Turing instability of the positive equilibrium are given, respectively. Based on the stability theory of partial differential equations, this paper explores the influence of fear factor on the density of prey and predator population, and finds that the fear coefficient is inversely proportional to the population size. Numerical simulations are carried out to illustrate the main results.

15:30-15:50 SatA02-7 1013 Dynamic System Modeling Based on Recurrent Neural Network Wenjie Cao Wuhan Textile Univ.Cheng Zhang Wuhan Textile Univ.Zhenzhen Xiong Wuhan Textile Univ.Ting Wang Wuhan Textile Univ.Junchao Chen Wuhan Textile Univ.Bengong Zhang Wuhan Textile Univ.Recurrent neural networks are widely used in time series prediction and classification. However, it has problems such as insufficient memory ability and difficulty in gradient back propagation. To overcome this drawback, this article proposed a new recurrent neural network model called RNN-SKIP. It can strengthen the ability to remember information from past moments and help the gradient to propagate backwards more smoothly. By testing arrhythmia data and analyzing the model effects of different parameters through experiments, we found that the new RNN-SKIP model can optimize the structure and improve the accuracy of the recurrent neural network, and effectively solve the exploding gradient and vanishing gradient problem.

15:50-16:10 SatA02-8 1074 Prediction of Tomato Yield in Chinese-Style Solar Greenhouses Based on Wavelet Neural Networks and Genetic Algorithms Ruimin Xiao Shenyang Agricultural Univ.Yonggang Wang Shenyang Agricultural Univ.Yuhang Liu Shenyang Agricultural Univ.Yizhi Yin Shenyang Agricultural Univ.Tan Liu Shenyang Agricultural Univ.Nannan Zhang Shenyang Agricultural Univ.Yield prediction for tomatoes in greenhouses is an important basis for making production plans, and yield prediction accuracy directly affects economic benefits. To improve the prediction accuracy in Chinese-style

Technical Programmes CCDC 2021 solar greenhouses (CSGs), a wavelet neural network (WNN) model optimized by a genetic algorithm (GA-WNN) is applied. Eight variables are selected as input parameters, such as the CO2 concentration and ambient humidity. The tomato yield is the prediction output. The GA is used to optimize the initial weights, thresholds, and translation factors of the WNN. The experiment results show that the mean relative errors (MREs) of the GA-WNN model, WNN model, and backpropagation BP neural network model are 0.0067, 0.0104, and 0.0242, respectively. The results root mean square errors (RMSEs) are 1.725, 2.520, and 5.548, respectively. The EC values are 0.9960, 0.9935, and 0.9868, respectively. Therefore, the GA-WNN model has a higher prediction precision and a better fitting ability when compared with the BP and the WNN prediction models. The GA-WNN model can overcome the shortcomings of slow convergence and finding local optimums with typical WNN models. The research of this paper is useful from both theoretical and technical perspectives for quantitative tomato yield prediction in the CSGs.

16:10-16:30 SatA02-9 1415 Weighted Sum of Squares Reconstruction Based on Regional Mutual Information in parallel MRI Chunli Wu Northeastern Univ.Zhiming Bai Northeastern Univ.The quality of image reconstruction is very important in parallel magnetic resonance imaging (MRI). Sum of Squares (SOS) reconstruction is considered as a popular reconstruction method in parallel MRI, however, it does not have a good suppression on motion artifacts caused by the autonomous and involuntary movement of patient. This paper presents an improved weighted sum of squares (WSOS) reconstruction algorithm based on regional mutual information (RMI). Every phased array coil image is divided into several small regions. The mutual information of regions is selected as the similarity metric between images to calculate the damage degree of coil data. Different coil image data are given different weights, and then the suitable optimal weighted coefficients will be redistributed to the WSOS algorithm formula to reconstruct the images. The purpose is to minimize the impact of coil damage data on the final reconstructed image, eliminate artifacts effectively, and strive to overcome the shortcomings of the SOS algorithm. To demonstrate the validity of the proposed improvement algorithm to eliminate artifact, two groups of 8-channel brain and cardiac receiver coils k-space under-sampling data have been reconstructed by this proposed method. Experimental results show that the reconstructed images based on the regional mutual information (RMI) have a better uniformity over the field of view than the conventional WSOS reconstruction based on mutual information (MI). In addition, the performance of the two reconstruction methods is also quantitatively evaluated by the normalized mean squared error (MSE), and the MSE value further confirm the feasibility and effectiveness of the improved algorithm.

16:30-16:50 SatA02-10 1539 Stationary patterns of a diffusive model with spatiotemporal delay Qiuyue Zhao Shanxi Univ. of Finance and EconomicsXinglong Niu North Univ. of ChinaPeng Liu North Univ. of China

A diffusive model with spatiotemporal delay subject to Neumann boundary condition is analyzed in this paper. The stability of the positive constant solutions is discussed. By maximum principle and Harnack inequality, a priori estimate of nonconstant positive steady states is given. Then, the nonexistence and existence of nonconstant positive steady states of the reaction-diffusion model are established, which demonstrates that the large diffusion rate can create stationary patterns.

SatA03 Room03 Networked-based Intelligent Control of Autonomous Systems: Theory & Application (Invited Session) 13:30-17:10 Chair: Chunxi Yang Kunming Univ. of Science and Tech.

13:30-13:50 SatA03-1 827 Policy Gradient Reinforcement Learning for Parameterized Continuous-Time Optimal Control Xindi Yang Tongji Univ.Hao Zhang Tongji Univ.Zhuping Wang Tongji Univ.This paper investigates the optimal control for nonlinear continuous-time systems with unknown dynamics. By using reinforcement learning, the knowledge of system dynamics is relaxed by offline datasets and online interactive data. In order to improve online learning performance and remove the condition of persistent excitation, the action-state value function and parameterized control law are drawn into learning algorithm and parameters analysis. Then, policy gradient reinforcement learning algorithm is presented to learn the optimal parameterized control law under system operating and update it real-time. It is also proven that any of the iterative control law can stabilize the system. Neural networks are used to approximate the action-state value function, parameterized control law, respectively. The weights are obtained by using methods of weighted residual. Finally, the numerical results and analysis are presented to illustrate the performance of the developed method.

13:50-14:10 SatA03-2 1090 Residential Load Pattern Clustering Based on Smart Meter Data Using Weighted Self-Organizing Map Qing Peng Huazhong Univ. of Science and Tech.Ming Chi Huazhong Univ. of Science and Tech.Mingxi Zhu State Grid International Development Co., Ltd.Yunfan Yu Huazhong Univ. of Science and Tech.Diandian Wan Huazhong Univ. of Science and Tech.Zhi-Wei Liu Huazhong Univ. of Science and Tech.With arrival of big data of smart meters, a large number of residential power consumption data are collected according to different sampling frequency, namely Residential Load Profiles (RLPs). In this paper, RLPs of smart meter customers are analyzed by clustering, which is of great significance to load management of smart grid. A twostage Weighted Self-Organizing Map (WSOM) clustering algorithm and a clustering performance evaluation method, SSE-DBI, combining Sum of Squares Error (SSE) and Davies-Bouldin (DBI) are proposed. In first stage, Principal Component Analysis (PCA) is used to reduce the dimension of the RLPs. When dimension reduced data is fed into SOM network for clustering, update of weights of SOM is weighted according to PCA, and these clustering centers, namely Typical Residential Load Profiles (TRLPs) of each customer are obtained after some iterations of training. In second stage, above processing is repeated for TRLPs of each customer, TRLPs of all customer are obtained. According to SSE-DBI, final optimal cluster number and clustering performance score of the model are determined. Compared with several benchmark methods, the proposed method obtains optimal performance.

14:10-14:30 SatA03-3 1134 Dynamic path planning of USV with towed safety boundary in complex ocean environment Rui Chen Kunming Univ. of Science and Tech.Chunxi Yang Kunming Univ. of Science and Tech.Shichang Han Kunming Univ. of Science and Tech.Jian Zheng Shanghai Maritime Univ.In order to solve the problem of USV dynamic path planning with towed safety boundary in complex marine environment, an adaptive USV dynamic path planning algorithm is proposed. Firstly, the obstacle avoidance problem of the USV with the towed safety boundary and the bounded tension problem of the cable array are analyzed respectively, and then correspondent safety boundary model and tension boundary condition are established. Secondly, a fuzzy mechanism strategy is developed to automatically choose an appropriate local searching radius according to the degree of the complex obstacles. Moreover, for some constraints such as large-angle turn and path demand in the process of path planning, the USV perspective self-adjusting mechanism and smooth optimization schedule of the path are designed to make it more feasible. Finally, simulation results show that the proposed algorithm can enable the USV with towed safety boundary to carry out dynamic path planning in the complex ocean environment effectively.

14:30-14:50 SatA03-4 1250 Domain Adaptive Rolling Bearing Fault Diagnosis based on Wasserstein Distance Chunliu Yang Kunming Univ. of Science and Tech.Xiaodong Wang Kunming Univ. of Science and Tech.Jun Bao Kunming Univ. of Science and Tech.Zhuorui Li Kunming Univ. of Science and Tech.The rolling bearing usually runs at different speeds and loads, which leads to a corresponding change in the distribution of data. The cross-domain problem caused by different data distributions can degrade the performance of deep learning-based fault diagnosis models. To address this problem, this paper proposes a multilayer domain adaptive method based on Wasserstein distance for fault diagnosis of rolling bearings operating under different operating conditions. First, the basic framework uses deep Convolutional Neural Networks (CNN) to extract domain invariant features and then an adaptation learning procedure is used for optimizing the basic CNN to adapt cross different domains. According to the experimental results, the network model has excellent fault diagnosis capability and adaptive domain capability and is able to obtain a high fault diagnosis rate under different working conditions. Finally, for investigating the adaptability in this method, we use t-SNE to reduce the high dimension feature for better visualization.

14:50-15:10 SatA03-5 1279 Leader-following consensus of linear multi-agent systems with strict negative imaginary uncertainties Kai Yan Beijing Inst. of Tech.Zhuoyue Song Beijing Inst. of Tech.This paper considers the leader-following consensus problem of linear multi-agent systems with strict negative imaginary (SNI) uncertainties. It is assumed that the followers have homogeneous nominal dynamics while subject to different model uncertainties with SNI property, leading to weakly heterogeneous multi-agent systems. In the case of undirected topology, the control protocol is designed for multi-agent systems based on the relative states of neighbouring followers and the leader. It is shown that the control protocol can achieve leader-following state consensus for

Technical Programmes CCDC 2021 networked systems that followers have SNI uncertainties. A necessary and sufficient condition for the nominal networked system to be negative imaginary (NI) and satisfying gain condition is presented by decoupling the feedback controller from communication topology. Furthermore, a sufficient condition in terms of linear matrix inequalities (LMI) is derived for the leader-following consensus controller. Simulation example is provided to validate the effectiveness of the results.

15:10-15:30 SatA03-6 1389 Adaptive Parameter Estimation for Flight Reconfiguration Module Yiming Li Kunming Univ. of Science and Tech.Chunxi Yang Kunming Univ. of Science and Tech.Shichang Han Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Haoran He Kunming Univ. of Science and Tech.Consider the rotor aircraft is not able to deliberately change its own shape for self-adapting different circumstances, tasks, or recovering from damage, a reconfiguration module aircraft that can rearrange its parts according to variable requirements is developed. In this work, a general model of flight reconfiguration module is built by using Newton s and Euler s laws. Moreover, there are some limitations to the structure such as the barycenter of a single module is not its geometric center due to the placement of battery-electric and circuit board devices. As a result, these constraints make its moment of inertia becoming difficult to detect. Therefore, an adaptive law combing with a first-order low pass filter is proposed to estimate these parameters in order to improve estimate accuracy and at the same time avoiding the measurement of high order signal. Finally, a simulation based on the model is given to show the efficiency of the proposed estimation method.

15:30-15:50 SatA03-7 1452 A Low-cost Tractor Navigation System with Changing Speed Adaptability Hao Wang Beijing Academy of Agriculture and Forestry SciencesWenxiang Niu

Heilongjiang Academy of Land Reclamation Sciences

Weiqiang Fu Beijing Academy of Agriculture and Forestry SciencesYaxin Ren Beijing Academy of Agriculture and Forestry SciencesShupeng Hu Beijing Academy of Agriculture and Forestry SciencesZhijun Meng Beijing Academy of Agriculture and Forestry SciencesAn auto-navigation system with variant speed adaptability is developed to improve the robustness of the autonomous agricultural machinery. Based on a commercial agricultural tractor, New Holland 1404, an RTK-GNSS based autonomous navigation system is presented in this research. The system consists of a dual-antenna GNSS receiver with centimeter-level positioning accuracy, an on-board computer, an electronic steering wheel, a steering angle sensor, and so on. A vehicle control unit is designed to analyze the driver operations, to transmit the commands to actuators, and to broadcast tractor statuses following the ISO 11783 protocol. The control algorithm is modified from the pure pursuit path tracking method using a vehicle dynamic model with slip angle. The look-ahead distance of the pure pursuit method is adaptive to the vehicle speed. On the farmland at 4.5m/s speed limitation, the lateral error is about 3.3 cm. Experiments reveal that the proposed auto-navigation algorithm with adaptive look-ahead distance is robust to variant working conditions. In addition, consistent operation accuracy is achieved by the speed adaptive path tracking algorithm and updated vehicle parameters during headland turn. The navigation system is evaluated in auto-steering mode and fully autonomous mode. It has been proved to be a cost-efficient solution for controlling a conventional tractor.

15:50-16:10 SatA03-8 1463 Design of an Elastically Suspended Backpack with a Tunable Damping and a Tunable Stiffness Xiaolong Li Huazhong Univ. of Science and Tech.Jian Huang Huazhong Univ. of Science and Tech.Yu Cao Huazhong Univ. of Science and Tech.Bo Yang Huazhong Univ. of Science and Tech.Hongge Ru Huazhong Univ. of Science and Tech.The extra load during walking will lead to increased metabolic consumption and even damage to joints and muscles. Most of the current backpack systems only focus on reducing the shoulder s static load or dynamic load, but there are few systems with these two functions. To realize these two functions, it is necessary to adjust the stiffness, damping, and other backpack parameters, which brings great difficulties to its mechanism design. In this paper, we design an active suspension backpack with variable stiffness and damping. It can adjust the stiffness and damping between the human body and the backpack to reduce the dynamic load of the shoulder only through a linear drive. At the same time, it can transfer part of the load acting on the shoulders to the hip, further reducing the user s metabolic consumption. To obtain the stiffness and the damping parameters that minimize the consumption of mechanical energy, we establish a dynamic model of the backpack. Then, based on the mechanism of variable stiffness and damping, we prove the stability of the system within the operating range. The simulation results show that the backpack with variable stiffness and damping can transfer the load from shoulder to hip. Under the variable speed movement, the backpack can dynamically adjust the stiffness and the damping to minimize the metabolic consumption, which improves the

energy efficiency of loaded walking.

16:10-16:30 SatA03-9 1608 Distributed hybrid secondary control strategy for DC microgrid group based on multi-agent system Shuangye Mo Guangxi Univ.Wu-Hua Chen Guangxi Univ.Xiaomei Lu Guangxi Univ.In order to solve the problem of current sharing cooperative control among distributed DC microgrid groups, fifirstly, the distributed generation units in DC microgrid are regarded as heterogeneous multi-agent, and the physical model of DC microgrid system is established. Secondly, according to the hierarchical control design principle of power system, a hybrid secondary control strategy based on discrete-time pulse cooperative control is proposed to design DC microgrid The reference output voltage of each distributed generation unit is designed by pulse control method in the secondary controller, and the voltage tracking controller of power converter is designed by double PI voltage and current control method in the primary controller. Thirdly, based on the stability theory of hybrid system, the suffificient conditions of time and current sharing cooperative control of DC microgrid are obtained. Finally, the effectiveness of the algorithm in DC microgrid is verifified by Matlab / Simulink.

16:30-16:50 SatA03-10 1635 Study on Cause Analysis and Evaluation Method of a Class of Overvoltage in Low-voltage Distribution Network Yanping Zhang Electric Power Science Research Institute

of Yunnan Power Grid Co., Ltd.North China Electric Power Univ.

Cheng Guo North China Electric Power Univ.Ke Yin Electric Power Science Research Institute

of Yunnan Power Grid Co., Ltd.North China Electric Power Univ.

There are many kinds of overvoltage problems in low-voltage distribution network, and the influence is serious. The causes of overvoltage are various and difficult to identify. In the paper, a kind of overvoltage phenomenon of low-voltage distribution network caused by three-phase imbalance and excessive transformer grounding resistance is analyzed by theory and measured data, and the severity of overvoltage problem in the substation area is calculated by analytic hierarchy process. The phenomenon widely exists in low-voltage distribution network. In order to quickly identify such high-voltage phenomenon from massive distribution transformer monitoring terminal data, the paper proposes a discriminant method based on three-phase imbalance state index and transformer grounding resistance state index, and introduces the risk preference type utility function to evaluate the influence degree. The method can determine which factors lead to the overvoltage problem of 380V three-phase four-wire low-voltage distribution network in a certain period of time, and evaluate the influence degree of these factors on the overvoltage problem. Finally, the effectiveness of the method is verified by analyzing the measured data.

16:50-17:10 SatA03-11 970 Observer Based Fault-tolerant Control for DC Microgrids with Sensor Fault Mingyu Huang Wuhan Univ.Li Ding Wuhan Univ.

An active fault-tolerant control scheme is proposed for voltage regulation in DC islanded microgrids subjected to sensor fault. Firstly, a state observer designed by the static output feedback technique is introduced to estimate the uncorrupted states of voltage and current. Then, an observer based state feedback controller is proposed to guarantee the stability of voltage regulation under sensor fault. Simulation studies are carried out to evaluate the performance of the proposed fault-tolerant control scheme. Simulation results show that our proposed control strategy can significantly improve the reliability of the DC microgrid systems.

SatA04 Room04 Energy Big Data and Energy System Digitalization (Special Session) 13:30-16:50 Chair: Dongsheng Yang Northeastern Univ.

13:30-13:50 SatA04-1 510 Research on Demand Response Strategy of Integrated Energy System Based on Hybrid Particle Swarm Optimization with Roulette Wheel Operator Shuying Ren Shenyang Inst. of EngineeringYan Zhao Shenyang Inst. of Engineering

Key Laboratory of Regional Multi-energy System Integration and Control

Tieyan Zhang Shenyang Ligong Univ.Yuri Wang State Grid Liaoning Electric Power Co.Lin Tong State Grid Liaoning Electric Power Supply Co.As one of the core elements of the technology base of the smart energy

Technical Programmes CCDC 2021 industry, the comprehensive energy system standard plays a link and catalytic role in promoting China's energy equipment manufacturing, the safe and stable operation of power systems, and the industrialization of scientific and technological achievements. Demand response can play an important role in the consumption of renewable energy in integrated energy systems. This paper establishes an objective function to minimize the cost of integrated energy system and optimize user power consumption. Under the multiple objective functions and constraints, the traditional single objective optimization model can no longer meet the present problem research. In view of the demand response in the integrated energy system, a multi-objective optimization method, the hybrid particle swarm algorithm with roulette operator, is adopted. This algorithm can make the game easier. The partial optimal solution is spread globally to find the optimal scheduling strategy.

13:50-14:10 SatA04-2 511 Research on Fault Diagnosis Method of Distributed Power Distribution Network Based on HHT and CNN Xiaodong Sun Shenyang Inst. of EngineeringHuanxin Guan Shenyang Inst. of EngineeringYan Zhao Shenyang Inst. of Engineering

Key Laboratory of Regional Multi-energy System Integration and Control

Tieyan Zhang Shenyang Ligong Univ.Yuri Wang State Grid Liaoning Electric Power Co.Bo Yang State Grid Zhangye Power Supply CompanyAccurate and reliable fault diagnosis is very important for improving relay protection performance and ensuring safe and stable operation of power system. This paper presents a fault diagnosis method for distributed power distribution network based on convolutional neural network. When fault occurs in the distribution network, FTU acquisition unit is used to sample the fault current and voltage signals of the distribution network. Hilbert-Huang transform is applied to the fault signal, and a two-dimensional time-frequency energy matrix is constructed. Then the time-frequency energy matrix is input into the convolutional neural network to realize the fault type identification of transmission lines in the distribution network with distributed power supply. The simulation results show that the model in this paper improves the speed of fault diagnosis, makes the model more robust, and can determine and judge fault types more accurately and quickly.

14:10-14:30 SatA04-3 513 Research on the Strategy of Energy Storage System Participating in the Secondary Frequency Regulation of Power Grid Based on Fuzzy Control Ya Zeng Shenyang Inst. of EngineeringYan Zhao Shenyang Inst. of Engineering

Key Laboratory of Regional Multi-energy System Integration and Control

Tieyan Zhang Shenyang Ligong Univ.Yuri Wang State Grid Liaoning Electric Power Co.Qianzhi Shao State Grid Liaoning Electric Power Supply Co.Yuanzhuo Du State Grid Xinyuan CompanyThe continuous development of science and technology has led to great changes in the demand for loads in the power system, placing higher demands on the grid for frequency regulation. The disadvantages of traditional FM generators such as slow response speed and dead zone in frequency modulation are becoming more and more prominent. However, the energy storage system has timely tracking, reversible charging and discharging, and rapid output. The auxiliary grid frequency modulation through the energy storage system helps to maintain the active power balance of the power system, improve and increase power. The frequency adjustment capability of the system provides reliable support for the safe and efficient operation of the power system. This paper first analyses the mechanism of secondary frequency regulation of energy storage assisted power grid and establishes an equivalent model of secondary frequency regulation of thermal power units assisted by energy storage system. In addition, an energy storage system-assisted grid secondary frequency regulation control strategy based on double-layer fuzzy control is designed to address the shortcomings of the existing energy storage assisted grid frequency regulation control strategy. Finally, the simulation analysis of the frequency regulation effect is carried out to verify the rationality and effectiveness of the proposed control strategy.

14:30-14:50 SatA04-4 612 Method for LSTM-Based Cascade Hydropower Plant Scheduling Zhi Cai Beijing Key Laboratory of Research and

System Evaluation of Power Dispatching Automation Tech.

Guofang Zhang State Grid Sichuan Electric Power SupplyCompany

Yi Lu State Grid Sichuan Electric Power SupplyCompany

Yuxuan Li Beijing Key Laboratory of Research and System Evaluation of Power Dispatching

Automation Tech.With the increase of the number of cascade hydropower plants and the increase of the discrete number of state variables in the time period to

obtain more high-precision solutions, the calculation time increases exponentially, which makes it difficult to optimize through physical models. Therefore, a data-driven cascade hydropower plant scheduling method with self-learning abilities is proposed. Firstly, perform cluster pre-processing of the historical dispatching data of hydropower plants in the scheduling scope with K-means algorithm; secondly, establish a deep learning model of cascade hydropower plant schedules based on long short-term memory (LSTM), and build the mapping model among system load, water regimen and hydropower plant schedule through historical data training, to make a decision; finally, continuously correct the model by accumulating the historical data, so that it has the ability of self-evolution and self-learning. We make case analysis based on the actual power grid data, and the calculation results show the effectiveness of the proposed method.

14:50-15:10 SatA04-5 815 Wind Turbine Power Optimization Based on Extreme Gradient Boosting Model and Periodic Adjustment Strategy Yihan Qin Zhejiang Univ.Zhifeng Sun Zhejiang Univ.Fengli Ma Zhejiang Univ.Shihao Ma Wuzhong Baita Wind Power Corporation LimitedIn recent years, with the rapid development of wind energy-related technologies, wind farms have been established in more and more countries and regions. However, some wind farms still face the problems such as low power generation and low wind energy utilization. To increase the profit of wind farms and improve the efficiency of wind turbines, we propose a power optimization method based on machine learning algorithm and periodic adjustment. In this study, the optimization process consists of two steps. First, a FF-PCA-XGBOOST model based on data from the supervisory control and data acquisition (SCADA) and Pearson correlation analysis is presented for power prediction. Principal component analysis (PCA) is used to reduce the dimensionality of the power data, and we use feature fusion (FF) to remain key features. Second, we propose a novel data-driven method, which uses power prediction information obtained from the prediction model to adjust pitch angle periodically for enhancing power output. We demonstrate that the optimization based on our model increases the energy production of the wind plant.

15:10-15:30 SatA04-6 886 Fault Diagnosis for Solar Panels using Convolutional Neural Network Zhendong Huang Univ. of Science and Tech. of ChinaShihui Duan China Academy of Information and Comm

unications Tech. of MIITKey Laboratory of Internet and Industrial I

ntegration and Innovation of MIITFei Long Chinaso Inc.

State Key Laboratory of Media Convergence Production Tech. and Systems

Yongjun Li SNEGRID Technology Co.Jin Zhu Univ. of Science and Tech. of ChinaQiang Ling Univ. of Science and Tech. of ChinaFault diagnosis of solar panels is essential for production capacity and safety of solar energy, and has caught considerable attention. This paper presents an efficient method based on a convolution neural network (CNN) to detect solar panels’ edges in infrared images. An accurate classifier is proposed to identify defective solar panels with the obtained obtained edges of solar panels. The location and classification algorithms of solar panels with faults are realized through a lightweight CNN, and implemented on embedded devices. With many images collected from different photovoltaic plants, we verified the effectiveness of the proposed fault diagnosis algorithms.

15:30-15:50 SatA04-7 1194 A Data-mining Method to Assess Automatic Generation Control Performance of Power Generation Units Zijiang Yang Shandong Univ. of Science and Tech.Jiandong Wang Shandong Univ. of Science and Tech.Song Gao Shandong Electric Power Research Inst. for

State Grid Corporation of ChinaXiangkun Pang Shandong Electric Power Research Inst. for

State Grid Corporation of ChinaAutomatic generation control (AGC) of power generation units aims at providing a satisfactory response of generated active power to desired active power dispatched from a power grid center. This paper proposes a data-mining method to estimate three metrics assessing the AGC performance of power generation units in terms of response latency, rapidity and accuracy. The proposed method is composed by two parts. The first part is to select data segments via a matrix profile technique from long-term data samples of the desired and generated active powers. The second part is to estimate performance metrics from a dynamic model between the desired and generated active powers, where the model is built by a system identification technique from the selected data segments. The proposed method resolves a major challenge that the performance metrics are defined for step responses, but the desired active power in practice changes in various forms, many of which are not suitable for performance assessments. An industrial example is provided

Technical Programmes CCDC 2021 to support the proposed method.

15:50-16:10 SatA04-8 1350 Data Quality Improvement Method of Distributed PV Generation Based on Time Correlation and Spatial Correlation Min CAO Southeast Univ.Zhifeng LIANG National Electric Power Dispatching and Control

CenterZhi LI Department of Dispatch Center State Grid Anh

ui Electric Power Co.Yi QU Southeast Univ.Kaifeng ZHANG Southeast Univ.In order to solve the problem of data anomaly and data missing in distributed photovoltaic (PV) active power, this paper proposes a data quality improvement method based on time correlation and spatial correlation. In terms of anomaly detection, back propagation (BP) neural network is used to establish a correlation model based on the correlation among active power and meteorological data on the time scale, and the difference method is used to detect and eliminate anomaly data. In terms of missing data repair, Random Forest is used to establish a correlation model based on the spatial correlation among active power of power station to be repaired and its surrounding power station’s, and the direct repair method is adopted to repair the missing data. The experimental results show that the anomaly detection method based on time correlation can achieve an AUC value of more than 0.93, and the repair method based on spatial correlation can achieve more accurate repair results.

16:10-16:30 SatA04-9 1398 UAV Routine Optimization and Obstacle Avoidance Based on ACO for Transmission Line Inspection Tianyi Liu Northeastern Univ.Dongsheng Yang Northeastern Univ.Bowen Zhou Northeastern Univ.Guangdi Li Northeastern Univ.Mingxi Zhu State Grid International Development Co.In this paper, light fixed-wing medium-range Unmanned Aerial Vehicle (UAV) is equipped with infrared thermal imager and obstacle avoidance Lidar scanning equipment to transmission line inspection tasks. A mathematical model for the UAV is established to calculate the linear motion and the kinematic corner parameters, different array monitoring solutions are provided for single target and multiple targets. Intelligent algorithms are used to plan the optimal solution of UAV obstacle avoidance routes. Simulation results show that the optimal route can be quickly found when the number of iterations increases and suitable self-adaptive parameters are adopted, which shows the effectiveness of the route optimization.

16:30-16:50 SatA04-10 1406 Path Planning of Ship Collision Avoidance for Minimized Energy Consumption Yiliang Li Dalian Maritime Univ.Yi Zuo Dalian Maritime Univ.Qihe Shan Dalian Maritime Univ.Tieshan Li Dalian Maritime Univ.

Univ. of Electronic Science and Tech. of China

The energy consumption and fuel pollution in ship navigation have brought great pressure to the development of shipping industry. In order to further reduce the energy consumption of ships in navigation, this paper proposes a ship path planning model based on genetic algorithm (GA). In this model, the ship maneuverability is considered. A ship collision avoidance path is composed of two distances, two steering angles and three rudder angles. The collision avoidance path includes three curved sections and two straight sections. Experimentally, the trajectory of ship turning motion is simulated by a ship motion mathematical model called NOMOTO model. We adapt GA to optimize ship collision avoidance path. A collision avoidance path of the least energy consumption can be found with meeting ship safety and the international regulations for preventing collision at sea (COLREGs: Convention on the International Regulations for Preventing Collision at Sea). This method helps to promote the development of ship energy optimization and ship collision avoidance path planning.

SatA05 Room05 Variable Structure Control 13:30-16:50 Chair: Yuan Li Beijing Inst. of Tech.CO-Chair: Yanmin Wang Harbin Inst. of Tech.

13:30-13:50 SatA05-1 106 Neural Networks-based Adaptive Backstepping Super-twisting Sliding Mode Control of Uncertain Nonlinear Systems with Unknown Hysteresis Mengmeng Li Beijing Inst. of Tech.Yuan Li Beijing Inst. of Tech.Qinglin Wang Beijing Inst. of Tech.An adaptive neural network output feedback tracking control scheme is

proposed for uncertain nonlinear systems with unknown hysteresis, unmeasurable states, and external disturbances. Radial basis function neural networks (RBFNNs) are used to approximate the unknown nonlinear functions, and a neural network state observer (NNSO) and a nonlinear disturbance observer (NDO) are designed to estimate the unmeasurable states and unknown compounded disturbances, respectively. Based on the NNSO and NDO, and combing the backstepping technique an super-twisting algorithm, a neural networks-based adaptive backstepping super-twisting sliding mode control (NNABSTSMC) scheme is proposed without constructing the hysteresis inverse. The problem of “explosion of complexity” inherent in the backstepping method is eliminated by using dynamic surface control (DSC) technique. The presented controller not only guarantees that all signals of the controlled system are semi-globally ultimately uniformly bounded (SUUB) via the Lyapunov analysis method, but also ensures that the observer and tracking errors fast converge to a neighborhood of the origin. A numerical example is provided to demonstrate the effectiveness of the proposed control scheme.

13:50-14:10 SatA05-2 1423 Trajectory Tracking of Series Elastic Actuators Using Terminal Sliding Mode Control Hui-Jie Sun Sun Yat-sen UniversityJing Ye Shenzhen Milebot RoboticsGong Chen Shenzhen Milebot RoboticsIn this paper, a continuous-time state-feedback control scheme is proposed by using a new form of adaptive terminal sliding mode for the trajectory tracking control problem of robot manipulators driven by a series elastic actuator. By applying a conditional integral sliding mode surface and online adaptive law for disturbance boundary identification, a terminal sliding mode controller is established by the lyapunov stability theory. A key feature of the proposed controller is the use of varying boundary layer for solving the chattering problem. An application and experimental verification to a series elastic actuator is provided to demonstrate the control effectiveness and significant performance improvements compared with the conventional PID controller.

14:10-14:30 SatA05-3 181 Adaptive Sliding Mode Methods for Active Power Filter Using Finite-Time Disturbance Observer Yun Chen Hohai UniversityJuntao Fei Hohai UniversityActive power filter (APF) is a typical nonlinear system with unknown external disturbances, its mathematical model is difficult to establish accurately. In order to compensate the external disturbance of the APF, a finite-time disturbance observer (FTDO) is designed to estimate matched and mismatched disturbances in this paper. The advantages of observer and sliding mode controller are combined. First, a nonsingular fast terminal sliding mode control (NFTSMC) based on the FTDO is proposed. Then a dynamic terminal sliding mode control (DTSMC) based on the FTDO is proposed. Finally, a double hidden layer recurrent neural network (DHLRNN) is introduced to achieve the optimal sliding mode switching gain. Simulation studies show the satisfactory compensation performance of the proposed FTDO using the NFTSMC and DTSMC methods

14:30-14:50 SatA05-4 1465 Sliding mode control for Markovian jump systems under deception attacks Bei Chen Shanghai University of Engineering ScienceYugang Niu East China University of Science & Tech.Zhiru Cao East China University of Science & Tech.This research article considers the design of static output-feedback sliding mode control for Markovian jump systems, in which the attacker may inject false information into the communication channel between the controller and the actuator. The key issue is how to design the feasible sliding mode control law to overcome the effects of unknown and time-varying attacks. To this end, an on-line estimation scheme is introduced to deal with the unknown network attack patterns. And then, a linear sliding surface is constructed based on the measured output information and an output-feedback sliding mode controller is correspondingly designed. It is shown that the reachability of the specified sliding surface can be achieved and the asymptotic stability of the closed-loop system can be ensured under the derived sufficient conditions. Finally, simulation examples are provided to verify the developed static output-feedback sliding mode control strategy.

14:50-15:10 SatA05-5 1561 Continuous Sliding Mode Control of Buck Converter with Constant Switching Frequency Mingyang Yang Harbin Inst. of Tech.Yanmin Wang Harbin Inst. of Tech.Jialing Le Harbin Inst. of Tech.Zihan Cai Harbin Inst. of Tech.In this paper, a novel continuous sliding mode (SM) control approach is proposed to address the two problems of chattering and non-constant switching frequency for buck converters. Instead of using the commonly

Technical Programmes CCDC 2021 used sub-optimal algorithm and hysteresis modulation (HM) method, a continuous SM controller is designed on the basis of the averaging model of buck converters to solve the chattering problem and to achieve control continuity. In phase plane, the control performance is analyzed around the equilibrium point simultaneously, leading to a variable hysteresis width for HM. The influence relationship of the switch frequency with the hysteresis width and controller parameter of buck converters is investigated. Simulations validate the proposed approach.

15:10-15:30 SatA05-6 1627 Response Time Estimation of NTSM Controlled Buck Converters via Phase Trajectory weiqi Zhang Harbin Inst. of Tech.Yanmin Wang Harbin Inst. of Tech.Xin Hui Harbin Inst. of Tech.Haoran Cai Harbin Inst. of Tech.In this paper, a novel estimation approach for the response time of sliding mode controlled buck converters is proposed by analyzing the phase trajectory. Due to its global fininte-time convergence, a non-singular terminal sliding mode (NTSM) controller is designed on the basis of the averaging model of buck converters. In phase plane, the phase trajectories starting from different original states are analyzed and the critical surfaces are proved to exist. The distribution rule of phase trajectories is investigated so that the system convergence to the equilibrium point and response time is determined. Simulations validate the proposed approach.

15:30-15:50 SatA05-7 271 Finite time synchronization of fractional-order brushless DC motor systems by soft variable structure control Wei Han Northeast petroleum UniversityBingkun Gao Northeast petroleum UniversityHaoxuan Guo Northeast petroleum UniversityIn this paper, finite-time synchronization of two fractional order brushless DC motor (BLDC) systems with control constraints is investigated. First, a fractional-order BLDC model is proposed, a soft variable structure control scheme is introduced for the finite time synchronization of two fractional order chaotic systems with control constraints. Considering the constraints of the controller, the control parameters of the controller are limited by the soft variable structure method. The finitetime stability of the error systems is rigorously proven. Then, the controller is proposed for the finite-time synchronization of fractional-order brushless BLDC model systems with disturbances. Finally, numerical simulation results are presented to demonstrate the effectiveness and feasibility of the proposed strategy.

15:50-16:10 SatA05-8 698 Aerodynamic Coefficients Modeling and Attitude Controller Design for an Air-breathing Generic Hypersonic Vehicle Jianhua Wang Space Engineering Univ.Chenghu Yun Space Engineering Univ.Zejie Cui Space Engineering Univ.Long Cheng Space Engineering Univ.A rotational attitude controller scheme for an air-breathing generic hypersonic vehicle (GHV) is proposed in this paper. The compact analytical models of the GHV’s aerodynamic moment coefficients are established based on the least square error criterion and quasi-newton modified method, and the inherent relations between aerodynamic moment coefficients and control surfaces are converted into an approximate linear form. For implementing the disturbance rejection control scheme, an Euler angle sliding surface vector is conducted andthe pseudo-commands with respect to roll, yaw, and pitch rates are derived. Then, the sliding surface vector of the three-channel body rates is designed. A control allocation method is derived and the commanded control surface fin deflections are denoted. In derivation, the model and aerodynamic uncertainties are estimated by extended state observers. The actual control surface fin deflections are denoted via a second order actuator model. Finally, the effectiveness and robustness of the newly proposed control scheme are verified and validated using nonlinear simulation studies.

15:10-16:30 SatA05-9 839 A New Exponential Power Combined Reaching Law Sliding-mode Control for Permanent Magnet Synchronous Motor Chunbo Xiu Tiangong Univ.Liu Yuan Tiangong Univ.Jiaming Li Tiangong Univ.In order to meet the high precision speed regulation requirement of permanent magnet synchronous motor (PMSM) and to overcome the influence of chattering caused by disturbance on speed, a new variable-exponent power combination reaching law is proposed. The reaching law has the characteristics of fast reaching speed and small chattering. The sliding-mode speed controller of permanent magnet synchronous motor designed based on this law is analyzed by using Lyapunov stability criterion, and compared with fast power law, double power law and variable exponential fast power law. The results show that the sliding-mode controlled variable can converge to the given value in

finite time, and the convergence speed of the reaching law controller is faster when the system has bounded disturbance. It is proved that the new exponential power combination reaching law controller has fast response speed, strong robustness and good dynamic and static characteristics.

15:30-16:50 SatA05-10 1027 Adaptive Super Twisting Sliding Mode Control for Flying-Wing UAV Yang Zhang Huazhong Univ. of Science and Tech.Mao Su Designing Inst. of Hubei Space Tech. AcademyZhongtao Cheng Huazhong Univ. of Science and Tech.Lei Liu Huazhong Univ. of Science and Tech.Bo Wang Huazhong Univ. of Science and Tech.Aiming at the attitude control problem of flying-wing UAV, an adaptive super-twisting non-singular fast terminal sliding mode control law is proposed. Flying-wing UAVs are pool in static stability due to the cancellation of the flat tail and vertical tail, which brings great challenges to the attitude control of flying-wing UAVs. Therefore, the non-singular fast terminal sliding mode control method is used for control. At the same time, using the improved super-twisting algorithm to attenuate the chattering, an adaptive super-twisting non-singular fast terminal sliding mode control law is designed: the finite-time convergence of the closed-loop system is proved using Lyapunov stability theory; Finally, through simulation analysis, it is proven that the adaptive super-twisting non-singular fast terminal sliding mode control law proposed in this paper has a better control effect. Compared with the traditional sliding mode method, the control is smoother and can suppress disturbance effectively and weaken chattering.

SatA06 Room06 Smart Manufacturing and Industrial Intelligence (Special Session) 13:30-17:15 Chair: Shixin Liu Northeastern Univ.CO-Chair:Ziyan Zhao Northeastern Univ.

13:30-13:45 SatA06-1 190 A Cost Optimization Strategy for Workflow Scheduling in Cloud Fuquan Sun Northeastern Univ.Zhenghao Lu Northeastern Univ.Jikui Pan Northeastern Univ.Zijian Wang Northeastern Univ.Workflow scheduling in cloud is one of the most challenging issues of recent times. It focuses on executing workflow applications in a manner that maps interdependent tasks to virtual machines under specified quality of service requirements. Cloud service providers offer resources with different performance at different prices. The same workflow with different resources can result in different makespan and cost. One of the main problems of workflow scheduling in cloud is to find a cheaper scheduling method on the premise of meeting the deadline. In this paper, an improved L-ACO algorithm (imL-ACO) is adopted. It optimizes the parameters of L-ACO algorithm and can solve the above problem well for specific workflow. The experiment shows that imL-ACO algorithm can make the scheduling cost lower, allocate resources more effectively, and the effect is better than L-ACO algorithm.

13:45-14:00 SatA06-2 295 A Cost-Aware Scheduling Algorithm for Reliable Workflow in IaaS Clouds Lingjuan Ye Beijing Institute of Tech.Yuanqing Xia Beijing Institute of Tech.Liwen Yang Beijing Institute of Tech.With the development of cloud computing, cloud services have been widely used to deal with various largescale complex workflow applications with the powerful computing capability. However, it is very challenging for cloud service providers to effectively schedule and deploy workflow applications while guaranteeing quality-of service (QoS) for different users. In this paper, a novel cost-aware reliable workflow scheduling algorithm (CRWS) is designed to minimize workflow scheduling cost and satisfy the workflow reliability constraints. In CRWS algorithm, we rank tasks to form a scheduling order and decomposes the workflow reliability constraint into tasks sub-reliability constraints. Virtual machines (VMs) are heuristically selected to scheduling workflow tasks for reducing cost while meeting the task sub-reliability constraint. Simulation experimental results demonstrate that CRWS achieves the lowest cost for workflow with the reliability constraint, as compared to the state-of-the-art algorithms.

14:00-14:15 SatA06-3 296 Unsupervised Anomaly Detection in Multivariate Time Series through Transformer-based Variational Autoencoder Hongwei Zhang Beijing Institute of Tech.Yuanqing Xia Beijing Institute of Tech.Tijin Yan Beijing Institute of Tech.Guiyang Liu Alibaba GroupModern industrial devices often use multiple sensors to detect the status of system, which produce a large amount of multivariate time series. Due to the complex temporal dependency of intra-channel and

Technical Programmes CCDC 2021 inter-correlations among different channels, few of proposed algorithms have addressed these challenges for anomaly detection in multivariate time series. Besides, previous work does not consider future dependency, which has been shown to be critical for sequential data modeling. In this paper, we develop an unsupervised anomaly detection algorithm TransAnomaly, which integrates Transformer, variational autoencoder (VAE) and nonlinear state space model. TransAnomaly not only reduces the computational complexity and allows for more parallelization but also provides explainable insights. To the best of our knowledge, it is the first model that combines VAE and Transformer for multivariate time series anomaly detection. Extensive experiments on several public real-world datasets show that TransAnomaly outperforms state-of-the-art baseline methods while training cost is reduced by nearly 80%.

14:15-14:30 SatA06-4 563 Stability Optimization and Verification Based on SPTG of Constant Distance Control Strategy in Train-Train Communication Train Control System Wanli Lu Beijing Jiaotong Univ.Jidong Lv Beijing Jiaotong Univ.Haixia Dong Beijing Jiaotong Univ.Hongjie Liu Beijing Jiaotong Univ.Ming Chai Beijing Jiaotong Univ.Shuai Su Beijing Jiaotong Univ.Xiwang Guo Liaoning Shihua Univ.Next Generation Train Control System (NGTC) based on train-train communication can efficiently realize train tracking control through constant distance strategy. How to ensure the stability of the platoon with the control strategy is very crucial since the leader train action is random in the platoon control. Based on Stochastic Priced Timed Game (SPTG), we propose a train-train communication control strategy modeling and verification method. Firstly, according to the control strategy’s requirements, a platoon control model including a leader train and follower trains are established and the stability are verified by SPTG automata. Secondly, using time expansion as the cost function, the optimal operation strategy of the platoon is obtained through combination of the cooperative game of the platoon's SPTG automata model and Q-learning. Finally, the stability optimization objection of the platoon is simulated in single-train operation, two-train operation and multi-train operation tracking scenarios. Compared with the random operation of the platoon, the result shows that this method can make the platoon stability error smaller.

14:30-14:45 SatA06-5 619 Research on Production Big Data Acquisition and Transmission System in Intelligent Jobshop Genyin Liu Shenyang Univ. of Tech.Yanhong Wang Shenyang Univ. of Tech.Chenghao Liu Shenyang Univ. of Tech.Yue Cui Shenyang Univ. of Tech.The production data acquisition and transmission mechanism in intelligent jobshops is a special topic. The data are complex in origin, diverse in types, and distributed with manufacturing resources. In order to efficiently utilize these massive, heterogeneous and multi-source production data, a production big data acquisition and transmission system is proposed. In the data acquisition, to realize a unified and standardized description of the functions for heterogeneous manufacturing resources, the operation process and functional characteristics of jobshop manufacturing resources are studied, and a resource attribute description model is designed. Further, a unified data acquisition system with abstract and hierarchical data interfaces are designed. Meanwhile, in the aspect of data transmission, a Kafka-based manufacturing data transmission system is designed and developed out, which can provide higher throughput and lower delay than traditional ones. Furthermore, the monitoring layer analyzes the data and provides a visual display of real-time status data by the visualization interfaces. Finally, the architecture has been tested in a simulation digital jobshop, The simulation results show that the proposed data acquisition and transmission can improve the scalability of the system, increase data throughput and reduce transmission delay.

14:45-15:00 SatA06-6 748 A Model Predictive Control System for Virtual Coupling Xiaolin Luo Beijing Jiaotong Univ.Tao Tang Beijing Jiaotong Univ.Hongjie Liu Beijing Jiaotong Univ.Ming Chai Beijing Jiaotong Univ.Xiwang Guo Liaoning Shihua Univ.Virtual Coupling (VC) is a novel train operation mode which makes trains move synchronously as a platoon via Train-to-Train (T2T) communication and they can be seen as a single train if they have same route ahead. This method dramatically enhances track capacity by decreasing headway between consecutive trains. How to realize VC operation is a hot topic recently. This paper proposes a model predictive control (MPC) system for VC to track reference driving profile or predecessor’s trajectory, i.e., driving as fast as they can, to a certain extent. Finite-time horizon prediction horizon is expanded to Quasi-infinite-time prediction horizon by penalizing the last state error. Input constraints (acceleration

and jerk constraints) and output constraints (safety headway constraints) are considered. This system also guarantees asymptotical stability and string stability. The results show that each train in platoon can automatically adjust its dynamic state as anticipation under safety.

15:00-15:15 SatA06-7 856 Multi-objective Discrete Brainstorming Optimizer for Multiple-product Partial U-shaped Disassembly Line Balancing Problem Kun Wu Liaoning Petrochemical Univ.Xiwang Guo Liaoning Petrochemical Univ.Shixin Liu Northeastern Univ.Liang Qi Shandong Univ. of Science and Tech.Jian Zhao Univ. of Science and Tech. LiaoningZiyan Zhao Northeastern Univ.Xu Wang Hebei Univ. of Environmental EngineeringTo promote the development of disassembly industrialization, reasonable disassembly line layout and disassembly process planning are essential. According to the concept of lean production, this paper presents a multiple-product partial U-shaped disassembly line balancing (MP-UDLB) problem. In this MP-UDLB problem, different types of end-of-life products are disassembled on the same U-shaped disassembly line at the same time. A mathematical model is constructed for the MP-UDLB problem to maximize the disassembly profit and minimize disassembly energy consumption simultaneously. A multi-objective discrete brainstorming optimizer (MDBO) is proposed to solve the MP-UDLB problem. The proposed MDBO is applied to real-world cases, i.e., a ballpoint pen and washing machine, and compared with other multi-objective algorithms. The experimental results show that the proposed MDBO outperforms peer algorithms.

15:15-15:30 SatA06-8 880 Health stages division and remaining useful life prediction of rolling element bearings based on hidden semi-Markov model Hongwei Wu Wuhan Univ. of Science and Tech.Zhenxing Liu Wuhan Univ. of Science and Tech.Yong Zhang Wuhan Univ. of Science and Tech.Ying Zheng Huazhong Univ. of Science and TechCong Tang Wuhan Univ. of Science and Tech.Health stages division and Remaining Useful Life (RUL) prediction are two important parts in safety study of rolling element bearings. In this paper, the Hidden Semi-Markov Model (HSMM) is proposed to divide the degradation stages of rolling element bearings. Firstly, we extract the root mean square feature from the original vibration signal, then utilize Viterbi algorithm to divide the degradation stages. Secondly, Fault occurrence time is determined according to the degradation stage and RUL is predicted with HSMM. In order to verify the effectiveness of this method, IEE-PHM-2012 challenge data sets are adopted and the comparison with the existing methods is carried out.

15:30-15:45 SatA06-9 925 An End-to-End Steel Strip Surface Defects Detection Framework: Considering Complex Background Interference Rongqiang Liu Northeastern Univ.Min Huang Northeastern Univ.Peng Cao Northeastern Univ.Automatic defect detection is an important step in production and manufacturing analysis. To remove the negative impacts of the information redundancy and enhance the discrimination of features, a surface defect detection framework on steel strip with self-attention mechanism is proposed in this work. By introducing self-attention mechanism, effective spatial-wise semantic relationships between any two positions of the feature maps are captured, and global contextual interdependencies are explicitly modeled. In the task of defect detection, our proposed framework can automatically identify the specific class and the location of six kind of typical surface defects on steel strip. Compared with Faster R-CNN with FPN, the proposed framework has higher detection accuracy and better localization ability. Keywords: surface defect detection, steel strip, self-attention, Faster R-CNN.

15:45-16:00 SatA06-10 995 Data-Driven Optimization Method for Aluminum Alloy Casting Process Parameters and Alloy Composition Shengchao Li Northeastern Univ.Shen Yan Northeastern Univ.Zhonghua Cao Ansteel Beijing Research Institute Co., Ltd.Shixin Liu Northeastern Univ.Dali Chen Northeastern Univ.Melting and casting are important parts of aluminum alloy production, in which process parameters and alloy composition are key factors in determining ingot performance and are essential to aluminum alloy product quality. In this paper, a data-driven method for in-depth optimization of process parameters and alloy composition in an aluminum alloy casting process is presented, which can automatically optimize the process parameters and alloy compositions to improve the ingot performance. The method includes three parts: data pre-processing, relational model construction, and process parameters/alloy composition

Technical Programmes CCDC 2021 optimization. At the data pre-processing stage, feature engineering, outlier handling, and missing value handling are used in turn to obtain a high-quality dataset. At the relational model construction stage, XGBoost is used to construct a relational model between the process parameters/alloy composition and ingot performance. At the process parameters or alloy composition optimization stage, the genetic algorithm is used to optimize the process parameters and alloy compositions respectively. To verify the effectiveness of the proposed algorithm, the melting and casting production process dataset of aluminum alloy is constructed. Experiments show that our algorithm can automatically optimize the process parameters or alloy compositions in a short time, and significantly improve the performance of ingots. The proposed method is an important guideline for practical production.

16:00-16:15 SatA06-11 1117 Mixed Bundled Pricing Strategies for Product and Service Considering Network Effects Xuwang Liu Henan Univ.Chengna Zhu Henan Univ.Wei Qi Henan Univ.Xiwang Guo Liaoning Shihua Univ.The network effects of product and service have a significant impact on the pricing of product and service. As an effective sales strategy, bundling has become a new way for enterprises to increase profits. We study the optimal pricing, the optimal market share and the maximum revenue of the hybrid bundling strategy for both product and service in the presence of network effects. Based on MNL model, we analyze the optimal solution and profit difference of mixed strategy binding. The results show that the market share of mixed bundling strategy is concave in terms of revenue function when negative network effect is taken into account, and the optimal pricing, optimal market share and optimal revenue solution are obtained. Finally, the importance of considering negative network effects in product and service pricing is illustrated by numerical simulation. The research results can provide theoretical basis and decision-making reference for enterprises with negative network effect product in terms of product and service pricing.

16:15-16:30 SatA06-12 1138 Aluminum Strip Crown Prediction in Hot Rolling Process Based on Data-driven Methods Minghao Yao Northeastern Univ.Shixin Liu Northeastern Univ.Zhonghua Cao Ansteel Beijing Research Institute Co., Ltd.Shen Yan Northeastern Univ.Dali Chen Northeastern Univ.Aluminum process parameters and crown are two important factors that determine the product performance in hot rolling process of aluminum strip. In this paper, we propose a data-driven model fitting method for the relationship between process parameters and crown of aluminum strip hot rolling process. The method includes two parts: the data preprocessing and the relational model fitting. In data preprocessing, we use feature selection algorithm, outlier handling algorithm and missing value padding algorithm to preprocess the given data and obtain high-quality data for analysis. In the relationship model fitting, we use six typical machine learning methods to fit the relationship model between process parameters and crown. Based on the relationship model, we can accurately predict the crown by given process parameters. In order to verify the effectiveness of the proposed algorithm, we construct the dataset of aluminum strip hot rolling process. A large number of experimental results show that this proposed method can be used to build an accurate relationship model between process parameters and crown, and realize the automatic prediction of crown.

16:30-16:45 SatA06-13 1146 A Q-learning-based Automatic Heuristic Design Approach for Seru Scheduling Rongxin Zhan

Beijing Institute of Tech.

Zihua Cui Guangdong Shenling Environmental Systems Co., Ltd.Tao Ma Inner Mongolia First Machinery Group CorporationDongni Li Beijing Institute of Tech.Seru production is a new mode of production with the advantages of quick response, high flexibility and high efficiency. It is well suited to the market that fluctuates frequently. The seru scheduling is an important issue for seru production system configuration problem because it reflects the management and control principle of seru production systems, which called just-in-time operation system. This paper studies a seru scheduling problem, which can be described as how to determine the sequence of serus in limited space for multiple orders considering worker overlapping. The objective is to minimize the maximum completion time. A Q-learning-based genetic programming algorithm is proposed to solve the above problem. Experimental results show the effectiveness of the proposed algorithm.

16:45-17:00 SatA06-14 1162 Heuristic Algorithms for a New and Practical Wire Rod and Bar Rolling Scheduling Problem

Yafeng Zhao Northeastern Univ.Shixin Liu Northeastern Univ.Xianming Zhao Northeastern Univ.A scheduling problem in a wire rod and bar rolling process is very important and hard to solve in practical steel production systems. This paper considers a new scheduling problem originated from a wire rod and bar rolling process considering job release time and due time, as well as setup time between consecutive jobs. The objectives are to minimize both the number of late jobs and total setup times. Their linear combination is used to measure a schedule. To solve such a practical scheduling problem, four effective algorithms were designed. The experimental results show that all of the presented algorithms can well solve the considered problem. Among them, iterated greedy algorithm shows the best solution accuracy. Its great performance implies its readiness to be used in practical industrial scheduling systems.

17:00-17:15 SatA06-15 1212 An Efficient Route Control Model of the Train-centric Control System Qi Wang Beijing Jiaotong Univ.Ming Chai Beijing Jiaotong Univ.Hongjie Liu Beijing Jiaotong Univ.Jidong Lv Beijing Jiaotong Univ.Xiwang Guo Liaoning Shihua Univ.The Train-centric control system integrates the main functions of the whole system into the vehicle, so the train can control the route resources independently. In this system, the route control method which is mainly reflected in the control of the switch is the root to ensure driving safety. This paper focuses on the route control method of the Traincentric system, to find ways to assure the safety of the switch control process and compare track capacity of different control methods. Firstly, we discuss the structural and compositional differences between the Train-centric control system and the traditional Communication Based Train Control (CBTC) system, present the route control process of the new system, as route control method matters the security of the whole system, one of the key problem in this process is the logic to add resource lock to switches, we propose a model to calculate safe distance, and propose two route control approaches, using Simulink/Stateflow hybrid modeling approach to model for both of them, get the visual curve, then build math model to measure and compare the efficiency of the models.

SatA07 Room07 Control and Decision of Intelligent Manufacturing Process (Invited Session) 13:30-16:50 Chair: Jianyan Tian Taiyuan Univ. of Tech.

13:30-13:50 SatA07-1 794 Neural Network Tracking Controls of SCARA Manipulator System Yongfeng Lv Taiyuan Univ. of Tech.Yingbo Huang Kunming Univ. of Science & Tech.Xiaolong Wu Nanchang Univ.Long Jian Taiyuan Univ. of Tech.In this paper, the neural network optimal tracking controller of SCARA manipulator system is designed. First of all, the SCARA manipulator system is modeled, and the reference motion trajectory of the joint is given, and the steady-state control is designed to ensure that the manipulator can keep up with the reference trajectory, but it can not guarantee the various performance in operation. Given the performance index of position and speed tracking error, the neural network approximate feedback control is obtained by learning the optimal performance index function with three-layer neural network based on reinforcement learning (RL). According to the steady-state control and approximate feedback control, the neural network optimization controller of the manipulator is designed to achieve the tracking effect of the position speed of the manipulator with the minimum overshoot and chattering, and the lowest energy consumption.

13:50-14:10 SatA07-2 1290 Simulation and Experimental Analysis of Polishing Contact Force of Industrial Robots Jing-jing Zhang Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningJia Liu Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningSheng-qiang Yang Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningZhi-jie Qiao Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningJing-zheng Li Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningIn order to solve the sudden change of force at the moment of contact with the robot polishing complex curved surface, which causes the manipulator arm to vibrate, PID and fuzzy PID methods are used for force tracking. First, a processing model of the industrial robot polishing process is established. Secondly, the force control model of the PID is established, the PID algorithm is simulated and analyzed by MATLAB/Simulink to verify the force tracking effect. Third, fuzzy PID algorithm is used to improve the force tracking accuracy during the

Technical Programmes CCDC 2021 polishing contact process and reduce the force fluctuation at the moment of contact. In the polishing process, the polishing tool is a flexible louver, using SPRUTCAM offline programming software, the robot arm moves on a calculated trajectory, and the force sensor collects the polishing force in real time. In the polishing process, the louver spindle speed, robot feed speed and polishing contact compression are used as the input parameters, and the contact force is the output. The simulation and experimental results show that the established fuzzy PID model can well track the polishing force. Though selecting appropriate polishing parameters, the variation range of the actual abrasive contact force and the steady fluctuate contact force is 0.5N, which can meet the polishing quality requirements.

14:10-14:30 SatA07-3 1292 Fuzzy Impedance Control for Robot Impact Force Jing-zheng Li Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningJia Liu Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningSheng-qiang Yang Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningZhi-jie Qiao Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningAiming at the high efficiency and quality machining of complex blade, which is an important part of aero-engine, steam turbine and gas turbine, the industrial robot is used for polishing and grinding. Based on the traditional impedance control, a combined control strategy, fuzzy impedance control, is obtained by fusing the fuzzy system and impedance control. On this control strategy, a simulation model is built on Simulink to carry out the collision and impact simulation experiment between the robot and the workpiece. The simulation results show that, compared with the traditional impedance force control, the proposed strategy accelerates the convergence speed, significantly reduces the impact between the robot and the workpiece, and is more beneficial to protect the robot, polishing tool and the workpiece.

14:30-14:50 SatA07-4 1293 Robot-assisted Blade Polishing and Process Control Based on SprutCam Offline Programming Software Zhi-jie Qiao Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningJia Liu Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningSheng-qiang Yang Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningJing-zheng Li Taiyuan Univ. of Tech.

Shanxi Key Laboratory of Precision MachiningThis paper proposes a regional polishing method based on SprutCam offline software for blade free-form surface parts. The experiment described the establishment of the robot linkage coordinates system, the analysis of inverse kinematics, the establishment of the digital model of the blade and the division of the processing area using the Kawasaki robot as an example. The signal feedback control between the PLC and the robot is used to realize the coordinated movement between the robot arm and the polishing platform, and the blade is polished in different areas. The offline programming software is used to adjust the polishing process in real time and plan the processing trajectory to achieve the whole processing of blades avoids collisions during processing and improves processing efficiency.

14:50-15:10 SatA07-5 1367 Dynamic average consensus algorithm for networked euler-lagrange systems with continuous-time distributed event-triggered control Long Jian Taiyuan Univ. of Tech.Yongfeng Lv Taiyuan Univ. of Tech.Jifu Li Taiyuan Univ. of Tech.Xinpeng Zhai Taiyuan Univ. of Tech.This paper investigates the dynamic average consensus problem for networked Euler-Lagrange (EL) systems with undirected communication graph topology. Based on the price of communication and the existing condition under discrete-time communication, a novel distributed event-triggered controller is proposed to enable multiple EL agents to track the dynamic average of multiple time-varying reference signals. The feasibility of the proposed controller is further verified to eliminate Zeno behavior. Numerical examples illustrate the results.

15:10-15:30 SatA07-6 1369 Set-based Adaptive Parameter Estimation for a Class of Systems with Nonlinear Parametrization Mingrui Chen Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Yashan Xing Institut de Rob`otica i Inform`atica IndustrialRamon Costa-Castell´ Institut de Rob`otica i Inform`atica IndustrialGuanbin Gao Kunming Univ. of Science and Tech.In this paper, an alternative adaptive parameter estimation framework is developed for nonlinearly parametric systems. The proposed estimation

approach is derived from the perspective of the set-based method, where the unknown constant parameters contains a fixed value and an unknown uncertainty. Based on this property, the mean-value theorem can be used to linearize the nonlinearly parameterized function with respect to the uncertainty. Inspired by this fact, the procedure of estimation method is that the nonlinearly parameterized function in the predictor error equation is first linearized by using the mean-value theorem. Subsequently, a low-pass filter is employed in the linearized function in order to derive parameter estimation errors and avoid using the predictor error derivative. Moreover, the estimator is designed based on the derived parameter estimation errors. Under the persistent excitation condition, the exponential convergence for the proposed estimator can be guaranteed. Finally, two simulation examples are provided to illustrate the effectiveness of the proposed method.

15:30-15:50 SatA07-7 1377 Research and Application of X-ray Thickness Measurement Technology for Iron based Nanocrystalline Ultra-Thin Strip Zhi-en Li Taiyuan Iron & Steel (Group) Electric Co.,Ltd.Bo Li Taiyuan Iron & Steel (Group) Electric Co.,Ltd.Yong-qian Feng Taiyuan Hongbo Technology Co., Ltd.Jiang Li Taiyuan Hongbo Technology Co., Ltd.At present, in the domestic production of iron-based nanocrystalline strip, there is a lack of on-line thickness measurement system in the process of strip making, or the thickness measurement accuracy can not meet the requirements. The control of the strip thickness mainly depends on the experience and operation level of the strip maker. The consistency of the strip before and after production is poor, the thickness hit rate is low, and the thickness can not be fully monitored in real time, which has a great impact on the quality of the strip. In this paper, a set of X-ray thickness measurement system for nanocrystalline strip is developed. Based on the principle of X-ray penetrability measurement, the thickness of nanocrystalline strip is measured quickly and accurately. It is successfully applied to the strip production site to realize online measurement and real-time display of thickness data.

15:50-16:10 SatA07-8 1390 Instance Segmentation Method of Adherent Targets in Pig Images Based on Improved Mask R-CNN Xinpeng Zhai Taiyuan Univ. of Tech.Jianyan Tian Taiyuan Univ. of Tech.Jifu Li Taiyuan Univ. of Tech.Aiming at the segmentation problem of adherent targets in pig images, thus an instance segmentation method based on improved Mask R-CNN is proposed to separate adherent pig body. Firstly, ResNet-50 is selected as the feature extraction network of Mask R-CNN due to the single detection category. Secondly, in order to strengthen the connection between the feature extraction channels and improve the utilization of features, the SENet network structure is embedded in ResNet-50. Finally, IoU boundary loss is introduced to construct a new Mask loss function to improve the edge detection accuracy of independent pigs. Training, validation, and test are performed on 3000 pig images. The ratio of training set, validation set, and test set is 3:1:1. The experiments show that the segmentation accuracy of the proposed method in this paper reaches 91.7% on the validation set and 88.9% on the test set. The results showed that the adherent targets in the pig images can be segmented into independent individuals, which provides a basis for the monitoring and automatic tracking of the pig.

16:10-16:30 SatA07-9 1399 Parameter Fusion Modeling Method for Hot Strip Rolling Process and its Application Huadong Qiu Taiyuan Iron and Steel Group Co., Ltd.Jianyan Tian Taiyuan Univ. of Tech.Long Jian Taiyuan Univ. of Tech.According to the production characteristics of different hot-rolled strip products and the characteristics of products, based on the in-depth study of the hot-rolled mechanism of strip, a modeling strategy combining multiple intelligent modeling methods with mechanism model is proposed, and a high-performance model of hot-rolled process parameters of strip is established. For common steel, neural network is used to modify the mechanism model adaptively; for special rule steel, fuzzy rule algorithm is added based on the output of neural network; for small batch of special steel, case-based reasoning method is added to modify the mechanism model. Finally, the parameter fusion modeling method of hot strip rolling process is formed and successfully applied to 1549mm hot rolling production line of Taiyuan Iron and Steel Group Co., Ltd (TISCO). The practical application results show that the fusion modeling method improves the modeling accuracy, speed and adaptive ability, and achieves good economic benefits.

16:30-16:50 SatA07-10 1634 An Improved Mean Filtering Algorithm for Measuring Data of Robotic Arm Force Sensor Li-hong Li Taiyuan Univ. of Tech.Ji-fu Li Taiyuan Univ. of Tech.Jia-cai Gao Taiyuan Univ. of Tech.

Technical Programmes CCDC 2021 Force sensor plays a very important role in the development and application of intelligent robot. Aiming at the problems of long sampling period, a large amount of calculation and poor real-time performance in the data processing of six axis force sensor of robotic arm, an improved mean filtering algorithm for the measurement data of robotic arm force sensor is proposed. By calculating the average value of the original signal and the original signal translated by n AD sampling intervals, the interference signal whose period is even multiple of the sampling interval can be quickly filtered. The six axis force sensor with cross beam is selected as the research object to carry out the weight loading experiment in Fz direction. The experimental results show that the proposed algorithm reduces the operation time by 2/3 compared with the conventional mean filtering algorithm, and can quickly filter out the interference signal in the sensor signal processing circuit and retain the effective component of DC signal, which is suitable for the data processing of six axis force sensor.

SatA08 Room08 Signal Processing and Information Fusion (I) 13:30-16:50 Chair: Hongli Li Tiangong Univ.CO-Chair: Jianxiong Wei Harbin Inst. of Tech.

13:30-13:50 SatA08-1 1060 Design of Holter System Based on the Internet of Things and EMD Algorithm Hongli Li Tiangong Univ.Man Ding Tiangong Univ.Quanzeng Wang Tiangong Univ.Xin Ma Tiangong Univ.Ronghua Zhang Tiangong Univ.Lixiang Ma Tianjin JFOD Automation Tech. Co. LTDThe real-time Holter monitoring system is designed based on Internet of Things and low-power wireless communication technology. The electrocardio signal acquisition, filter, analysis, prognosis and transfer can be realized by this system. The system has low-power dissipation and smaller volume. The wearable device is used to collect electrocardio signal in real-time, and transmit them to the local monitoring mobile phone platform via Bluetooth. The mobile phone is used as a preprocessing and communication platform to display the electrocardiogram (ECG). The EMD (Empirical Mode Decomposition) algorithm is used to process the ECG and perform preliminary diagnosis of the QRS waveform of the ECG signal. Thus the local monitoring of the ECG signal is realized. If the abnormal ECG is detected, it can be transmitted to remote community medical server by WIFI. The community doctor will in-depth analyze and diagnose the abnormal ECG. In the meantime, the alarm and cue will be given to remind the patient to take medicine. The experiment shows that the system has the characteristics of convenient wear, sensitive monitor, timely diagnosis and high stability. The system will improve the living quality of heart disease patients and extend the average life span. The long-term monitoring of community people with heart disease risk can be effectively solved.

13:50-14:10 SatA08-2 530 Research on AUV Cooperative Positioning Technology Based on Improved-EKF with Error Estimation Yuan Hui Beihang Univ.Changyun Wen Kunming department of Xi'an Research

Inst. of Precision MachineryWei Jianxiong Harbin Inst. of Tech.Shao Jianbo Harbin Inst. of Tech.Fan Shiwei Harbin Inst. of Tech.Yu Fei Harbin Inst. of Tech.Huang Wenjun Science and Tech. on Near-Surface

Detection LaboratoryAiming at the data fusion problem in the Leader-fellow cooperative positioning system of multiple autonomous underwater vehicles (AUV), the mathematical model of the Leader-fellow cooperative positioning system is established, and then the speed error and heading error are analyzed to position the fellow AUV At the same time, an improved-EKF filtering algorithm with the ability to estimate the measurement error of navigation equipment is designed.The distance information between the leader and fellow AUVs obtained by acoustic ranging is used as the measurement information to estimate the position, speed error and heading error of the fellow AUV. In order to verify the effectiveness of the algorithm, the cooperative positioning algorithm with error estimation capability is verified by simulation experiments and offline data from ship navigation experiments on the lake. The results show that the proposed improved-EKF algorithm can effectively reduce the positioning error of the fellow AUV. It is especially obvious when the AUV is autonomously positioned, which greatly improves the navigation and positioning capabilities of the fellow AUV.

14:10-14:30 SatA08-3 229 Level Set Method for Image Segmentation Model Based on Improved Signed Pressure Force Function Jiaxin Wang Dalian Maritime Univ.Jing Liu Dalian Maritime Univ.Teng Wu Dalian Maritime Univ.

A new level set model is proposed to solve the problems of selective binary and gaussian filtering regularized level set (SBGFRLS), which cannot segment the image with weak boundary or uneven gray scale. A new signed pressure force function (SPF) which fuses global and local statistical information is constructed by introducing the generalized global mean gray value and adding local image gray value. In order to speed up the curve evolution, the velocity function is improved. The experimental results show that the new model can effectively segment targets with fuzzy boundaries and multi-target images with different gray scales, and has some anti-noise properties.

14:30-14:50 SatA08-4 240 3D Visual Simulation of Swaying Motion of Surface Vessel Based on Given Power Spectral Density Zhilin Liu Key Laboratory of Intelligent Tech. and

Application of Marine EquipmentHarbin Engineering Univ.

Zhongxin Wang Key Laboratory of Intelligent Tech. andApplication of Marine Equipment

Harbin Engineering Univ.Jiayue Jiang Key Laboratory of Intelligent Tech. and

Application of Marine EquipmentHarbin Engineering Univ.

Shouzheng Yuan Key Laboratory of Intelligent Tech. andApplication of Marine Equipment

Harbin Engineering Univ.When the surface vessel is parked on the sea, it can be affected by various external interference, e.g. the six-degree-of-freedom swaying motion can be produced by sea waves. In order to better study the swaying motion of the surface vessel under the irregular waves, based on the given swaying power spectral density, a method of converting power spectral density into time-domain signals is proposed, and then the spectrum analysis method can be adopted to carry out the research of the surface vessel motion. The virtual simulation technology is used so as to visually observe the motion of the surface vessel under random interference. The model required for simulation is established in the Multigen Creator software, and the environment required for simulation is configured in Vega Prime. Moreover, VC++ is used to develop the software framework, human-computer interaction, and other functions, where the Matlab engine should be called for the design of algorithm modules. Finally, serial communication technology is used to realize real-time physical communication, completing the design of the 3D visual simulation system. The simulation results show that the power spectral density can be effectively converted into a time-domain signal through the given algorithm, and the swaying motion of surface vessel can be reproduced under the converted time-domain signal using the three-dimensional simulation software.

14:50-15:10 SatA08-5 305 Guaranteed Cost Robust Centralized Fusion Kalman Predictor for Systems with Moving Average Colored Measurement Noise, Missing Measurements and Uncertain Noise Variances Yang Chunshan Guilin Univ. of Aerospace Tech.Zhao Ying Guilin Univ. of Aerospace Tech.Li Shude Guilin Univ. of Aerospace Tech.The paper is concerned with the guaranteed cost robust centralized fusion (CF) prediction problem for systems with moving average colored measurement noise, missing measurements and uncertain noise variances. The considered system is converted into one only with uncertain noise variances by model transformation method. The method includes rewriting moving average model of state space form, augmenting method and fictitious noise technology. By parameterizing the perturbations of uncertain noise variances, two classes of guaranteed cost robust CF Kalman predictors are presented based on the minimax robust estimation principle. Two problems can be converted into optimisation problems with constraints, and they can be solved by Lagrange multiplier method and linear programme method. The proposed guaranteed cost predictors can concurrently give maximal lower bound and minimal upper bound of accuracy deviations. The proof of the guaranteed cost robustness is proved by the Lyapunov equation approach. A simulation example used to UPS shows the correctness and effectiveness of the proposed results.

15:10-15:30 SatA08-6 371 Adaptive Tracking Algorithm with Radar Position Errors and Measurement Noise Covariance Matrix Xingyu Bao Shanghai Jiao tong Univ.Hao Chen Beijing Inst. of tracking and

telecommunication tech.Jianxun Li Shanghai Jiao tong Univ.Radar position errors and measurement noise are two key parameters which should be estimated accurately in practical target tracking system. The wrong parameters will result in substantial estimation errors or even filtering divergence. Although some methods were proposed to estimate the two parameters respectively, there are few ways to estimate two parameters simultaneously. Relevant works are less in the case of target maneuvering. In this paper, Interacting Multiple Method (IMM) is used to identify maneuver, and the augmented-state filtering algorithm is established to realize online estimation of radar position errors.

Technical Programmes CCDC 2021 Meanwhile, Sage-Husa adaptive filtering algorithm is introduced to estimate measurement noise covariance in real time. Based on the estimation of these two parameters, an adaptive maneuvering target tracking method is established, which makes the filtering algorithm more consistent with the reality. Monte Carlo simulation verifies the performance and practicability of the new method.

15:30-15:50 SatA08-7 442 Absolute Phase Unwrapping with Deep Neural Network for Structured Light 3D Reconstruction Wan Chang Wuhan Univ. of Science and Tech.

Engin. Research Center of MetallurgicalAuto. and Measurement Tech.

Sen Xiang Wuhan Univ. of Science and Tech.Engin. Research Center of Metallurgical

Auto. and Measurement Tech.Huiping Deng Wuhan Univ. of Science and Tech.

Engin. Research Center of MetallurgicalAuto. and Measurement Tech.

Jin Wu Wuhan Univ. of Science and Tech.Engin. Research Center of Metallurgical

Auto. and Measurement Tech.Phase-coding structured light is an important technique in 3D reconstruction. However, a great challenge is the wrapped phase that causes geometry ambiguity. Conventional unwrapping methods such as spatial and temporal approaches face the problem of error propagation and low efficiency. In this paper, we propose to solve the phase unwrapping problem with a deep neural network. To be specific, the phase unwrapping problem is cast to a semantic segmentation task, where the wrapped phase is the input and the fringe index for every pixel is the output. An encoderdecoder architecture, which is like U-net, is adopted as the network. We further propose a combined loss function by considering cross entropy loss, phase consistency loss and edge consistency loss. With 10000 artificially synthesized samples, the proposed method converges well. Experimental results demonstrate that the trained model well predicts fringe orders on both simulation data and real captured data. In addition, it unwrapps every pixel independently and avoids phase error propagation, and further achieves accurate 3D reconstruction.

15:50-16:10 SatA08-8 444 Shock Response Analysis of the Unlocking Device of Satellite and Rocket Based on EEMD Jianhai Zhang State Key Laboratory of Astronautic Dynamics

Xi’an Satellite Control CenterShaofei Qin Xi’an Satellite Control CenterShouhang Sun Xi’an Satellite Control CenterFeng Shi Xi’an Satellite Control CenterNan Ye Xi’an Satellite Control CenterIn order to make the device of satellite work normally, the natural frequency of the unlocking separation structure should be avoided as far as possible when designing the instrument equipment of satellite. A new method based on the Ensemble Empirical Mode Decomposition (EEMD) was used to analysis the shock response of an unlocking device of satellite and rocket was proposed. The shock response signals was decomposed into different Intrinsic Mode Function (IMF) by EEMD, and the time-frequency spectrum of each IMF was further analyzed. The example shows that this method can quickly and accurately identify the natural frequency of the unlocked separation structure, and has great engineering application value for the optimization design of the impact environment of the rocket body in the industrial sector.

16:10-16:30 SatA08-9 1093 A Gait Events Detection Algorithm Based on the Invariant Characteristic of Hip Joint Kinematics Ningcun Xu Beijing Inst. of Tech.Xiwei Peng Beijing Inst. of Tech.Liang Peng Chinese academy of ScienceZengguang Hou Chinese academy of ScienceIn order to make up for the shortcomings of a single inertial sensor, which is easily disturbed and unable to directly describe the periodic characteristics of gait, a novel gait events detection algorithm is proposed which is based on the invariant characteristics of hip joint kinematics. Four healthy volunteers conducted a walking experiment over the ground, who were equipped with motion capture device and insole with foot-switch. The hip angle derived from motion capture device were applied to detect gait events, heel stride (HS) and toe off (TO). And the gait events detected by ground reactor force (GRF) were taken as the reference standard. The mean absolute difference of HS is 28±42ms, and the mean absolute difference of TO is 27±43ms. And the confidence levels of the two gait events are 97.5% and 99.2%, respectively. The results demonstrate that the proposed gait events detection algorithm is reliability and has potential clinical application value.

16:30-16:50 SatA08-10 1123 Unsupervised Infrared and Visible Image Fusion with Pixel Self-attention

Saijia Cui Beijing Inst. of Tech.Zhiqiang Zhou Beijing Inst. of Tech.Linhao Li Beijing Inst. of Tech.Erfang Fei Beijing Inst. of Tech.In this paper, we propose a convolutional neural network (CNN) based unsupervised infrared and visible image fusion method. The proposed method optimizes both network structure and loss functions to obtain better fused images. Specifically, an effective pixel self-attention module is applied to emphasize the importance of different pixel locations of the feature map, which enables the network to better integrate the salient information in infrared images and the detail information in visible images. As to the loss function, we adopt the perceptual loss and texture loss to preserve the detail information as well as improve the visual perception of the fused image. Experimental results demonstrate that our method can achieve a superior performance compared with other fusion methods in both subjective and objective assessments.

SatA09 Room09 Fault Diagnosis and Predictive Maintenance (I) 13:30-16:50 Chair: Yiqun Dong Fudan Univ.CO-Chair: Yuping Cao China Univ. of Petroleum (East China)

13:30-13:50 SatA09-1 387 Deep Neural Networks-based Air Data Sensors Fault Detection for Aircraft Yiqun Dong Fudan Univ.Jiongran Wen Fudan Univ.Youmin Zhang Concordia Univ.Jianliang Ai Fudan Univ.A deep neural networks (DNN) based fault detection (FD) scheme for aircraft air data sensors (ADS) is proposed. We first investigate kinematic relations of the aircraft air data. Measurements of inertial reference unit (IRU) are modeled as equivalent inputs to the relations. We then model the FD task as a mapping process. Whilst ADS fault cases are exported directly in the process, the inputs to the mapping involve the ADS outputs, and other measurable states including accelerations/angular speeds along different axes of the aircraft body. We adopt both convolutional neural network and long-short time memory blocks to construct the mapping. We detail the database that is established in training the DNN. Training history and testing results of the DNN are also illustrated. Testing performances of the proposed DNN-based FD scheme present itself promising with the high FD accuracy and robustness to different aircraft/flight conditions.

13:50-14:10 SatA09-2 483 A Fault Diagnosis Method Based on Mutual Information and Canonical Variate Analysis Yingqi Zhao China Univ. of Petroleum (East China)Yuping Cao China Univ. of Petroleum (East China)Xiaogang Deng China Univ. of Petroleum (East China)Canonical variate analysis (CVA) is a widely used multivariate statistical method for fault diagnosis. CVA features are extracted by maximizing the correlations between two sets of variables. However, traditional CVA extracts features on the basis of the covariance matrix, which only considers variables’ linear correlations, and can not reflect general practical production process variables’ nonlinear correlations fully. For above problem, we introduce mutual information into CVA and propose a mutual information and CVA based fault diagnosis method. Mutual information is a nonlinear measure, which can describe random variables’ dependence degree. The proposed method extracts features based on mutual information, and builds monitoring statistics. The effectiveness of the proposed method is verified by a continuous stirred tank reactor.

14:10-14:30 SatA09-3 1339 Online Anomaly Detection with Streaming Data based on Fine-grained Feature Forecasting Keying Liu Henan Normal Univ.Wentao Mao Henan Normal Univ.

Engineering Lab of Intelligence Business &Internet of Things of Henan Province

Huadong Shi Henan Normal Univ.Chao Wu Henan Normal Univ.Jiaxian Chen Henan Normal Univ.In the industrial applications like fault diagnosis and health management, monitoring data generally reachessequentially in a streaming form. To recognize fault occurrence in real time without system halt, it is necessary to improve the accuracy and stability of anomaly detection with streaming data. To solve this problem, a new online anomaly detection method with streaming data is proposed based on fine-grained feature forecasting. First, to conduct fine-grained decomposition of features, a denoising autoencoder network is run to extract multiple-dimensional deep features of online data in the initial period of normal state. Second, a forecasting model with tensor Tucker decomposition and ARIMA is conducted to predict the fluctuation trend of all feature sequences. Finally, the deviation degree between the prediction values and sequentially-arrived data is calculated, and an alarm threshold is built according to the 95% confidence interval of the maximum deviation. Then the anomalous state data can be detected in real time. This paper adopts the problem of

Technical Programmes CCDC 2021 bearing early fault online detection as an example, and run comparative experiments on the IEEE PHM Challenge 2012 bearing dataset. The results show that the proposed method has good detection accuracy and is with no false alarm, while the model training does not rely on any offline data. Then the proposed method is applicable to the problem of online anomaly detection.

14:30-14:50 SatA09-4 1595 Fault Estimation and Fault-tolerant Control for Discrete-time Stochastic Systems with Actuator and Sensor Fault Bo Ding Yangzhou Univ.Sutong Li Yangzhou Univ.This paper studies the fault estimation (FE) and fault-tolerant control (FTC) for discrete-time stochastic systems, where the actuator fault and sensor fault are both considered. By introducing virtual noise, the faults are extended to the state as the augmented system. Next, the Kalman filter (KF) is applied to the augmented system, then the optimal state estimation and fault estimation are obtained. Based on the state and fault estimation, an active fault-tolerant controller is designed to ensure the stability of the system. Numerical simulation verifies the effectiveness of this method.

14:50-15:10 SatA09-5 1323 Improved Outlier-tolerance Method of Typical Error Variance Estimation Algorithms for Process Sampling Data Xiaomin Huang Xi’an Univ. of Tech.Jian Li Norinco Group Test and Measuri

ng AcademyShaolin Hu Xi’an Univ. of Tech.

Guangdong Univ. of Petrochemical Tech.

Jiahui Liang Xi’an Univ. of Tech.The error variance estimation is commonly used to evaluate the quality of process sampling data. In this paper, two typical variance estimation algorithms: the least squared (LS) and the variable-difference method are analyzed from the perspectives of optimality, unbiasedness and outlier-tolerance, and an improved outlier-tolerance method based on the Huber weight function is proposed. The simulation results show that the variance estimation algorithm based on LS and variable-difference method have no outlier-tolerance ability, the improved method can effectively suppress the influence of abnormal data and has better outlier-tolerance. The above results are very valuable for the quality evaluation of radar measurement data.

15:10-15:30 SatA09-6 1275 Fault Diagnosis of Electric Actuator Based on Spiking Neural Network Guolian Hou North China Electric Power Univ.Zhiheng Lv North China Electric Power Univ.Wenguang Zhang North China Electric Power Univ.Actuator is an important part of the control system, and its fault diagnosis is essential to guide the safe and stable operation of the control process. This paper designed a fault diagnosis method for electric actuators based on the spiking neural network (SNN) of the adaptive threshold neuron model. First, the required training Dataset and test Dataset were obtained from the established experimental platform. Through the local mean decomposition (LMD) of the Dataset, the signal was decomposed into product function components with real physical meaning, so as to obtain the main characteristics of the Dataset; then the adaptive threshold was introduced to optimize the neuron structure, and through the relative ordering learning algorithm (ROL), the accuracy of fault recognition was improved. Finally, the test Dataset was used for verification. The experimental results showed that the method made full use of the effective information of the data and could be effectively applied to the fault diagnosis of the electric control valve.

15:30-15:50 SatA09-7 287 Remaining Useful Life Prediction of Turbofan Engine with GA Optimized Hybrid Neural Network Hong Huang Huazhong Univ. of Science and Tech.Junyang Jin HUST-Wuxi Research InstituteYong Zhang Wuhan Univ. of Science and Tech.Ye Yuan Huazhong Univ. of Science and Tech.In recent years, deep learning has been widely used in industrial remaining useful life (RUL) prediction. However, most methods does not distinguish the importance of samples at different time steps. In order to solve this problem, this paper introduces an attention layer in neural network, and uses the multi-network collaborative training method to predict the remaining useful life. Convolutional Neural Networks (CNN) are used to extract the data information under the spatial span, and Bidirectional Gated Recurrent Units (BGRU) are used to capture the time correlation. Through parameter sharing and loss feedback, the improved genetic algorithm learns the attention weights of different time steps. The proposed method is validated on turbofan engine data and the experimental results show that the method outperforms several state-of-the-art methods.

15:50-16:10 SatA09-8 374 Research on Accelerated Degradation Test and Reliability Evaluation Method of Electromechanical Products Yi Wang System Engineering Research Inst.

Nanjing Univ. of Aeronautics and AstronauticsAccording to the characteristics of long life and high reliability of aviation electromechanical products, the reliability of electromechanical products was evaluated by designing step down stress accelerated degradation test (SDSADT) and modeling analysis of performance degradation data. An accelerated degradation test scheme combining pre-test and formal test was proposed. Based on the degradation data of product performance, a nonlinear Wiener process degradation model with measurement error was established. The random drift parameters and random initial values caused by individual differences were considered. A multi-stage parameter estimation method was proposed and the product failure time distribution was derived. The simulation results were compared with the traditional Wiener model. Finally, the test verification and life evaluation of a certain type of resolver were carried out, and the reliability curve of this type of resolver under normal temperature storage environment was obtained. The results show that the proposed accelerated degradation test scheme can significantly shorten the test time, improve the test efficiency and data validity, the performance degradation model used is more accurate than the traditional Wiener model, and the reliability evaluation results are reliable.

16:10-16:30 SatA09-9 381 Design of CNNs with Adaptive Activation Function and Application in Fault Detection Wenxuan Zhang Shanghai Jiao Tong Univ.

Key Laboratory of System Controland Information Processing

Shanghai Engineering ResearchCenter of Intelligent Control and

ManagementFei Xiao Shanghai Jiao Tong Univ.

Key Laboratory of System Controland Information Processing

Shanghai Engineering ResearchCenter of Intelligent Control and

ManagementJianxun Li Shanghai Jiao Tong Univ.

Key Laboratory of System Controland Information Processing

Shanghai Engineering ResearchCenter of Intelligent Control and

ManagementActivation function plays an important role in deep convolutional neural network. It realizes the nonlinear mapping of eigenspace. The traditional activation function is a fixed form in the network model. In this paper, the methods of linear combination activation functions and gated adaptive activation functions are proposed to learn the activation function. By combining basic activation functions and using gating ideas to allow activation functions to adapt to the input. Parameters are learned through back propagation. Using the learning parameters, a nonlinear mapping suitable for the specific data distribution is established. Compared with the traditional activation function, more complex nonlinear representation or linear representation can be obtained, which can help a network model more suitable for input data. Moreover, the proposed gated activation function can adjust the form of the activation function according to the specific input, which can increase the ability of feature extraction and generalization of network model. Experiments show that the proposed adaptive CNN model has better performance in fault detection.

16:30-16:50 SatA09-10 1618 An LSTM-Based Approach for Capacity Estimation on Lithium-ion Battery Mengda Cao National Univ. of Defense Tech.

Hunan Key Laboratory of Multi-energySystem Intelligent Interconnection Tech.

Yajun Zhang National Univ. of Defense Tech.Hunan Key Laboratory of Multi-energy

System Intelligent Interconnection Tech.Jianjiang Hui Beijing Inst. of Tracking and

Telecommunication Tech.Yajie Liu National Univ. of Defense Tech.

Hunan Key Laboratory of Multi-energySystem Intelligent Interconnection Tech.

With the wide application of lithium-ion batteries, the safety and stability of batteries have attracted much attention in recent years. Accurate capacity estimation can help understand the health status of lithium-ion batteries. Model-based methods can perform well on capacity estimation with enough prior knowledge. However, the internal electrochemical mechanism of lithium-ion battery degradation is so complicated that the cost of prior knowledge is usually expensive. Due to no need for prior knowledge, data-driven methods introduce new solutions to solve this problem. Among these, deep learning methods (DL) show great potential for their advantages of high-dimensional feature mapping. In this paper, an LSTM (Long Short-Term Memory) network model is proposed to estimate the capacity, in which seven time-related health features in the

Technical Programmes CCDC 2021 charge stage are used as input of the model. On the one hand, health features extracted from charge time data can decrease the difficulties in data acquisition. On the other hand, the LSTM model has the ability to learn the long-term dependencies between data so that suit for constructing mapping relationship between health features and capacity. The validation experiments are conducted based on two simulation datasets. The results show that the proposed model can accurately track the capacity recovery effect and estimate the RUL of lithium-ion batteries.

SatA10 Room10 Optimal Control and Optimization (I) 13:30-16:50 Chair: Zhiqiang Geng Guizhou Provincial Key Laboratory of

Public Big DataBeijing Univ. of Chemical Tech.

CO-Chair: Wang Kai Xi’an Univ. of Tech.

13:30-13:50 SatA10-1 542 Operation Optimization of Ethylene Cracking Furnace based on NNIA Algorithm Zhiqiang Geng Guizhou Provincial Key Laboratory of Public

Big DataBeijing Univ. of Chemical Tech.

Qiuyi Wang Guizhou Provincial Key Laboratory of PublicBig Data

Beijing Univ. of Chemical Tech.Yongming Han Guizhou Provincial Key Laboratory of Public

Big DataBeijing Univ. of Chemical Tech.

Jie Yu Guizhou Provincial Key Laboratory of PublicBig Data

Feng Xie Guizhou Provincial Key Laboratory of PublicBig Data

Kai Chen Guizhou Provincial Key Laboratory of PublicBig Data

In the petrochemical industry, pyrolysis is the main way to produce ethylene and propylene, and the ethylene cracking furnace is the critical device in the thermal cracking process. In order to increase the product yield and control the production cost, this paper optimizes the ethylene product yield, propylene product yield, naphtha flow rate and steam flow rate of the ethylene cracking furnace based on the NNIA algorithm to gain Pareto solution set. The text results present that the method of this paper can deal with the multi-objective optimization model of the ethylene cracking furnace with effect. While increasing the ethylene production rate, the multi-objective optimization model can reduce the naphtha flow rate and steam flow rate. At the same time, the propylene output has only a small reduction, which can bring higher levels in actual production and economic benefits.

13:50-14:10 SatA10-2 280 Optimization on Cooling Process of LTCC Substrate Reflow Soldering Kai Wang Xi’an Univ. of Tech.Dan Zheng Xi’an Univ. of Tech.Jiale Shi Xi’an Univ. of Tech.In order to optimize cooling process of a soldered assembly, the corresponding finite element thermal analysis model has been established in this paper. And the relevant thermal physical parameters have been investigated too. Taking the minimum first principal stress produced in low temperature co-fired ceramic (LTCC) substrate as the optimization object, the optimized cooling process parameters have been obtained by using the orthogonal test method. According these process parameters the reflow soldering cooling process has been simulated. On this basis, the experimental research also has been carried out on cooling process. Experiments verify the correctness of the simulation analysis. The substrate’s first principal stress can be reduced effectively, thereby soldered quality has been improved definitely.

14:10-14:30 SatA10-3 672 Research on Artificial Potential Field Route Planning Method Based on Threat Level Assessment Xiao Wang China Aerodynamics Research and

Development Center

Enmi Yong China Aerodynamics Research andDevelopment Center

Kaifeng He China Aerodynamics Research andDevelopment Center

Tao Liu China Aerodynamics Research andDevelopment Center

Chenzhou Xu China Aerodynamics Research andDevelopment Center

Northwestern Polytechnical Univ.Fengqi Zheng China Aerodynamics Research and

Development CenterShenshen Liu China Aerodynamics Research and

Development CenterThis paper first studies the uncertain probabilistic inference model based on Bayesian networks, constructs a threat level evaluation algorithm, and combines fuzzy inference technology to evaluate the relative threat levels

of different threat sources in the battlefield environment. Then the threat level assessment is combined with the path planning method, the two-dimensional Gaussian probability density function is used to model each threat source, and the artificial potential field method is combined to establish a UAV trajectory planning method based on threat situation assessment. Finally, simulations verify the effectiveness of the proposed method.

14:30-14:50 SatA10-4 676 Output Tracking of Singular Boolean Control Networks Rong Zhao Shandong Univ.Jun-e Feng Shandong Univ.Yiliang Li Shandong Univ.This paper investigates the output tracking problem of singular Boolean control networks (SBCNs). First, the concept of control invariant subset of SBCNs is proposed. Based on properties of control invariant subsets, an algorithm for finding the maximum control invariant subset is given. Second, two necessary and sufficient conditions for the solvability of output tracking problem are derived by using the maximum control invariant subset. Third, in light of a criterion for output tracking under a state feedback controller, an approach to designing the time-optimal state feedback controller is obtained. Finally, an example is provided to verify the validity of the main results.

14:50-15:10 SatA10-5 285 Predefined-Time Distributed Optimization of Multi-Agent Systems With Exogenous Disturbance Shiling Li Central South Univ.Xiaohong Nian Central South Univ.Zhenhua Deng Central South Univ.Zhao Chen Central South Univ.In this paper, we consider the predefined-time distributed optimization problem of disturbed multi-agent systems under undirected and connected communication networks. In order to control all agents converge to the optimal solution at predefinedtime, we propose a distributed predefined-time control law based on time-base generator technique and internal model. Then we guarantee the convergence of the proposed algorithm by the properties of Laplace matrix and orthogonal matrix. Finally, we provide example to illustrate the effectiveness of the proposed distributed algorithm by choosing arbitrary convergence time.

15:10-15:30 SatA10-6 313 Direct Solution of High-pressure Air Cold-launch Optimal Control Problem Qin Chen China Aerodynamic Research and

Development CenterFang Zhao China Aerodynamic Research and

Development CenterJun Wu The 63926 ArmyGuoqing Li The 63926 ArmyZebing Ren China Aerodynamic Research and

Development CenterHigh-pressure air cold-launch can provide advantages for commercial launches, from both safety and cost considerations. In military applications, missiles’ structures are solid to be accelerated more than 10g by engine exhaust flow. The critical part of implementing commercial systems is precisely controlling energy-releasing from the highpressure gas steady through valves, as payloads and commercial rockets demand strictly limited acceleration and dynamic pressure in the launching process. This paper tried to solve the cold-launch problem with acceleration constraints. After elaborating the differential equations for the system, we introduce a direct solution by particle swarm optimization. The simulated results prove the method’s feasibility. The first version of demonstration experiments based on current work will allow exploring the problem with more details.

15:30-15:50 SatA10-7 355 Research on Improved Potential Field Ant Colony Algorithm for UAV Path Planning Tao Chen North Minzu Univ.Xinyu Lv North Minzu Univ.Shengying Wang North Minzu Univ.Na Ta North Minzu Univ.Jing Zhao Ningxia Univ.Xinpei Chen Ningxia Preschool Education CollegeMingxia Xiao North Minzu Univ.Haicheng Wei North Minzu Univ.In order to solve the problems of slow convergence speed and prone to local optimization in ant colony algorithm, an improved ant colony algorithm of potential field is proposed in this study. Firstly, the method of pheromone affected by artificial potential field is introduced to reduce the blindness of the ant colony. Then, the potential field heuristic information is used to accelerate the convergence speed of ant colony algorithm. Finally, the decrease coefficient of potential field resultant force is used to solve the local optimal problem. Compare with the traditional algorithm, the experiments show that the improved algorithm in this paper can get better results, in terms of path length and the number of convergence

Technical Programmes CCDC 2021 iterations, the average search time for convergence is reduced by 44.83% in the 20x20 map.

15:50-16:10 SatA10-8 363 The Optimal Chassis Configuration Matching for Distributed Hybrid Truck Working Conditions Based on Energy Consumption Jiwei Liu Beijing Inst. of Tech.Junqiu Li Beijing Inst. of Tech.Ruichuan Wei Beijing Inst. of Tech.Meng Qiu JIANGLU Machinery & Electronics Group

Co, LTDThe distributed hybrid truck which carries the hub motor driving system has advantages of high efficiency, high controllability and high integration. It also shows potential to reducing energy consumption and range anxiety, becoming one of the future development trends of commercial vehicles. However, due to the large number of chassis components and the complex power units, the problem of economy has too many dimensions to find the optimal solution. In this paper, reasonable chassis configurations are selected and a distributed hybrid truck model is established. Then this paper classifies the working conditions into three types by using the historical test data, while divides the sample working conditions and extracts the features. Giving priority to energy consumption, the global optimal chassis configuration for each working condition is obtained based on the dynamic programming (DP) algorithm. Then the sample working conditions and the matching chassis configurations are used to train the long short-term memory (LSTM) neural network to study the relationship between the working condition and the corresponding optimal chassis configuration. Finally, concludes the whole paper and points out the direction of future work.

16:10-16:30 SatA10-9 383 An Optimal Tracking Control Method for Unmanned Helicopter Ship Approach Yuqing Qiu Northwestern Polytechnical Univ.Yan Li Northwestern Polytechnical Univ.Yuxian Liu Northwestern Polytechnical Univ.Jinxi Lang Northwestern Polytechnical Univ.This paper presents a control algorithm to enable autonomous ship approach for an unmanned helicopter under the initial state disturbances and ship motions. The nonlinear dynamic model of a single rotor unmanned helicopter is used to develop the optimal controller. The proposed control algorithm is based on the indirect Legendre pseudospectral method (ILPM) combined with receding horizon optimization (RHO) strategy. The issue in regard to the real-time calculation of the optimal control input is addressed. An important part of this research is the design of on-line control strategy, which ensures the fast convergence of tracking errors to zero under initial state disturbances. Simulations discuss how to acquire a trade-off between the control performance and computation time by selecting the LGL points’ number and the horizon length, and the effectiveness of the developed approach is validated with appropriate parameters.

16:30-16:50 SatA10-10 586 Optimization of deep stall landing trajectory for morphing aided UAV Weijia Zeng Beijing Inst. of Tech.Junhui Liu Beijing Inst. of Tech.Jiayuan Shan Beijing Inst. of Tech.Lan Wei Beijing Inst. of Tech.In order to recover small unmanned aerial vehicles (UAVs) at a confined area, deep stall landing is investigated for UAV aided by morphing wing. Inspired by perching maneuver of birds, morphing wing is adopted to gain extra controllability during deep stall landing. Constraints like terminal velocity, sink rate, terminal attitude, down range of approach, input saturation, etc are considered. Then, deep-stall landing problem of small UAV aided by morphing wing in longitudinal plane has been formulated as an optimal control problem. The optimal trajectory and nominal reference control is solved numerically based on Gauss pseudo spectral method. Numerical results verify the effectiveness of proposed optimization method, and characteristics and mechanisms of optimized deep-stall landing trajectory for morphing aided UAV are discussed under various flight conditions and limits.

SatA11 Room11 Intelligent Control, Computation and Optimization (I) 13:30-16:50 Chair: Dong Wang Dalian Univ. of Tech.CO-Chair: Yueling Zhao Liaoning Univ. of Tech.

13:30-13:50 SatA11-1 796 Differentially Private Distributed Online Optimization in Time-varying Networks via Dual Averaging Push Yang Liu Dalian Univ. of Tech.Zhu Wang Dalian Univ. of Tech.Chaowen Zhu Wanhua Chemical Group Co, LtdDong Wang Dalian Univ. of Tech.Wei Wang Dalian Univ. of Tech.Distributed online optimization allows several nodes in the network to

collaboratively minimize the sum of time-varying local cost functions at each moment through information exchange. However, frequent interactions are prone to leakage of sensitive information. Meanwhile, the actual communication networks can be time-varying and unbalanced. To address such a problem, we proposed the differentially private online distributed dual averaging push algorithm (DP-ODDAP), where push-sum protocol and differential privacy are employed on the basis of dual averaging method. By introducing the differential privacy, the data privacy of individuals is protected, which means that malicious nodes gain little sensitive information. Moreover, the impact of the imbalance caused by time-varying directed networks is eliminated by applying push-sum protocol, thus DP-ODDAP can reach the sub-linear expected individual regret bound with order of O(√T). In addition, the tradeoff between privacy level and accuracy is revealed. Finally, simulation experiments is provided to verify the effectiveness of above results.

13:50-14:10 SatA11-2 697 Modeling and simulation of maximum wind energy capture by segmented slope duty-cycle perturbation method Yueling Zhao Liaoning Univ. of Tech.Sijia Kong Liaoning Univ. of Tech.Dinghua Liu Beijing Inst. of Space Launch Tech.Dong Guo Liaoning Univ. of Tech.Due to the change of external wind speed, the power speed output characteristic curve will also change. In order to improve the tracking speed of the maximum power point and reduce the oscillation problem near the maximum power point in the maximum wind energy capture process of direct drive permanent magnet synchronous wind power generation system, this thesis proposes a MPPT algorithm based on piecewise slope duty-cycle disturbance method Methods by judging the critical value range of the slope of power to speed to select the appropriate duty cycle disturbance value, the maximum power point can be achieved quickly and stably. Finally, the effectiveness and superiority of the segmented duty-cycle disturbance method are verified by building MATLAB /Simulink model.

14:10-14:30 SatA11-3 745 Simulated Annealing Strategy in Chaotic Neural Network with Legendre Function Yaoqun Xu Harbin Univ. of CommerceZhenhua Yang Harbin Univ. of CommerceXinxin Zhen Harbin Univ. of CommerceAs to the monotonous activation funcation of chaotic neural network, a new transient chaotic neuron model is constructed by combining multiple Legendre polynomials and sigmoid functions as a new non-monotonic excitation function, and the dynamic characteristics of chaotic neurons are analyzed. A new transient chaotic neuron network model is constructed by using this model. This model improves the ability of the network to fall into local minima by using the idea of piecewise simulated annealing. The effectiveness of this model has been verified by the application of multi-user detectors in nonlinear function optimization, traveling salesman problem (TSP) and direct sequence-code division multiple access (DS-CDMA) on multi-user detector.

14:30-14:50 SatA11-4 110 Robust Deep Stochastic Configuration Network Modeling Method Based on Kernel Density Estimation Jingcheng Guo Beijing Univ. of Tech.

Engineering Research Center of DigitalCommunity

Beijing Laboratory for Urban Mass TransitAijun Yan Beijing Univ. of Tech.

Engineering Research Center of DigitalCommunity

Beijing Laboratory for Urban Mass TransitIn order to alleviate the negative impact of noise on the accuracy of deep stochastic configuration network modeling, a robust deep stochastic configuration network modeling method based on kernel density estimation is proposed. The output weights of each hidden layer of deep stochastic configuration networks are obtained by solving the weighted least square problem, where the kernel density estimation method is employed to set the penalty weights of training samples. In addition, the alternating optimization technique is applied to update the penalty weights and hidden layer output weights. The effectiveness of the proposed method is tested and evaluated by using function approximation and the benchmark dataset. The results show that the proposed method can effectively alleviate the impact of noise on modeling accuracy, which is valuable for applications in the field of robust modeling.

14:50-15:10 SatA11-5 170 A New Carrier Deck Pitch Motion Compensation Strategy Yongtao Yu Shenyang Aerospace Univ.Zhiyong Yao Shenyang Aerospace Univ.Tian Feng Shenyang Aerospace Univ.Guo Yuetao Shenyang Aerospace Univ.A new carrier deck pitch motion compensation strategy was designed to

Technical Programmes CCDC 2021 compensate the adverse effects that pitch motion of carrier acts on aircraft landing. The desired approach velocity of aircraft was calculated by velocity controller when aircraft was approaching, and the aircraft approached with desired approach velocity to guarantee the purpose that carrier pitch reached to 0° when aircraft landing, which means the carrier pitch motion compensation is accomplished. The excess adjust of velocity was avoided by restricting the range of desired approach velocity, which reduce the influence that compensation strategy acted on the flight path adjustment ability of aircraft. The new compensator with proposed strategy was build and added into automatic carrier landing system, and a full approach simulation results verified the effectiveness of the new design.

15:10-15:30 SatA11-6 402 A Average Response Time Prediction Method For Seasonal Non-Stationary Concurrency Based On Improved RBF Algorithm Jun Guo Northeastern Univ.Jiayi Wang Northeastern Univ.Jina Wang Liaoning Province’s Construction and Engineering

Center for Advanced Equipment Manufacturing BaseAixuan Dong Northeastern Univ.Bin Zhang Northeastern Univ.In the cloud service platform, seasonal non-stationary concurrency is widespread, and the amount of concurrency peaks in the form of a cycle. As the seasonal non-stationary constants continue to change, the service performance of the cloud service platform will be affected. The predicted average response time tends to lag behind the real time load condition by the traditional load balancing strategy. This paper proposes an average response time prediction model based on the improved RBF(Radial Basis Function) algorithm for seasonal non-stationary concurrency. The model uses the RNN-LSTM(Recurrent Neural Network, Long Short Term Memory) algorithm to predict the concurrency. The average response time is predicted by the improved RBF algorithm. In the prediction of seasonal non-stationary concurrency, the mean relative error of RNN-LSTM are 0.0467. In the prediction of average response time, the mean relative error of improved RBF are 0.0125. Experimental results show that the method proposed in this paper has higher prediction accuracy and lower prediction error.

15:30-15:50 SatA11-7 327 Optimal PID Controller Design for AVR System Based on Multi-objective Optimization and Multi-attribute Decision Making Yan Sun Central South Univ.Xiaojun Zhou Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyZhaoke Huang Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyIn this paper, a novel two-stage approach is proposed to design an optimal Proportional-Integral-Derivative (PID) controller for the Automatic Voltage Regulator (AVR) system by combining multi-objective optimization with multi-attribute decision making. First, the optimal PID controller design for the AVR system is formulated as a multiobjective optimization problem. Then a new multi-objective state transition algorithm based on decomposition is used to find the Pareto optimal solutions. In order to prevent integral saturation and large oscillation of the controller, the PID controller output is constrained by integral separation method. Finally, the best compromise solution is obtained by using a multi-attribute decision making method named AHP-Entropy-TOP SIS. The simulation results verify the effectiveness and superiority of the proposed approach.

15:50-16:10 SatA11-8 368 Classification of skin diseases based on improved MobileNetV2 Yujia Cheng Shanghai Inst. of Tech.Wei Lin Shanghai Inst. of Tech.Yuanzhen Liu Shanghai Inst. of Tech.Lu Sun Shanghai Inst. of Tech.In order to realize the classification research of skin diseases on portable devices, the neural network model can be better applied to embedded and mobile devices. This article explores the impact of ECA module on the model complexity and accuracy. By embedding ECA module into the MobileNetV2 model, the accuracy of the model is significantly improved without increasing the complexity of the model. Due to the complex network structure of neural network, it has a high requirement on hardware equipment and has a great amount of calculation, which cannot be run on mobile and embedded devices, and cannot deal with the problem of portable identification of skin lesion images on mobile devices. In this article, a deep learning model based on improved MobileNetV2 is proposed, which has higher accuracy and faster recognition rate, and can realize the classification and recognition of skin lesion images on portable devices. By comparing with the current advanced network model, it is verified that this model can improve the network performance without increasing the complexity of the model and at the same time improving the recognition rate.

16:10-16:30 SatA11-9 126

Traffic Flow Prediction via Weighted Combination of ARIMA and WASDNN Models Xiao Liu Sun Yat-sen University

Guangdong Key Laboratory of ModernControl Technology

Key Laboratory of Machine Intelligenceand Advanced Computing

Yunong Zhang Sun Yat-sen UniversityGuangdong Key Laboratory of Modern

Control TechnologyKey Laboratory of Machine Intelligence

and Advanced ComputingMin Yang Sun Yat-sen University

Guangdong Key Laboratory of ModernControl Technology

Key Laboratory of Machine Intelligenceand Advanced Computing

Zhongxian Xue Sun Yat-sen UniversityGuangdong Key Laboratory of Modern

Control TechnologyKey Laboratory of Machine Intelligence

and Advanced ComputingChengxu Ye Qinghai Normal Univ.The modeling and prediction of traffic flow are important in transportation study and traffic network management. Different from individual models, a hybrid model which is a combination of autoregressive integrated moving average (ARIMA) and weights and structure determination neural network (WASDNN) models, is proposed for modeling and prediction of traffic flow. Compared with individual models like ARIMA and WASDNN, the proposed ARIMA-WASDNN model has better performance in modeling and prediction of traffic flow of Shenzhen city as an example. In addition, prediction results of different activation functions of hidden layer neurons are provided. What is more, the proposed ARIMA-WASDNN model also performs better than the popular long short-term memory (LSTM) model in predicting traffic flow of Shenzhen city. The effectiveness and superiority of ARIMA-WASDNN model are substantiated by results of numerical experiments.

16:30-16:50 SatA11-10 222 An Active Queue Management Mechanism for Minimizing Queueing Delay Siqiao Hu Nanjing Univ. of Science and Tech.Jinsheng Sun Nanjing Univ. of Science and Tech.Real-time Internet applications, such as online gaming, VoIP (Voice over Internet Protocol) and teleconferencing, prefer short latency over high throughput and have been steadily rising in popularity as of 2021. Yet current Active Queue Management (AQM) algorithms inherently create delay-inducing standing queue, thus cannot effectively minimize the latency. We propose an adaptive mechanism named MID (Multiplicative Increase/Decrease) in this paper, to minimize queueing delay by eliminating the standing queue created by AQMs relying on fixed targets, based on the assumption that standing queue can be removed without substantially affecting the network performance in other aspects. The proposed mechanism discovers the optimal control target by gently reducing the target as long as the buffer is not empty, otherwise the target will be increased and the discovery process will be temporarily suspended. All parameters of MID are insensitive to network environments hence require no tuning. The paper validates the performance of the proposed algorithm by applying it to PI and PIE in simulations, and compares the performance between the MID-enhanced AQMs and their original counterparts. Results confirm that MID is capable of efficiently minimizing queueing delay at an acceptable cost.

SatAIS Room12 Interactive Session 13:30-16:50

SatAIS-01 738 Feature selection of Wrapper based on GA and prediction of Burning Through Point of integrated multi-kernel support vector machine Zhongwei Wu Northeastern Univ.Ping Zhou Northeastern Univ.Burning Through Point (BTP) is a crucial parameter in sintering process, and its position directly determines the quality of sinter. Due to the large hysteresis of sintering process, it is of great significance to predict BTP in advance to guide workers to control operating parameters in adavance to improve the quality of sinter. Firstly, in order to remove redundant features and select feature combinations that make the subsequent prediction model more accurate, a feature selection method of Wrapper based on GA was adopted in this paper. Secondly, in order to overcome the problem of low prediction accuracy of single learner, this paper aopts the support vector machine algorithm of integrated learning, in order to enhance the diversity of base learner, five different kernel functions of support vector machine are adopted, in order to improve the performance of base learner, the parameters c and g of support vector machine are determined by grid search algorithm. Finally, in this paper, the actual sintering data are used to verify the feasibility of the proposed algorithm and compare it with other methods to verify the effectiveness and advancement of the proposed method.

Technical Programmes CCDC 2021 SatAIS-02 1088 Prediction of Rolling Force for Hot Strip Rolling Based on RFR Yan Ren Inner Mongolia Univ. of Science and Tech.Nan Su Inner Mongolia Univ. of Science and Tech.Jing Yang Inner Mongolia Univ. of Science and Tech.Xiaowen Gao Inner Mongolia Univ. of Science and Tech.Huimin Wang Inner Mongolia Univ. of Science and Tech.Meixia Yue Inner Mongolia Univ. of Science and Tech.Donghao Lv Inner Mongolia Univ. of Science and Tech.In the hot strip rolling, rolling force is one of the important factors affecting the shape of steel strip. There are some errors in the mechanism model used to predict the secondary rolling force of hot strip rolling in a steel company. In this paper, in order to improve the setting accuracy of the rolling force in production line, the rolling force prediction based on historical production data is studied in this paper. First, in order to reduce the computational complexity of the model and improve the prediction accuracy, correlation analysis and local outlier factor algorithm are used to preprocess the data. Secondly, a rolling force prediction model based on random forest regression algorithm is proposed in this paper,Experimental results based on actual data show that the proposed method has a better predictive effect.

SatAIS-03 1330 Study on HS-RNN in Vibration Prediction of Mechanical Spindle Jinxiang Pian Shenyang Jianzhu Univ.Masuma Rifah Tamanna Shenyang JIanzhu Univ.Alkasim Usman ABDULMAJID Shenyang JIanzhu Univ.Mechanical spindle in the high-speed operation due to an uneven distribution of the rotor mass, causing the sindle to vibrate thus affecting its accuracy. Therefore, the model of mechanical spindle vibration prediction is of great significance. The mechanism of mechanical spindle vibration prediction is complex, and the amplitude of vibration varies nonlinearly with the speed. It is difficult to establish accurate model of mechanical spindle vibration prediction. In this paper, RNN (Recurrent Neural Network) recurrent neural network is used to establish the model of mechanical spindle vibration prediction. The vibration amplitude of the built-in balance block at different position and spindle speeds is predicted. Harmony Search (HS) Through self-learning update, thereby improving the accuracy of the mechanical spindle vibration prediction model. The experimental results show that the HS-RNN-based mechanical spindle vibration prediction method proposed in this paper can automatically determine the network structure and accurately predict the vibration amplitude of the mechanical spindle.

SatAIS-04 83 Fuzzy Adoptive Command Filtered Backstepping Fault-tolerant Control for MIMO Nonlinear Systems with Actuator Fualt Sheng Ning Qingdao Univ. of Science & Tech.Ai Zi Dong Qingdao Univ. of Science & Tech.Zhang Dian Qingdao Univ. of Science & Tech.Zu Li Nan Qingdao Univ. of Science & Tech.This paper focus on MIMO nonlinear systems with actuator fault. Use the fuzzy logic system to deal with the nonlinear unvertainty, use the command filtered to eliminate the computational complexity through the backstepping design methodology; two kinds of actuator faults are considered in the simulation, the simulation results are manifested the effectiveness of the proposed method; the stabilization analysis are verified all the signals in the closed-loop systems are bounded even suffering from the actuator fault.

SatAIS-05 782 FeO Content Classification of Sinter Based on Semi-Supervised Deep Learning Qian Ding Central South Univ.

Zhongye Changtian International Engineering Co.Zongping Li Zhongye Changtian International Engineering Co.

Central South Univ.Liming Zhao Baosteel Zhanjiang Iron and Steel Co.Sinter is the main raw material for blast furnace ironmaking. The FeO content in sinter is used as an important indicator for evaluating the quality of sinter, especially the strength and reducibility of sinter. In view of the existing sinter FeO content detection methods that have strong hysteresis, reliance on manual experience, and possible cognitive limitations, this paper uses Dense Convolutional Network (DenseNet), taking the section image of sintering machine tail as input to classify FeO content. For the problem of insufficient labeled data, the semi-supervised learning method FixMatch is used to train the model to achieve accurate classification of FeO content. The experimental results indicate that the FeO content classification results are within an acceptable range and can be fed back in real time at the sintering production site. The use of the above methods also provides references for sintering production and related research.

SatAIS-06 870 Research on Voiceprint recognition method of buried drainage pipe based on MFCC and GMM-HMM

Jiarui Yang Kunming Univ. of Science and Tech.Zao Feng Kunming Univ. of Science and Tech.Jiande Wu Kunming Univ. of Science and Tech.Yugang Fan Kunming Univ. of Science and Tech.An voiceprint recognition model based on short-term energy and Gaussian Mixed Hidden Markov models was proposed in this paper aiming to solve the problem that the number and position of the of the blockages, pipe components and pipe end in the buried drainage pipeline are uncertain and difficult to identify. This method first performs wavelet threshold de-noising method on the sound pressure signal collected in the operating pipeline, and then uses the short-term energy as the discriminant parameter for endpoint detection, then divide into segmentations obtain the sound pressure signal corresponding to different types of individuals in the pipeline, and then extracts the Mel-frequency Cepstrum coefficient to constructs feature vector sets that can characterize the blockage, lateral connection and the pipe end, and finally the feature vector sets are used to train the Gaussian Mixed hidden Markov model to obtain the classification results. The experimental results have shown that the proposed method can effectively identify blockages, pipe fittings such as lateral connection and pipe end, furthermore, it avoids the problems of low recognition rate of new samples caused by insufficient known conditions included in the training samples.

SatAIS-07 1041 Research on Standard Model of Engineering Lighting Solution Based on Network Collaboration Limin Zhao Fujian Hongbo Opto-electronics Tech. Co.

Fujian Key Laboratory of LED Packaging Technology and Application Research

Qingmei Chen Fujian Hongbo Opto-electronics Tech. Co.Fujian Key Laboratory of LED Packaging Technol

ogy and Application ResearchGuoliang Wang Shanghai Univ. of Engineering ScienceWeiwu Yan Shanghai Jiao Tong Univ.The rapid development of the industrial internet has put forward the digital, networked and intelligent transformation needs of enterprises, especially for the real industrial enterprises that rely on customized engineering projects to obtain orders. This paper discusses the advantages of this innovative model in the engineering lighting industry by analyzing the architecture, solution process and business scenario logic of common applications. Moreover, by using the network collaboration, the enterprises can achieve the rapid response of engineering projects to solutions, prices and deliveries. The response time to market is shorten by establishing a customized collaborative application model for engineering projects and forming a standardized database to realize standard solutions for engineering projects. The effect of this innovative model is verified by examples.

SatAIS-08 1065 Fractal-based combined kernel function model for the polyester polymerization process Junxian Geng Donghua Univ.Lei Chen Donghua Univ.Kuangrong Hao Donghua Univ.Hengqian Wang Donghua Univ.The polymerization process plays a significant role in polyester production, and the melt intrinsic viscosity is an important indicator of product quality. This paper introduced the fractal dimension feature selection method and combined the kernel function model based on STL decomposition (Seasonal-Trend decomposition procedure based on Loess) for the polyester polymerization process. The fractal dimension is used to analyze the spatial characteristics of the data and select features to construct the feature space, and the STL decomposition is used to analyze the time characteristics of the data to construct the combined kernel function. The two methods are combined with Gaussian process regression model, and the simulation experiment of industrial data shows the effectiveness of the proposed algorithm.

SatAIS-09 1236 Temperature Test Method and Experimental Research of Resistance Central Heating Cigarette Equipment Chunxin Shi Kunming Univ. of Science and Tech.Weiquan Deng Kunming Univ. of Science and Tech.Xiang Li Kunming Univ. of Science and Tech.Jiande Wu Kunming Univ. of Science and Tech.In view of the lack of experimental analysis on the temperature control curves of heating elements under different working conditions, and there is no temperature measurement method for heating elements under load in the industry. Using high temperature ceramic glue to fix the thermocouple on the surface of the heating element, the temperature control curve of heating cigarette equipment’s heating element at the position of 6mm (middle) and 10mm (tip) from the base of heating element under three states of no load, under load without puff and under load with puff is tested. The results show that: The puffing action has a greater impact on the sheet type center heating cigarette equipment IQOS 2.4 Plus than the stick type center heating cigarette equipment lil SOLID mini. In the heating stage, the temperature of the heating element under load lags the temperature of the heating element under no load,

Technical Programmes CCDC 2021 and the hysteresis of the tip of the stick type center heating cigarette equipment lil SOLID mini is more prominent.

SatAIS-10 1245 Fast Fault Identification Method of Wind Turbine Rolling Bearing based on VMD and Time Domain and Frequency Domain Feature Selection Ruichen Liu Northeast Electric Power Univ.Zhenhao Tang Northeast Electric Power Univ.Shengxian Cao Northeast Electric Power Univ.Hongzhi Han Urumqi Electric Power Construction and Commiss

ioning Inst. of Xinjiang Xinneng Group Co.One-third of the wind turbines faults occurred in the rolling bearings. The rapid identification of rolling bearings faults is of great significance to the safe production of wind power generation. To take full usage of the rolling bearing vibration signals, a rapid identification method based on Variational Mode Decomposition (VMD) and the time-domain and frequency-domain feature selection is developed. Firstly, the original vibration signals are processed by adaptive variational mode decomposition to obtain their components. Then, the time-domain and frequency-domain features of the original signal and components are computed through statistical methods and Fast Fourier Transform (FFT). Finally, the selected the time-frequency domain features via the random forest algorithm are given into the extreme learning machine network to construct a rapid fault identification model. Experiments demonstrate that the proposed method can identify bearing faults with a fault recognition rate of 99.7% at 0.268 seconds.

SatAIS-11 1247 Prediction of short-term wind power based on ESN improved by VMD Xu Gao Northeast Electric Power Univ.Zhenhao Tang Northeast Electric Power Univ.Hongzhi Han Urumqi Electric Power Construction and Commiss

ioning Inst. of Xinjiang Xinneng Group Co.Bing Bu Guodian Hefeng Wind Power Development Co.Wind power generation is discontinuous and uncertain. Short-term power prediction is an important basis for power equipment structural design, system scheduling, system planning, and market transactions. Accurate prediction of short-term wind power is of great significance to optimize the operation and dispatch of the power system. There combine prediction methods including nonlinear data analysis, data decomposition, and reconstruction is proposed to improve the reliability of short-term wind power prediction. The original time series is decomposed by the variational modal decomposition(VMD). The echo state network (ESN) prediction model is based on the new subsequence and key factors as input variables. We use wind power data from real wind farms in northeast China to verify the feasibility and effectiveness of the proposed method.

SatAIS-12 1327 Data-driven Pressure Control for Manufacturing System of 16 µm Ultra-thin Alloy Bo Li Taiyuan Iron & Steel (Group) Electric Co.Jian-yan Tian Taiyuan Univ. of Tech.Yong-feng Lv Taiyuan Univ. of Tech.Zhi-en LI Taiyuan Iron & Steel (Group) Electric Co.Xian-he Liu Taiyuan Univ. of Tech.Gao-peng Han Taiyuan Univ. of Tech.The manufacturing process of ultra-thin alloy is a high-temperature, high-speed and high-precision process. Since the thickness of the ultra-thin alloy is only 16 µm, slight pressure fluctuations during the manufacturing process will cause the thickness of the ultra-thin alloy to be uneven. In order to solve this problem, this paper establishes a data driven control system to produce the 16 µm ultra-thin alloy effectively, which avoids the accurate mathematical model for the ultra-thin alloy making. Based on the maintenance of steel nozzle seam pressure and flow rate consistent, 16 µm ultra-thin alloy manufacturing pressure automatic control system, established a set of nonlinear pressure control curve. According to the pressure curve, gas proportional valve is controlled through S7-200 PLC, the closed-loop control is realized by using pressure sensor feedback S7-200 PLC to achieve the stable state of 16 µm ultra-thin alloy thickness in plane flow casting. The practical application results show that the automatic pressure control system can control the thickness deviation of the ultra-thin alloy at 2 µm, and greatly improve the overall pass rate of the product, and obtain the good economic benefit.

SatAIS-13 1333 Design of an Automatic T-beam Erection System Based on NB-IoT for Bridge-Erecting Cranes Xu Li Kunming Univ. of Science and Tech.Guanbin Gao Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Xin Chen Kunming Univ. of Science and Tech.Bridge-erecting cranes are often used for beam erection in the construction of expressways. To improve the speed and quality of T-beam

erection and ensure the safety of bridge-erecting cranes, an automatic T-beam erection system based on the Internet of Things (IoT) is designed in this paper. Narrow Band Internet of Things (NB-IoT) communication technology is used to integrate laser-ranging sensors, batteries, and communication modules into base station subsystems, which are installed in specific locations of the bridge-erecting crane. The position of the T-beam can be measured in real-time by the laser ranging sensors, with which a closed-loop control system is constructed for the T-beam erection system. The information of the running state including the position of the T-beam, the installation progress, and the position of the bridge-erecting crane is transferred to the cloud computing platform by NB-IoT, which can be viewed by mobile terminals. The experimental tests show that the distance measurement range of the system is 0.045m～30m, and the measurement accuracy is 2mm. Compared with the manual operation, the automatic T-beam erection system can reduce the risk of the T-beam erection and improve efficiency.

SatAIS-14 1338 Intelligent Optimization Algorithm for Distribution Reactive Power Cheng Guo Yunnan Electircal Power Experiment Inst., CO.Yantian Li Kunming Univ. of Science and Tech.Linzhen Zhong Kunming Univ. of Science and Tech.Jun Zhao Kunming Univ. of Science and Tech.This paper proposes a method of combining particle swarm optimization (PSO) and genetic algorithm (GA) to optimize the reactive power of IEEE 33 node system. Although the optimal solution can be obtained by using PSO, it is easy to fall into the local optimal value when dealing with multi-variable problems. To overcome this problem, by combining with GA, the convergence speed has been significantly improved, and the stability of convergence is also better. To this end, the system model is first constructed, where the distribution reactive power optimization objective function is derived. To realize the distribution reactive power optimization, the PSO combined with GA are proposed. Simulation results shows that the algorithm combining PSO and GA is more reliable than GA or PSO alone in the distribution reactive power optimization.

SatAIS-15 1384 A Pipeline Blockage Identification Model Learning from Unbalanced Datasets Based on Random Forest Mingyue Fang Kunming Univ. of Science and Tech.Zao Feng Kunming Univ. of Science and Tech.Xiaodong Wang Kunming Univ. of Science and Tech.Jun Ma Kunming Univ. of Science and Tech.Aiming at the problem of decreased accuracy of operating state recognition caused by the unbalanced data acquisition between the normal and blocked failure states of urban drainage pipelines, a new method of pipeline blockage state recognition based on unbalanced data is proposed. The experimental results show that the random forest algorithm, which uses bootstrap sampling and simple voting methods to integrate decision trees, has a good effect on the pipeline blocking state recognition of unbalanced data.

SatAIS-16 1455 Event-triggered adaptive compensation control for nonlinear cyber-physical systems under false data injection attacks Pengbiao Wang Beijing Inst. of Tech.Xuemei Ren Beijing Inst. of Tech.Dongdong Zheng Beijing Inst. of Tech.In this paper, we study the event-triggered adaptive compensate control problem for cyber-physical systems constructed by nonlinear systems with unknown parameters under false data injection attacks. First, a new adaptive eventtriggered scheme (AETS) is designed to save limited network resources, and its threshold can be continuously adjusted according to the change of system state. In particular, the proposed adaptive event-triggered scheme can degenerate into the existing event-triggered scheme with the fixed threshold. Then, an adaptive controller and adaptive laws are designed to effectively compensate for false data injection attacks. Furthermore, it is proved that the tracking error of the system can be exponentially converged within a compact set with an adjustable radius. Finally, a simulation example shows the effectiveness of the proposed method.

SatAIS-17 1481 Optimization of non-linear industrial based on WT-KECA for process fault detection Yinghua Yang Northeastern Univ.Qingyan Kong Northeastern Univ.Xiaozhi Liu Northeastern Univ.In this paper a new method is proposed for the optimization of non-linear process fault detection based on kernel entropy component analysis (KECA) with wavelet denoising. The method starts with data pre-processing using the wavelet transform signal noise removal method, Then, in order to reduce the influence of the nuclear size parameters during fault detection, the calculation of the optimal nuclear size parameters is carried out. Finally, a simulation test study was carried out using the Tennessee Eastman process and the results verified the feasibility and effectiveness of the proposed method.

Technical Programmes CCDC 2021 SatAIS-18 1607 Data Cleaning for Wind Turbine Systems Based on Iterative learning and Neural Network Yijun Shen Zhejiang Univ. Of Tech.Shijian Dong China Univ. of Mining and Tech.Qi Wu Zhejiang Univ. Of Tech.Fei Chu China Univ. of Mining and Tech.The power curve is an important indicator for evaluating the power generation performance of wind turbine systems. Equipment failure and human intervention are the main factors leading abnormal data of power curve, resulting in serious deterioration of the data availability. In order to solve the negative impact of abnormal data, a novel data cleaning framework is proposed for wind turbine systems. First, the nonlinear regression model between the wind speed and the power is established by the back propagation (BP) neural network. Then, the distances between the real data and the nonlinear regression model are approximated by employing the designed auxiliary points. The candidate abnormal data and the current normal data are separated by a threshold judgment strategy after sorting all the distances in descending order. At the same time, based on the set of candidate abnormal data, the candidate normal data is selected through another threshold judgment strategy after sorting all the distances in ascending order. On the basic of both the current normal data and the candidate normal data, the neural network based on iterative learning is applied to update the nonlinear regression model. Finally, the effectiveness of the proposed method is verified upon two sets of data collected by real wind turbine systems.

SatAIS-19 1645 Robot Visual Servo Control System Based on Deep Detection Network and Spatial Pose Estimation Chao Zhang Yunnan Univ.Dapeng Tao Yunnan Univ.Linfei Wang Yunnan Univ.Yiqiang Wu Yunnan Univ.Visual servo control system (VSCS) is an indispensable ability for robots, which has become a hot topic in machine intelligence and attracts extensive attentions. While the enormous success has been witnessed in last years, however, there are still several essential challenges, such as visual detection error caused by unsatisfied accuracy of traditional algorithm, hysteresis of control caused by expensive time-consuming of deep learning. To address above issues, we propose a novel visual servo control framework based on light weight detection network and spatial pose estimation for object following. Specifically, we select stable YOLO network as backbone and introduce Kalman Filterbased pose estimation module into proposed framework to ensure accurate and real-time object detection. In addition, a double loop-based cascade PID algorithm is adopted to drive the robot following the specific objects in real-time. Experimental results demonstrate the effectiveness of proposed framework on mobile object detection and pose estimation.

SatAIS-20 1660 Active Disturbance Rejection Control of The Novel Variable Speed Direct Drive Pumping System Haigang DING China Univ. of Mining and Tech.

Jiangsu Province and Education Ministry Co-sponsored Collaborative Innovation Center of I

ntelligent Mining EquipmentYanbin ZHAO China Univ. of Mining and Tech.Robin China Univ. of Mining and Tech.Ziwen SANG China Univ. of Mining and Tech.Chengcheng YANG China Univ. of Mining and Tech.Concrete pump truck is a special construction machinery to realize the rapid delivery and pouring of concrete, which is widely used in transportation, energy, construction, national defense engineering and other fields. The pumping system is the core of concrete pump truck. The paper presents a new structure of variable speed direct drive pumping system, then describes the structure and working principle of the new concrete pumping system, establishes the mathematical model, and creates multiphasic simulation model with AMESim, The operation rule of pumping system is expounded. An Active Disturbance Rejection Control is used to precisely control the displacement of concrete cylinder. The control characteristics of ADRC are studied in a simulated system under the condition of time-varying parameters and load disturbance, and the influence of controller parameters on the control characteristics of the system is analyzed. The simulation results show that ADRC is very suitable for electro-hydraulic servo system, with high control accuracy and easy to implement. The study provides a reference for the application of ADRC in the field of construction machinery.

SatAIS-21 908 Research on Identification Method of Sewage Blockage Based on Low Frequency Active Acoustic Wave Xuefeng Zhu Kunming Univ. of Science and Tech.Jiande Wu Kunming Univ. of Science and Tech.Zao Feng Kunming Univ. of Science and Tech.Guoyong Huang Kunming Univ. of Science and Tech.Urban drainage system is an important link in urban rainwater discharge,

water pollution control and water ecological environment protection system. It is an important infrastructure to ensure the survival and sustainable development of the city. However, some buried sewage have been laid for a long time, and the sewage blockage due to the proliferation of microorganisms, chemical corrosion, sediment deposition and valve not fully opened. The blockage will reduce the cross-section of the drainage network, increase the pressure at the peak of drainage, and increase the flow resistance. With the passage of time, it is easy to cause multiple blockage, which leads to the serious consequences of overload transportation and surface water flooding. Therefore, how to accurately and quickly realize the intelligent detection of sewage blockage condition, locate the blockage position, improve the detection and maintenance accuracy, and reduce the intensity of artificial labor, is a worthy study subject. It is a fast, nondestructive, non-invasive and low-cost detection method to detect the sewage blockage conditions by using low-frequency active acoustic wave. It is suitable for straight line, curve shape and different length of pipeline, and it is suitable for low, medium and high density fluid, with good controllability. The acoustics detection of pipe blockage is established on the following grounds: A computer with Labview software is used to control the DAQ of NI-PXIe-6363 multi-function IO module for outputting analog voltage signals. After signal amplification with power amplifier, the sound card is driven to generate audio signals, which are then transmitted into the pipeline via the loudspeaker. Upon encounter of interface changes (conventional lateral connections, blockages, leaks) during the sound wave propagation in the pipe wall, the echoed sound wave signals are received by the microphone installed at the pipe head end, which are then uploaded to the computer for storage following filter passage. The received acoustic signals are processed in three steps, namely signal analysis, feature extraction and pattern recognition, thereby attaining identification of the the sewage blockage conditions. However, due to the complex surrounding and internal environment of the actual pipeline, the collected acoustic signal contains a lot of interference and overlapping information, which is non-stationary, nonlinear, non Gaussian, strong attenuation and strong coupling. It is difficult to extract acoustic characteristics of sewage blockage effectively. Separating and removing the redundant information is an important step in the data pre-processing and feature extraction stage, and it is also a key part to ensure the efficiency and accuracy of the pattern recognition system. Targeting the challenge of determining the degree of blockage in buried pipelines and the difficulty of effectively extracting blockage features, a blockage detection method integrating information gain and Mel frequency cepstral coefficients (MFCC) is proposed. Firstly, a sinusoidal sweep signal was used as the excitation signal, the sound pressure data was collected at the receiving end to measure the acoustic impulse response of the pipe. Each one hundred sets of sampling are performed on the nine statuses of sewage to obtain 9*100 sets of sample data in total. Then variational mode decomposition (VMD) was applied to obtain decomposed acoustic signal components in different frequency bands. VMD operation is performed on the one hundred sets of sampled data in nine statuses, thereby deriving 12 components. By utilizing information gain, 7 effective components are filtered from the 12 components. Information gain values of the components are selected as per the principle of decision tree selection. A threshold is set up, which is then compared with the filtered information gain values. Filtering is terminated if the information gain value of a component signal is less than the threshold. After VMD operation, multiple components are obtained. Some of them are closely linked to the information about pipeline blockage, while others are unrelated to the blockage or are noise interference components. Asthis necessitates the filtration of component information, an information gain-based component selection method is put forward. The sound pressure level was then calculated to form the 7 effective components from the Mel-frequency cepstrum coefficients. Finally, the extreme learning machine (ELM) was adopted due to its advantages of simple structure and fast learning speed, the pipe conditions can be effectively identified, as well as the different degrees of blocking of the pipe through the above methods.

SatAIS-22 1532 Closed-loop distributed data-driven modeling and control for islanded microgrids Dong-Dong Zheng Beijing Inst. of Tech.Lingjie Nanjing Univ. of Aeronautics and Astronautics.Xuemei Ren Beijing Inst. of Tech.The integration of the renewable energy resources into the electrical grid is performed via microgrids (MG). An MG is a distributed multivariable nonlinear system with unknown and time varying parameters, which makes voltage and frequency control as well as power sharing among different distributed generation units a challenging control problem. In this paper a new distributed nonlinear identification method based on neural networks is proposed. The system identification process can be done using the available closed-loop system input/output data recorded during normal operation without additional external excitation. Moreover, based on the nonlinear identified model, a novel frequency regulation and active/reactive power sharing control framework is developed. The new control strategy does not rely on the classical droop-based hierarchical control structure, such that improved transient performance and accurate power sharing for microgrid with mixed lines can be achieved. The effectiveness of the proposed method is demonstrated via simulations.

SatAIS-23 599

Technical Programmes CCDC 2021 Optimization of electric-heat-gas model based on interval linear programming Mingtong Li Shenyang Agricultural Univ.Lidi Wang Shenyang Agricultural Univ.Sitong Zhu Shenyang Agricultural Univ.Yilin Miao Shenyang Agricultural Univ.Xinyao Wang Shenyang Agricultural Univ.In order to improve the coupling relationship between energy sources and improve the energy utilization rate, this paper controls the parameter variables of energy storage equipment and renewable energy power generation technology, compiles the corresponding optimization program, obtains the optimal combination of different energy types distribution work, and realizes the economic optimization goal. The program analysis shows that: the existence of renewable energy power generation technology will greatly reduce the wind energy abandonment rate and make the wind energy output regulation within the controllable range; the existence of energy storage equipment will reduce the overall economic consumption of the system; the joint output of different energy sources can not only meet the energy demand, but also improve the efficiency of the whole system, so the system has flexible energy regulation ability, At the same time, the economic budget is within the controllable range and has practical value.

SatAIS-24 1257 A Bionic Simultaneous Location and Mapping with Closed-Loop Correction Based on Dynamic Recognition Threshold Weilong Li Air Force Engineering Univ.Dewei Wu Air Force Engineering Univ.Haonan Zhu Air Force Engineering Univ.Huaxing Wu Air Force Engineering Univ.Boxin Zhao Air Force Engineering Univ.Chuanjin Dai Air Force Engineering Univ.A bionic simultaneous location and mapping with closed-loop correction based on the dynamic recognition threshold is proposed to solve the problem that there exist missing matching and false matching in the model of the rat simultaneous location and mapping for the continuous experienced scenes in the bio-inspired navigation. An enhanced threshold factor and an attenuated threshold factor are introduced in the stage of forming local view cells. The recognition results for the first few frames are used as the excitation to dynamically adjust the template recognition threshold of the current frame image in real time, and to judge whether new local view cells need to generate according to the threshold. Then the experience map is corrected through closed loop detection by integrating the recognition results for the continuous experienced scenes. Experimental results and a comparison with the method under the fixed threshold show that the proposed method not only avoids false matching, but also increases the correct recognition rate, which urges the agent to modify the experience map in a timely manner and to improve the accuracy of the experience map.

SatAIS-25 1478 Robot Assisted Training for Upper Limbs using Impedance Control based on Iterative Learning Kamran Maqsood Univ. of SussexJingkang Xia Southwest Jiaotong Univ.Deqing Huang Southwest Jiaotong Univ.Yanan Li Univ. of SussexThis paper proposes an approach to improve robot assisted physical training subject to human uncertainties. This approach is based on impedance control which is used to regulate the dynamic relationship between the robot’s position and contact force. Repetitive exercise is considered and impedance parameters are adapted in accordance with the human user to provide physical training as needed. Different from the existing approaches, the proposed one has the capacity to deal with time-length-varying cycles, which is a critical issue in physical training of human’s upper limbs. By theoretical analysis and experimental results, we show that the approach can effectively learn the required robot’s impedance parameters and improve the performance of physical training.

SatAIS-26 1540 An Optimization Algorithm for Second-order Multi-robot Collaborative Object Lifting Jinxin Liu Nanyang Technological Univ.Guoqiang Hu Nanyang Technological Univ.In this paper, we formulate the task of multi-robot object collaborative lifting as a distributed optimization problem. The considered total objective function is the sum of the private objective functions corresponding to each robot, which are used to evaluate their location choices. To avoid the large horizontal component force on the multi-robot system during lifting, a coupled equality constraint is introduced to the formulated distributed optimization problem. The feasibility constraints of the optimal location are also considered in this paper. A novel distributed continuous-time algorithm is proposed to solve the collaborative object lifting task for second-order multi-robot systems. Each robot only needs to access its local evaluation function, local variables, and the information exchanged between neighbors. The proposed algorithm does not require exchanging location variables or velocity variables, which effectively prevents privacy leakage. Moreover, we prove the asymptotic convergence of the proposed algorithm to the exact optimal solution.

Finally, the efficacy of the proposed algorithm is verified through the numerical simulation.

SatAIS-27 144 Intelligent Remote Monitoring Agricultural Greenhouses Based on the Internet of Things Aijuan Song Northeastern Univ. at QinhuangdaoA new remote intelligent greenhouse control system based on based on the Internet of Things is presented this paper. In this system data and commands are transmitted through ZigBee, which not only realize higher efficiency for the real-time monitoring of multiple greenhouses, but also effectively improve the degree of intelligent greenhouse environment control In the system, the integrated use of the technology of the Internet of things, sensors, automatic control and video surveillance to realize the agriculture field environment remote monitoring, early warning and control. In addition, the environmental factors can be analyzed to guide the agricultural production.

SatAIS-28 896 Assessment of Subspace Signal Detection in Jamming Environment Zheran Shang Academy of Military Sciences PLA ChinaAizhi Liu Academy of Military Sciences PLA ChinaZhige Xie Academy of Military Sciences PLA ChinaLei Zhao Academy of Military Sciences PLA ChinaJun Lou Academy of Military Sciences PLA ChinaThis paper deals with the problem of detecting a subspace signal in jamming environment which characterized by generalized eigenrelation (GER). Since no uniformly most powerful test exists for the problem at hand, we devise and assess three adaptive subspace detectors designed according to the GLRT test, the Rao test, and the Wald test. Numerical examples indicate that the detectors obtained by using the GLRT criteria can achieve better detection performance in jamming environment than the existing detectors. The proposed Wald detector have the best performance in detecting mismatched signal. The proposed Rao detector can work only when secondary data is sufficient.

SatAIS-29 915 The Research on Social Distance Detection on the Complex Environment of Multi-Pedestrians Xueliang Pan Jianghan Univ.Zhengtong Yi Jianghan Univ.Jun Tao Jianghan Univ.To keep a safe social distance plays an important role in the prevention of high-risk diseases. Aiming at the outbreak of COVID-19, in order to regulate the social distance between pedestrians and reduce the risk of COVID-19 spreading among pedestrians, a multi-pedestrians distance measurement method based on monocular vision is reasonably proposed to realize the measurement of the distance between multiple pedestrians under the monitoring perspective. The pedestrian detection model is used by that method to capture the multi-pedestrians target under the monitoring perspective, and the monocular distance measurement principle is also used to achieve the distance measurement between the multi-pedestrians. Through analyzing these distances, the social distance between pedestrians can be regulated. The experimental results show that this method can efficiently and quickly detect people who do not meet the social distance norms.

SatAIS-30 939 Research on Helmet Wearing Detection in Multiple Scenarios Based on YOLOv5 Zhentong Yi Jianghan Univ.Gui Wu Jianghan Univ.Xueliang Pan Jianghan Univ.Jun Tao Jianghan Univ.Wearing a helmet can effectively protect the human head in industrial production and traffic activities. In order to monitor whether relevant people are wearing helmets in real time, YOLOv5 target detection algorithm is combined with helmet wearing detection in this paper, and the head and helmet dataset are pre-trained through its own yolov5l.pt weight file to obtain the characteristics of the head and helmet. Then the test pictures of different scenarios are sent to the network for performance verification of the algorithm, and compared with YOLOv4 in the quantitative analysis. Finally, the score of mAP equals to 0.924 is obtained. The results show that the reference value for auxiliary real-time detection of helmet wearing can be provided by this algorithm.

SatAIS-31 1045 Adaptive Dynamic Output Feedback Based Cooperative Tracking for Heterogeneous Multi-agent Systems Kun Liu Changchun Univ. of Tech.Yulian Jiang Changchun Univ. of Tech.Zhiyi Wang Changchun Univ. of Tech.Chang Liu Changchun Univ. of Tech.Dongyan Xue Tonghua Agricultural Mechanization Tech.This paper studies the tracking control for a class of heterogeneous multi-agent systems(HMASs) without relying on the global information of

Technical Programmes CCDC 2021 network topologies. Firstly, based on the measurable output information, a local observer is designed for each agent, and a tracking control protocol based on the observer is proposed when the network topology is directed. Then both adaptive controller and observer gain matrices are designed cooperatively, and the asymptotic consensus tracking condition for HMASs is obtained simultaneously. Finally, the effectiveness of the designed cooperative tracking controller is proved by the simulation experiment.

SatAIS-32 1144 Parameter Adjustment Method of Kalman Filter Based on Quadratic Curve Fitting Xinli Xiong Chongqing Changan Automobile Co.Kuan Wang Chongqing Changan Automobile Co.Jianbin Chen Chongqing Changan Automobile Co.Tao Li Chongqing Changan Automobile Co.Haoyun Deng Chongqing Changan Automobile Co.Fan Ren Chongqing Changan Automobile Co.This paper proposes a method for adjusting parameters of Kalman filter based on quadratic curve fitting. Among them, the two main processes of the Kalman filter parameter adjustment are to obtain the true value of the target and how to fit the observation noise in the Kalman filter based on the quadratic curve. First of all, this paper realizes the installation and calibration of the sensor based on the installation characteristics of the sensor. Secondly, it realizes the transformation between the sensor coordinate system and the reference coordinate system based on the stereo calibration principle in computer vision , and uses Real-Time-Kinematic (RTK) to obtain the real position of the target in the scene. Then the observation noise in the Kalman filter is fitted based on the quadratic curve, and the Kalman filter parameters are adjusted based on the observation noise. Finally, the effectiveness of the method proposed in this paper is verified based on qualitative and quantitative methods. Experimental results show that the method proposed in this paper can effectively realize real-time Kalman filter parameter adjustment.

SatAIS-33 1188 The system of Robotic arm watering flowers based on OneNET Internet of Things Yanyan Wu Shenyang Aerospace Univ.Tianle Cui Shenyang Aerospace Univ.Yajie Wang Shenyang Aerospace Univ.Yinghao Song Shenyang Aerospace Univ.Zhengyan Li Shenyang Aerospace Univ.In order to realize the intelligent management of flowers maintenance in family, a watering system using mechanical arm was designed based on OneNET Internet of Things. Firstly, soil moisture sensor, air temperature sensor and light sensor were used to collect the environmental information of flowers. Secondly, Arduino controller was adopted to process parameter informations collected by all kinds of sensors. Also, the water pump was used to pumping water intermittently, the intelligent watering was finished by the mechanical arm, when the water is shortage or too much ,the system can alarm automatically. Finally, the real-time monitoring was implemented through OneNET. Practice shows that the system is accurate and sensitive, and can ensure good growth environment for family flowers.

SatAIS-34 1205 Distractor-aware Visible and Infrared Tracking based on Multi-feature Fusion Yongfang Hu Nanjing Univ. of Science and Tech.Shuangshuang Li Nanjing Mobile Communication & Computing In

novation Inst. of ICTGaopeng Zhao Nanjing Univ. of Science and Tech.It is still challenging for tracking the object of interest in complex environments. Combinations of different features can extract more discriminative information for the tracking problems. Fusion of the visible and infrared features is a feasible way since they are complementary. In this paper, a distractor-aware visible and infrared tracking method is presented. The multi-feature distractor-aware object model is designed by combining the information of the object, the distractors, the color feature of the visible image, and the intensity feature of the infrared image. Two probability map fusion methods are presented including the fixed weights and the adaptive weights based on the peak to sidelobe ratio (PSR). A scale updating strategy based on feature selection is also designed. Experiments on the public Object Tracking and Classification Beyond the Visible Spectrum (OTCBVS) datasets are conducted. The results demonstrate that the proposed tracking methods perform well in terms of accuracy and robustness in real surveillance scenes and show better results compared to several state-of-the-art methods.

SatAIS-35 1412 Enhancing the differentiation of walking and standing via the ratio of plantar pressures Shuxing Bao Nanjing Univ. of Posts and TelecommunicationsJingjin Shen Nanjing Univ. of Posts and TelecommunicationsZhenyu Zhou Nanjing Univ. of Posts and Telecommunications

Xin Zheng Nanjing Univ. of Posts and TelecommunicationsDifferentiation of standing and walking based on plantar pressures is helpful in developing strategies to reduce health risks in the workplace. In order to improve the differentiation ability, the paper proposes a new metric for posture differentiation, i.e., the pressure ratio on the two anatomical plantar regions. The plantar pressures were collected from 30 persons during walking and standing. Two-way repeated measures ANOVA was conducted for the pressure ratios and pressure metrics. The advantage of the pressure ratio over two conventional pressure metrics (the average pressure and the peak pressure) is demonstrated by its much larger size effect. Furthermore, the pressure ratio permits to establish value ranges corresponding to walking and standing, which are less influenced by specific person factors, thus facilitating the design of a standardized posture recognition system. The underlying mechanism underlying the pressure ratio is discussed from the aspect of biomechanics of movement.

SatAIS-36 1542 Research on Gesture Recognition Based on YOLOv5 Li LING Jianghan Univ.Jun Tao Jianghan Univ.Gui Wu Jianghan Univ.The gesture recognition technology based on YOLOv5 adopts the Backbone network to train and learn. The graphical interface developed by python Tkinter module has made the real-time gesture recognition of videos with high precision and frame rate achievable. In terms of the testing dataset, the overall average recognition precision of model can be up to 99.6%, which is up to the precision of current mainstream deep learning target detection algorithms. Moreover, it is superior to those algorithms in terms of the recognition speed. From the experimental results, it can be concluded that YOLOv5 takes account of both precision and speed requirements of recognition, which has an advantage in dealing with the natural interaction information. Thus the technology provided by this paper proposes effective means for the human-computer interaction techniques.

SatAIS-37 1663 A Weak Target Tracking Algorithm Implement on Networked Multi-Agent Control Framework Qiang Ren Inst. of Computer Application, China Academy

of Engineering PhysicsHailei Ren Inst. of Fluid Physics, China Academy of Engin

eering PhysicsBaoran An Inst. of Computer Application, China Academy

of Engineering PhysicsWith the rapid development of computer science and technology, target tracking technology related to computer vision has become a hot research topic. Target tracking technology is to find the region of interest from each frame of video sequence to analyze and find the location of the target to be tracked. This paper presents a weak target tracking method under the condition of strong light spot interference. Firstly, the edge extraction method is used to locate and extract the strong spot. Secondly, the pixels of each strong spot are filled with the medium value pixels in the field, which can eliminate the strong spot. When the weak target coincides with the strong spot, it can avoid the problem of tracking target transfer the target due to the strong spot significance. Finally, to evaluate the performance of the proposed method, we carried out a large number of experiments on our networked multi-agent control framework. Through the qualitative and quantitative analyses of the experimental results, we verified that the weak targets tracking performance of the method is effective and robust.

SatAIS-38 1664 Real-time Panoramic Video Mosaic system Based on Mapping Table and GPU Acceleration Hailei Ren Inst. of Fluid Physics, China Academy of Engin

eering PhysicsQiang Ren Inst. of Computer Application, China Academy

of Engineering Physics As an important research direction of computer vision, panoramic mosaic technology has a very important position in many fields such as disaster detecting and terrain reconnaissance, target detection and tracking, drone aerial reconnaissance, satellite survey, virtual reality and so on. Although many researchers have made a lot of achievements in the field of panoramic stitching, it is found that its universality and real-time performance need to be improved in practical engineering applications. In order to solve the problems that the existing algorithms have poor performance when the feature points are scarce and the stitching speed is slow, we realized a real-time panoramic video mosaic system based on the mapping table (MT) and the GPU acceleration method. Qualitative and quantitative experimental analysis shows the effectiveness of our method.

SatAIS-39 1698 A novel birefringent fused cone Sagnac ring refractive index sensor Lianqin Li Optical Engineering Laboratory, Physical and El

ectronic Information Engineering Department

Technical Programmes CCDC 2021 Zenghui Wang Optical Engineering Laboratory, Physical and El

ectronic Information Engineering DepartmentQianqian Ma Zhejiang Normal Univ.Mengjiang Wang Optical Engineering Laboratory, Physical and El

ectronic Information Engineering DepartmentBaojin Peng Optical Engineering Laboratory, Physical and El

ectronic Information Engineering DepartmentA novel birefringent fused-cone coupling Sagnac-interference refractive index sensor is proposed in this study. First, single-mode optical fiber is fused with photonic crystal fiber (PCF) to form SMF–PCF–SMF interference. Second, the single-mode optical fiber at both ends of the PCF is folded in half to form a ring and placed on the translation stage of optical fiber for taper coupling to form a Sagnac interferometer connection for PCF. Finally, strong evanescent field characteristics of fiber coupling and the presence of birefringence in the coupling region are used and the prepared sensor is placed in a solution with different refractive indices. Experimental results showed that sensitivity reaches 186.656 nm/RIU in the refractive index range of 1.339–1.367. The sensor structure with high sensitivity, simple manufacture, and low cost plays an important role in food safety and chemical industry.

SatAIS-40 1699 High power output fiber optic torque sensor with high sensitivity Qian-qian Ma Zhejiang Normal Univ.Meng-jiao Wang Zhejiang Normal Univ.Zeng-hui Wang Zhejiang Normal Univ.Chun-ting Lin Zhejiang Normal Univ.Bao-jin Peng Zhejiang Normal Univ.In order to realize high precision real-time measurement of torque, a high sensitivity torque sensor based on high power output fiber is proposed. The excitation mode, output power, torque sensitivity and other aspects of the sensor are systematically studied. A Single mode-Multimode-Single mode cascade torque sensor is proposed. A cascade single mode-multimode-single mode (SDS) microstructure was fabricated by using a high power output fiber with a core diameter of 135 µm and a cladding diameter of 155 µm instead of the traditional multimode fiber. This kind of fiber can excite more high order modes, which is beneficial to the coupling of multiple modes. When the fiber is twisted, the fiber torque changes, which causes the change of optical path difference in the fiber and makes the interference spectrum drift, so as to realize torque sensing. The experimental results show that the sensitivity of the sensor is 18.907 nm rad / in the rotation angle range of 30 ° -55 ° . The sensor has the advantages of simple design, low price, high sensitivity and strong anti-jamming ability in which case it has strong research value.

SatAIS-41 1161 Deep regression prediction model based on spatiotemporal sensor data—PM2.5 forecast Xuebo Jin Beijing Tech. and Business Univ.Xinghong Yu Beijing Tech. and Business Univ.Wentao Gong Beijing Tech. and Business Univ.Tingli Su Beijing Tech. and Business Univ.Yuting Bai Beijing Tech. and Business Univ.Jianlei Kong Beijing Tech. and Business Univ.PM2.5 refers to solid particles with a diameter of 2.5 microns or less in the atmosphere. Because PM2.5 has a small diameter, contains toxic substances, and stays in the air for a long time, it has become an important indicator of air quality evaluation. Therefore, the analysis and prediction of PM2.5 changes is a hot topic of research. Relevant studies have shown that changes in the concentration of PM2.5 and meteorological elements, pollutant emissions, geographical conditions, and other factors make the prediction of PM2.5 more complicated, especially in the long-term forward forecast. It isn't easy to obtain a high precision prediction. Through the feature selection method based on the maximum mutual information coefficient, this paper analyzes the important factors that affect PM2.5 changes as the input of the deep network prediction model and builds a long and short-term memory network based on Bayesian deep learning theory to model PM2.5 concentration change. We evaluated the model with data from 2017 to 2019 obtained by multiple air pollution monitoring sites and meteorological element monitoring sites in Beijing. We found that PM2.5 was performed using highly correlated air pollutant factors and meteorological elements in neighboring areas. The Bayesian long and short-term memory network model has also been superior to the traditional long and short-term memory network, convolutional long and short-term memory network, and other models.

SatAIS-42 1190 Research on STF-DBSCAN based personal semantic location acquisition Zixuan Yao Beijing Univ. of Civil Engineering and Architecture Dong Wei Beijing Univ. of Civil Engineering and Architecture Yibing Ran Beijing Univ. of Civil Engineering and Architecture With the development of Tech. and economy, the planning and construction of smart cities have generated new demands and goals. Artificial intelligence-led Techologies and applications are changing people's lives where location-based services are one of the most important Techical supports. In recent years, the popularity of portable

devices with GPS (Global Positioning System) function and the development of positioning Tech. make a large amount of individual track data accessible. Consequently, collecting personal semantic location by mining these tracklogs would be the application direction and research focus of location-based services. Personal semantic location is a practical significant location that users will visit frequently, stay for a long time such as a home address, workplace, etc.). The acquisition of personal semantic location involves two steps, which are acquiring location and matching semantics. Currently, most of the relevant research use density clustering-based algorithms to collect location data. When the original DBSCAN algorithm is used to acquire location, all the trajectory points in the raw dataset need to be included in the clustering samples, which increases calculation and reduces the program efficiency. This paper addresses this problem and proposes an advanced density-based clustering algorithm STF-DBSCAN. First, the trajectory points in the raw dataset are filtered to obtain the dwell points. Then, the dwell points are clustered and analyzed to achieve the purpose of improving the running efficiency. For the semantic recognition issue, this paper pre-defines the daily behavior pattern of individual and analyzes the temporal features of the trajectory points in each class. When using the same trajectory data samples for location acquisition, the running time of the proposed algorithm in this paper is one-fifteenth of the original algorithm.

SatAIS-43 1218 Data processing method of 3D motion capture based on bone constraint Pengyue Dong Beijing Univ. of Civil Engineering and Architecture Yu Zhang Beijing Univ. of Civil Engineering and Architecture Zihao Zhang Beijing Univ. of Civil Engineering and Architecture In recent years, human 3D motion Tech. has been widely used in computer games, film production and animation industry. However, the data collected by professional motion capture equipment inevitably has noise and outliers. To solve this problem, this paper proposes a method of 3D motion capture data processing based on bone constraints. Firstly, the original data captured by the equipment is transformed into joint angle sequence by inverse kinematics method; secondly, the discrete joint angles are processed by Gaussian filtering; secondly, the human joint sequence is reconstructed by forward kinematics combined with the characteristics of human skeleton; finally, the experiment shows that the path of the joint point sequence processed by this method is significantly lower than that of the original data. The experimental results verify the feasibility and accuracy of the method.

SatAIS-44 891 On-line modification of T-S fuzzy models based on gradient descent algorithm Haodong Feng Beijing Univ. of Civil Engineering and Architecture Dong Wei Beijing Univ. of Civil Engineering and Architecture Huanyan Jiao Beijing Univ. of Civil Engineering and Architecture The gradient descent algorithm is applied to the model parameter correction algorithm in view of the shortcoming that T-S fuzzy model cannot be used for real-time adaptive parameter correction. In this paper, an online model correction method combining T-S fuzzy model with gradient descent algorithm is proposed, and the operation data of a subway air-conditioning system in Wuhan is used for verification. The experimental results show that the T-S fuzzy model modified by gradient descent algorithm has better accuracy and higher operational efficiency.

SatAIS-45 1077 Research on Energy Saving Control of Lighting Systems in Public Buildings Shengyan Li Beijing Univ. of Civil Engineering and Architecture Hongyan Ma Beijing Univ. of Civil Engineering and Architecture Yongxue Ye Beijing Univ. of Civil Engineering and Architecture Jiaming Dou Beijing Univ. of Civil Engineering and Architecture Jingjian Yang Beijing Univ. of Civil Engineering and Architecture Most indoor users are unwilling to adjust the lighting equipment and shutters in daily life, resulting in increasing energy consumption of buildings. In this paper, in order to reduce the energy consumption, using fuzzy control to solve this problem. By using fuzzy control, it could control the opening of indoor lamps, and let the shutters could determine the opening degree of the shutters according to the difference between the set illuminance values and the actual illuminance values on the working surface. The results show that the fuzzy control of classroom lighting system can reasonably control the running status of lamps according to the actual situation of the environment and reduce the energy consumption of the lighting system. Compared with the single fuzzy controller, the double fuzzy controller of the shutter can make the difference between the illuminance value of indoor working surface, and the set illuminance value smaller, control the light sources in different areas of the room, and improve the uniformity of natural light in the room. The method proposed in this paper can solve the energy consumption problem.

SatAIS-46 1082

Technical Programmes CCDC 2021 Energy Consumption Prediction of Public Buildings Based on MEA-BP Neural Network Shuai Wang Beijing Univ. of Civil Engineering and Architecture Hongyan Ma Beijing Univ. of Civil Engineering and Architecture Jiaming Dou Beijing Univ. of Civil Engineering and Architecture Yongxue Ye Beijing Univ. of Civil Engineering and Architecture Jingjian Yang Beijing Univ. of Civil Engineering and Architecture Data mining Tech. is often adopted in construction engineering to provide relevant information and reference basis for public building energy consumption prediction. This paper proposes an optimization algorithm applying back propagation (BP) neural network and mind evolutionary algorithm (MEA) to establish a model to predict building energy consumption. Five main weather data are used as inputs to the BP neural network and daily electricity consumption is used as an output. The MEA is employed to optimise the structure of the network and to predict the energy consumption of buildings. The results demonstrate that the optimized BP neural network has higher fitting accuracy, and the predicted load value is more consistent with the actual value.

SatAIS-47 855 Expression Recognition Method Based on Attention Neural Network Zhibo Shi Beijing Univ. of Civil Engineering and Architecture Zhi Tan Beijing Univ. of Civil Engineering and Architecture Convolutional neural network has been widely used in facial expression recognition. However, the expression ability of facial expression features extracted from convolutional neural network is insufficient, the recognition accuracy is not high, and the network parameters are large. In order to solve these problems, an expression recognition method based on attention mechanism and residual network is proposed. In this method, the expression image is processed by dual pooling channel attention module to deepen the expression of discriminant features, and then the residual structure convolution layer is used to extract features. Finally, Softmax is used to realize expression classification. The experimental results show that compared with the original ResNet model, the recognition effect is better, and the recognition performance is improved by 4.14% and 6.07% on CK+ and JAFFE facial expression data sets, respectively. Compared with other attention models, it is lighter and has better recognition performance.

SatAIS-48 545 Image Super-Resolution Reconstruction Based on Improved Dense Residual Network Tianshun Yao Beijing Univ. of Civil Engineering and Architecture Xiaoxuan Ma Beijing Univ. of Civil Engineering and Architecture In order to solve the problems of low image feature utilization, low quality and blurred texture details after reconstruction by super-resolution Tech., a network model based on a combination of dense residual blocks and multi-scale convolutional layers is proposed. Firstly, dense residual blocks are introduced to enable the model to make more use of feature information and to improve the quality of the reconstructed image; then multi-scale convolutional layers are used to extract information of different scales to enhance the learning ability of the network; then Charbonnier loss is used as the loss function; Finally, the network structure is optimized by removing the batch normalization layer in the network model. Compared the reconstruction results of our model in this article with classic methods such as SRCNN, FSRCNN, and SRResNet on test data sets such as set5 and set14, the experimental results show that the image reconstructed by this model has a certain improvement in objective evaluation indicators and subjective visual effects on the test data set.

SatAIS-49 1222 Application of image processing Tech. in gas pipeline inner wall damage detection Chenglin Zhang

Beijing Univ. of Civil Engineering and Architecture

Yahui Wang Beijing Univ. of Civil Engineering and Architecture Donghao Liu Beijing Univ. of Civil Engineering and Architecture In order to detect the damage of gas pipeline, this paper uses image processing Tech. as a research tool, and proposes an image processing system for pipeline damage detection. Because the environment of the gas pipeline may be dark, humid, even sand and other complex environmental factors, as well as the noise generated by the electronic devices themselves, resulting in the quality of the image transmitted from the front end is greatly reduced, so this paper first uses image processing Tech. to denoise the image. In order to distinguish the damaged and intact inner wall of the pipeline and facilitate the measurement of the target area, the image segmentation processing is also needed for the image. The purpose is to provide highquality and easy to process images for the follow-up work. Finally, the image features of gas pipeline inner wall damage can be extracted to complete the image processing.

SatAIS-50 1227 Research on Fault Diagnosis of Gas Pressure Regulator Based on Digital Twin Jiecheng Nian Beijing Univ. of Civil Engineering and Architecture Yahui Wang Beijing Univ. of Civil Engineering and Architecture

Jindong Wang Beijing Univ. of Civil Engineering and Architecture The gas pressure regulator plays an extremely important role in the gas transmission and distribution pipeline network. In order to respond to the failure of the pressure regulator in a timely and accurate manner, the digital twin Tech. is used to carry out the numerical simulation of the three-dimensional model of the gas pressure regulator. In this paper, grid generation and numerical simulation under normal conditions for a certain type of voltage regulator are carried out. The simulated data are consistent with the field experimental results. The research and design of the voltage regulator model under the fault condition of the sawtooth grooves, and the development of the experimental plan, the field test platform also meets the experimental needs, it is feasible to analyze the failure of the specific location of the voltage regulator through the digital twin method.

SatAIS-51 1237 Research and Optimization of Gas Regulator Fault Diagnosis Based on Multiple Combination Algorithm Jingxuan Huang

Beijing Univ. of Civil Engineering and Architecture

Yahui Wang Beijing Univ. of Civil Engineering and Architecture Yun An Beijing Univ. of Civil Engineering and Architecture Aiming at the problem of low accuracy of single model in fault diagnosis of intelligent gas regulator, a fault diagnosis method based on multi-combination algorithm is proposed in this paper. In the existing fault diagnosis on the basis of the theory and method, using principal component analysis (PCA) and linear discriminant method (LDA) two methods of data processing, with radial basis function neural network (RBF) and support vector machine (SVM) is the basis of the two kinds of fault diagnosis algorithm, taking the sub-high pressure gas regulator as the research object, and manufacturer cooperation has carried on the concrete experiment, optimize and improve the recognition rate of single fault diagnosis model. At the same time, aiming at the problem that the single fault diagnosis model has different adaptability under different flow rates in actual operating conditions, two new intelligent combination diagnosis schemes are proposed, which saves the manpower and material resources of gas companies, improves the safety and accuracy, and lays a foundation for subsequent applications.

SatAIS-52 1319 Research On Auxiliary Emergency Disposal Information System For Gas Pressure Regulating Station Box Sijie Li Beijing Univ. of Civil Engineering and Architecture Chenglin Zhang

Beijing Univ. of Civil Engineering and Architecture

Yahui Wang Beijing Univ. of Civil Engineering and Architecture In view of the lack of emergency disposal process and disposal mode of gas pressure regulating station box, this paper puts forward an emergency disposal card based on Gas Pressure Regulating Station Box, Emergency disposal process, and compiles a basic software to help maintenance personnel make auxiliary decision. Firstly, different events of Surge Tank Station Box are classified. Then, according to the data and regulations of the gas industry, the specific inspection, control and report of different events are carried out. The equipment names of emergency resources are summarized. Finally, Android Studio software is used to realize the visualization of emergency disposal.

SatAIS-53 422 Look-ahead Horizon based Energy Management Strategy Jointly with Speed Planning for Connected and Automated HEVs Fuguo Xu Sophia Univ. Tielong Shen Sophia Univ. This paper develops a look-ahead horizon based optimal energy management strategy to jointly improve the efficiencies of powertrain and vehicle for hybrid electric vehicles (HEVs) with connectivity and automated driving on the road with slope. Both speed planning and torque split of hybrid powertrain are provided by the proposed approach. A constrained optimal control problem is formulated to minimize the fuel consumption and the electricity consumption under the satisfaction of inter-vehicular distance constraint between ego vehicle and preceding vehicle. Moreover, the preceding vehicle speed in the look-ahead horizon is predicted by extreme learning machine with real-time data obtained from communication of vehicle-to-vehicle and vehicle-to-infrastructure. The optimal solution is derived through the Pontryagins maximum principle and verified in a traffic-in-the-loop powertrain platform to show the effectiveness of the proposed approach. It is found that the fuel economy for proposed strategy is improved compared to the energy management strategy without prediction of preceding vehicle speed.

SatAIS-54 157 Lane detection based on Improved FCN Long Yan Foshan Univ. of Science and Tech. Shaolin Hu Guangdong Univ. of Petrochemical Tech. Caixia Zhang Foshan Univ. of Science and Tech. Computer vision research has a long history and is still in progress. Lane detection is one of the typical applications of computer vision in the field of traffic, and has a wide range of applications. As one of the important

Technical Programmes CCDC 2021 intelligent driving Techique, lane detection can not only assist autonomous driving safely, but also give out warning when vehicle is yawing. For lane detection, there are two kinds of major methods, one is based on image processing and the other is based on image segmentation. Because of its good representation and learning ability, the last one has been made great progress in lane detection field recently. Based on the analysis of fully convolutional network (FCN) and conditional random field (CRF), a novel lane detection algorithm is proposed in this paper, which is based on the combination of FCN and CRF. Some extensive experimental results show that the proposed method is robust against shadow, occlusion and slope variations in the public lane dataset.

SatAIS-55 189 Deadbeat Control Combined with Finite Control Set Model Predictive Control for Permanent Magnet Synchronous Machine Based Two-Level Inverter Jianqiao Zou Wuhan Second Ship Design and Research Institute In this paper, the relationship between deadbeat control (DBC) and finite control set model predictive control (FCS-MPC) is found out to reduce the computational burden of FCS-MPC, where the optimal voltage vector of FCS-MPC is nearest to the solution of DBC. Then, in order to ensure the permanent magnet synchronous machine (PMSM) can work in safe region without overcurrent, the current limit is inserted into FCS-MPC by adding penalty function. Moreover, because the FCS-MPC causes high current ripple by only applying one voltage vector, the DBC is introduced to control PMSM when the current stays in the current limit. Finally, the effectiveness of this control strategy is verified by experiments.

SatAIS-56 501 Short-Term Traffic Flow Prediction Based On IWOA-WNN Qin Yu Wuhan Univ. of Tech.Yuepeng Chen Wuhan Univ. of Tech.Qingyong Zhang Wuhan Univ. of Tech.Li Li Wuhan Univ. of Tech.Wenqing Ma Wuhan Univ. of Tech.Aiming at the nonlinear and random characteristics of short-term traffic flow, an improved whale optimization algorithm (IWOA) is proposed to replace the gradient descent method to optimize the wavelet neural network (WNN) for short-term traffic flow prediction. Firstly, in view of the slow convergence speed and low convergence accuracy of the traditional whale optimization algorithm (WOA), a nonlinear convergence factor a is introduced to balance the global search and local search ability of the algorithm. At the same time, Kent chaotic mapping is used to increase population diversity and enhance the ability of jumping out to fall into local optimum. Secondly, aiming at the problem that the gradient descent method in the wavelet neural network is sensitive to the initial values of the weights and wavelet factors, and is easy to fall into local minimum values, the network weights of the wavelet neural network are optimized by improving the whale optimization algorithm. Finally, the wavelet threshold denoising algorithm is used to process the noise in the raw traffic flow sequence data, and the IWOA-WNN is used to test the short-term traffic flow data set after processing. The results show that the mean square error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) of the model are 18.03, 2.82 and 13.13 %, respectively. Experimental results show that the improved algorithm has higher accuracy than the raw algorithm, and the model can effectively improve the prediction accuracy of short-term traffic flow.

SatAIS-57 829 Study on characteristics and calibration method of vehicle honking detection systems Jing Wu Liaoning Provincial Institue of measurementThis paper introduces the application scope and principle of the vehicle honking detection system, and through the study of vehicle honking snap feature, it provides the honking detection system calibration method, the method is simple and feasible, it can detect the sound pressure level and locating resolution of the measurement, the method can provide the Techical support for the traffic police department.

SatAIS-58 1119 The Application of multi-network integration in Rail Transit Vehicles Fujing Zhang CRRC Dalian R&D Co., LTD Techical Center Yong Shi CRRC Dalian R&D Co., LTD Techical Center Ping Zhou Northeastern Univ. With the development of Ethernet Tech., there are more and more applications of Ethernet in rail transit vehicles. At present, the vehicle subnet of PIS system generally adopts Ethernet, the ethernet maintenance network of the whole vehicle is also popular. However, the function of vehicle control using Ethernet to transmit process data is still in its infancy. At present, most of the vehicle ethernet networks transmitting process data coexist with the traditional vehicle networks. The integration of all kinds of vehicle networks and the unified use of Ethernet communication remain in the laboratory verification stage, and there is no precedent for the actual application of rail transit vehicles. In this paper, the implementation mechanism and effect of multi-network integration in rail transit vehicles are described in detail based on the

application case of multi-network integration in actual vehicle.

SatAIS-59 1121 Experimental Platform for Intelligent Application of Rail Transit Fujing Zhang CRRC Dalian R&D Co., LTD Techical Center Shizhi Li CRRC Dalian R&D Co., LTD Techical Center Ping Zhou Northeastern Univ. The intelligent of rail vehicles is the future direction, it is mainly in the automation and driveless of vehicle line operation, the intelligent of rail vehicles and the intelligent operation and maintenance of rail vehicles. At present, there is an increasing number of driverless subway lines built, and the pure domestic driverless system has also been officially operated on the Yanfang line of Beijing subway, which plays a demonstration role for other domestic driverless subway lines.With the development of 4G/5G communication Tech., it becomes feasible that the vehicle operating state data is transmitted to the ground in real-time, which is followed by the need for remote real-time monitoring of vehicles,all life cycle failure diagnosis and intelligent operation and maintenance based the big data of vehicles. In order to better carry out the application of intelligent of rail vehicle, experimental platform with corresponding functions need to be established to support the practical application of intelligent. In this paper, combining the construction of an experimental platform for intelligent rail vehicle, the functions and application effects of the experimental platform for intelligent rail vehicle will be elaborated.

SatAIS-60 1342 A Deep Learning-Based mmWave Beam Selection Framework by Using LiDAR Data Yadan Zheng Beijing Univ. of Posts and TelecommunicationsShanzhi Chen Beijing Univ. of Posts and TelecommunicationsRui Zhao Beijing Univ. of Posts and TelecommunicationsMillimeter Wave (mmWave) Tech. plays an non-substitutable role in 5G communication system because of its effectiveness to increase spectral efficiency. Furthermore, internet of things (IoT) applications will be further developed via mature 5G Tech. for which 5G has a competitive information interaction delay. Beam selection is one of crucial proposals that can optimize the power dissipation in 5G multi-antenna devices (includes base station and user equipment), particularly for smart mobile devices, such as autonomous vehicles. In this study, a novel 3D convolutional neural networks is proposed for beam selection in a vehicle-to-everything (V2X) application scenario, which is based on 5G mmWave communication system and Light detection and ranging (LiDAR) sensors. The experimental results demonstrate that our proposed methods outperform than the previous methods.

SatAIS-61 1622 Research on Signal Light Adaptive Optimal Timing Strategy Based on Phase Saturation Ya Fang Univ. of JinanShi-Yuan Han Univ. of JinanLu-Jun Qiu Univ. of JinanJin Zhou Univ. of JinanIn order to improve the traffic efficiency of intersections, a signal light adaptive optimal timing algorithm based on phase saturation is proposed. First, the signal timing problem is formulated as a quadratic optimal programming problem, in which the phase saturation is set as the weight parameter for each phase to allocate the green light time. After that, according to the road load capacity and the solution of the designed quadratic optimal programming problem, the adaptive timing strategy of signal lights is proposed. Considering the most common two adjacent and four adjacent intersections in urban traffic, the saturation dynamic prediction model is established based on the influence mechanism of two adjacent and four adjacent intersections, and the optimal timing algorithm is designed based on the proposed signal light adaptive timing strategy. Finally, the simulation results show that the design of adjacent intersections with the optimal algorithm improves the overall efficiency of traffic.

SatAIS-62 151 Current Situation and Potential of Seawater Desalination Loads to Consume Renewable Energy Junci Tang Shenyang Univ. of Tech.Shuai Chu Shenyang Univ. of Tech.Shitan Zhang Northeast Electric Power Univ.Weichun Ge Shenyang Univ. of Tech.Dai Cui Shenyang Univ. of Tech.Chuang Liu Northeast Electric Power Univ.With the growing proportion of renewable energy in the electricity supply, the excess energy generated by renewable energy is also increasing. Interruptible loads are seriously needed for renewable energy consumption. Desalination loads are interruptible loads. They can deal with the intermittence of new energy and be used to consume excess renewables. The paper proposes to use desalination loads to consume excess capacity generated by renewable energy. First, this paper introduces the development status of desalination Techiques and the energy consumption component of desalination systems. Then, different desalination Techologies are studied to figure out how to combine them

Technical Programmes CCDC 2021 with different renewable energy sources to produce more efficient systems. Finally, considering the flexible operation characteristics, the potential of desalination loads participating in the power market is exploited in this article.

SatAIS-63 218 Distributed operation optimization for multi Microgrids with energy trading Feng Wang State Grid Shandong Electric Power Research Inst.Lisheng Li State Grid Shandong Electric Power Research Inst.Yang Liu State Grid Shandong Electric Power Research Inst.Min Huang State Grid Shandong Electric Power Research Inst.Luhao Wang Univ. of JinanGuanguan Li Shandong Univ.Recent advancement of renewable energy has motivated microgrids to trade energy with each other and main grid to minimize their operational cost. This paper propose a new methodological framework for operation optimization to incentivize energy trading among MGs. Different from the existed studies, the proposed approach allows MGs to autonomously trade energy without the government of energy market or aggregator. To address the privacy problem, a distributed ADMM algorithm is adopted to solve the energy trading problem. The developed method is implemented on a 3 MGs system and its effectiveness is demonstrated through the case studies.

SatAIS-64 233 Data-driven Method of Renewable Energy Based on Generative Adversarial Networks and Energy PLAN Liu Yang Northeastern Univ.Huaguang Zhang Northeastern Univ.Yunfei Mu Northeastern Univ.This paper proposes for the first time to combine the concept of deep learning to analyze the data under the energyPLAN platform. In the process of data simulation, the Generative Adversarial Networks (GAN) algorithm is introduced to compensate for the missing data of individual energy indicators, so as to realize the intelligent monitoring and renewable energy. It is significant for secondary utilization of energy. Regarding innovation of Energy Management Module (EMM) and availability of renewable energy development, we have achieved phased results, but there are still challenges: (1) The feature extraction of the data is broken down or stolen, and the distortion problem occurs. The original data of the training sample is abnormal, and the integrity of the data needs to be expanded; (2) Large-scale data relies solely on simulation. There are still feasibility problems, and further verification is needed. In the article, the realtime power data and simulation collected by the international platform energyPLAN are used to verify the deep learning, and the compensation data is integrated into the verification at the same time.

SatAIS-65 247 Optimal Dispatch of Integrated Electricity-Gas-Heat System Considering Power to Gas Ming Li Yangjiang PolyTechicLiwan Huang Yangjiang PolyTechicYonghua Li Yangjiang PolyTechicThe integrated energy system and energy hub (EH) can not only implement energy complementary coordination, but also improve power quality. In this paper, an integrated energy system considering power to gas (PtG) is composed of electric power distribution system, district heating system, natural gas distribution system and energy hub. In order to realize the optimal allocation of resources, we have constructed an optimization model of two layers. By constructing a Lagrange function, the model is transformed into a mixed integer linear program (MILP) combined with Karush-Kuhn-Tucker (KKT) optimality conditions, and then common commercial software CPLEX is used to solve this model. Finally, a case study is used to prove the effectiveness of the proposed model and method. The performances of PtG promoting the overall economic benefits and enhancing the reliability level of electrical loads are also analyzed．

SatAIS-66 270 LMI-Based Robust Control Strategy for Wind Energy Conversion System through Communication Network Zhihong Huo Hohai Univ.Chang Xu Hohai Univ.Yi Li Chongqing Academy of Metrology and Quality Inspe

ctionZhijie Jin Hohai Univ.Hongyu Guo Hohai Univ.Many processes include time-delay and parametric uncertainties phenomena in their inner dynamics. The existence of time-delay and parametric uncertainties usually deteriorate the performance of closed-loop systems and may be a source of instability. This paper presents LMI-based methods for designing a robust controller for Wind Energy Conversion System taking input uncertain time-delayed communication network and parametric uncertainties into account. The robust design methods handle the parametric uncertainties and provide

both stability and performance guarantees. The method was chosen as they are based on time-delay and structured uncertainty description, which makes the controllers less conservative than unstructured uncertainty description, making this description favorable. After detailed theoretical analysis, Simulation results show the capability of the proposed approach to enhance the performance of the wind energy conversion system with parametric uncertainties and uncertain time-delayed communication network.

SatAIS-67 576 High Performance of Three-Phase Four-Leg Inverter Based on Repetitive Control Strategy Liupeng Zheng Dongguan Univ. of Tech.Zhi Zhang Dongguan Univ. of Tech.Yati Chen Shezhen Rspower Tech. Co.,LtdZhaoyun Zhang Dongguan Univ. of Tech.Xiao Tang Dongguan Univ. of Tech.In the paper, three-phase four-leg inverter based on improved repetitive control strategy is proposed to improve the quality of output voltage waveform. First, the three-dimensional space vector modulation (3-D SVM) method based on ABC rectangular coordinate system is adopted. Meanwhile, due to the independence of each phase under the topology structure, double closed-loop controller is performed on each phase considering the nonlinear loads or unbalance loads to simplify the control process. At last, Matlab / Simulink simulation model is built to verify the correctness and the feasibility of the proposed improved repetitive method, and high performance can be achieved under three-phase balanced or imbalanced load conditions.

SatAIS-68 579 Seamless Switching of Three-phase Inverters Grid-connected and Off-grid Based on Virtual Synchronous Generator Tech. Sheng Gao Dongguan Univ. of Tech.

Guangdong Univ. of TechZhi Zhang Dongguan Univ. of Tech.

Guangdong Univ. of TechYaohua Hu Dongguan Univ. of Tech.

Guangdong Univ. of TechXiao Tang Dongguan Univ. of Tech.Zhaoyun Zhang Dongguan Univ. of Tech.In the microgrid, virtual synchronous generator (VSG) can mimic the external characteristics of synchronous generator to improve the grid-connection capability of microgrid, which has become a hot spot of recent research. VSG can work in both grid-connected and off-grid modes and seamless switching is essential function to ensure the stable and uninterrupted operation of load. VSG is off-grid mode, frequency, phase and amplitude are not consistent with the grid, at the moment when the inverter is connected to the grid, huge impulse current will be generated, which will cause damage to the inverter devices. Therefore, this paper proposes a pre-synchronization control strategy for frequency and phase compensation to realize the seamless switch of VSG. Finally, the proposed method is verified by MATLAB/SIMULINK, simulation results verify the effectiveness of the proposed strategy.

SatAIS-69 632 Distributed Optimal Economic Dispatch for Integrated Energy System Considering Communication Delays and Event Triggering Mechanism Jun Yang Northeastern Univ.Xinhao Zhuang Northeastern Univ.This paper investigates the economic dispatch problem of Integrated Energy System (IES) in a distributed fashion. A distributed double consistency algorithm is proposed to address this issue. This algorithm aims to minimize the operating cost of IES, achieving the same incremental cost of electricity and heat respectively. Meanwhile, an event triggering mechanism based on state information is proposed, which can effectively reduce the communication resources between nodes. At the same time, the time-varying delay of communication between nodes is considered, which is more practical. Finally, the effectiveness of the algorithm is verified by an IES simulation system with 30-32 nodes.

Sunday, 23 May, 2021

SunA01- SunB01 Room01 Presentations by Finalists of Zhang Si-Ying Outstanding Youth Paper Award 08:30-12:20 Chair: Guang-Hong Yang Northeastern Univ.

08:30-09:00 SunA01-1 596 Monocular Visual Servo of Unmanned Surface Vehicles with View-field Constraints Hongkun He Dalian Maritime Univ.Ning Wang Dalian Maritime Univ.

Harbin Engineering Univ.In this article, a prescribed performance monocular visual servo (PPMVS) scheme is exclusively created such that an unmanned surface vehicle (USV) can be regulated to reach the desired configuration in the

Technical Programmes CCDC 2021 presence of both view-field constraints and unknown image depth. To satisfy view-field constraints, feasible specifications on system outputs are elaborately predefined by virtue of the prescribed performance control technique, and thereby resulting in an equivalent unconstrained visual servo system by incorporating barrier functions. To facilitate stable closed-loop system, the unknown image depth and its reciprocal are respectively accommodated by combining with the backstepping philosophy and Lyapunov synthesis. Completely independent of tuning parameters, the proposed PPMVS scheme makes not only feature points uniformly keep in the image plane, but also the USV asymptotically approach the desired pose. Simulation studies and comparisons on a prototype USV comprehensively demonstrate the effectiveness and superiority of the proposed PPMVS scheme.

09:00-09:30 SunA01-2 800 Input-to-State Stabilization for an ODE Cascaded by a Parabolic PIDE with Disturbances Hanwen Zhang Beijing Inst. of Tech.Junmin Wang Beijing Inst. of Tech.Jianjun Gu Changshu Inst. of Tech.This paper investigates the input-to-state stabilization problem of an ODE cascaded by a parabolic partial integro-differential equation (PIDE) subject to disturbances. First, the backstepping approach and the sliding mode control method are used to design the discontinuous boundary feedback control law. Then, a Galerkin approximation scheme is constructed to show the existence of solution to the closed-loop system. With the Lyapunov approach, the input-to-state stability of the closed-loop system is obtained. Finally, the simulation results are presented to illustrate the effectiveness of the proposed control law.

09:30-10:00 SunA01-3 985 Economic Dispatch of an Integrated Microgrid Based on the Dynamic Process of CCGT Plant Zhiyi Lin Zhejiang Univ.Chunyue Song Zhejiang Univ.Jun Zhao Zhejiang Univ.Chao Yang Zhejiang Univ.Huan Yin Zhejiang Univ.Intra-day economic dispatch of an integrated microgrid is a fundamental requirement to integrate distributed generators. The dynamic energy flows in cogeneration units present challenges to the energy management of the microgrid. In this paper, a novel approximate dynamic programming (ADP) approach is proposed to solve this problem based on value function approximation, which is distinct with the consideration of the dynamic process constraints of the combined-cycle gas turbine (CCGT) plant. First, we mathematically formulate the multi-time periods decision problem as a finite-horizon Markov decision process. To deal with the thermodynamic process, an augmented state vector of CCGT is introduced. Second, the proposed VFA-ADP algorithm is employed to derive the near-optimal real-time operation strategies. In addition, to guarantee the monotonicity of piecewise linear function, we apply the SPAR algorithm in the update process. To validate the effectiveness of the proposed method, we conduct experiments with comparisons to some traditional optimization methods. The results indicate that our proposed ADP method achieves better performance on the economic dispatch of the microgrid.

10:00-10:30 SunA01-4 1022 Direct adaptive output sampled control for a class of uncertain nonlinear systems based on the characteristic model Yafei Chang Beijing Inst. of Control Engineering

Science and Tech. on Space Intelligent Control Laboratory

Yulian Gong Beijing Inst. of Control EngineeringScience and Tech. on Space Intelligent Control L

aboratoryThe design of a novel direct adaptive output sampled control scheme for a class of uncertain systems using characteristic modeling theory is presented.The interest here is to achieve a direct adaptation law which uses tracking-control-error for the characteristic parameters estimation.Particularly, to reject the undesired influence of the time-variation of the characteristic parameters on the system stability and convergence, a nonlinear term consisting a saturation function is added to the controller and the characteristic parameter adaption law. Stability analysis is presented using Lyapunov theory. Simulation results show that the newly suggested characteristic model-based direct adaptive method can achieve better control performance in dynamic characteristics and control accuracy than the standard one which is designed based on the prediction-error.

10:30-11:00 SunA01-5 1276 Payoff Control in Repeated Games Renfei Tan Peking Univ.Qi Su Univ. of PennsylvaniaBin Wu Beijing Univ. of Posts and TelecommunicationsLong Wang Peking Univ.Evolutionary game theory is a powerful mathematical framework to study

how intelligent individuals adjust their strategies in collective interactions. It has been widely believed that it is impossible to unilaterally control players’ payoffs in games, since payoffs are jointly determined by all players. Until recently, a class of so-called zero-determinant strategies are revealed, which enables a player to make a unilateral payoff control over her partners in two-action repeated games with a constant continuation probability. The existing methods, however, lead to the curse of dimensionality when the complexity of games increases. In this paper, we propose a new mathematical framework to study ruling strategies (with which a player unilaterally makes a linear relation rule on players’ payoffs) in repeated games with an arbitrary number of actions or players, and arbitrary continuation probability. We establish an existence theorem of ruling strategies and develop an algorithm to find them. In particular, we prove that strict Markov ruling strategy exists only if either the repeated game proceeds for an infinite number of rounds, or every round is repeated with the same probability. The proposed mathematical framework also enables the search of collaborative ruling strategies for an alliance to control outsiders. Our method provides novel theoretical insights into payoff control in complex repeated games, which overcomes the curse of dimensionality.

SunA02 Room02 New Energy Control and Smart Energy (Special Session) 08:00-10:20 Chair: Qiuye Sun Northeastern Univ.

08:00-08:20 SunA02-1 1636 Research on Photovoltaic Cell Models: A Review Jingxun Fan Hubei Minzu Univ.Shaowu Li Hubei Minzu Univ.Xianping Zhu Hubei Minzu Univ.Yan Li Hubei Minzu Univ.To carry on the theoretical analysis and practice verification to the photovoltaic (PV) power generation system, the accurate model of the PV cell should be established first. Nowadays, a large number of scholars at home and abroad have carried out research on PV systems and PV cells, and have made a lot of breakthroughs and innovations in mathematical and circuit model optimization, maximum power point tracking (MPPT), and parameter extraction methods of PV cells. However, the model used cannot be completely compatible with the required precision, computational complexity, and environmental conditions. In this paper, the circuit models, mathematical models, and parameter extraction method of nine commonly used PV cells are reviewed, and the engineering simplified model and shading model of PV cells are reviewed. The above are the steady-state models of PV cells, but the maximum power point tracking is a dynamic optimization process. Therefore, the dynamic characteristics of PV cells (especially the small-signal output impedance) are summarized in this paper. Then a method is proposed which is different from the traditional PV cell model classification, that is to say, all the above models are nonlinear PV cells, which need complex iteration and calculation to extract parameters and research. Therefore, this paper summarizes the linearization model proposed by scholars. By comparing all the above models, a series of conclusions are drawn. Finally, the future development direction of PV cells has prospected. The research results can lay a model foundation for the wide application of PV cells.

08:20-08:40 SunA02-2 1672 Multi-Objective Optimized Operation of Integrated Energy System Based on Economic and Exergy Analysis Yongling Li Baoding Electric Power Voc. & Tech. CollegeMi Zhang North China Electric Power Univ.Chunhua Qiu Huaneng（Fujian） Energy Development limited c

ompany Fuzhou branchPengyu Shen Baoding Electric Power Voc. & Tech. CollegePeng Gao Beaulieu Rizhao Floorcoverings Co., Ltd.Yu Huang North China Electric Power Univ.Integrated energy system can effectively reduce operation cost and improve energy utilization through the multi-energy complementation and cascade utilization of various types of energy. Based on the exergy analysis method, this paper establishes the energy-saving index of the reciprocal of the minimum exergy efficiency of the system, and combines the economic index to construct a multi-objective operation model of the integrated energy system. The Pareto front is obtained by using the ε-constraint method and the entropy method is used to determine the optimal operating strategy of the system. Simulation results show that the multi-objective operation model can achieve the trade-off between the system operation cost and the exergy efficiency compared with the single objective operation.

08:40-09:00 SunA02-3 1456 An Overview of Photovoltaic Models Under Partial Shading Conditions Bingqing Chen Xi’an Jiaotong-Liverpool Univ.Huiqing Wen Xi’an Jiaotong-Liverpool Univ.Photovoltaic (PV) phenomenon becomes more and more important since it can take the sunlight as the input and yield electric energy at the output. Recently, intensive researches have been conducted in this field to achieve high energy yield efficiency and tracking accuracy. However, the

Technical Programmes CCDC 2021 actual output power from PV systems under partial shading conditions (PSCs) will be significantly affected. Thus,in order to address this issue, several PV models are introduced with distinct characteristics. This paper will give a comprehensive review and comparison of different PV models under the PSCs from different perspectives such as the accuracy, complexity and the utilization of them in practical. In addition, pros and cons of them are evaluated based on certain defined classification.

09:00-09:20 SunA02-4 666 A Resilient Distributed Consensus Control Scheme for DC Microgrids Over Fading Channels Jingang Lai RWTH Aachen Univ.Xiaoqing Lu Wuhan Univ.Guo-Ping Liu Wuhan Univ.Yaonan Wang Hunan Univ.Xinghuo Yu RMIT Univ.DC microgrids are constructed from inverter-based distributed generations (DG) to achieve a various range of applications. Such configuration eliminated DC/AC conversion and simplified the control objectives design, being DC-grid voltage regulation and curretn sharing. This paper develops a stochastic distributed washout filter-based control scheme that can achieve current sharing of DGs and voltage restoration of DC bus in mean square subject to additive noises and time delays over fading networks. Since the cyber channels are exposed to inherent additive noises and time delays, the information exchange among multiple and cooperative DGs may be disturbed in fading communication networks, where fading may either be due to noises, delays, and shadowing from obstacles affecting the wave propagation. As a results, fading makes every DG receives incomplete/imprecise information from its neighbors. To dispel the adverse influences of noises and time delays over fading networks, a washout filter-based distributed noise-resiliency control algorithm is developed for DC microgrids, in which only the current state variable is exchanged among DGs with the local decentralized voltage controller. By introducing stochastic theory and Lyapunov stability function, the criteria for the stability analysis and delays boundedness considering noise interferences is derived to ensure the stability operation of the whole closed-loop DC network. The obtained results show the effectiveness of the proposed strategy by a tested DC microgrid in OPAL-RT real-time simulator.

09:20-09:40 SunA02-5 1258 Smart Port Energy Management Strategy Considering Cold Chain System Xin Zhang Dalian Maritime Univ.Qihe Shan Univ. of Electronic Science and Tech.Tieshan Li Dalian Maritime Univ.Fei Teng Dalian Maritime Univ.In this paper, a novel energy management framework with cold chain system of smart port is presented, which has the characteristics of supply-demand balance and generator set and load diversification. Aiming at the above model, this paper studies the optimization algorithm of port energy management based on dynamic programming, which can not only realize the self-management of the temperature control of reefers within the appropriate temperature range, reduce the contact between workers and frozen products in the process of cold chain operation, but also ensure the reduction of power demand during the period of high load of the system, and solve the optimization operation problem with port energy uncertainty to realize the cost minimization. Finally, simulation results illustrate the effectiveness of the proposed solution.

09:40-10:00 SunA02-6 518 Prediction of wind power based on LSTM-CNN joint model Yan Guo Central South Univ.Mi Dong Central South Univ.Dongran Song Central South Univ.The prediction of wind power not only helps maintain the stability of the entire power grid when the wind power is connected to the grid, but also plays an indispensable role in ensuring the performance and service life of wind turbines in the wind farm. This paper considers the power prediction of multiple wind turbines as a multi-location prediction problem. Proceeding from the wind power time series, a two-stage modeling strategy is proposed, and a deep neural network that uses spatiotemporal correlation to simultaneously predict the power of multiple wind turbines is built. Specifically, the network is a joint model composed of Long Short-Term Memory Network (LSTM) and Convolutional Neural Network (CNN). At the start of the joint model, LSTM captures the temporal dependence of the historical power sequence. After processing, the spatial features in the data are extracted by the CNN, thereby achieving the prediction of the power for multiple wind turbines. Taking the measured power data of an offshore wind farm in China as the experiment data, the prediction results of the model are compared with the true values and a number of error indicators are defined to evaluate the comprehensive performance of the prediction model. Compared with other methods, the proposed method is not only correct and feasible, but also provides better prediction results.

10:00-10:20 SunA02-7

187 Active Stabilization Control for Distributed Generators in DC Microgrids With Constant Power Loads Rui Wang Northeastern Univ.Qiuye Sun Northeastern Univ.Chenghao Sun Northeastern Univ.Yushuai Li Northeastern Univ.High penetration of renewable energy sources has been widely embedded into DC microgrids, which results in weak grid features and instability phenomenon. Meanwhile, numerous constant power loads will exacerbate this phenomenon. Based on this, this paper proposes an active stabilization control for distributed generators (DGs) in DC microgrids with constant power loads. Firstly, the generalized state-space function with weak grid feature is built, which eliminates the impact of weak grid. Based on the generalized state-space functions, an active stability control for distributed generators (DGs) in DC microgrids is proposed to ensure the system stability, which is based on sliding mode control. Eventually, the simulation and experimental results are provided to verify the performance of the proposed control approach.

SunA03 Room03 Pattern Recognition and Intelligent Machines (I) 8:00-10:00 Chair: Bin Zhu Jiangxi college of applied tech.CO-Chair: Zixi Jia Northeastern Univ.

8:00-8:20 SunA03-1 10 An improved U-Net for Vision based Indoor Localization Bin Zhu Jiangxi college of applied tech.Farisi Zeyad Jiangxi college of applied tech.Jianrong Zhang Jiangxi college of applied tech.Guohu Luo Jiangxi college of applied tech.Lei Chen Jiangxi college of applied tech.In indoor environment, the location information can be expressed by the path region feature in the image. In this paper, a semantic image based indoor localization method is presented. In order to segment path region semantically in indoor environment, an improved U-Net is proposed, in which squeeze and excitation (SE) block is added into the classical UNet. Then, the fully connection layers of VGG16Net is employed for the feature aggregation and comparison of the path region. At last, the ArcFace loss function is applied for feature classification. In SE block, by learning feature weight according to loss function, the important feature map is assigned to a large weight and the invalid or less important feature map is assigned to a small weight, so the path region can be extracted more accurately. Experimental results show: compared to classical UNet, our algorithm can retain more edge details of the extracted path region, compared to RGB image based indoor localization method, our algorithm can enhance the accuracy of location points in the center of the scene which have obvious path features.

8:20-8:40 SunA03-2 1215 An Improved Indoor Navigation Scheme Based on Vision-Language Localization Ziheng Xu Northeastern Univ.Zixi Jia Northeastern Univ.Xuegang Zhou Northeastern Univ.Huan Wen Northeastern Univ.Yanan Li Northeastern Univ.This paper proposes an improved indoor localization and navigation method based on image matching and text extraction. Compared with traditional indoor localization methods based on WIFI, Bluetooth or laser, the improved method is mainly realized by image matching in computer vision and text extraction in natural language processing. The improved bidirectional A* algorithm is adopted to realize the optimal path planning, and the specific location of indoor localization and path planning diagrams are displayed through an app. Experimental results show that this method which can realize indoor location determination in real scenes, is convenient to use, and has low cost. It can overcome the problems of high hardware requirements, high cost, and poor transferability in the traditional indoor navigation field in terms of localization.

8:40-9:00 SunA03-3 377 DRSO-SLAM: A Dynamic RGB-D SLAM Algorithm for Indoor Dynamic Scenes Naigong Yu Beijing Univ. of Tech.

Beijing Key Lab of the Computational Intelligence and Intelligent System

Mengzhe Gan Beijing Univ. of Tech.Beijing Key Lab of the Computational Intelligence

and Intelligent SystemHejie Yu Beijing Univ. of Tech.

Beijing Key Lab of the Computational Intelligence and Intelligent System

Kang Yang Beijing Univ. of Tech.Beijing Key Lab of the Computational Intelligence

and Intelligent SystemIn order to improve low position accuracy and insufficient robust performance of service robots in indoor dynamic scenes, a dynamic RGB-D simultaneous localization and mapping (SLAM) algorithm based

Technical Programmes CCDC 2021 on semantic information and optical flow (DRSO-SLAM) is proposed. Firstly, the Mask R-CNN semantic segmentation network is used to obtain semantic information in indoor dynamic scenes, establishing a priori semantic information database of objects and the system's rough self-motion estimation is performed based on the established prior semantic information database. Secondly, the feature points optical flow field is calculated to track the dynamic objects optical flow, the pixel-level semantic information obtained through the semantic segmentation network serves as mask information in the optical flow tracking and the fundamental matrix is further calculated. Finally, the epipolar geometry is used to filter out the actual dynamic feature points and only static feature points are retained in the tracking, local mapping and loop detection threads. Experiments are carried out in high and low dynamic scenes of the TUM dataset. Compared with the ORB-SLAM2, the root mean square error of DRSO-SLAM in high dynamic scenes is improved by an average of 95.02% and it also has considerable positioning accuracy in low dynamic scenes. Experimental results have shown that DRSO-SLAM can effectively improve the position accuracy and robustness of SLAM system in dynamic indoor scenes.

9:00-9:20 SunA03-4 1246 Joint classification and regression based on multitask learning in the diagnosis of Alzheimer's disease Chuyuan Wang Northeastern Univ.Ying Wei Northeastern Univ.

Key Laboratory of Medical Imaging Calculation of the Ministry of Education

Xiang Li Northeastern Univ.Qian Hu Northeastern Univ.Yue Liu Northeastern Univ.Yuefeng Wang Northeastern Univ.Alzheimer's Disease (AD) is one of the most common senile diseases in the world. With the aging of global population becoming more and more serious, the incidence of AD is also increasing. Mild Cognitive Impairment (MCI) is the early manifestation of AD. Mild dementia symptoms are not obvious thus is often mistaken for normal aging and further missed the best treatment period. Therefore, the diagnosis and early intervention of MCI patients as well as early AD patients are of great significance for delaying the development of AD. Recent years, deep learning has made great progress in medically assisted diagnosis. In this paper, we proposed a joint regression and classification of multitask learning method accessed for auxiliary diagnosis of Alzheimer's disease. In particular, we adopted the autoencoder to imitate the representative features for the classification and regression tasks. Simultaneously, we combine the demographic information of each subject in the process of learning to achieve more information. The results show that our method has achieved good results in both disease classification and clinical score regression.

9:20-9:40 SunA03-5 332 A Convolutional Neural Network for Small Sample’s Ring Structured Light Denoising Shuyang Lin Northeastern Univ.Xu Han Northeastern Univ.Yixiao Wang Northeastern Univ.Zongyi Lu Northeastern Univ.Yichun Zhang China Inst. of Arts Science and Tech.Tong Jia Northeastern Univ.Denoising is an indispensable step when measuring depth by using omnidirectional ring structured light depth perception system. However, it is difficult to obtain uniform, clear and continuous structured light stripe by using general image denoising algorithm. To solve this problem, our paper adopts the method of combining Deep Convolutional Generative Adversarial Networks (DCGAN) and Denoising Convolutional Neural Network-Ring Structured Light (DnCNN-RSL) to denoise. We first use DCGAN network to generate more data sets, and then use DnCNN-RSL network to denoise images so that noiseless images can be obtained. DnCNN-RSL network is suitable for removing the noise of small sample data sets, which reflects in improving PNSR value of images, and SSIM value is also closer to 1. Experiments show that DnCNN-RSL network can get better denoising results than traditional image processing methods and obtain clear, continuous structured light stripe. In this way depth information obtained by denoised structured light stripe is more accurate.

9:40-10:00 SunA03-6 1624 DTB-Net: A Detection and Tracking Balanced Network for Fast Video Object Detection in Embedded Mobile Devices Fu Huang Yunnan Univ.Dapeng Tao Yunnan Univ.Linfei Wang Yunnan Univ.Recently, object detection is of great significance and full of challenges in the task of video analysis with the continuous breakthrough of convolutional neural networks. However, detecting video objects in embedded mobile devices still remains challenging. The two major reasons limit the video detection performance of existing methods in embedded mobile devices: 1) The computing capability of embedded mobile devices is limited; 2) The motion and change of the interested object causes a lot of noise and blur. To address these problems, we

propose a novel method for fast video object detection in embedded mobile devices, called a Detection and Tracking Balanced Network (DTB-Net). DTB-Net performs an accurate and fast object detection in embedded mobile devices by a balance module to regulate the working state of tracker and decide on the final output of network. In addition, we build a new Robot Detection Dataset for video object detection, called the FIST-RD. A series of detailed evaluation experiments in Nvidia Jetson TX2 platform based on FIST-RD dataset demonstrate the competitive performance of proposed DTB-Net.

SunA04 Room04 Connected Vehicle and Future Smart Transportation (Special Session) 08:00-10:00 Chair: Yujie Sun State Nuclear Elect. Power Planning Desi

gn & Research Inst. Co., LtdCO-Chair: Yan Pang Dalian Univ. of Tech.

08:00-08:15 SunA04-1 131 Optimal Selection of Clustering Analysis Model for Traffic Congestion Patterns Yujie Sun State Nuclear Elect. Power Planning Design & Resea

rch Inst. Co., LtdHao Luo State Nuclear Elect. Power Planning Design & Resea

rch Inst. Co., LtdBased on the four parts of clustering analysis, three clustering analysis models of traffic congestion patterns were established. In order to verify the accuracy of the three clustering analysis models of traffic congestion pattern and select the best model, evaluation indexes should be found to conduct quantitative evaluation on the three models.First, this paper proposes three evaluation indexes according to the results of cluster analysis, “coefficient of intra-class variation”, “coefficient of inter-class variation”, and “coverage”.Then, three models were used to evaluate the clustering results of the data in the second quarter of 2018, and a relatively superior model was obtained. Finally, the model is applied to the data of the same period in 2019 to verify its stability. The results show that model III is the best model for clustering analysis of traffic congestion patterns. By comparing the data of the same period in 2019, it is shown that the model is reliable and stable.

08:15-08:30 SunA04-2 135 Study on Model Adaptability Based on K-means Clustering Method and Exponential Continuous Time - Varying Characteristics Yujie Sun State Nuclear Elect. Power Planning Design & Res

earch Inst. Co., LtdHao Wang SPIC China Power Complete Equipment Co., Ltd.The model based on k-means clustering method and exponential continuous time-varying characteristics is a clustering analysis model of traffic congestion patterns. From the previous clustering results, it can be seen that the model has ideal applicability and stability for Beijing. In order to analyze the adaptability of the model in other large cities, the model was applied to Shanghai, Guangzhou, and Shenzhen successively, and it was found that the model has a good effect on the analysis of traffic congestion patterns in these cities, and there are certain traffic congestion patterns between different cities.

08:30-08:45 SunA04-3 139 Local Path Planning Algorithm for UGV Based on Improved Covariance Matrix Adaptive Evolution Strategy Jiangbo Zhao Beijing Institute of Tech.Jiaquan Zhang Beijing Institute of Tech.Junzheng Wang Beijing Institute of Tech.Xin Zhang Techical center, Xinxing Cathay International

Group Co., Ltd.Yanlong Wang Beijing North Vehicle Group Co., Ltd.Local path planning algorithm is one of the key Techologies for unmanned ground vehicle(UGV). In order to reduce the computational complexity of the local path planning algorithm and ensure the real-time performance of the algorithm, a non-uniform column grid lines modeling method is introduced, and on this basis, a modeling method for planning paths is proposed. Aiming at the path planning problem in a multi-obstacle environment, the evaluation function of the path is constructed from four aspects, and the covariance matrix adaptive evolution strategy(CMA-ES) is used to solve the nonlinear optimization problem. In order to further reduce the amount of calculation, the CMA-ES algorithm is improved by the dynamic adjustment strategy of population size. Experiment results show that the path planning algorithm can effectively realize the local path planning in complex environment.

08:45-09:00 SunA04-4 361 A Survey on Deep Reinforcement Learning for Traffic Signal Control Wei Miao GS-Unis Intelligent Transportation System&Co

ntrol Tech. Co,.LtdLong Li GS-Unis Intelligent Transportation System&Co

ntrol Tech. Co,.LtdZhiwen Wang Lanzhou Univ. of Tech.Traffic congestion is one of the most important and complex problems in urban governance for a long time. Although traffic lights are used at

Technical Programmes CCDC 2021 intersections, traffic bottlenecks still appear with the increasing number of private cars. In recent years, with the continuous development of related Techologies in the field of intelligent transportation, more attention has been paid to the automatic driving scheme with intelligent vehicle infrastructure cooperative systems as the core. As a kind of advanced artificial intelligence method, deep reinforcement learning (DRL) is applied to traffic signal control (TSC) to achieve the purpose of optimizing roadside traffic timing. In this paper, we introduce the background of TSC (i.e., main parameters, methods and simulation tools), and then summarize the representation of DRL model (i.e., state, action and reward) and the application of DRL in TSC. The research scenarios of TSC are divided into single-agent and multi-agent. Finally, according to existing works in this field, the problems to be solved are put forward and the paper is summarized.

09:00-09:15 SunA04-5 435 A Modified Vehicle Following Control System on the Curved Road Based on Model Predictive Control Zongwei Cui Hubei Key Laboratory of Advanced

Tech. of Automotive ComponentsXiaotian Xia Wuhan Univ. of Tech.Xiaofei Pei Hubei Key Laboratory of Advanced

Tech. of Automotive ComponentsIn this paper, a decoupled control framework is developed to fulfill the automated vehicle following. Human driver’s habits on curved road are incorporated into controller. Firstly, the overall structure of vehicle following control system is given. Secondly, in the upper-level controller the motion relation of vehicle longitudinal is set up by Preview G-Vectoring Control (PGVC). Hence, the acceleration of host vehicle is adjusted according to the variety of road contour during vehiclefollowing. Then, an acceleration-predictor of preceding vehicle in prediction horizon is also incorporated into MPC structure. Moreover, a practical lower-level controller for acceleration follow is proposed respectively. Finally, simulation results demonstrate the control framework has good performance of vehicle-following ability on the curved road, and its velocity characteristic is more similar with human’s driving behavior.

09:15-09:30 SunA04-6 449 A Driving Assist System for Path Tracking via Active Rear-wheel Steering Yufeng Hong Beijing Institute of Tech.Junqiu Li Beijing Institute of Tech.Weichen Wang Beijing Institute of Tech.Jianwen Chen JIANGLU Machinery & Electronics Group Co.,LTD.Ruichuan Wei Beijing Institute of Tech.In this paper, we propose a framework for shared control to assist driving by using an active rear-wheel steering system. In this framework, the active rear-wheel steering is used to correct improper operation and improve driving performance on path tracking for the driver. An adaptive model-predictive control (AMPC) method is adopted to balance the tracking performance and the influence of unnecessary intervention from the controller. We describe the solution of the rear wheel angle as an optimization problem in a moving horizon, which enables the rear-wheel system to provide timely help without excessive intervention.

09:30-09:45 SunA04-7 495 Longitudinal Control of Connected Truck Platoon based on PreScan Junhong Fan Chongqing

Univ. of Posts and TelecommunicationsYongfu Li Chongqing

Univ. of Posts and TelecommunicationsHao Zhu Chongqing

Univ. of Posts and TelecommunicationsShuyou Yu Jilin Univ.This paper proposes a general framework of connected truck platoon control based on PreScan, which includes scenario building, control algorithm loading, graphical user interface (GUI) developing, and 3D visualized display. In particular, a distributed longitudinal controller for connected truck platoon is developed by considering the car-following interactions between trucks based on the combination with feedback and feedforward. Then, the plant stability and string stability of the truck platoon are analyzed, respectively. Finally, a scenario of platoon with five trucks is constructed in PreScan to conduct the co-simulations. Results verify the effectiveness of the proposed platoon controller in terms of velocity, acceleration, and spacing error profiles.

09:45-10:00 SunA04-8 544 Autonomous Driving Traffic Control Based on Human-in-the-loop Decisions Yan Pang Dalian Univ. of Tech.Gaochang Zhang Dalian Univ. of Tech.Hao Xia Dalian Univ. of Tech.With the development of science and Tech., autonomous driving vehicles have received a lot of attention. Before autonomous driving vehicles can completely replace human-driven vehicles, we will experience the situation where human-driven vehicles and autonomous driving vehicles appear on the road at the same time. It is particularly important for the

autonomous vehicles to understand drivers’ intentions well, make corresponding decisions, complete the driving task safely and effectively. In this regard, the human-in-the-loop control idea is considered, and a control strategy based on stochastic model predictive control is proposed. It is applied on two autonomous driving scenarios: lane merging and left-turn. In the simulation, safety, effectiveness and robustness of the proposed strategy are verified.

SunA05 Room05 Adaptive Control and Learning Control (I) 8:00-10:00 Chair: Junqiu Li Beijing Inst. of Tech.CO-Chair:Binghui Che

China Aerodynamics Research and DevelopmentCenter

8:00-8:20 SunA05-1 11 Adaptive learning controller design for uncertain nonlinear active suspension system with actuator input delay and time-varying state constraints Huijun Guo Xi'an Univ. of tech.

Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

Xi'an Shechtman Nobel Prize New Materials Inst.Jintao Liang Guilin university of electronic tech.This paper investigates the adaptive tracking control problem for active suspension system（ASS） with time-varying constraints via the neural network (NN) approach. The nonlinear terms in the spring and damper coefficients, input time delay of the actuator and the suspension performances are also considered simultaneously. The NNs are used to approximate the unknown function caused by the uncertain car-body mass. It is known to all that ensuring the vertical displacement and speed states in the constraint regions can guarantee the stability and safety of the ASSs. Thus, by introducing the symmetric barrier lyapunov function from the time-varying constraints, an adaptive controller based on the NNs and nonlinear backstepping technique is developed to guarantee the states within constraints. Then the stability of the whole system, including the semi-global ultimately uniformly boundedness of the all signals of the ASSs, is analyzed by the Lyapunov stability analysis. Finally, simulation results are provided to verify the feasibility of the adaptive learning controller.

8:20-8:40 SunA05-2 185 Finite-time Adaptive Synchronization Control of Complex Dynamical Networks With Nonlinear Couplings and Time-varying Delay Yong Bao Tianjin College, University of Science and

Tech. BeijingYuejiao Zhang Tianjin College, University of Science and

Tech. BeijingQiang Li Tianjin College, University of Science and

Tech. BeijingIn this paper, the problems of finite-time synchronization control of complex dynamical networks with nonlinear couplings and time-varying delay are investigated using adaptive control methods. It is less conservative to solve the nonlinear coupling function between nodes in complex network by one-sided Lipschitz condition. The adaptive laws are derived to estimate unknown coupling strength and topological structure of complex dynamical networks. Finally, the effectiveness of the control method is verified by experimental simulation.

8:40-9:00 SunA05-3 274 Simulation and Experimental Research on Adaptive Active Vibration Control for Test Model in wind tunnel Binghui Che China Aerodynamics Research and Development

CenterYong Huang China Aerodynamics Research and Development

CenterLujun Huang China Aerodynamics Research and Development

CenterThe tail support test system of wind tunnel test will produce vibration under unsteady wind load excitation. The vibration of larger magnitude will threaten the test safety and affect the test results. In order to suppress the vibration of the model and support system, an active vibration suppression control method based on adaptive filtering algorithm is studied. The structure and working principle of adaptive feed forward control and adaptive internal model control based on adaptive filtering algorithm are analyzed, and the control effect and feasibility of the two control algorithms are compared through simulation of the control algorithm. In order to verify the effect of active vibration control, an experimental system of active vibration control based on adaptive internal model control algorithm was established with cantilever beam structure as the control object. Signal generator is used to simulate system excitation and vibration response is obtained by acceleration sensor, which verifies the actual control effect of the control algorithm. The results show that the internal model control method based on adaptive filtering algorithm has a good active control effect on the vibration of the structure, and can be easily applied in engineering practice, with the characteristics of simple implementation and fast operation.

9:00-9:20 SunA05-4

Technical Programmes CCDC 2021 359 Four Wheel Steering Vehicles Stability Control Based on Adaptive Radial Basis Function Neural Network Qi Li Beijing Inst. of Tech.Junqiu Li Beijing Inst. of Tech.Sifan Wang Beijing Inst. of Tech.Xiaopeng Zhang JIANGLU Machinery & Electronics Group Co,

LTD.Jiwei Liu Beijing Inst. of Tech.Due to the nonlinear and strong coupling characteristics of four-wheel steering vehicles, there are poor control accuracy and robustness in traditional control methods. This paper proposes a hierarchical controller of lateral stability based on mechanical-differential combined distributed drive four-wheel steering vehicle. The upper controller trains the RBF neural network offline based on the ideal sideslip angle, the parameters adaptively adjusted online according to the error and calculates the ideal rear wheel angle by using the front wheel angle in real time. In addition, a lower controller is established for the differential torque distribution of the hub motor on both sides with the PI controller. Finally, the performance is evaluated via MATLAB/Simulink. The simulation results prove that the four-wheel steering strategy based on adaptive RBF neural network controller (ADP-RBF) can effectively improve steering mobility at low speed and the lateral stability at high speed of the vehicle.

9:20-9:40 SunA05-5 1010 Predictor-based Model Reference Adaptive Controller Design for the Longitudinal Motion of a Hybrid Autogyro Jiajia Hou Science and Tech. on Space Physics Laborat

oryXinglu Xia Beihang Univ.Chaoyi Sun Science and Tech. on Space Physics Laborat

oryXin Li Science and Tech. on Space Physics Laborat

oryThe model reference adaptive control (MRAC) scheme is a popular method to control systems with uncertainties. It is improved by adding a predictor to be a controller of predictor-based model reference adaptive control (PMRAC). The PMRAC controller can compensate for the system uncertainty and does not need an accurate model so that it has a better performance than the traditional PID controllers. This paper focuses on the PMRAC controller design for a hybrid autogyro whose characteristics is similar with the fixed-wing unmanned aerial vehicle. To show the excellent performance of the PMRAC controller, a traditional PID controller is designed for comparison. Numerical simulations show the anti-jamming ability of the proposed PMRAC controller.

9:40-10:00 SunA05-6 504 Adaptive control for nonlinear large-scale systems with unavailable states and unknown virtual control coefficients Honghong Wang Qingdao Univ.Gang Xu Weifang Vocational CollegeBing Chen Qingdao Univ.Chong Lin Qingdao Univ.Yumei Sun Shandong Univ. of Science and

Tech.Base on the approximation capability of fuzzy logical systems (FLSs), adaptive decentralized control is discussed for a class of large-scale nonlinear systems with unavailable states, input saturations and unknown virtual control coefficients. In order to overcome the challenges coming from unknown virtual control coefficients and system states, the convex combination method and the observer are applied, respectively. Combing Lyapunov stability theory and backstepping technique, a decentralized output feedback control strategy is proposed to stabilize the resulting interconnected systems. Finally, an example is given to illustrate the effectiveness of our results.

SunA06 Room06 IntelliSense and Advanced Sensing, Detection Technology (Special Session) 08:00-10:00 Chair: Yong Zhao Northeastern Univ.

08:00-08:20 SunA06-1 284 Improved Disparity Estimation Algorithm Based on PSMNet Juan Du South China Univ. of Tech.Yongchao Tang South China Univ. of Tech.Bohang Li South China Univ. of Tech.Dengping Lin South China Univ. of Tech.Juan Huang Guangdong Univ. of Tech.When designing a network for disparity prediction, increasing the number of convolutional layers can effectively improve the accuracy. However, a too deep network is likely to cause too many parameters and long running time, which is not conducive to real-time disparity estimation. To solve this problem, an improved disparity estimation algorithm based on PSMNet is proposed. First, the dimensionality of the input image is adjusted through a simple convolutional layer. The multi-level residual network structure is used for feature information extraction, and rich image feature information of left and right images is obtained. Then the SPP structure is used to extract and fuse image features at multiple scales. Finally, the concatenated left and right image features are input

as a cost volume to the 3D convolutional layer for disparity regression calculation, and the predicted disparity is obtained. The experimental results show that our algorithm reduce the number of parameters by 46.6% compared to the PSMNet. At the same time, the accuracy of the model in the local environment has increased by 7%. This algorithm reduces the number of model parameters while ensuring the accuracy of disparity estimation.

08:20-08:40 SunA06-2 309 Wildlife Small Object Detection based on Enhanced Network in Ecological Surveillance Wan Dai Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsHongpeng Wang Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsYulin Song Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsYunwei Xin Nankai Univ.Visual tele-observation is an effective way for intelligent monitoring and protection of ecology and the natural environment. Different from pedestrian or rigid body detection, wildlife detection in natural scenes face more complex problems, such as the existence of wild background clutter, local or global vegetation occlusion of animals, small object, rotation, deformation, and other interfering factors. In this paper, We mainly propose a novel method for the small object problem. For our small object wild species data set, we use an SSD detector for object detection. Firstly, the K-means algorithm is used to adjust the anchor box of the SSD network. Secondly, for the situation that SSD is not good for small object detection, in conv4_3 layers of SSD, the feature enhancement module is added. Finally, aiming at the size of a small object, the network level of SSD is deleted. We have proved the feasibility of each of the three methods through experiments and combined with the three methods to verify, the recognition rate of the small target has increased by 2.67%.

08:40-09:00 SunA06-3 310 Object Segmentation based on Anti-disturb Network in Ecological Surveillance Wan Dai Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsHongpeng Wang Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsChongshan Fan Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsYunwei Xin Nankai Univ.Visual tele-observation is an effective way for ecological protection. Different from pedestrian or rigid body detection, wildlife recognition in natural scenes face more complex problems, such as the similarity of background and foreground, wild background clutter, deformation, and other factors, which will lead to a reduction in recognition accuracy. In this paper. We propose a novel segmentation network that is named T-S network to segment background and foreground. The network mainly consists of two parts: one is a tracking network. Considering the context similarity of images in the data set, the purpose of tracking is to ensure that the position of the object is accurately known when the same species in the images are synthesized into a video with interval frames; the other is the saliency network, which is the basis of the most likely position of the object obtained from the tracking network. Then, the saliency network is used to complete the object segmentation. To verify the effectiveness of the network and consider the size of the data set, a lightweight CNN is used to identify the network. Experiments show that the accuracy of task recognition is improved by 1.89% by using the segmentation method proposed in this paper.

09:00-09:20 SunA06-4 460 Lane-Level Three-Dimensional Semantic Mapping Based on Stereo Vision Ruirong Wang Beijing Inst. of Tech.Chunlei Song Beijing Inst. of Tech.Yuwei Zhang Beijing Inst. of Tech.Jianhua Xu Beijing Inst. of Tech.Autonomous vehicles need to clarify their position and recognize objects in the urban scene, making it increasingly rely on maps to provide them with prior semantics for advanced tasks such as positioning and navigation or planning control. Semantic segmentation and geometric reconstruction techniques required for mapping have gradually developed. They have been combined to a certain extent for applications such as generating semantic maps. Still, they are rarely refined to the lane level, even if the lane can be used as a necessary constraint for vehicles to maneuver on the road. Important clues. This paper proposes a three-dimensional lane-level semantic mapping system that utilizes stereo vision and use the scrolling grid representation to save computing time and memory. We use the deep neural network to obtain the stereo disparities and lane-level semantics and then combined pose and depth to transmit the semantics into the three-dimensional space. Experiments on the KITTI data sequence show that our system can continuously identify and reconstruct objects on the road, even if their strong structure prior or few appearance clues.

Technical Programmes CCDC 2021 09:20-09:40 SunA06-5 628 A method of lung parenchyma segmentation based on multi-feature extraction and improved Graham two-dimensional convex hull algorithm Cong Liu Northeastern Univ.Jun Gong Northeastern Univ.Hongyang Pei Northeastern Univ.Dan Yang Northeastern Univ.XueYan Wang Northeastern Univ.Aiming at the problem that it is difficult to segment the proximal pleural nodules accurately due to the adhesion between the proximal pleural nodules and the pulmonary parenchymal contours, this paper mainly proposes a multi-feature extraction and improved lung parenchyma segmentation method Graham two-dimensional convex hull algorithm. First, the lung parenchyma in the sample image is segmented, and then the near pleural nodule in the lung parenchyma is segmented. The image was pre-processed to remove the black background image of the four corners of the image by bimodal selection. Then, the lung duct was removed by the combination of CLAHE and OTSU algorithm, and the background images of bone and bed were removed by the OTSU algorithm and morphology. Then the lung lobe adhesion was separated based on the contour center distance and the lung lobe adhesion part in the image such as longitudinal connection thin; Finally, an improved Graham two-dimensional convex hull algorithm is proposed to segment and smooth the lung parenchyma twice, and two kinds of images of the contour groove and the non-contour groove are obtained. Then the nodule image is obtained by different or operation and multiple nodule feature screening. The segmentation accuracy, recall rate and F value of the proposed algorithm for near pleural nodules are 99.7%,94.7%,97.7%, respectively. The experimental results show that this algorithm can accurately segment the near pleural nodule image in the sample image, and has good adaptability and robustness.

09:40-10:00 SunA06-6 629 A U-Net model with multi-scale convolution kernel for lung nodule segmentation Jun Gong Northeastern Univ.Hongyang Pei Northeastern Univ.Dan Yang Northeastern Univ.Cong Liu Northeastern Univ.In order to accurately segment the lung nodules in CT images, an improved U-Net network segmentation method of lung nodules is proposed. This method introduces a multi-scale convolution kernel, strengthens the network's extraction and utilization of different receptive field features, extracts target features of different sizes on the image, and uses an improved hybrid loss function to alleviate the problem of class imbalance. The experimental results on the LIDC-IDRI lung nodule public database show that the Dice similarity coefficient value, Precision and Recall achieved by this method are 0.856, 0.871 and 0.853, respectively. Compared with other segmentation networks, this method can accurately segment lung nodules and has good segmentation performance.

SunA07 Room07 Fractional Calculus and Fractional-order System (Special Session) 08:00-10:00 Chair: Dingyu Xue Northeastern Univ.

08:00-08:20 SunA07-1 42 The existence and uniqueness of solution for a class of fractional differential equation with fractional-order impulsive term Nan Zhang Taiyuan Univ. of Tech.Lingling Zhang Taiyuan Univ. of Tech.

Beijing Inst. of TechnIn this paper, compared with the impulsive differential equations of integer order impulsive terms in the previous literatures, we investigate boundary value problem of fractional differential equation where the impulsive item is fractional order. By giving the Green function of corresponding equation, using the fixed point theorem and contraction mapping principle, we obtain the existence and uniqueness of solution for the fractional impulsive differential equation.

08:20-08:40 SunA07-2 43 Fixed point results based on set Ph,e and application in nonlinear fractional differential equation Gaofeng Xing Taiyuan Univ. of Tech.Lingling Zhang Taiyuan Univ. of Tech.

Beijing Inst. of TechnXin Zhao Taiyuan Univ. of Tech.In this paper, a fixed point theorem of mixed monotone operator based on the set Ph,e is obtained by using monotone iterative methods and cone theory on Banach Space. In particular, the operator dose not need to satisfy the conditions of continuity and compactness or existence of upper and lower solutions. As an application, we employ the result obtained to study existence and uniqueness of solutions for a class of nonlinear fractional differential equation.

08:40-09:00 SunA07-3

175 Stabilization of a class of Takagi-Sugeno fuzzy fractional order systems based on fuzzy controller switching Zhe Wang Northeastern Univ.Feng Pan Northeastern Univ.Dingyu Xue Northeastern Univ.Jiwei Nie Northeastern Univ.In this paper, the stability of fuzzy fractional order systems (FOSs)is discussed. Firstly, a fuzzy model of FOSs with switching controllers is presented. Then, the stability criteria of fuzzy FOS with order 0 < α < 1 and 1 < α < 2, respectively, are given. The stability conditions are composed of bilinear matrix inequalities (BMIs) and can be converted to linear matrix inequalities (LMIs). Moreover, the stabilizing switching laws of the state-dependent form are designed. Finally, two examples are given to verify the effectiveness of the proposed results.

09:00-09:20 SunA07-4 234 A Tutorial on a Universal Blockset for Fractional-order Systems Tingxue Li Northeastern Univ.

Shenyang City Univ.Dingyu Xue Northeastern Univ.Xinshu Cui Northeastern Univ.Fractional-Order systems have been studied more universally in recent years and the tools designed to study them have been developing hand in hand. One of the effective tools is the FOTF toolbox in Matlab/Simulink which is used to deal with calculation and simulation of fractional-order calculus and fractional-order transfer functions. FOTF Toolbox proposed by the presenter is now extended and simulation modelling pattern for some more universal blocks are created, such as Universal block diagram pattern for Caputo differential equations; Modelling of fractional-order delayed systems and systems with variable delays; Modelling and simulation of multivariable control systems; Vectorized fractional block for the modelling of fractional-order state space model; the (s + a)α block for some kinds of irrational transfer function models. In this paper, the catalogues and the FOTF-library of the toolbox are introduced first and then a few examples of usage of the library in simulation are given for further illustration.

09:20-09:40 SunA07-5 376 LMI-Based Criterion for Global Mittag-Leffler Synchronization of Discrete-time Fractional-Order Complex-Valued Neural Networks with Time Delay Xingxing You Sichuan Univ.Songyi Dian Sichuan Univ.Kai Liu Sichuan Univ.Guofei Xiang Sichuan Univ.Bin Guo Sichuan Univ.Haipeng Wang State Grid Intelligent Tech. Co.Xu Zhang State Grid Intelligent Tech. Co.In this paper, the global Mittag-Leffler synchronization of discrete-time fractional-order complex-valued neural networks with time delay is investigated. First of all, a new lemma for Caputo fractional difference is derived in complex field. Then, by constructing appropriate Lyapunov function and designing a novel complex-valued linear feedback controller, the LMI-based sufficient criterion is provided to ensure the global Mittag-Leffler synchronization of proposed networks. A simulation example is given to show the effectiveness of the control scheme.

09:40-10:00 SunA07-6 408 An improved SOC estimation method based on noise-adaptive particle filter for intelligent connected vehicle battery Zhongyue Zou Henan Suda Electric Vehicle Tech. Co.Mingbo Zhou Xi’an Jiaotong Univ.Junyi Cao Xi’an Jiaotong Univ.In order to effectively use the cloud data of connected vehicle to estimate the battery state of charge (SOC), an estimation method based on noise adaptive particle filter (N-APF) is proposed in this paper. Firstly, several cells are connected in series under the laboratory environment to simulate the grouping of battery packs in real vehicle. Besides, the federal test procedure (FTP) operating current for battery pack is obtained through software simulation combined with the actual vehicle parameters. Then, the Thevenin equivalent circuit model is established and the reliability of online identification of model parameters based on 10s interval data is verified. Furthermore, the effectiveness of the proposed noise adaptive particle filter method for adjusting the process noise and enhancing the stability of the SOC estimation is proved. Finally, the reliability of the improved SOC estimation method for the connected vehicle is verified based on the 10s interval cloud data, which shows the proposed noise adaptive particle filter estimation method can stabilize the SOC estimation error below 5% except for some high-current discharge phases.

SunA08 Room08 Signal Processing and Information Fusion (II) 08:00-10:00 Chair: Tong Zhou Chongqing Vocational Inst. of EngineerigCO-Chair: Le Wang Xi'an Electronic Engineering Research Inst.

08:00-08:20 SunA08-1

Technical Programmes CCDC 2021 165 Shadow Suppression for Vehicle Target Detection in Open-air Expressway Scenes Tong Zhou Chongqing Vocational Inst. of EngineeringYuxuan Li Chongqing Vocational Inst. of EngineeringMei Deng Chongqing Univ.Siyuan Pang Chongqing Univ.The accurate extraction of the vehicle target area is key to the detection of expressway anomaly events based on video surveillance. In the expressway scenes, the existing vehicle shadow interference areas, often make the vehicle area distortion, expansion, connectivity or even lost. In addition, there is much more noise interference in the open-air images and most of the images’ quality is lower, resulting in that the traditional shadow suppression method is still difficult to apply. In order to reduce the noise in open-air scenes, a new shadow suppression method is proposed, based on a combination of color grayscale feature and HLGP (Histogram of Local Gradient Binary Patterns) feature. The new algorithm first uses chromaticity and brightness similarity to determine the shadow preliminarily so as to solve the problem of misjudgment in the vehicle area. Then, based on the noise robustness LGBP and local gradient histogram feature, the HLGP feature is obtained, which is proved to be good robustness against illumination and applied to correct the results of color shading for reducing the false detection area. The experimental results indicate that the proposed method can overcome the noise interference and improve the accuracy of vehicle target detection.

08:20-08:40 SunA08-2 555 A New Roll Angle Fusion Method Based on High Confidence Fusion Strategy Le Wang Xi'an Electronic Engineering Research Inst.Yaojun Li Xi'an Electronic Engineering Research Inst.Yu Xie North Industries Co. Ltd.Feng Ruan Xi'an Electronic Engineering Research Inst.Zhenyu Yang Northwestern Polytechnical Univ.Roll angle is one of the important parameters for the guidance of rotating carrier, and the precision of measurements is positively related to the precision of hitting for two-dimensional guidance. In order to improve the precision of measurements of roll angle and eliminate the influence of measuring errors in online data, it is necessary to make fusion of the roll angle measurements from Beidou receiver. In this paper, a new roll angle fusion method based on high confidence fusion strategy is proposed. By designing the high confidence fusion strategy and its fusion process, a set of high confidence fusion strategy is formed naturally, which integrates the general data fusion methods such as variance, information entropy, Euclidean distance and so on. Based on the high confidence fusion strategy, adaptive weighted optimization and normalized fusion are carried out. In the final, the high confidence integrated decision-making results with rejection probability are obtained as a results. The experiment test shows that the method proposed can significantly enhance the accuracy and reliability of roll angle estimation.

08:40-09:00 SunA08-3 1612 WiDG: An Air Hand Gesture Recognition System Based on CSI and Deep Learning Zhengjie Wang Shandong Univ. of Science and Tech.Xue Song Shandong Univ. of Science and Tech.Jingwen Fan Shandong Univ. of Science and Tech.Fang Chen Shandong Univ. of Science and Tech.Naisheng Zhou Shandong Univ. of Science and Tech.Yinjing Guo Shandong Univ. of Science and Tech.Da Chen Shandong Univ. of Science and Tech.Hand gesture recognition has become a hot research topic because it plays a crucial role in human-computer interaction applications. Channel State Information (CSI) is attracting more attention since it depicts more accurate communication links and can be leveraged to recognize target action in its coverage area. In this paper, we propose a device-free hand gesture recognition system based on CSI and deep learning models, called WiDG. This system can recognize handwritten digits from 0 to 9 in the air according to CSI changes caused by different hand movements. We build deep learning models to identify hand gestures. We conduct experiments in both non-through-the-wall and through-the-wall scenarios to evaluate system performance. The experimental results show that Convolutional Neural Networks (CNN) achieves 97.2% and 95.7% recognition accuracy in the non-through-the-wall scene and through-the-wall scene, respectively. In addition, we discuss the system parameters affecting recognition accuracy and compare system performance with WiNum. The results show that deep learning models can realize hand gesture recognition with a satisfactory performance using CSI.

09:00-09:20 SunA08-4 1613 Crowd Counting Based on CSI and Convolutional Neural Network Zhengjie Wang Shandong Univ. of Science and Tech.Jingwen Fan Shandong Univ. of Science and Tech.Xue Song Shandong Univ. of Science and Tech.Naisheng Zhou Shandong Univ. of Science and Tech.Fang Chen Shandong Univ. of Science and Tech.Yinjing Guo Shandong Univ. of Science and Tech.Da Chen Shandong Univ. of Science and Tech.

With the rapid development of people-centered internet of things technology, crowd counting has attracted more and more attention and plays an important role in smart home and public security management. This paper present a crowd counting system based on WiFi Channel State Information (CSI) and Convolutional Neural Network (CNN). The system uses the convolutional neural network to process input data and achieves a recognition accuracy of 97% in a six people environment. We analyze the influence of distance on recognition accuracy and discuss the data processing methods with the other two neural networks. Furthermore, we compare this system with the other system in the similar parameters and validate the effectiveness of our system in crowd counting using CSI.

09:20-09:40 SunA08-5 1650 Multi-target Track-to-Track Association Based on Relative Coordinate Assignment Matrix Jintao Chen Sun Yat-sen Univ.Yan Zhang Sun Yat-sen Univ.Yang Yang Sun Yat-sen Univ.Chengzhi Qu Sun Yat-sen Univ.Multi-sensor data fusion has been widely recognized to improve the performance of multi-target tracking. Track-to-track association (TTTA) must be completed before performing track-to-track fusion. However, the TTTA problem will be complicated by the systematic sensor biases which are different from random error. This article focuses on the TTTA problem in the presence of sensor biases and dense false tracks. A novel multi-target TTTA method based on the relative coordinate assignment matrix (RCAM) is proposed. More specifically, the RCAM leverages the relative coordinate information between two targets instead of the absolute position information of each target. We theoretically analyze the influence of systematic azimuth and range biases on target measurement information and RCAM of target, and test the proposed method in different challenging scenarios. Simulation results demonstrate better performance of the new method against systematic sensor biases and false tracks compared with other competing methods.

09:40-10:00 SunA08-6 1661 Event-triggered Distributed Sequential Estimation for Wireless Sensor Networks Ni Wang Heilongjiang Univ.Chunyu Liu Heilongjiang Univ.Shuli Sun Heilongjiang Univ.

Key Laboratory of Information Fusion Estimationand Detection in Heilongjiang Province

In this paper, we propose an event-triggered distributed sequential fusion estimator based on information filters. This algorithm not only uses the distributed parallel structure on sensors, but also uses the real-time processing of sequential fusion on state estimates. Therefore, it has the advantages of reliability and reduced computational burden. The event-triggered mechanism is designed based on observations of each sensor. Moreover, the equivalence on estimation accuracy of the proposed event-triggered distributed sequential fusion filter and event-triggered centralized fusion filter is proven. A simulation example shows the effectiveness of the proposed algorithm.

SunA09 Room09 Fault Diagnosis and Predictive Maintenance (II) 08:00-10:00 Chair: Zhitao Liu State Key Laboratory of Industrial Control

Tech.CO-Chair: Yu Zhang Northeastern Univ.

08:00-08:20 SunA09-1 1033 Prediction of Remaining Useful Life of Lithium-ion Battery Based on Improved Auxiliary Particle Filter Huan Li Zhejiang Univ.Zhitao Liu State Key Laboratory of Industrial Control

Tech.Hongye Su State Key Laboratory of Industrial Control

Tech.In order to effectively predict the remaining useful life of lithium-ion batteries, particle filter algorithm is introduced in this paper. However, the standard particle filter algorithm is difficult to ensure the accuracy of battery life prediction due to its weight degradation, particle exhaustion and other problems. In this paper, a method based on the improved auxiliary particle filter algorithm and the double exponential capacity degradation model to predict the remaining useful life of lithium-ion batteries is proposed. Based on the standard particle filter, the algorithm introduces an auxiliary variable and performs two weighting operations to make the particle weight change more stable. Then, using the nonlinear mapping ability of BP neural network, the particle weights are split and adjusted to improve the particle diversity. The experimental results show that the improved algorithm is more reliable than the auxiliary particle filter, and the estimated relative error is smaller, that is, the remaining useful life of lithium-ion battery can be predicted more accurately.

08:20-08:40 SunA09-2 912

Technical Programmes CCDC 2021 Sensor Fault Detection for Superconducting Cable Monitoring System based on Swarm Optimization BP Neural Network Xihua Zong Shanghai Electric Cable Research Institute Co.,

Ltd.Xize Zhang Shanghai International Superconducting Science

and Tech. Co., Ltd.Dayi Zhang Shanghai International Superconducting Science

and Tech. Co., Ltd.Bin Xu Northeastern Univ.Yu Zhang Northeastern Univ.A reliable sensor network is essential for the superconducting cable monitoring system and designing an efficient sensor fault detection method is very important for the stable operation of the system. Aiming at the problem of sensor fault detection in superconducting cable monitoring system, a sensor fault detection method using particle swarm optimization BP neural network algorithm is presented. In the experiment, the real data collected in the cable monitoring system is used and the result show that the method has better performance compared with traditional BP neural network algorithms. A prototype software for model verification and application is established. It shows that the sensor fault detection system can detect a variety of sensor faults accurately and quickly.

08:40-09:00 SunA09-3 207 A Deep Learning Model for Bearing Fault Diagnosis Based on Convolution Neural Network with Multi-channel and Residual Network Jianyong Tuo Beijing Univ. of Chemical Tech.Yu Hu Beijing Univ. of Chemical Tech.Xin Ma Beijing Univ. of Chemical Tech.Youqing Wang Shandong Univ. of Science and Tech.Traditional bearing fault diagnosis algorithms mostly rely on expert experience and prior knowledge, which can no longer meet the actual requirements of industrial big data. This paper proposes a new deep learning model that combines the multi-channel and wide first layer structures, and uses dropout technology, regularization, batch normalization, and other methods to solve the problem of overfitting in the network structure problem, and the introduction of the residual network to solve the problem of network degradation. Experimental results show that the model has an average accuracy of 100% in the bearing data set of Western Reserve University, showing good adaptive ability. The comparison results with mainstream diagnostic algorithms shows that the proposed method has good anti-noise ability.

09:00-09:20 SunA09-4 225 Distributed DBN-HSBoost Model for Hot Rolling Process Operating Performance Assessment with Partial Communication Chuanfang Zhang Key Laboratory of Knowledge Automation for

Industrial Processes of Ministry of EducationKaixiang Peng Key Laboratory of Knowledge Automation for

Industrial Processes of Ministry of EducationUniv. of Science and Tech. Beijing

Jie Dong Key Laboratory of Knowledge Automation forIndustrial Processes of Ministry of Education

Liang Ma Key Laboratory of Knowledge Automation forIndustrial Processes of Ministry of Education

The traditional hot rolling process (HRP) monitoring technology is confronted with monumental challenges in the face of high standard safety and quality requirements. How to evaluate the operating performance in real time and trace the causes of non optimal operating performance has become a new hotpot. Considering the extremely imbalanced and small minority (EISM) data problem in the FMP, a distributed operating performance assessment method with partial communication is proposed by utilizing deep belief network and hybrid sampling boosting (DBN-HSBoost). First, according to the process knowledge, variables of the HRP are divided into several sub-blocks, and the communications of different sub-blocks are also considered. Then, DBN is used to extract the features in each sub-block, which is used as the input of the HSBoost algorithm to establish the operating performance assessment model. When the process is in non-optimal condition, a difference index is calculated to trace the non-optimal variables. Finally, the effectiveness of the proposed method is verified by the process data from a real HRP.

09:20-09:40 SunA09-5 1282 Research on Fault Modeling and Simulation of Electric Control Valve Yang Wang North China Electric Power Univ.Shenghui Wang Huaneng Taiyuan Dongshan Gas Power

Company LimitedWenguang Zhang North China Electric Power Univ.Yuguang Niu North China Electric Power Univ.As the executive terminal of industrial production instructions, the failure of electric control valve will seriously affect the performance of the control system. In order to study the fault diagnosis algorithm of electric control valve, the mathematical models of motor, transmission mechanism and valve of electric control valve are deduced based on mechanism analysis, and the Simulink simulation model of electric control valve is built by using modular modeling method and verified on the semi-physical test

platform. On this basis, the fault models of internal leakage and external leakage of the electric control valve are established, and compared with the corresponding fault simulation results of the semi-physical test platform to verify the accuracy and effectiveness of the fault model.

09:40-10:00 SunA09-6 1682 Design of Fault-tolerant Control Algorithm for Aeroengine Performance Degradation Zhenghong Han Civil Aviation Univ.of ChinaWei Wang Civil Aviation Univ.of ChinaFor aeroengine performance degradation problems, this paper constructs a certain type of single axis aeroengine model, and introduces the concept of health parameters. Through the establishment of deviation index function designed in the face of aeroengine fault detection algorithm was proposed to performance decline at work, at the same time design based on rotating speed and health parameters double scheduling gain scheduling of PI controller, effectively improved the aeroengine decline in the performance of the control performance.

SunA10 Room10 Optimal Control and Optimization (II) 08:00-10:00 Chair: Longquan Yong Shaanxi Univ of Tech.

Shaanxi Key Laboratory of IndustrialAutomation

CO-Chair: Chunhui Yang Naval Univ. of Engineering

8:00-8:20 SunA10-1 635 An Improved Harmony Search Based on Teaching-Learning Strategy for Unconstrained Binary Quadratic Programming Longquan Yong Shaanxi Univ of Tech.

Shaanxi Key Laboratory of IndustrialAutomation

Unconstrained binary quadratic programming (UBQP) problem plays an important role in operational research due to its application potential and its computational challenge. This paper presents a new hybrid algorithm based on Harmony Search (HS) and Teaching-Learning-Based. Optimization. The main features of the proposed algorithm called harmony search with teaching-learning (HSTL) are the integration of teaching-learning strategy in the basic harmony search. Thishybridization has led to an efficient hybrid framework which achieves better balance between the exploration of HS and the exploitation capabilities of the Teaching-Learning-Based Optimization. Experiments on numerous benchmark problems having 50 to 2500 variables show the effectiveness of the proposed framework and its ability to achieve good quality solutions.

8:20-8:40 SunA10-2 351 Study on Dynamic Optimization of Management Activities of Marine Ship-Mounted Equipment Chunhui Yang Naval Univ. of EngineeringKun Gao Naval Univ. of EngineeringHanfu Cong Naval Univ. of Engineering

Tao Hu Naval Univ. of EngineeringAiming at characteristics that it is difficult to efficiently complete the maintenance and overhaul of the equipment during the operation of ships at sea, this paper constructs the dynamic management model of the equipment by analyzing the requirements of the management activities of the equipment, and the maintenance work is disassembled and allocated reasonably. Finally, an example is given to verify that the model can output the activity plan that can satisfy the maintenance time and minimum work intensity of the crew, which is helpful to guide the organization and implementation of ship equipment management activities.

8:40-9:00 SunA10-3 603 Hybrid Fuzzy Controller Design on Oxygen Excess Ratio Control of PEMFC Air Feed System by using PSO Algorithm Kang Wu Nanjing Univ. of Aeronautics and AstronauticsJing Zhu Nanjing Univ. of Aeronautics and Astronautics

Ministry of Industry and information Technology Jiangsu Key Laboratory of Internet of Things

and Control TechnologiesBin Jiang Nanjing Univ. of Aeronautics and Astronautics

Ministry of Industry and information Technology Jiangsu Key Laboratory of Internet of Things

and Control TechnologiesThe fluctuation of oxygen excess ratio (OER) of the proton exchange membrane fuel cell (PEMFC) system in air feed subsystem affects the cell safety and performance directly. In this paper, we concern about the OER control of PEMFC air feed system and propose a hybrid fuzzy controller optimized by using particle swarm optimization (PSO) algorithm, which can track the optimal OER quickly. In the design of the hybrid fuzzy controller, both the feedback and feedforward controller are used. In the feedback control loop, the switching device selects the optimal controller between the fuzzy logic controller (FLC) and fuzzy self-tuning PID (FSTPID) controller in real time upon the OER tracking

Technical Programmes CCDC 2021 error, while in the feedforward control loop, the fuzzy feedforward controller (FFC) is used to reduce the current disturbance. The quantification and scale factors in fuzzy controllers are efficiently optimized by using PSO algorithm. Simulation results show the quick tracking performance of OER and the good dynamic performance of air feed systems in PEMFC.

9:00-9:20 SunA10-4 1420 LQG Control for Markov Jump Linear System with Input Delay Song Zhang Univ. of JinanChunyan Han Univ. of JinanYue Liu Northeastern Univ.This paper studies the linear quadratic gaussian (LQG) control for Markov jump linear system (MJLS) with input delay. Two cases are considered here: the one-step input delay case, and the d-step input delay case (special case). The main contribution is that an extended version of the maximum principle which is applicable to the MJLS with input delay and additive noise is developed. And the LQG controllers for case 1 and case 2 are obtained by solving two sets of coupled difference Riccati equations (CDREs). No state augmentation is required herein.

9:20-9:40 SunA10-5 1510 UAV Path Planning in 3D Complex Environments Using Genetic Algorithms Shanshan Leng Southeast Univ.Hui Sun Southeast Univ.This paper studies the path planning problem of an unmanned aerial vehicle (UAV) flying in a mountainous region, in which obstacles both on the ground and in the air are included. Two objectives, i.e., minimizing total length and minimizing radar detection threat, are considered in path planning. Two genetic algorithms (GAs) with different population initialization methods and genetic operations are proposed to solve the problem. Computational results show that both algorithms can find safe and flyable paths in three dimensional (3D) complex environments. Furthermore, one of them generally leads to a better performance.

9:40-10:00 SunA10-6 1568 Distributed Optimal Power Flow Algorithm for Mesh Networks Tianle Gao Zhejiang Univ.Yuwei Chen Zhejiang Univ.Ji Xiang Zhejiang Univ.In this paper, a distributed algorithm that can obtain the optimum solution of optimal power flow (OPF) problem over mesh network is proposed without requiring the form of central coordination. The mesh network OPF problem is solved in each radial network through the quadratic convex (QC) relaxation method simultaneously, with the interchange of boundary message between neighboring regions. The distributed quadratic convex relaxation algorithm is based on the modified alternating direction multiplier method (ADMM). The effectiveness of the algorithm is tested via numerical simulations in the IEEE 14-bus mesh network system.

SunA11 Room11 Intelligent Control, Computation and Optimization (II) 08:00-10:00 Chair: Dinghui Wu Jiangnan Univ.CO-Chair: Shixi Hou Hohai Univ.

8:00-8:20 SunA11-1 1573 Prediction of Blast Furnace Gas Output Based on GA-Elman Neural Network Yong Zhu Jiangnan Univ.Ruijie Ma Jiangnan Univ.Dinghui Wu Jiangnan Univ.Yanxia Shen Jiangnan Univ.Blast furnace gas (BFG) output in steel industry fluctuate sharply and there are many influencing factors. It is difficult to establish a suitable mechanism model to predict it effectively, and the prediction accuracy of a single neural network model is low. In order to improve the accuracy, an Elman neural network (ENN) prediction model based on Genetic algorithm(GA) is proposed. By optimizing the initial weights and thresholds of the ENN, the possibility of network falling into local optimum is reduced greatly. Combining the characteristics of BFG output with actual data to simulate and analyze, the simulation results show that compared with other models, the model proposed in this paper has higher accuracy.

8:20-8:40 SunA11-2 1615 Type-2 Neuro-Fuzzy Control for a Class of Nonlinear Systems Na Wang Tianjin Polytechnic Univ.

Key Laboratory of Advanced Electrical Engineering and Energy Tech

Shixi Hou Hohai Univ.Cheng Wang Hohai Univ.Suwei Zhai Electric Power Research Inst. of Yunnan

Power Grid Co.,LtdYundi Chu Hohai Univ.In this paper, a type-2 neuro-fuzzy control for a class of nonlinear

systems is studied. Firstly, an integral-type sliding mode controller (SMC) is designed to ensure that the error converges in a finite time. At the same time, the saturation function as an effective way to alleviate chattering is utilized. Moreover, a type-2 neuro-fuzzy networks (T2NFN), in which network parameters can be adjusted online, is used to approximate the designed SMC. In order to improve the generalization ability, T2NFN combines a recursive feature selection algorithm. In particular, due to the added robust compensator, the issue of the approximation error also can be overcome. Finally, the T2NFN controller is applied to the active power filter (APF) to show its superiority.

8:40-9:00 SunA11-3 414 Novel non-singular terminal sliding mode observer based on support vector regression parameters optimization for PMSM sensorless control Yue Zhang Dongfang Electric Machinery Co. LtdJiewen Si Xi’an Jiaoting Univ.Bo Hu Dongfang Electric Machinery Co. LtdZhiming Liang Dongfang Electric Machinery Co. LtdLing Liu Xi’an Jiaoting Univ.Yishan Ma Xi’an Jiaoting Univ.Qilian Lin Xi’an Jiaoting Univ.In order to improve the stability and dynamic performance on the sensorless control system of permanent magnet synchronous motor, a novel nonsingular terminal sliding mode observer is proposed in this paper. It discards the differential term with the integral term which avoids the noise caused by the differential signal and the singular problem that may occur in the sliding mode process. Then, a support vector regression machine is used to optimize the most important parameters which affect novel nonsingular terminal sliding mode observer’s performance. Simulation results show that sensorless control system using novel nonsingular terminal sliding mode observer has high tracking accuracy and small error in rotor position and speed, and has advanced dynamic performance and robustness.

9:00-9:20 SunA11-4 278 ZND-ZeaD Models and Theoretics Including Proofs for Takagi Factorization of Complex Time-Dependent Symmetric Matrix Zhuosong Fu Sun Yat-sen Univ.

Guangdong Key Laboratory of ModernControl Technology

Key Laboratory of Machine Intelligenceand Advanced Computing

Min Yang Guangdong Key Laboratory of ModernControl TechnologySun Yat-sen Univ.

Key Laboratory of Machine Intelligenceand Advanced Computing

Jinjin Guo Sun Yat-sen Univ.Guangdong Key Laboratory of Modern

Control TechnologyKey Laboratory of Machine Intelligence

and Advanced ComputingJianrong Chen Sun Yat-sen Univ.

Youjiang Medical Univ. for NationalitiesYunong Zhang Sun Yat-sen Univ.

Guangdong Key Laboratory of ModernControl Technology

Key Laboratory of Machine Intelligenceand Advanced Computing

Takagi factorization (TF) is a kind of matrix factorization (also termed, decomposition) method. In this paper, the problem of TF for complex time-dependent symmetric matrix (simply termed, time-dependent Takagi factorization) is presented and theoretically studied. To simplify the tough problem, we first consider converting it into an equivalent equation system. Then, a continuous-time (CT) TF (CTTF) model is presented and derived theoretically, by adopting ZND (Zhang neural dynamics, or termed, Zhang neural network) method and a dimensional reduction technique. In order to facilitate the implementation on digital hardware platforms, the corresponding discrete-time (DT) ZND solution models (or termed, algorithms) with different discretization precisions of the CTTF model are presented and taken into theoretical consideration. Specifically, we present and investigate a discrete-time TF (DTTF) model, termed eight-instant DTTF (8IDTTF) model, by using a new eight-instant Zhang et al discretization (ZeaD, also termed, Zhang time discretization, ZTD) formula to discretize the CTTF model. Besides, two other DTTF models, termed four-instant DTTF (4IDTTF) and six-instant DTTF (6IDTTF) models, are presented respectively, on the basis of two corresponding multiinstant ZeaD formulas. Moreover, the effective solution step-size intervals of the three DTTF models are further presented and proved.

9:20-9:40 SunA11-5 282 Data-driven Control for the Consensus of a Class Multi-agent Systems With Unknown Dynamics Jia Wu Xiangtan Univ.

Anhui Polytechnic Univ.Ning Liu Xiangtan Univ.Wenyan Tang Xiangtan Univ.

Technical Programmes CCDC 2021 Kun Li Xiangtan Univ.This study investigates the consensus problem for a class of unknown heterogeneous nonlinear discretetime multi-agent systems with unknown dynamics. A novel distributed data-driven protocol is proposed by using model free adaptive control method, which only uses the input/output data of neighbouring agents and desired signal. Under the proposed protocol, the sufficient conditions that guarantee the consensus errors of unknown multi-agent systems uniformly bounded are analyzed. Finally, the effectiveness of the proposed protocol is illustrated through simulations.

9:40-10:00 SunA11-6 328 A Frame Level Feature Aggregation Method for Video target Detection Jun Guo Northeastern Univ.Wenfeng Liu Northeastern Univ.Shijie Xin Research Inst. of Electronic Information

Products InspectionZixuan Zhao Northeastern Univ.Bin Zhang Northeastern Univ.Video target detection method at the feature level improves the accuracy of video target detection through frame-level feature aggregation. The method of frame-level feature aggregation is to propagate the features of adjacent frames to the current frame and then aggregate them together with the features of the current frame. Multi-frame commonly used while in polymerization of adjacent frame weight average method and the cosine similarity weight allocation method, but the two methods did not consider the video frames the appearance quality of distribution, and lack of the pixel weight of less number of parameters. In the case of motion blur, or low pixels, even lens zoom, the existing methods cannot accurately detect the moving target under the above complex scene. In this paper, a frame-level aggregation method for scaling cosine similarity weight is proposed. At the same time, video frame quality and optical flow quality are modeled. The comparison experiment proves that the scaling cosine similarity weight proposed in this paper is robust to video difficult scenes.

SunAIS Room12 Interactive Session 08:00-10:00

SunAIS-01 874 Spatiotemporal Two Scale Sequential Charging and Discharging Strategy based on User Behavior Xin Li Shenyang Univ.Xiaoning Qin Shenyang Dongling Power Supply Branch CompanyNowadays, more and more EVs (electric vehicles) are connected to the power grid, bringing some load to the grid that cannot be ignored. At the same time, the charging and discharging optimization of EVs becomes extremely complicated due to the random and uncertain behavior of EV users. In order to solve the above problems, the orderly charging and discharging of EVs from both time and space is studied. Considering the impact of the spatial layout of the charging station on the charging and discharging decisions of EVs, a strategic study on the spatial level is added. Firstly, based on user behavior, a time-level model is constructed. In this model, the user expense satisfaction function and the total grid load fluctuation function are established. Then, the user travel satisfaction function and the charging equipment utilization function of the charging station are established in the space layer. Among them, the influence of "mileage anxiety" psychology on user satisfaction is considered in the user travel satisfaction model. Makes user satisfaction greatly improved. The result shows that the decision-making mechanism works well, improving user satisfaction, the economic benefits of the power grid, and the equipment utilization of charging stations.

SunAIS-02 890 Short-term wind power ramp event prediction based on LSTM and error correction algorithm Lin Tong Liupanshui Normal Univ.Yang Yao Liupanshui Normal Univ.Chao Li Guizhou Vocayional Tech. College of Electronices

and InformationPeng Wang Liupanshui Normal Univ.Dengguo Xu Liupanshui Energy AdministrayionAiming at the problems of low wind power ramping occurrences, complex features, and low prediction accuracy of the prediction method for small sample ramp events, a wind power ramp event prediction model based on the combination of long and short-term memory network (LSTM) and error correction algorithms is proposed. First, taking into account a variety of meteorological factors, such as temperature, atmospheric pressure, humidity, wind speed, wind direction and other multi-dimensional meteorological factors, the LSTM network is used to predict the original wind power data, and based on the prediction error, the error is used to correction algorithm finds the error distribution law and corrects the predicted value of wind power to obtain the final predicted value of wind power. Experiments show that compared with BP, SVM and a single LSTM model, the proposed method has better prediction accuracy. At the same time, based on the definition of wind power ramp events, compared with the other three models in a variety of wind power ramp rediction indicators, the effectivenss of the model is verified.

SunAIS-03 904 Optimization Method of Calibration Parameters for Hybrid Electric Vehicle Shenlong Li China North Vehicle Research InstituteWeizhi Liang Jilin Univ.Shuang Jia China North Vehicle Research InstituteXiaohua Zeng Jilin Univ.Qianrui Yun Jilin Univ.In order to solve the problems of cumbersome calibration process and the inability of calibration parameters to meet the requirements of different driving cycles, an optimization method of calibration parameters is proposed. A hybrid electric bus is taken as the research object, and the power threshold is regarded as the calibration parameter to be optimized. In order to study the relationship between calibration parameter and driving cycles, one hundred new driving cycles are constructed based on the common driving cycles of bus. Twenty four characteristic parameters are screened to represent these driving cycles. In order to improve the fuel economy of hybrid electric vehicle, the minimum energy loss of the system is taken as the optimization target to solve the optimal power threshold. Then, the linear relationship between the optimal power threshold and these characteristic parameters is found through regression analysis. The optimal power thresholds under different driving cycles can be obtained by the regression equation. The research in this paper improves the adaptability of calibration parameters to different driving cycles, and shortens the development cycle of the vehicle controller. The solution method of the optimal calibration parameter is also of great significance to the fuel-saving control of hybrid electric vehicle.

SunAIS-04 936 Research on Energy Management Strategy of PHEV Considering Battery Life Based on Driving Cycle Recognition Shufeng Liu Jilin Univ.Xiaohua Zeng

State Key Laboratory of Automotive Simulation andControl

Xingqi Wang State Key Laboratory of Automotive Simulation and Control

Lili Yang State Key Laboratory of Automotive Simulation and Control

Weizhi Liang State Key Laboratory of Automotive Simulation and Control

Aiming at the problems of deterioration of the use conditions of the power battery and increase of the life cycle cost of the vehicle caused by complicated driving conditions, the battery life is proactively extended from the perspective of energy management optimization. The optimization goal is to minimize the comprehensive fuel consumption and battery life attenuation. DP algorithm is used to achieve the global optimization. Based on the global optimization results of three representative driving routes composed of urban driving cycle, suburban driving cycle and high-speed driving cycle, the training is applied to the NN controller, a real-time optimization strategy for energy management under the corresponding routes is developed, and a random forest model is trained to identify the driving cycle. The simulation results show that the comprehensive fuel consumption obtained by simulation with the operating condition recognition strategy is only increased by 1.4%, the effective Ah-throughput flowing through the battery is increased by 1.3%, the ODCV is increased by 1.3%, and the simulation calculation time is reduced by more than 95% compared with the global optimization results. The developed energy management strategy based on operating condition recognition achieves good control effect and optimizes the overall performance of PHEV.

SunAIS-05 961 Dynamic Resource Allocation for Power Distribution Internet of Things: a Game-Theoretic Model Zhi Li State Grid Information & Telecommunication Group Co.,

Ltd.Zhu Liu State Grid Information & Telecommunication Group Co.,

Ltd.Yanzhu Liu

Great Wall Computer Software and Systems Inc.

Nan Zhang

State Grid Information & Telecommunication Group Co., Ltd.

Jing Guo Aostar Information Techologies Co., Ltd.With the rapid development of smart terminals and sensors, the data scale of Power Distribution Internet of Things is increasing exponentially. The types of data are becoming more and more abundant. Processing and applying these data have become a hot spot in the industry and academia. Based on the Edge-Cloud Collaboration Architecture, the Power Distribution Internet of Things takes the Edge Internet of things Agent equipment as the core to realize the comprehensive perception, data fusion and intelligent application of the distribution network, thus effectively supporting the rapid development of the energy Internet. However, compared to Cloud Data Centers, the virtual computing resources of Edge IoT Agents are limited. To adapt the different service requirements, the Power Distribution Internet of Things system must coordinate limited virtual computing resources to improve the utilization of cloud and edge resources, thereby improving the quality of user services. In this paper, based on the research of the Power Distribution Internet of

Technical Programmes CCDC 2021 Things system architecture, a Four-Tier network model of the Power Distribution Internet of Things in the Edge-Cloud collaboration environment is constructed. Starting from the overall benefits of the Power Distribution Internet of Things system, an Edge-Cloud collaborative virtual computing resource allocation game model is constructed. Based on the dynamic differential game theory, the feedback Nash equilibrium solution of the dynamic game model is also proved and solved. Finally, the dynamic resource allocation model’s effectiveness verified through simulation and evaluated its performance in an experimental environment.

SunAIS-06 1210 Distributed event-triggered strategy for fixed-time economic dispatch in Islanded Microgrids Haoran Liu Huazhong Univ. of Science and Tech.Huijian Fan Huazhong Univ. of Science and Tech.Bo Wang Huazhong Univ. of Science and Tech.The fixed-time economic dispatch problem is extremely important for microgrids with highly dynamic renewable energy. To reduce unnecessary communication while realizing fixed-time economic dispatch, a distributed event-triggered fixed-time algorithm (abbreviated as disEXAM) is established in the paper. Firstly, based on optimal analysis, the economic dispatch problem can be reformulated by choosing the incremental cost of each generation unit as the consensus variable. Then, a distributed fixed-time economic dispatch algorithm is proposed by employing a fixed-time consensus Techique and an event-triggered communication strategy. The algorithm obtains an upper bound for the estimation of the settling time which is irrelevant to the initial status. Finally, simulation results based on the IEEE 14-bus system are presented to illustrate the effectiveness of the distributed optimization algorithm.

SunAIS-07 1295 Alternating Direction Method of Multipliers Improved Online Power Management in a Fuel Cell Hybrid Bus Xiaodong Wei Hunan Univ.Chao Sun Beijing Institute of Tech.Weiwei Huo Beijing Information Science and Tech. Univ.Fengchun Sun Beijing Institute of Tech.ADMM is an important convex optimization method in solving complex control problems. In fuel cell and battery hybrid vehicles, the power distribution control of fuel cell engine and the battery is critical, which is called energy management. This paper, for the first time, proposed an ADMM-based energy management strategy for a fuel cell hybrid bus. An augmented lagrangian equation and scaled dual form of the system model are established, to satisfy the standard form of ADMM. Algorithms are developed to validate the performance of proposed method under the driving cycles CHTC-HT and C-WTVC. Simulation results show that the ADMM-based energy management approach is able to realize real-time implementation, while maintaining over 95% optimality of hydrogen consumption compared to DP offline results.

SunAIS-08 1372 Perturb and Observe MPPT Algorithm of photovoltaic System: A Review Zhipeng Fan Hubei Minzu Univ.Shaowu Li Hubei Minzu Univ.Hang Cheng Hubei Minzu Univ.Liwei Liu Hubei Minzu Univ.The perturbation observation method is one of the most commonly used methods to track the maximum power of photovoltaic (PV) system, but there are few reviews on perturb and observe (P&O) algorithm. In this paper, the maximum power point tracking (MPPT) based on P&O algorithm is discussed. The classical fixed-step P&O algorithm, improved classical P&O algorithm and hybrid P&O combine with intelligent algorithms are analyzed in terms of accuracy and fast tracking. By comparing the study of MPPT method in P&O algorithm, it is found that in recent years, researchers have improved the classical P&O algorithm through the improved classical P&O algorithm and the hybrid algorithm of P&O combined with machine learning/meta-heuristic. These two methods can reduce the oscillation of MPP, accelerate the convergence speed, improve the tracking accuracy, reduce the power loss and improve the conversion efficiency of PV system.

SunAIS-09 1408 Research progress of the ultra-short term power forecast for PV power generation: A review Xianping Zhu Hubei Minzu Univ.Shaowu Li Hubei Minzu Univ.Yan Li Hubei Minzu Univ.Jingxun Fan Hubei Minzu Univ.With the continuous improvement of the permeability of photovoltaic(PV) power generation in the power grid, it is difficult to ensure the accuracy of the prediction in the traditional short-term power prediction for PV power generation, which will have an important impact on the safety, stability of the power system and the dispatching. However, few previous studies have classify and summarize the ultra-short-term power prediction of PV

power generation. To address these questions, this study focuses on the ultra-short-term power prediction method of PV power generation and the influence of prediction errors on the power grid. The factors affecting the accuracy of PV power prediction are classified, and the research status of PV power prediction is summarized. On this basis, the requirements of the evaluation index of PV power prediction results are analyzed, and the error evaluation index should reflect the prediction quality in the whole time period. Finally, the prediction methods are summarized and the future research direction is prospected.

SunAIS-10 1453 Non-Intrusive Residential Load Decomposition Method Based on Steady State Feature Extraction Wei Sun Shenyang Institute of EngineeringXinfu Pang Shenyang Institute of EngineeringHenan Geng State Grid Zhangjiakou Electric Power Supply Comp

anyYanbo Wang State Grid Dandong Electric Power Supply CompanyLi Liu Shenyang Institute of EngineeringSong Ju Audit Center of State Grid Liaoning Electric Power C

o., Ltd.As an important part of intelligent power Tech., non-intrusive residential load monitoring Tech. has gradually become an important research direction of intelligent power Tech. because it can obtain specifically detailed load monitoring data and further analyze the load components of residential users. At present, the transient characteristics are employed to analysis, which has high accuracy requirements for data acquisition equipment, so it is difficultly applied to practical problem. This paper proposes a non-intrusive household load decomposition method based on the identification of working modes of electrical appliances. Firstly, the steady-state characteristics of current waveform, active power and reactive power of household appliances are extracted. Then, a new method is proposed to determine the working mode of electrical appliances. In addition, the artificial neural network is employed to recognize the working mode of electrical appliances, and the load of household appliances is decomposed accurately. Finally, the simulation results show that the proposed method can effectively identify household appliances and decompose electrical load according to the steady-state characteristics.

SunAIS-11 1581 Microgrid event trigger optimization and control based on asynchronous-sampling data system approach Yiwei Feng Lanzhou Univ. of Tech.Xin Wang Lanzhou Univ. of Tech.As an important part of modern power system, microgrid has received more and more attention because of its flexibility, stability and independent operation. For the proposed microgrid model, network control systems (NCSs) are introduced, and linear quadratic gaussian (LQG) controllers are designed. The asynchronous sampling data system method is adopted to establish an event trigger system, and an optimization method based on event trigger is proposed, and the delay caused by the network in the channel and the interference in the model are considered. Set the cost threshold as the trigger condition, use particle swarm optimization (PSO) to balance the control cost and communication cost, reduce the event rate, and save communication resources. The simulation results verify the effectiveness of the controller and optimization method.

SunAIS-12 1600 Intelligent MPPT Control Methods for Photovoltaic System:A review Hang Cheng Hubei Minzu Univ.Shaowu Li Hubei Minzu Univ.Zhipeng Fan Hubei Minzu Univ.Liwei Liu Hubei Minzu Univ.How to improve the maximum power point tracking (MPPT) efficiency of photovoltaic (PV) system is the core problem of PV power generation, many scholars have studied the intelligent algorithm in the maximum power tracking control of PV system. However, there is little literature on the classification of intelligent algorithms in the maximum power tracking control of PV systems. Therefore, this paper summarizes the published research results, classifies these intelligent algorithms, epitomizes the advantage and disadvantage of these intelligent algorithms. Finally, the application of intelligent algorithms in PV system MPPT control is prospected in the future. Through this paper, more comprehensive information of PV intelligent algorithm can be obtained.

SunAIS-13 1602 Designing and performance investigations on an ejector with auxiliary inlet for PEMFC hydrogen recirculation system Zhiqiang Du Shandong Univ.Chao Zhang Weichai Power CoFuqiang Xi Weichai Power CoLei Wang Shandong Univ.Xinli Wang Shandong Univ.The ejector driven by the high-pressure gas potential energy can reliably recirculate the unreacted hydrogen without consuming parasitic power in

Technical Programmes CCDC 2021 proton exchange membrane fuel cell (PEMFC) system. The ejector-driven hydrogen recirculation system must maintain sufficient recirculate capacity to take out the water generated in the fuel cell to ensure the stable operation of the PEMFC system. However, when the output power of the fuel cell system is relatively large, the inside of the ejector may be blocked, causing the recirculation performance of the ejector to decrease. In this paper, an ejector with auxiliary entrainment is developed for the hydrogen recirculation system to improve the performance under the larger output power. The ejector is numerically investigated, using a two-dimensional numerical model verified by experiment, to reveal the flow structure and evaluate its performance. The simulation results show that the ability of recirculation performance of the ejector is improved about 18% than the traditional ejector at 84 kW output power. Furthermore, as the increase of the auxiliary secondary flow inlet diameter, the recirculation performance of the ejector gradually increases. When the diameter of the auxiliary secondary inlet is above 6mm, the recirculation ratio of the ejector does not increase significantly. The developed ejector has better performance than the traditional ejector in the PEMFC system, which significantly promotes the development of fuel cell being widely adopted in automobiles.

SunAIS-14 1629 Optimization Control Methods Based on Wind-solar Hybrid Systems Combined with New Energy Vehicles: A Review Liwei Liu Hubei Minzu Univ.Shaowu Li Hubei Minzu Univ.Hang Cheng Hubei Minzu Univ.Zhipeng Fan Hubei Minzu Univ.With the development of new energy vehicle industry, the existing photovoltaic vehicles can not meet the need of the market in terms of performance. To improve the performance of photovoltaic vehicles, the possibility and practicability of combining the wind-solar hybrid power generation Tech. with new energy vehicles are summarized. At the same time, the optimization methods of related the wind-solar hybrid Tech. are collected in detail, and the effectiveness of optimization strategies proposed for new energy vehicles is specifically analyzed, to adjust the configuration and operation of the wind-solar hybrid system in-vehicle power in the references, To improve the theoretical basis of the research on the combination of the wind-solar hybrid power generation and new energy vehicles.

SunAIS-15 1685 UAV Countermeasure Tech. Based on Partial-band Noise Jamming Zhipeng Lei State Grid Liaoning Electric Power Co., Ltd.Peng Ding State Grid Liaoning Electric Power Co., Ltd.Wei Zheng State Grid Liaoning Electric Power Co., Ltd.Xuan Fei State Grid Liaoning Electric Power Co., Ltd.Houyang Fan State Grid Liaoning Electric Power Co., Ltd.With the rapid development of the drone market, the black flight of UAV incident has become the norm. How to deal with the various safety problems caused by the black flight and the potential danger of the black flight has caused various aspects of the society highly anticipated. To this end, this thesis proposes a UAV countermeasure Tech. based on Partialband noise jamming , taking civilian UAVs as the research object, and according to the characteristics of UAV communication signals, using partial-band noise jamming to cut off the controller from the UAV Control, make the drone enter the out-of-control state, and then use the GPS repeater deception jamming Tech. to counteract the drone of black flight for the rotor drone whose out-of-control behavior is in the return state. System Tech. to analyze the test results. Experimental results show that this Tech. can trap black flight drones more stably.

SunAIS-16 1058 Optimization model of Wind-Solar-Thermal Power joint dispatching control considering output randomness Kaicheng Zhang Yunnan Univ.Peng Li Yunnan Univ.Lian Gao Yunnan Univ.Xin Shen Yunnan Power Grid CorpConsidering the economy and reliability of dispatching and the randomness of wind power and photovoltaic power output, this paper proposes a wind-solar-thermal power dispatching model combining day-ahead optimization and day optimization. Based on the forecast values of wind power output, photovoltaic output, and load in different periods, the model uses Mixed Integer Linear Programming (MILP) algorithm to optimize the output of each unit to find the optimal output that meets the constraint conditions and takes the output as the planned value of daily dispatch. The model predictive control (MPC) is used to establish the intraday rolling optimization model to realize the real-time control of wind power and photovoltaic output, to achieve the tracking plan value of wind power output and photoelectric output. Thermal power as a backup power supply can meet the power balance requirements of the power supply to the load. The simulation results show that the real-time wind-solar-thermal joint scheduling model can not only meet the economic dispatch but also effectively suppress the output fluctuation of renewable energy, which provides a strategy for the optimal scheduling with renewable energy.

SunAIS-17

1264 Order Batching Optimization Model and Algorithm for Copper Ingot Production Duan Xu Shanghai Baosight Software Limited CompanyMingming Chen Northeastern Univ.Shixin Liu Northeastern Univ.Using the similarity of order grades and specifications to batch multi-species and small-lot customer orders into high-volume production orders is one of the effective ways to solve the contradiction between customer demand for multi-species and small-lot copper products and high-volume production methods. The order batching problem is abstracted into a bin-packing problem with due-date constraints, an integer programming model is established, the concept of dominant and subordinate orders is introduced to eliminate symmetry, and the due-date constraints are eliminated from the constraints by data pre-processing, thus reducing the difficulty of solving the model. A heuristic algorithm based on dynamic programming is designed to solve the model by adding the concept of dynamic maximum corps. The correctness of the model and the effectiveness of the algorithm are verified by computational experiments.

SunAIS-18 1460 Optimization Model and Algorithm for Ingredient Optimization of Copper Strip Production Duan Xu Shanghai Baosight Software Limited CompanyMingming Chen Northeastern Univ.Shixin Liu Northeastern Univ.Ingredient optimization directly affects the quality of copper strip products and production cost. A multiobjective optimization model with the objectives of minimizing raw material cost and deviation of the target ingredient of alloying elements was established by considering the feeding volume limit, inventory, impurity content and alloying element ingredient constraints. A modified non-dominated sorting genetic algorithm II (NSGA-II) is designed to solve the model. The validity of NSGA-II is verified by experiments. The results show that NSGA-II can effectively solve the multi-objective optimization model and provide satisfactory Pareto solution set.

SunAIS-19 182 Simplified Model Predictive Control for Permanent Magnet Synchronous Machine based Back-to-Back Converter Jianqiao Zou Wuhan Second Ship Design and Research InstituteTo decide the most appropriate switching vector, traditional model predictive control (MPC) algorithms have to suffer extreme time burden for 8-time searching in each sampling period. Meanwhile, the traditional MPC methods have some other demerits, such as complex cost function to get reasonable good performance. In order to solve such defects, one new model predictive control method based switching table (MPC-ST) is proposed in this paper. Firstly, the MPC-ST and MPC are applied to permanent magnet synchronous machine (PMSM) drive system based back-to-back converter. Secondly, in the same conditions, a detail comparison between MPC-ST and MPC is conducted by comprehensive simulation. It fully demonstrates that the new MPC-ST considering other performance parameters can simplify cost function greatly.

SunAIS-20 319 A Robot Grasp Relationship Detection Network Based on the Fusion of Multiple Features Jianning Chi Northeastern Univ.Xingrui Wu Northeastern Univ.Changqing Ma Northeastern Univ.Xiaosheng Yu Northeastern Univ.Chengdong Wu Northeastern Univ.Grasp is one of the main ways for robots to interact with the real world. Recently, there are many approaches in grasp detection using deep learning. They can successfully detect one or multi grasp locations from an RGB image. When it comes to a scene with multiple objects, they still need the relationships of the objects to instruct robotics in grasping objects. In this paper, we present a new deep convolutional neural network approach for detecting all potential objects and predicting grasp relationships of them from an RGB image. For each object pair, we firstly generate not only their visual features, but also their spatial masks and semantic embedding vectors from three branches. Then we integrate these features as input to obtain their grasp relationship. Experimental results show that our proposed approach outperforms the state-of-the-art and achieves 73.78% accuracy on the Visual Manipulation Relationship Dataset (VMRD).

SunAIS-21 401 A Method for Surface Detect Classification of Hot Rolled Strip Steel based on Xception Xinglong Feng Northeastern Univ.Xianwen Gao Northeastern Univ.Ling Luo Northeastern Univ.Hot rolled strip steel is an important raw material for automobile, home appliance and other manufacturing industries, and the quality of its surface and plate shape has a vital impact on the products produced by

Technical Programmes CCDC 2021 end users. In actual industrial production, different measures should be taken for different kinds of strip steel surface defects. Therefore, it is of great significance to classify the surface defects of hot rolled strip accurately. An improved method based on Xception algorithm is presented. The algorithm can classify the hot rolled strip defects and is more suitable for the imbalance between categories to some extent. Compared with 91.18% of the original Xception algorithm, the classification accuracy of the improved algorithm reached 93.87% on the hot rolled strip defect dataset. The improved scheme solves the problem of unbalanced dataset samples to a certain extent and improves the classification accuracy of dataset significantly.

SunAIS-22 476 Image Enhancement Algorithm of Vehicle Recognition under High Light Based on Deep Learning Chunhe Shi Northeastern Univ.

Shenyang Univ.Chengdong Wu Northeastern Univ.Yuan Gao Northeastern Univ.An adaptive image enhancement algorithm under high light conditions is proposed to solve the complex light problem of traffic checkpoint surveillance cameras. This algorithm mainly includes three modules: rapid image classification module, image enhancement module and image quality evaluation module. The rapid image classification module divides the high light image into daytime high-light type and night high-light type on the basis of SqueezeNet network of deep learning, reducing the computation burden while ensuring the classification accuracy. The image enhancement module makes use of the integrated image enhancement algorithm for secondary enhancement of the classified images by adopting the separate handling thought. The image quality evaluation module adopts objective evaluation indexes to measure and designs a weighted comprehensive evaluation index, which is measured from three aspects, namely, structural similarity, normalized mutual information, and normalized mean square error. The experimental results indicate that the proposed algorithm can still obtain a relatively accurate recognition rate for high-light images under different conditions, improving the subsequent recognition of vehicle faces and vehicles.

SunAIS-23 82 Target detection based on DB-YOLO in road environment Yu Liu Jilin Univ.Wei Hong Jilin Univ.In order to improve the accuracy pedestrian and vehicle, traffic light detection especially for targets that are too large or too small in autopilot assisted driving system, an improved YOLOv3 target detection algorithm is proposed in this paper. The COCO data set is used as the training set. Firstly, various data expansion methods such as random cropping, filling and mixing are adopted for the original data, and generating corresponding annotation information. Then sing k-means algorithm to generate anchors. The processed data is input into the feature extraction network based on the improved Densenet layer structure. The 19*19 feature map extracted from the feature extraction network was input into the receptive field mechanism constructed by the spatial pyramid pooling. Finally, input to FPN network to get the prediction results. During training, CIOU loss function is used to guide weight updating, while detection, the prediction of the non-maximum suppression processing network can be used to obtain the final detection result. The experiment proves that the improved method proposed in this paper can improve the detection accuracy of YOLOv3 algorithm effectively for pedestrian and automobile and traffic lights.

SunAIS-24 490 Small-scale Image Semantic Segmentation Method Based on Multi-level Superposition and Enhancement Fusion Xiaodong Su Harbin Univ. of Commerce

Heilongjiang Province Key Laboratory of Electronic Commerce and Intelligent Information Processing

Hongyu Liang Harbin Univ. of CommerceHeilongjiang Province Key Laboratory of Electronic

Commerce and Intelligent Information ProcessingGuilin Yao Harbin Univ. of Commerce

Heilongjiang Province Key Laboratory of Electronic Commerce and Intelligent Information Processing

Hui Li Harbin Univ. of CommerceHeilongjiang Province Key Laboratory of Electronic

Commerce and Intelligent Information ProcessingShizhou Li Harbin Univ. of Commerce

Heilongjiang Province Key Laboratory of Electronic Commerce and Intelligent Information Processing

Aiming at the problem of small-scale target loss and boundary discontinuity in image semantic segmentation, this paper proposes an image semantic segmentation method based on deep learning multi-level superposition and enhancement fusion. This method uses DeepLabV3+ network structure and constructs multi-level feature extraction. The method of multi-level superposition and jump connection improves the ability to acquire local features, so that while segmenting large targets, local feature information can also be obtained, and the segmentation accuracy of local target boundaries is improved. The network is trained, validated and tested on the public data set PASCAL VOC2012.

Experiments show that the visual segmentation effect of this method on small-scale images is significantly better than most semantic segmentation methods that have appeared. At the same time, in the overall accuracy of all categories and the accuracy of several typical categories, the method in this paper is also superior to many previous methods.

SunAIS-25 1125 Boosting Small Ship Detection in Optical Remote Sensing Images via Image Super-Resolution Linhao Li Beijing Inst. of Tech.Zhiqiang Zhou Beijing Inst. of Tech.Saijia Cui Beijing Inst. of Tech.Small ships in optical remote sensing images are hard to detect due to the lack of sufficient detail information. In this paper, we adopt the image super-resolution technology to solve this problem. Specifically, an effective superresolution network is designed to generate clear super-resolution ship images from small blurry ones produced by the ship detector. Inspired by the idea of generative adversarial network (GAN), the super-resolution network is trained together with a discriminator network in an adversarial way, aiming at generating more realistic super-resolution images. Moreover, to eliminate false detections, the discriminator network is also used to distinguish ship and non-ship images via an additional classification branch. Experimental results demonstrate the effectiveness of the proposed method.

SunAIS-26 964 Subject-independent Emotion recognition based on Entropy of EEG Signals Haihui Yang Heilongjiang Univ.Panxiang Rong Heilongjiang Univ.Guobing Sun Heilongjiang Univ.Emotion recognition is always an academic focus in the research of human-computer interaction (HCI). Due to poor generalizability of electroencephalogram (EEG) features from EEG different subjects, the aim of this paper is to further explore different characteristics in EEG signals to improve the accuracy of subject-independent emotion recognition. We extract the features from time domain and frequency domain, sample entropy and wavelet entropy and so on, and use SVM to evaluate the recognition performance. Results show the accuracy of public DEAP dataset is up to 70.1% in the valence dimension and 64.2% in the arousal dimension. This analysis is significant because we verify that the sample entropy and wavelet entropy are effective in subject-independent emotion recognition.

SunAIS-27 1020 FCOS-Lite: An Efficient Anchor-free Network for Real-time Object Detection Shuai Liu Northeastern Univ.Jianning Chi Northeastern Univ.Chengdong Wu Northeastern Univ.Fully Convolutional One-Stage detector (FCOS) is a simple and strong anchor-free detector. However, the outstanding performance comes at the cost of long inference time. In this paper, a more efficient anchor-free detector FCOS-Lite is proposed to speed up the inference time of FCOS. FCOS-Lite improves the three components of FCOS, namely the backbone, neck and head. We first apply an efficient attention module in the backbone to extract more important semantic information. Then, we propose an adaptive feature-fusion module in the neck to detect small objects accurately. Finally, we use some strategies in the head of FCOS-Lite to reduce the computation. With these methods on the FCOS-Lite, we achieve a speed-accuracy balance on the MS COCO dataset. FCOS-Lite s inference time is almost halved based on the FCOS with a slight accuracy drop. (33.2% mAP at 24 ms for FCOS-Lite compared to 37.4% mAP at 44ms for FCOS on COCO). FCOS-Lite suits for the real-time object detection, improving both accuracy and efficiency of the real-time detector YOLOv3 and FCOS-mobileNet (31.0% mAP at 29ms for YOLOv3 and 30.9% mAP at 27ms for FCOS-mobileNet on COCO).

SunAIS-28 315 Multi-Receptive-Fields Convolutional Network for Remote Sensing Images Super-Resolution Huan Wang Northeastern Univ.Qian Hu Northeastern Univ.Jianning Chi Northeastern Univ.Chengdong Wu Northeastern Univ.Xiaosheng Yu Northeastern Univ.Recently, single image super-resolution (SISR) has been widely applied in the field of remote sensing image processing and obtained remarkable performance, focusing on restoring the high-resolution (HR) image from a lowresolution (LR) image. However, we observe that the existing CNN-based SISR methods mainly focus on wider or deeper architecture design, neglecting to exploit features at global receptive field. Moreover, the LR inputs and features contain abundant low-frequency information, which are perceived equally in the same receptive field, hence limiting the representational ability of CNNs. To solve these problems, we propose a

Technical Programmes CCDC 2021 Multi-Receptive-Fields Super Resolution Network (MRFSR) for remote sensing image reconstruction. The proposed network employs non-local neural network to enhance low-level complex features by expanding the receptive field of the shallow convolution layer. Moreover, we propose the multi-branch up- and down-sampling modules to deal with LR features in multiple receptive fields, which can enhance the high-frequency components and learn abstract feature representations in multiple scales, respectively. Extensive experiments on NPU-RESISC45 dataset shows that the proposed MRFSR can provide state-of-the-art or even better performance in both quantitative and qualitative measurements.

SunAIS-29 318 Design of Defocus Binary Pattern Based on Genetic Algorithm and Tabu Search Tong Jia Northeastern Univ.Feng Liang Northeastern Univ.Zhikang Zeng Northeastern Univ.Ziwei Wu Northeastern Univ.Yichun Zhang China Inst. of Arts Science & Tech.The binary defocusing technique has enabled speed breakthroughs for 3D shape measurement, yet simultaneously achieving high accuracy and high speed remains difficult. To overcome this limitation, this paper proposes a binary pattern optimization algorithm based on Genetic Algorithm and Tabu Search. Firstly, the relative phase is obtained by defocusing the binary pattern through the projector to avoid the influence of the gamma effect on the phase quality. Secondly, for reducing the influence of the projector's defocus on the Gray code phase unwrapping, a complementary combined Gray code method is proposed, which can accurately unwrap the phase. The experimental results show that the method in this paper improves the three-dimensional measurement results of the digital fringe projection technology and improves the robustness.

SunAIS-30 333 Implementation of Real-Time Stereo Matching System Based on Speckle Structured Light Tong Jia Northeastern Univ.Juncai Ma Northeastern Univ.Yichun Zhang China Inst. of Arts Science & Tech.Wenhao Li Northeastern Univ.Zhikang Zeng Northeastern Univ.Junwen Huang Northeastern Univ.Real-time and accurate acquisition of target depth information is the prerequisite for subsequent 3D reconstruction and abnormal behavior detection. For this application, this paper designs and implements a binocular stereo real-time system based on speckle. The main hardware components are binocular and speckle projector, and the main algorithm is (Semi-Global Matching) SGM's GPU implementation. The detection range of the system is 1.2m-6m, the frame rate can reach 30, the parallax level is 256, and the image resolution can reach 1280 720 pixel.

SunAIS-31 357 Human Gait Analysis Method Based on Sample Entropy Fusion AlphaPose Algorithm Xinyu Lv North Minzu Univ.Shengying Wang North Minzu Univ.Tao Chen North Minzu Univ.Jing Zhao Ningxia Univ.Desheng Chen General Hospital of Ningxia Medical Univ.Mingxia Xiao North Minzu Univ.Xiaoye Zhao North Minzu Univ.Aiming at the problem of difficulty to evaluate the recovery of patients after their joint replacement accurately, a new gait analysis method was proposed on the basis of Sample Entropy fusion AlphaPose. In the algorithm, AlphaPose was encouraged to extract the trajectory of the key points from healthy people and patients, and convert the transform trajectories into feature vectors. After normalized, feature vectors were calculated and analyzed by Sample Entropy algorithm. The results demonstrated that there was significant difference in the entropy value of heel key point waveform between two groups, the entropy value of patients with joint disease was lower than that of healthy group. Comparing with the traditional gait analysis method, the proposed algorithm performs better on human pose recognition, because of its strong robustness and efficiency.

SunAIS-32 373 A Real-time Driver Fatigue Monitoring System Based on Lightweight Convolutional Neural Network Chunyu Zhou Chongqing Univ. of Posts and TelecommunicationsJun Li Chongqing Univ. of Posts and TelecommunicationsTraffic accidents caused by fatigue driving happen frequently. How to alarm in advance and reduce the accident rate is the focus on research. The traditional fatigue detection method occupies high computing resources, and has insufficient real-time performance. In the context of intelligent traffic safety, this paper collects facial feature data, and proposes a driver monitoring system based on MobileNetV3 and Jetson TX2 platform. The system can monitor the driver status by extracting the

driver's facial features. For the night and wearing sunglasses, a infrared camera is selected, and corresponding data is collected for model training. After that, tests are conducted on public datasets YawDD, CEW and self-acquired dataset. The results show that the model achieves a real-time speed of 22 fps on the Jetson TX2 platform, and the average accuracy of the algorithm can reach 94%.

SunAIS-33 395 Gesture Recognition of Subway Drivers Based on Improved Dense Trajectory Algorithm Da Suo Beijing Jiaotong Univ.Xiukun Wei Beijing Jiaotong Univ.Dehua Wei Beijing Jiaotong Univ.Metro drivers need to make corresponding gestures when the train is in different operation states. The purpose is to confirm whether the display of each instrument on the console is normal, and then determine whether the train is in good running condition, whether the switch or signal on the line is working normally, and whether the door is closed normally. This information needs to be fed back to the train control center in time. Therefore, it is necessary to recognize the drivers' gestures in the surveillance video of the drivers' cab. This paper proposes a gesture recognition model based on the monitoring video of subway train cab, which combines Improved Dense Trajectory (IDT) algorithm, Fisher vector coding technology and Support Vector Machine (SVM) classification technology to realize feature extraction, feature coding and classification of video clips. The model recognizes the drivers' gestures in the actual train monitoring video clips, and the accuracy can reach 100%.

SunAIS-34 404 Forest Fire Detection Based on Lightweight Yolo Shengying Wang North Minzu Univ.Tao Chen North Minzu Univ.Xinyu Lv North Minzu Univ.Jing Zhao Ningxia Univ.Xiaoyan Zou The Frist People’s Hospital of YinchuanXiaoye Zhao North Minzu Univ.Mingxia Xiao North Minzu Univ.Haicheng Wei North Minzu Univ.Early fire detection is quite important to fighting of forest fire. A lightweight YOLO forest fire detection model is proposed in this study. First, the YOLOv4 object detection model is selected as the overall framework for fire detection. In order to reduce the number of parameters and computation, this study replaced YOLOv4's backbone network with the lightweight MobileNetV3. Finally, the data set with flame and smoke is used for training model, and fire-like images are used as negative samples to improve the robustness of the model. The experimental results demonstrate that compared with YOLOv4, the number of parameters of the proposed architecture is reduced by 62.78%, and inference speed increases by 3.04 times. Moreover, the detection accuracy of the proposed algorithm achieves 0.666 mAP. The proposed method has advantages of less number of parameters, fast inference speed and low memory occupation. The method presented in this paper has referential value to realize real-time forest fire detection and object detection on embedded devices.

SunAIS-35 526 A Time Series Classification Method Based on 1DCNN-FNN Zihao Zhao Northwestern Polytechnical Univ.Geng Jie Northwestern Polytechnical Univ.Jiang Wen Northwestern Polytechnical Univ.With the rise of deep learning technology, the use of one-dimensional convolutional neural network (1DCNN) to process time series has the advantages of higher classification accuracy and stronger generalization ability. However, the 1DCNN constructs a classification model by identifying the feature vector of the data distribution, which lacks the reasoning ability on digital features. Because Fuzzy Neural Network (FNN) combines fuzzy inference with neural network and has stronger ability of fuzzy information inference, this paper proposes a hybrid classification model combining 1DCNN and FNN. The hybrid model uses 1DCNN and FNN models to process two kinds of feature information separately and effectively merge them on the fully connected layer. In this paper, WISDM data set is used to train and test the proposed 1DCNN-FNN hybrid classification model, and the results are compared with the results of the 1DCNN model. Experimental results show that the proposed method has better classification effect.

SunAIS-36 654 An Affinity Based Matting Method Based on Multi-Scale Space Fusion Guilin Yao Harbin Univ. of Commerce

Heilongjiang Provincial Key Laboratory of ElectronicCommerce and Intelligent Information Processing

Dongai Jiang Harbin Univ. of CommerceHeilongjiang Provincial Key Laboratory of Electronic

Commerce and Intelligent Information ProcessingJianliang Sun Heilongjiang College of Business and Tech.At present, affinity based image matting methods are mainly divided into

Technical Programmes CCDC 2021 Matting Laplacian methods and Nonlocal methods. However, their search ranges are fixed and cannot be changed according to the input trimap of the image. This paper combines the characteristics of the search of the Nonlocal method and Matting Laplacian method, and proposes an affinity based matting method based on the fusion of multi-scale spaces. This method firstly distinguishes some opaque foreground and opaque background in the image through preprocessing steps. Secondly, this paper uses a nonlocal method to perform an initial global search on the image, and uses a two-layer space search method, so that for different trimap input, more search ranges can be covered. Thirdly, this paper adopts the Matting Laplacian based on the local smoothness assumption of foreground and background colors to perform a further matting step for the local details of the image. Finally, a sparse linear equation is solved to calculate the final alpha result. Experiments show that, compared to other single-layer and single-search affinity based methods, the multi-layer fusion method proposed in this paper achieves a large final accuracy rate in calculating opaque pixels, mixed pixels, and overall results. At the same time, in terms of visual effects, the method in this paper is also superior to various methods that have appeared.

SunAIS-37 783 Computer-vision-based abnormal human behavior detection and analysis in electric power plant Yuan Cao Zhejiang Univ.Hao Xu Zhejiang Univ.Qiang Yang Zhejiang Univ.With the increasing demand for intelligent security in power plants, the rapid and accurate processing of massive surveillance video data is urgently needed. Researches on the detection and analysis of abnormal human behaviors in power plants still focus on traditional image processing technology, and most of them lack robustness. In this article, abnormal behavior detection and analysis system based on personnel information are proposed to solve the above problems. The proposed method using an improved YoLov3 algorithm first detects persons and extracts abnormal behavior information on this basis. In the implementation, some training tricks are introduced to improve performance. Experimental results show that the system can effectively detect abnormal human behaviors, and the improved YoLov3 algorithm can also effectively improve model performance. The proposed abnormal behavior detection and analysis system based on personnel information prove its effectiveness through experiments, which can efficiently perform in power plants with lower computing costs.

SunAIS-38 963 FAST and FLANN for feature matching based on SURF Shiguo Huang Heilongjiang Univ.Guobing Sun Heilongjiang Univ.Minglun Li Heilongjiang Univ.The aim of this paper is to Reduce Speeded Up Robust Features’ (SURF) time-consuming problem and get a high accuracy in image registration, the Features From Accelerated Segment Test (FAST) algorithm and the Fast Library for Approximate Nearest Neighbors (FLANN) are proposed respectively for extracting the feature points and increasing the accuracy. First, the FAST algorithm is applied to extract the features of the image, and then the SURF algorithm is used to construct the descriptor to realize the rapid extraction of image features with rotation invariance. And then an improved feature matching algorithm FLANN is proposed to accurately match the feature points. The experimental results show that our method is about two times faster than the traditional SURF algorithm and also has a higher accuracy. The main outcomes of this paper are the usage of FAST for a time-consuming problem and FLANN for a high accuracy in image registration.

SunAIS-39 1136 A Dual Self-Attention based Network for Image Captioning ZhiYong Li Beijing Univ. of Tech.JinFu Yang Beijing Univ. of Tech.YaPing Li Beijing Univ. of Tech.With the increasing demand for intelligent security in power plants, the rapid and accurate processing of massive surveillance video data is urgently needed. Researches on the detection and analysis of abnormal human behaviors in power plants still focus on traditional image processing technology, and most of them lack robustness. In this article, abnormal behavior detection and analysis system based on personnel information are proposed to solve the above problems. The proposed method using an improved YoLov3 algorithm first detects persons and extracts abnormal behavior information on this basis. In the implementation, some training tricks are introduced to improve performance. Experimental results show that the system can effectively detect abnormal human behaviors, and the improved YoLov3 algorithm can also effectively improve model performance. The proposed abnormal behavior detection and analysis system based on personnel information prove its effectiveness through experiments, which can efficiently perform in power plants with lower computing costs.

SunAIS-40 1178

An improved unified online multi-object tracking algorithm combined with attention mechanism Jianning Chi Northeastern Univ.Changqing Ma Northeastern Univ.Xingrui Wu Northeastern Univ.At present, the mainstream paradigm of multi-target tracking is still tracking-by-detection, which includes two parts: the detector for locating the target and the appearance embedding model for data association. Most methods implement the two modules separately, without considering the relationship between them. However, the biggest problem of this two-stage methods is the large amount of calculation, leading to slow running speed. In this paper, we build a unified online multi-object tracking system. By integrating the object detector and the apparent embedding model into the same shared model, we can get the bounding box and the embedding model simultaneously, so as to reduce the network complexity and speed up the operation. To further improve the performance of the detector, we add an attention mechanism to weight each dimension of the output channel, so as to highlight the important foreground information and ignore the influence of the background as much as possible. The experimental results demonstrate that we can achieve competitive results on MOT16 dataset, and the best trade-off between accuracy and speed.

SunAIS-41 1184 Malicious Domain Name Detection Model Based on CNN-GRU-Attention Yanshu Jiang Harbin Univ. of Science and Tech.Mingqi Jia Harbin Univ. of Science and Tech.Biao Zhang Harbin Univ. of Science and Tech.Liwei Deng Harbin Univ. of Science and Tech.Domain Generation Algorithm (DGA) domain name detection is one of the key technologies for detecting botnet C&C communications. It is well known that malicious websites can cause great harm, and from individuals to countries will be affected to varying degrees. Aiming at the problems of low detection accuracy and high complexity of traditional detection methods, this paper proposes a malicious domain name detection model (CNN-GRU-Attention). The model first used the CNN neural network to extract the spatial features of the domain name data; then used the GRU neural network to extract the temporal features of the domain name data; finally used the attention mechanism to improve the detection accuracy of the domain name. In the experiment, this article used Bigrams, LSTM artificial neural network, GRU neural network, LSTM-GRU four models to compare with the CNN-GRU-Attention model. The experimental results showed that the CNN-GRU-Attention model had better convergence and higher accuracy.

SunAIS-42 1206 Multi-Level Relation Learning with Confidence Evaluation for Few-Shot Learning Qingjie Zeng Northwestern Polytechnical Univ.Jie Geng Northwestern Polytechnical Univ.Kai Huang Northwestern Polytechnical Univ.Wen Jiang Northwestern Polytechnical Univ.Few-shot learning is developed to classify unknown categories through limited training samples. In this paper, a multi-level relation learning model with confidence evaluation (MLR-CE) is proposed in area of few-shot learning. In the proposed framework, multi-level features are extracted that contain semantic information of different depth, and multi-level relation pairs are built by stacking feature maps of support images and query images. To expand the support set, confidence evaluation by a Gaussian mixture model is developed to select samples with high confidences. Experiments on two data demonstrate that the proposed method can yield superior few-shot classification results.

SunAIS-43 1207 Stack LSTM for Chinese Image Captioning Wei Wu Inner Mongolia Univ.Deshuai Sun Inner Mongolia Univ.Image captioning has attracted considerable attention in recent years. However, little work has been done for Chinese image captioning which has unique cultural characteristics and wording requirements. This paper studies how to generate more accurate Chinese image captions. We propose a novel Chinese image captioning model, which uses the pre-trained ResNet50 to extract the visual information of the image and a double-layer LSTM to predict each Chinese word. Applying this approach to Chinese image captioning, we obtained the better results on the AIC-ICC dataset compared with other image captioning algorithms, the method proposed in this paper greatly improves the evaluation performances and achieves BLEU-4 /CIDEr scores of 39.9/121.7, respectively. The actual generation results also show that the model can generate accurate, diverse and vivid Chinese caption of images.

SunAIS-44 1228 Spacecraft Target Recognition and Tracking Based on Ellipse Detection Zhimin Wang Engineering Research Center of Digitized

Textile & Apparel Tech.

Technical Programmes CCDC 2021 Donghua Univ.

Tong Wang Engineering Research Center of Digitized Textile & Apparel Tech.

Donghua Univ.Kuangrong Hao Engineering Research Center of Digitized

Textile & Apparel Tech.Donghua Univ.

A method for identifying and positioning the spacecraft is designed based on ellipse detection. The ellipse detection algorithm identifies and locates the spacecraft from the first frame in the ellipse's shape. The bounding box's size and position are calculated by the parameters of the outer ellipse and the inner rectangle. The target tracking algorithm uses the size and position information in the first frame to predict the spacecraft's location in the subsequent frames. The experiments show the superiority of the proposed ellipse detection algorithm and the effectiveness of the spacecraft target recognition and tracking method.

SunAIS-45 1252 Compression algorithm for live face recognition model based on depth-separable convolution Jinhu Wei South China Univ. of Tech.Yan Zhou South China Univ. of Tech.Wei Xie South China Univ. of Tech.JinWei Yu South China Univ. of Tech.WeiSheng Li South China Univ. of Tech.In this paper, we present a new compression algorithm for live face recognition models. Firstly, a VGG11 convolutional neural network is improved based on depth-separable convolution. The depth-separable convolution layer consists of a depth convolution layer and a point-by-point convolution layer. The depth convolution is used for filtering, acting on each channel of the input. And the point-by-point convolution is used for transforming the channels, acting on the output feature mapping of the deep convolution. Compared with the original model, although the accuracy of the improved model decreases by about 1%, the model size reduces to about 1/5 of the original size. Secondly, since the float16 quantization approximates continuously valued floating-point model weights to a finite number of discrete values with a low loss of accuracy, the model volume is compressed by half with little impact on model accuracy through float16 quantization. The proposed method can compress the original face live recognition model by 90.94% and reduce the recognition time by 83.46% with 1.07% accuracy loss.

SunAIS-46 1256 Real-time Vehicle Detection and Tracking Based on YOLOv3 Pruning Model Mingxiu Lin Northeastern Univ.Jiayi Li Northeastern Univ.Jiaxin Zhang Northeastern Univ.Xinghui Li Northeastern Univ.Shiyao Ji Northeastern Univ.Yunli He Northeastern Univ.Real-time detection and tracking of vehicles is very important in the field of automatic driving. In this paper, a YOLOv3 network based on pruning algorithm is proposed to solve the problem of real-time vehicle detection. Through reducing the number of channels and layers in the backbone network, the computation of the model is reduced. And thus, the rate of detection is greatly increased. On the basis of vehicle detection, the real-time tracking of multiple vehicle targets is completed by using Kalman filter algorithm for prediction and Hungarian algorithm for data association. The experimental results show that compared with the original YOLOv3 network, the model size is compressed by 95% to 11.25 MB and the detection rate is doubled to 128.1 frames/s while the average accuracy is basically unchanged. The detection and tracking frame rate of the whole algorithm is 18fps, and the recall is 98.5%. The algorithm also has strong robustness on complex traffic roads, and can basically realize real-time detection and tracking of road vehicles.

SunAIS-47 308 Light Field Depth Estimation based on Occlusion Optimization Long Zhang Wuhan Univ. of Science and Tech.Huiping Deng Wuhan Univ. of Science and Tech.Sen Xiang Wuhan Univ. of Science and Tech.Shuang Li Wuhan Univ. of Science and Tech.Light field depth estimation has superiority for its distinctive refocus characteristics. However, it remains challenges because of presence of occlusion. In order to detect more accurate occlusion region, this paper introduces a structured forests-based edge detection method that is more consistent with human vision system then both consistency cue in angular patch and defocus cue in refocus image are combined to estimate initial depth map. Finally, a global optimization method based on Markov random field is applied to enhance the quality of the initial depth map. In the process of optimization, this paper designs an adaptive weight to protect the edge. Experiments on HCI 4D light field dataset demonstrate the proposed method can achieve sharp transition around object boundaries. The proposed method outperforms some state-of-the-art light field depth estimation methods in both qualitative and quantitative evaluations.

SunAIS-48

1349 A Fast Method for Detecting Accuracy of Cross Line Laser Wenlong Shi Southeast Univ.Yuan Wu Southeast Univ.Siyu Xia Southeast Univ.The laser line projector, as a kind of precise measuring instrument, is widely used in interior decoration, equipment installation and engineering construction. Traditionally, laser line projector is calibrated by manual method combined with ruler measurement. However, laser does harm to eyes, and manual judgment is inefficient. The process is cumbersome and lead to low automation level. To overcome the above problems, a visual calibration system of laser scanner based on image processing technology is designed. This paper designed a new calibration structure, which can not only ensure the brightness of the laser line, but also take pictures without distortion. In addition, we proposed a new method to separate the scale line and the laser line, and used the method of image processing to obtain the scale value of the laser line in the picture. Experiments show that compared with manual calibration method, our method can effectively avoid human errors with simple operation and a high degree of automation. It greatly improves the calibration efficiency and the calibration accuracy of laser line projector.

SunAIS-49 400 Application of attention mechanism for multi-time series in fault diagnosis Yang Yu Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing, Ministry of Education of China

Shanghai Engineering Research Center of Intelligent Control and Management

Nan Xia China Shipbuilding Industry Corporation 708th Inst.Jianxun Li Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing, Ministry of Education of China

Shanghai Engineering Research Center of Intelligent Control and Management

A large amount of time series data obtained by sensors in industry can be used for fault diagnosis.However, multivariate time series data is very complicated, and different features are related to each other.This article proposes an improved CNN-LSTM network for attention mechanism of multivariate time series.By adding the attention mechanism for multi-time series, the model can focus on the useful time series, so as to better extract the temporal characteristics of multi-time series data.Finally, through the experimental analysis of the fan blade icing problem, the results are compared with the traditional CNN-LSTM model, and the F1-score of the model is improved by 6.7%.The method presented in this paper is more accurate in solving the problem of multi-variable sequence classification.

SunAIS-50 1585 Indoor Dynamic Scene Visual SLAM Based On Human Detection and Geometric Constraints Jiantao He Guangdong Univ. of Tech.Li He Guangdong Univ. of Tech.Haifei Zhu Guangdong Univ. of Tech.Chaoqun Xiang Guangdong Univ. of Tech.Yisheng Guan Guangdong Univ. of Tech.The existence of dynamic objects dramatically depresses the performance of localization and mapping of SLAM systems. In this paper, we propose a novel indoor dynamic visual SLAM framework based on human detection and geometric constraints to solve the problem caused by the dynamic object. We first use semantic segmentation model, DeeplabV3, to detect human and then use spatial geometric constraints to further remove dynamic feature points. Several experiments on TUM datasets and Bonn datasets have shown that our performances, in terms of absolute trajectory error (ATE), are 91% to 99% higher than that of ORBSLAM2.

SunAIS-51 1588 HDR Image Generation Based on Polarization Image Chuangbin Chen Guangdong Univ. of Tech.Li He Guangdong Univ. of Tech.Haifei Zhu Guangdong Univ. of Tech.Chaoqun Xiang Guangdong Univ. of Tech.Yisheng Guan Guangdong Univ. of Tech.The existence of dynamic objects dramatically depresses the performance of localization and mapping of SLAM systems. In this paper, we propose a novel indoor dynamic visual SLAM framework based on human detection and geometric constraints to solve the problem caused by the dynamic object. We first use semantic segmentation model, DeeplabV3, to detect human and then use spatial geometric constraints to further remove dynamic feature points. Several experiments on TUM datasets and Bonn datasets have shown that our performances, in terms of absolute trajectory error (ATE), are 91% to 99% higher than that of ORBSLAM2.

SunAIS-52 1616

Technical Programmes CCDC 2021 Research on Collaborative Object Detection and Recognition of Autonomous Underwater Vehicle Based on YOLO Algorithm Leisheng TANG Chinese Academy of Science

Shenyang Jianzhu Univ.Hongli XU Northeastern Univ.Han WU Chinese Academy of ScienceDongxu TAN Shenyang Ligong Univ.Lei Gao Chinese Academy of ScienceAutonomous underwater vehicle (AUV) is an important tool for human to explore and research marine resources. Due to the influence of underwater environment, it is impossible to realize the rapid detection and recognition of AUV and the recognition of cooperative objects. To solve this problem, underwater light vision and underwater image enhancement technology are proposed to realize the rapid detection and recognition of underwater vehicles, so as to realize the recognition of cooperative objects. In this paper, YOLO algorithm which has a good effect on target detection precision and the recognition speed is used to recognize the underwater vehicle on the Yolo network. For the original data set, histogram equalization and CLAHE algorithm are used to enhance the image. They are respectively loaded into the same training model and trained on the yolov2 and yolov3 networks. The analysis of the experimental results shows that the underwater image enhancement technology based on the YOLOv3 network and the CLAHE algorithm can meet the requirements of rapid recognition for underwater vehicle detection and recognition.

SunAIS-53 1677 An Optimized Random Forest Classification Method for Processing Imbalanced Data Sets of Alzheimer's Disease Haijing Sun Northeastern Univ.

Shenyang Univ.Anna Wang Northeastern Univ.Yun Feng Northeastern Univ.

Shenyang Univ.Chen Liu Northeastern Univ.In this work we propose an optimized random forest classification method to solve the problem of imbalanced samples in the Alzheimer's disease (AD). The improved algorithm is based on Synthetic Minority Oversampling Technique (SMOTE) and multi-dimensional Gaussian probability density hypothesis combined with Random Forest (RF). Through this technique, some reasonable pseudo samples can be established for minority classes to balance the number of samples of each type before the RF classifier is used to classify them. This method can effectively solve the problem of sample imbalance, improve the accuracy and recall rate of the minority classes to a certain extent, and avoid overfitting of the classifier. For the purpose of demonstrating the effectiveness of the algorithm proposed in this paper, Support Vector Machine (SVM) algorithm, RF algorithm and the algorithm proposed in this paper are compared. Experimental results have proved that the algorithm proposed in this paper has obvious improvement in accuracy and recall rate compared with the other two algorithms.

SunAIS-54 477 Learning Depth for Multi-View Stereo with Adversarial Training Liang Wang Beijing Univ. of Tech.

Engineering Research Center of Digital CommunityDeqiao Fan Beijing Univ. of Tech.Jianshu Li Beijing Univ. of Tech.The deep learning-based Multi-View Stereo (MVS) methods have shown excellent performance in depth estimation. However, due to the heavy computational burden caused by 3D convolution, most of the existing methods are difficult to be applied to high-resolution scenarios. In this paper, a novel method of learning depth for multi-view stereo with adversarial training is proposed, which exploits the Wasserstein generative adversarial network (GAN) to overcome shortcomings of the original GAN. The proposed network mainly consists of the encoder, the generator, and the discriminator sub-network. With the help of the proposed loss function, the proposed network can merge more global information provided by the discriminator of GAN and local information provided by the generator. This can capture more details and estimate an accurate depth map in high-resolution scenarios. It also significantly reduces memory consumption by exploiting WGAN instead of performing 3D convolution. Extensive experiments validate the proposed method, which can achieve on par or even better performance than the state-of-the-art.

SunAIS-55 366 Gesture recognition matching based on dynamic skeleton Jingyao Wang Beijing Univ. of Tech.Naigong Yu Beijing Univ. of Tech.Firdaous Essaf Beijing Univ. of Tech.Gestures, as a basic human feature, occupy an important position in human-computer interaction and other fields as well. In order to accurately recognize gestures and eliminate environmental interference, this paper proposes a gesture recognition matching method based on dynamic bones. This method uses the Mask R-CNN model and exponential filtering to identify and calibrate the key points of the hand. Through the segmentation and feature extraction of real-time frame images, the combined network is used to obtain stable and accurate hand

bone key points. Based on the idea of constructing a Spatio-temporal graph convolutional network model based on ST-GCN, a skeletal point information gesture data set is constructed and sent to the network for training. Finally, template matching is used to realize gesture recognition. The experimental results show that the method can eliminate the environmental interference to the greatest extent, as well as the incomplete traditional data set, and the model accuracy defects caused by the lack of special samples. The recognition accuracy of the Chinese sign language database can reach 87.02%. Compared with previous researches on gesture recognition, it has improved accuracy and robustness.

SunAIS-56 298 A Graphical Convolutional Network-based Method for 3D Point Cloud Classification Liang Wang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Jianshu Li Beijing Univ. of Tech.Deqiao Fan Beijing Univ. of Tech.Point cloud data classification has been widely used in autonomous driving, robot perception, and virtual/ augmented reality. Due to its irregularity and disorder, the classification task of point clouds needs to transform the point cloud into a multi-view or voxel grid, and then use the traditional convolution neural network processing. However, this process is not only complex in operation but also low in classification accuracy. To solve this problem, a new point cloud classification method based on the graphical convolutional neural network (GCN) is proposed. Firstly, based on PointNet, KNN graph is introduced to obtain global deep features. Then the 3D point cloud is represented as a directed graph, local features are extracted by edge convolution. Finally, the extracted global and local features are aggregated to represent and classify point clouds. The proposed network is evaluated on the open dataset ModelNet40 and 3DMNIST. Experimental results show that the proposed network can achieve on par or better performance than state-of-the-art, such as PointNet, PointNet++, DGCNN, and PointCNN, for point cloud classification.

SunAIS-57 520 Efficient Off-policy Adversarial Imitation Learning with Imperfect Demonstrations Jiangeng Li Beijing Univ. of Tech.Qishen Zhao Beijing Univ. of Tech.Shuai Huang Beijing Univ. of Tech.Guoyu Zuo Beijing Univ. of Tech.Generative Adversarial Imitation learning (GAIL) enables the agent to learn a desired policy from the demonstrations provided by human beings. For complex continuous control tasks, such as robotic tasks, demonstrations are usually be obtained from multiple demonstrators to save time. However, the differences in the abilities of multiple demonstrators lead to the drift of demonstration distribution, which results in learning a suboptimal policy in the adversarial training process. In this paper, we proposes a new imitation learning (IL) algorithm, IM-GAIL, to enable the agent to learn effectively from imperfect demonstrations of multiple demonstrators. The proposed method rebuild the objective function in the adversarial training, making the policy match the optimal policy. Meanwhile, to improve the sample efficiency, off-policy training framework is adopted and a curious experience replay mechanism is designed for the training. The experiments are conducted on robotic simulation tasks, and results show that our method can efficiently learn from the given demonstrations and it achieves better performance than other state-of-the-art methods.

SunAIS-58 379 Research on Segmentation Algorithm of Workpiece Character Image Based on FCN-ELM Weibo Yu Changchun Univ. of Tech.Yu Li Changchun Univ. of Tech.Hongtao Yang Changchun Univ. of Tech.In an industrial scene, in order to deal with the character segmentation problem on each workpiece, multiple complex steps such as image preprocessing are required, and then image segmentation processing of the workpiece character is performed. Aiming at the complex preprocessing process in the workpiece character recognition process, a workpiece character image segmentation algorithm based on the combination of FCN and ELM is proposed, that is, the convolution layer is mainly responsible for the feature extraction function in the FCN, and the difference of the input image is extracted Deep image features, remove the last fully connected layer of the convolutional neural network, and then use ELM instead of the fully connected layer, as a classifier to quickly classify image pixels, improve the classification accuracy, and then connect the convolutional layer to the extreme learning machine. The classified image pixels are used as the input of the convolutional layer to perform an up-sampling operation, and the generated feature thumbnails need to be restored to the dimensions of the original image. The experimental results show that the improved algorithm improves the accuracy compared with the original FCN algorithm.

SunAIS-59

Technical Programmes CCDC 2021 674 Gait recognition based on the mean impact value and probability neural network Lei Liu Zhengzhou college of light industryYinmao Song Zhengzhou college of light industryPeng Yang Hebei univ. of Tech.Zuojun Liu Hebei univ. of Tech.In order to effectively control the intelligent lower limb prosthesis, the effective identification of gaits (including walk, up and down stairs or hills, etc.) is the key. Firstly, this paper extracts characteristic values of different gait, uses Mean Impact Value to screen the values, and identifies six feature values in accordance with the characteristics of the knee amputees, for the maximum value, mean degree in Initial Stance, mean degree in Middle Stance, standard deviation in Middle Stance, standard deviation in Swing Phase of hip (Mh, ISh, MSh, SWh, MSV, SWV). Secondly, use the probabilistic neural network (PNN) to recognize, of which can accurately identify the five kinds of gaits in this experiment, and compare with BP neural network, the results show that the method, which uses the means of Mean Impact Value to screen eigenvalues and recognizes by Probabilistic Neural Network , has good recognition rate and recognition speed. The recognition rate which compared to BP neural network, improves by more than 10%. So this method is effective and feasibility conveniently.

SunAIS-60 399 An Optimized Model based on Metric-Learning for Few-Shot Classification Wencang Zhao Qingdao Univ. of Science and Tech.Wenqian Qin Qingdao Univ. of Science and Tech.Ming Li Qingdao Univ. of Science and Tech.Few-shot learning makes up for the shortcomings of traditional deep learning that requires a large amount of labeled data, and has great potential in promoting machines to become more intelligent. Many existing few-shot learning methods have achieved benign performance in numerous classification tasks by training a classifier, yet some trained models are restricted by shallow networks which will gravely restrict their feature expression ability. In addition, what proves awful is that some previous few-shot learning methods do not use appropriate loss functions to train excellent models, which limits their performance to some extent. To settle above problems, we optimize the classical few-shot learning framework, that is, prototypical networks, from three aspects: data augmentation, increasing the network s feature expression ability and improving the training loss function. It is worth mentioning that besides keeping simple and efficient, our innovative metric-learning-based few-shot classification framework is capable to be integrated into the same model to achieve end-to-end training. Immense amounts of experimental results show that our model not only performs well in classification tasks, but also shows its amazing superiority and competitiveness compared with related technologies.

SunAIS-61 1051 Research on Distribution Strategy of Region of Interest in Panoramic Video Based on Improved Deepsort Jiangeng Li Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Zhibo Zang Beijing Univ. of Tech.Beijing Key Laboratory of Computational Int

elligence and Intelligent SystemHaizheng Xie Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Guangsheng Wang Beijing Univ. of Tech.Few-shot learning makes up for the shortcomings of traditional deep learning that requires a large amount of labeled data, and has great potential in promoting machines to become more intelligent. Many existing few-shot learning methods have achieved benign performance in numerous classification tasks by training a classifier, yet some trained models are restricted by shallow networks which will gravely restrict their feature expression ability. In addition, what proves awful is that some previous few-shot learning methods do not use appropriate loss functions to train excellent models, which limits their performance to some extent. To settle above problems, we optimize the classical few-shot learning framework, that is, prototypical networks, from three aspects: data augmentation, increasing the network s feature expression ability and improving the training loss function. It is worth mentioning that besides keeping simple and efficient, our innovative metric-learning-based few-shot classification framework is capable to be integrated into the same model to achieve end-to-end training. Immense amounts of experimental results show that our model not only performs well in classification tasks, but also shows its amazing superiority and competitiveness compared with related technologies.

SunAIS-62 108 Abnormal Behavior Detection in Crowd Scene Using YOLO and Conv-AE Yajing Li Beijing Inst. of Tech.Zhongjian Dai Beijing Inst. of Tech.This paper proposes a weighted convolutional autoencoder (Conv-AE)

and a novel regularity score based on the results of You Only Look Once (YOLO) network to detect abnormal behavior in crowd scenarios. The weighted Conv-AE extracts spatial features of video frames. In the training process, a weighted loss function is proposed based on the YOLO detection results, which emphasizes the foreground part, and thus overcomes the impact of complex background. In addition, a novel regularity score is put forward in the anomaly detection process. The regularity score takes into account the three factors of reconstruction errors obtained from weighted Conv-AE, speed information and category of objects detected by YOLO. Three scores respectively based on these factors are integrated to obtain anomaly detection results. The experimental results on UCSD ped1 and ped2 dataset verify that the proposed method achieves better performance than the most of semi-supervised methods.

SunAIS-63 434 Structured Attention Knowledge Distillation for Lightweight Networks Xiaowei Gu Beijing Inst. of Tech.Hui Tian China Mobile Xiongan Information Communication

Technologies Ltd.China Mobile Communications Group Co,.Ltd.

Zhongjian Dai Beijing Inst. of Tech.Knowledge distillation is to transfer the effective knowledge learned by the teacher networks to the student networks through the designed loss function, which helps the student network to get better performance with less calculation cost. However, when there is a great difference between the student network and the teacher network in terms of structure and calculation amount, the previous knowledge distillation methods can hardly improve the student network performance effectively. In order to improve this situation, this paper proposes structured attention distillation for lightweight networks. Structured attention distillation groups the features from models by channels, which helps student networks learn the feature extraction ability of teacher networks by refining the spatial attention maps. Our proposed method is evaluated on CIFAR100 and large-scale face recognition validation set (LFW, CFP-FP, Age-DB). Compared with other distillation methods, our proposed method achieves better accuracy on CIFAR100 and face recognition validation set.

SunAIS-64 59 An Image Segmentation method based on improved CV Mode Yang Xu Univ. of science and tech. LiaoningXiaowen Dong Univ. of science and tech. LiaoningChong Cheng The Liaoning Academy of Analytical SciencesXiaofan Fu Univ. of science and tech. LiaoningGrey scale value inside and outside the contour curve approximates as a constant in CV model. For the target with uneven gray scale, the global approximation method can't reflect the change of image gray scale and it is difficult to obtain a satisfactory segmentation results for this kind of image. In this paper the improved CV model is put forward and it overcomes some defects of the original, through the experiment in this paper, the improved algorithm is simpler than LBF algorithm in terms of computational complexity. Better segmentation results can be obtained by using LBF model and ILBF model, but the iterations of ILBF model are less than that of LBF model, and the time consuming of ILBF model is less than that of LBF model.

SunAIS-65 104 Magnetic Hard Disk Serial Number Recognition Method Based on Machine Vision Zhe Xu Beijing Univ. of Tech.Xiaoge Liu Beijing Univ. of Tech.Jian Tang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Pengsheng Li Beijing Univ. of Tech.Ziying Zhang Beijing Univ. of Tech.Degaussing used magnetic hard drives is a common means of destroying magnetic information. Customized degaussing for different magnetic hard drives can improve the efficiency of magnetic information destruction. In this scenario, it is necessary to quickly identify the used magnetic hard disk firstly. Aim at the above problem, this paper proposes to apply machine vision to the identification of magnetic hard disk serial number. At first, the magnetic hard disk serial number image is preprocessed. Then, the serial number is segmented and its HOG features are extracted. Finally, a support vector machine (SVM) algorithm is used to construct a recognition model. The effectiveness of the proposed method is verified by the magnetic hard disk serial number image.

SunAIS-66 158 Multi-Object Tracking Based on Feature Fusion and Hierarchical Data Association Yan Liu North Univ. of ChinaPinle Qin North Univ. of ChinaJianchao Zeng North Univ. of ChinaMulti-object tracking has been a fundamental topic in recent years. However, most methods can hardly have good performance due to the

Technical Programmes CCDC 2021 high appearance similarity, large number of the target objects and error detections in complex scenes. To solve these problems, we propose a multi-object tracking method based on multi-feature fusion and hierarchical data association. Specifically, it uses multi-feature fusion method to improve the anti-jamming ability of data association method and solve the difficulty of tracking accuracy decline caused by large number of objects and error detections. In order to improve the performance of the association method, we refined into multi-layer calculation to solve the problems of objects high similarity appearance and the failure of object trajectory assignment when occlusion occurs. Benefit from our proposed method, on the MOT16 test set, the accuracy of the multi-object tracking method is 44.9%. Compared with the method of multi-feature fusion only, the tracking accuracy of our hierarchical data association is improved by 10.7%.

SunAIS-67 281 Crater Obstacle Recognition and Detection of Lunar Landing Based on YOLO v4 Tao Hu Shanghai Inst. of Spaceflight Control Tech.

Shanghai Key Laboratory of Intelligent Control Tech.Changchun Zhao

Shanghai Inst. of Spaceflight Control Tech.Shanghai Key Laboratory of Intelligent Control Tech.

Zhouyuan Qian

Shanghai Inst. of Spaceflight Control Tech.Shanghai Key Laboratory of Intelligent Control Tech.

Liang He Shanghai Inst. of Spaceflight Control Tech.Shanghai Key Laboratory of Intelligent Control Tech.

Mengying Ni East China Normal Univ.During the landing process of an unmanned or manned lunar lander, the lunar image collected by the sensor has the characteristics of insufficient illumination, single surface texture, large scale changes and no prominent features, which makes it more difficult to detect landing obstacles in real time. Traditional image processing technology for obstacle recognition and detection is prone to missed and misdetected phenomena. Aiming at the problem of detecting crater obstacles during the descent of the lander, a lunar landing crater detection model based on YOLO v4 was proposed, and a crater data set was made, and the data set types were expanded through data enhancement algorithms, and the DenseNet network was used to enhance Feature extraction capabilities. Experiments on the crater data set show that the improved algorithm in this paper is better than YOLO v4 and YOLO v4 tiny in accuracy. Compared with YOLO v4, the average accuracy is increased from 72.65% to 77.24%, and good results are achieved.

SunAIS-68 299 S-BRRT*：A Spline-based Bidirectional RRT* with Strategies under Nonholonomic Constraint Yubo Zhang Beijing Univ. of Tech.

Beijing Key Lab of the Computational Intelligence and Intelligent System

Daoxiong Gong Beijing Univ. of Tech.Beijing Key Lab of the Computational Intelligence

and Intelligent SystemGlobal path planning in more general scene under the constraint condition has always been the difficulty in the field of path planning. Rapidly-exploration Random Tree and its derived algorithm (RRTs) is a popular planning algorithm of path planning in recent years, which is especially suitable for constrained conditions. A feasible path should consider the model and constraints of its execution object. To this end, we propose S-BRRT*: First, a bidirectional spanning tree is introduced into the basic RRT*. Second, nonholonomic constraints are considered into the program, including internal motion constraints and external collision constraints. Then, a new pruning strategy is put forward to conduct intelligent pruning of BRRT* 's zigzag path, including pruning under collision constraint and pruning under path constraint, so as to select optimal path nodes. Finally, the smooth strategy is proposed, using cubic Bézier curve to fitting the previous pruning path for the first time. Then, we fitting for the second time under collision optimization to ensure the final feasibly smooth path. Our S-BRRT * algorithm can realize the global path planning in a variety of scenarios. It is verified by experiments in sparse common scene as well as the scenarios with dense narrow path. We can conclude that the trajectory maintains robust and conforms to the nonholonomic constraints, meanwhile, we obtain improvement in length and feasibility.

SunAIS-69 392 Matching and Locating Complex Semantic Objects on a Novel Semantic Map Kaiqiang Wang National Univ. of Defense Tech.Zhiyong Liao National Univ. of Defense Tech.Yu Zhang National Univ. of Defense Tech.Xueke Yang National Univ. of Defense Tech.Tingting Li National Univ. of Defense Tech.The semantic map is composed of physical information and object-level semantic information in the environment. However, it neglects object attributes and the relationships between objects, which are critical for complex semantic objects matching and retrieval. In this paper, we propose a framework for matching and locating complex semantic objects on a novel semantic map (MLSM for short). We present a novel semantic map which transforms RGB-D image sequence into visual scene graphs

to represent the detailed semantic information. We show that the target object depicted in natural language text can be successfully matched and located on the semantic map after being transformed into a textual scene graph. Additionally, we demonstrate the framework and methods are significantly effective by experimenting in a real indoor environment.

SunAIS-70 396 Research on the Algorithm for Off-line Rectification of Welding Seam Based on Improved ICP Hongtao Yang Changchun Univ. of Tech.Chengxiang Zhu Changchun Univ. of Tech.Weibo Yu Changchun Univ. of Tech.In order to improve the low registration efficiency and poor accuracy caused by the size error and initial position s major difference of the workpiece in the registration process of off-line welding rectification, this paper adopts a coarse registration first and then fine registration s process strategy.On the basis of quaternion algorithm to achieve coarse registration, an iterative closest point (ICP) registration algorithm based on K-D tree nearest neighbor search method is proposed.In the precise registration algorithm, the K-D tree nearest neighbor search method is first used to improve the search speed of corresponding point pairs, and then the bidirectional search method is used to improve the registration accuracy. In order to verify the feasibility and effectiveness of the algorithm, the registration experiments are carried out on the point cloud data of circular weld, square weld and complex weld. The results show that the accuracy of the proposed method is 90% higher than the PCA algorithm when the time is similar, and compared with the traditional ICP algorithm, the registration accuracy and time of this method are improved by 35% and 55%.Therefore, the method presented in this paper has good efficiency, accuracy and stability in solving the problem of weld track point cloud data registration.

SunAIS-71 554 Behavior Identification based on Improved Two-Stream Convolutional Networks and Faster RCNN Hongchao Su Dalian Maritime Univ.Hu Ying Dalian Maritime Univ.Guoqing Zhu Dalian Maritime Univ.Chuyue Zhang Dalian Maritime Univ.With the rapid development of deep learning in the filed of security, the application of deep learning in the factory to identify the dangerous behaviors of employees can further reduce the safety accidents caused by dangerous behaviors. Considering that the existing behavior recognition network cannot solve the problems of multi-target behavior recognition, slow detection speed and low accuracy. In this paper, a multi-target recognition model based on improved Two-Stream Convolutional Networks and Faster RCNN is proposed. The main framework of this model is based on Two-Stream Convolutional Networks. The optical flow in the temporal stream is replaced by the motion history image to reduce the computing cost. The LRCN network is introduced to replace the spatial stream network to enrich the spatial stream motion information and improve the accuracy. In this paper, the experimental research on the public data set KTH shows that the algorithm can adapt to the behavior recognition of multiple targets, with higher speed and accuracy.

SunAIS-72 695 Gas Concentration Prediction in Continuous Monitoring Based on Echo State Network Qingfeng Wang Jilin Univ.Zhaojiang Luo Jilin Univ.In this paper, we introduce an echo state network (ESN) model approach for gas concentration estimation using Metal Oxide (MOX) sensors in Open Sampling System (OSS). Our approach focuses on the compensation of the slow response of MOX sensors, while concurrently solving the problem of estimating the gas concentration in OSS. By comparing with SVR algorithm model commonly used in the prediction system, it fully reflects high accuracy of the gas prediction model based on ESN algorithm and the well tracking performance with rapidly changing gases. A prediction model based on ESN can overcome these limitations because of its nonlinear and implicit storage characteristics. In this paper, the concentration and variation of gas mixture are predicted successfully.

SunAIS-73 699 Center Extraction Algorithm of Linear Structured Light Stripe Based on Improved Gray Barycenter Method Hongtao Yang Changchun Univ. of Tech.Zhen Wang Changchun Univ. of Tech.Weibo Yu Changchun Univ. of Tech.Peng Zhan Songyuan Equipment Manufacturing Branch of

Daqing Petroleum Administration Co. Ltd.In the online structured light 3D vision measurement, the most important step is the extraction of the laser stripe center. Due to the roughness of the detected object and the light source and other factors affect the precision of the linear structured light stripe center, an improved algorithm which can detection of laser fringe center based on gray barycentric

Technical Programmes CCDC 2021 method is proposed. The algorithm uses adaptive threshold segmentation method to extract strip targets, extract the skeleton of the target stripe by thinning method, use template method to detect the normal direction of laser stripes. Finally, using the gray barycentric method to accurately extract the laser center. It has been verified that the algorithm has the characteristics of high extraction accuracy, short running time and good stability.

SunAIS-74 854 Research on Pattern Recognition Algorithm Based on Visual Principle Li Hao Xijing Univ.Tuanbu Wang Xijing Univ.Yifei Li Xijing Univ.From the perspective of bionics, this paper introduces a pattern recognition algorithm based on visual theory, which regards the data set as an image and realizes pattern recognition by using the perception and recognition method of visual image. Experiments show that the new algorithm based on visual theory not only maintains good recognition effect for all kinds of data, but also is insensitive to noise data and has good robustness. Combined with the voting strategy, this method is applied to multi-category complex data, and compared with the traditional support vector machine method, the results show that the visual method has obvious improvement in classification accuracy and classification speed, especially suitable for complex classification problems with a large number of categories and difficult classification.

SunAIS-75 1103 The comparison of handwritten digital image classification based on different neural networks Shengwei Li Wuhan Univ. of Tech.Huajun Zhang Wuhan Univ. of Tech.Based on the wide application of neural network in image recognition and character recognition, this paper introduces three kinds of neural network models, compares and analyzes the performance of different neural network models in practical application. This paper first introduces the development of neural networks, especially the convolutional neural network, and the principles of the three neural network models in detail. Then building and training the three models that are based on the analysis. Finally, through experimental simulation, it shows that convolutional neural network has an outstanding advantage in handwritten digital image recognition.

SunAIS-76 1538 COFH—An improved feature descriptor for RGB information based on OVFH Yang Jiang Northeastern Univ.Weiping Li Northeastern Univ.Xingmao Wu Northeastern Univ.Xiao Chen Northeastern Univ.In this paper, we propose a novel point cloud feature descriptor for object recognition based on 3D RGB point cloud. Due to its strong recognition power, OVFH (Orthogonal Viewpoint Feature Histogram) feature descriptor is widely used in real-time 3D point cloud object recognition task, especially when dealing with the object of symmetric placement of mirror image, and has achieved good results. However, this descriptor does not take the color information of the point cloud into consideration, which is an essential information for object recognition. To solve this problem, we propose a novel feature descriptor named COFH (Color-OVFH). It consists of two parts, which are viewpoint component and extended color shape feature. The extended color shape feature integrates the surface shape feature and color feature. The performance of COFH descriptor is validated on the common dataset. The experimental result shows that COFH significantly improved the recognition ability in RGB point clouds with similar shapes but large color differences. It can be effectively applied to object recognition with RGB information.

SunAIS-77 1579 Building Recognition Based on Improved Faster R-CNN in High Point Monitoring Image Xu Li Univ. of Chinese Academy of SciencesLijun Fu Univ. of Chinese Academy of Sciences

Chinese Academy of SciencesYue Fan Univ. of Chinese Academy of Sciences

Chinese Academy of SciencesChunsheng Dong Univ. of Chinese Academy of Sciences

Chinese Academy of SciencesIn this study, the Faster R-CNN model is trained to recognize the building based on the monitoring video image data captured by the high-resolution camera (high point) of the 40 meter communication tower. Aiming at the problem that the building target in the foreground is small and the detection effect is not good, the Faster R-CNN network structure is improved, and the low-level features and high-level features are used to detect the target in different scales. The experimental results show that the average accuracy of this method is 75.58%, which can effectively detect buildings. It can be applied to the law enforcement and inspection

of illegal occupation of land, improve the work efficiency of government departments, and reduce the cost of manual inspection.

SunAIS-78 910 Link addition strategies in interdependent scale-free networks Chao-Yang Chen Hunan Univ. of Science and Tech.Yang Zhao Chinese Academy of SciencesWith the concept of interdependent networks being put forward, the robustness of interdependent networks has increasingly become a research hotspot. Compared with a single and isolated network, interdependent networks appear to have great vulnerabilities. In order to improve the robustness of interdependent networks under deliberate attacks, based on the characteristics of nodes and interdependent networks and the more similar the two sub-networks, the more robust, this paper proposes 14 edge addition strategies by two edge importance functions. Then, interdependent BA networks with assortative coupling (AC), disassortative coupling (DC), and random coupling (RC) are constructed to verify the feasibility of the proposed strategy. The study found that when the number of attacked nodes in the system is small, the harmonic closeness (SH) edge addition strategy has the highest efficiency. With the increase of the attack ratio, the degree (SD) edge addition efficiency gradually increases, and the effect is more obvious in DC and RC. In addition, through the comparison of different strategies, under DC and RC, the edge addition strategy for product is more effective than the addition strategy for sum. However, under AC, this phenomenon is not obvious. The results show that our proposed strategy can greatly enhance the robustness of interdependent networks, and our method provides an effective reference for the control and prevention of cascading faults in actual interdependent networks.

SunAIS-79 1426 Exponential stability of singularly perturbed systems with mixed impulses Li-Mei Wei Guangxi Univ.Wu Yang Guangxi Univ.

Huazhong Univ. of Science and Tech.This paper investigates the exponential stability problem for a class of singularly perturbed impulsive systems in which the flow dynamics is unstable and is affected at discrete time instants by impulses that have both destabilizing and stabilizing effects, which means that neither the flow dynamics nor the impulsive one is stable. We introduce a new impulse-dependent vector Lyapunov function to take full advantage of the jump matrix structure and its stabilizing effects on the slow dynamics and to better describe the behavior between two consecutive impulses as well as the jumps at impulse instants. Several numerically tractable criteria for stability of singularly perturbed impulsive systems are established based on vector comparison principle. Moreover, upper bounds on the singular perturbation parameter are derived. Finally, the validity of our results is verified by two numerical examples.

SunAIS-80 1285 A Distributed Secondary Controller for DC Microgrid Qifan Yuan Huazhong Univ. of Science and Tech.Yanwu Wang Huazhong Univ. of Science and Tech.Xiaokang Liu Huazhong Univ. of Science and Tech.Yan Lei Huazhong Univ. of Science and Tech.In this paper, a new distributed secondary control scheme has been designed for an islanded DC microgrid. Based on the dynamic average consensus algorithm, the proposed secondary controller can achieve both current sharing among the converters and voltage regulation of the DC bus without requiring the knowledge of the DC bus voltage. Simulation cases and experiment tests are carried out to demonstrate the effectiveness of the proposed secondary control strategy.

SunB02 Room02 Computer Games (Invited Session) 10:20-12:20 Chair: Jiao Wang Northeastern Univ.

10:20-10:40 SunB02-1 432 Mahjong Game Platform With Strong Applicability Xu Wang Shenyang Aerospace Univ.Zhongzhen Jin Shenyang Aerospace Univ.Wenkai Kang Shenyang Aerospace Univ.Zeyu Liu Shenyang Aerospace Univ.Na Guo Shenyang Aerospace Univ.Mahjong project is a new computer game project in China, which has a magnificent space for development. In order to facilitate the common improvement of the ultramodern mahjong games and the debugging of players on the platform, and get data of card scores and other information, we developed the Shen Hang Dazhong Mahjong platform. It is a platform that with strong applicability. It can not only combine online and offline, but also can carry through the game man-man, man-machine, machine- machine, retrospect operation. It also can be randomly licensed and load the predetermined card wall. Users can choose the corresponding function mode according to their needs.

Technical Programmes CCDC 2021 10:40-11:00 SunB02-2 441 Optimized VCST Algorithm for Connect6 Yihao Wu Univ. of Science and Tech. BeijingMiao Su Univ. of Science and Tech. BeijingXiaorui Li Univ. of Science and Tech. BeijingYunpeng Zhang Agricultural Bank of China Beijing BranchKe Zhou Univ. of Science and Tech. BeijingSince the concept of Connect6 was first introduced by Professor Wu at National Chiao Tung University in Taiwan, there has been massive progress in the study of Connect6. Recently VCF (victory by continuous four) algorithm has become a well-known game search method for Connect6. This method performs well in attacking the opponent and finding offensive ways to win. Based on the VCF method of Gomoku, this paper proposes an algorithm called VCST (victory by continuous single-threat-or-more moves) for Gomoku. For any feasible solution searched out, the solution is a winning strategy, and there is no need to continue traversing the tree. This method uses the AND/OR tree strategy to improve the search efficiency of the algorithm, and a parallel implementation method is designed for this strategy to speed up. The running speed of the algorithm. The experimental result shows that our improved VCST algorithm improves the program robustness and leads to an increment of winning chance. Our gaming program won second place in the 14th National Undergraduate Computer Game Competition.

11:00-11:20 SunB02-3 1260 A method of computing winning probability for Texas Hold'em poker Xiaochuan Zhang Chongqing Univ. of Tech.Song Du Chongqing Univ. of Tech.Hailu Zhao Chongqing Univ. of Tech.He Liu Chongqing Univ. of Tech.Fan Wu Chongqing Univ. of Tech.Texas Hold’em poker is one of the most popular card games in computer games. Most of the times hand evaluation is necessary for computing hand strength which is also called winning probability. We need to traverse all combinations of cards to get the accurate hand strength in conventional approach. Usually Monte Carlo simulation or look-up table could reduce the computation. To improve efficiency, this paper propose an algorithm for poker hand classification and introduce linear regression to calculate the approximate effective hand strength. Firstly getting the classifying result of 5 cards or 6 cards, then generating feature vector through data preprocessing, at last the predictive value of the effective hand strength calculated by the linear regression equation. The experiment results show that the average absolute error between the predicted value and the label is 0.034. Compared with the traditional evaluation approach, our method is more effective to computing winning probability without lookup table.

11:20-11:40 SunB02-4 1268 A Mahjong Game System Architecture Based on Empirical Knowledge Xiaochuan Zhang Chongqing Univ. of Tech.Hailu Zhao Chongqing Univ. of Tech.Chunyan Gan Chongqing Univ. of Tech.Junyu Chen Chongqing Univ. of Tech.Le Zeng JJWorld (Chengdu) Network Tech. CompanyTongyuan Huang Chongqing Univ. of Tech.I Imperfect-information game has always been the field that artificial intelligence computer game researchers want to crack. As a typical imperfect-information game, mahjong has also received extensive attention from researchers. This paper mainly takes mahjong as the research carrier, and proposes a mahjong game system architecture based on empirical knowledge, which mainly including professional mahjong game terms analysis, the design of mahjong computer game system and core game strategies. In the strategy, a combination of empirical algorithm and improved search tree algorithm is designed to formulate the player's game behavior in different periods. In the search tree algorithm, an evaluation function is constructed and a method to judge if the hand is in winning state is proposed. Finally, the experimental results show that the system architecture proposed in this article has a high level of game for the rules of popular mahjong, and it also has positive reference significance for other rules of mahjong game and imperfect-information items.

11:40-12:00 SunB02-5 1358 Research on the Characteristics of Incomplete Information Chess and Card Game Models Zhe He Harbin Univ. of Science and Tech.Xian Mei Harbin Univ. of Science and Tech.Xi Wu Harbin Univ. of Science and Tech.Lin Zhou Harbin Univ. of Science and Tech.Zixiang Pan Harbin Univ. of Science and Tech.Jianing Du Harbin Univ. of Science and Tech.This paper analyzes the fundamental concepts of the game of incomplete information in Computer Game and compares the model of the game of complete information and the game of incomplete information. What s more, this paper will describe the application of the model in terms of

engine decision, battle platform and chess book standard in detail.

12:00-12:20 SunB02-6 1366 Research and Implementation of Surakarta Computer Game Algorithm Xiyuan Li Shenyang Aerospace Univ.Linlin Wang Shenyang Aerospace Univ.Shang Liu Shenyang Aerospace Univ.Aiming at the drawbacks of human participation in the "machine-machine" game of Surakarta chess, the necessity of constructing a computer game platform for Surakarta chess is proposed. In this paper, Surakarta's battle platform is implemented, and the available position of each chess piece is optimized using a depth-first search algorithm. The algorithm divides them into two stages: endgame and non-endgame according to the number of pieces of both sides, which correspond to different value matrices. Furthermore, it changed the original program from the compiler to express the chessboard to a graphical interface, drawing the initial interface, chessboard, chess pieces, and moving range of the selected chess pieces, etc., so that the platform was significantly improved.

SunB03 Room03 Pattern Recognition and Intelligent Machines (II) 10:20-12:20 Chair: Wei Xie South China Univ. of Tech.CO-Chair: Ping Li Huaqiao Univ.

10:20-10:40 SunB03-1 1251 An Improved Face Liveness Detection Algorithm Based on Deep Convolution Neural Network Yan Zhou South China Univ. of Tech.Wei Xie South China Univ. of Tech.Jinhu Wei South China Univ. of Tech.Face recognition system has been frequently used in people s daily life. Therefore, face anti-spoofing technology on video and photo has attracted more and more attention. Based on the traditional VGG-11 model, we proposes an improved deep convolution neural network which can accurately detect the face liveness of single face image. Firstly, the training data set is enhanced by some methods such as random rotation, random brightness and saturation adjustment, which can improve the generalization ability of the network. Secondly, batch normalization and random deactivation are added into the traditional VGG-11 network, which can improve feature extraction and decisionmaking classification of real and false face images. Finally, we use the exponential attenuation learning rate during the process of network training which can avoid the optimization parameters wandering around the local optimization values. Experimental results against the state-of-the-art methods on NUAA and CASIA face liveness databases show that, the proposed method can achieve higher accuracy and lower recognition error rate on face liveness detection.

10:40-11:00 SunB03-2 1120 An Automatic Lung Field Segmentation Algorithm Based on Improved Snake Model in X-ray Chest Radiograph Jun Hu Huaqiao Univ.Ping Li Huaqiao Univ.In order to solve the problems such as sensitive selection of artificial initial contour and inaccurate segmentation of the concave area when traditional Snake model is applied to segmentation of lung field in X-ray chest radiograph, an automatic segmentation method of lung field in X-ray chest radiograph based on improved Snake model is proposed. First, Otsu method was used to binarize the original image to obtain the binarization image including lung field and background area. After image inversion, connected domain manipulation and morphological processing, the binary image containing only lung field area was obtained. Then, automatic initialization of Snake model contour can be completed through edge detection. Finally, segmentation results can be obtained through the evolution of Snake model. The experimental results show that this method can get rid of the dependence of Snake model on artificial initial contour, improve the robustness of segmentation, and at the same time, segment the sunken area more accurately and have better segmentation effect.

11:00-11:20 SunB03-3 1087 SORT with Depth Image Based Pedestrian Tracking Robots Rongxi Li Northeastern Univ.Zixi Jia Northeastern Univ.Yang Yang Northeastern Univ.Senwen Gan Northeastern Univ.Aiming at the problem of frequent position changes, crossing, and disappearance of pedestrians in the image coordinate caused by the low camera angle of view on the tracking robot, we have made a series of adaptive improvements to the SORT (Simple Online and Realtime Tracking), an online multiple object tracking algorithm, furthermore propose the SORT-DI (Simple Online and Realtimes Tracking with Depth Image). We use the RGB-D camera to obtain the position of the target in the robot coordinate system and use a simple and fast filter to calculate the difference between image features such as gradient features and

Technical Programmes CCDC 2021 color features, which solves the problem that the SORT is easy to identify swiches. This solution can also run faster and with low latency on the CPU and achieve the online tracking of designated pedestrians by the robot in large-scale pedestrians tracking scenarios.

11:20-11:40 SunB03-4 1365 Modified LSTM-CNN Model for Arrhythmia Classification With Mixed Handcrafted Features Fan Chen Beijing Univ. of Posts and TelecommunicationsJingjing Zheng PLA Rocket Force Characteristic Medical CenterYutao Tang Beijing Univ. of Posts and TelecommunicationsIn this paper, we propose a novel machine learning approach for the classification problem of cardiac arrhythmias. First, we develop a combined model consisting of long short-term memory (LSTM) networks and convolutional neural networks (CNNs) to extract the deep features of ECG signals. Then, this LSTM-CNN model is augmented by mixed handcrafted features, including Heart Rate Variability (HRV) and morphological features. The final model is trained and validated over the MIT-BIH arrhythmia dataset. It can achieve a classification performance with an average accuracy of 99.58%, an average sensitivity of 99.42%, and average specificity of 99.62%. Experimental results demonstrate the effectiveness of the proposed approach.

11:40-12:00 SunB03-5 1037 Two-stage Cascaded Network With Deep Supervision And Residual Attention For Brain Tumor Segmentation Bingbing Li Northeastern Univ.Jianning Chi Northeastern Univ.Chengdong Wu Northeastern Univ.Xiaosheng Yu Northeastern Univ.Accurate segmentation of brain tumor has important research value and significance for tumor growth evaluation and treatment. In this paper, we propose a novel two-stage cascaded network based on ResUNet, which is used to segment brain tumors and their substructures to solve the class imbalance problem in brain tumor data. Brain tumor segmentation includes the following two stages. In the first stage, we use a variant of U-Net with deep supervision to segment the whole tumor structure and construct a tumor mask for more tumor boundary information. In the second stage, we design a deep residual network which combines context extractor and attention mechanism to segment tumor substructure, aiming to focus on small target areas. Among them, the tumor mask of the first network is multiplied with the multimodal images as the input of the second network. The whole network is trained in an end-to-end fashion. The proposed method is evaluated on the BraTS 2018 training dataset and achieve competitive performance compared with the state-of-the-art approaches. The results of mean dice are 0.8813, 0.8056 and 0.7366 for the whole tumor, tumor core and enhancing tumor respectively.

12:00-12:20 SunB03-6 841 Improved Otsu Multi-Threshold Image Segmentation Method based on Sailfish Optimization Ke Li Chongqing Univ. of Posts and TelecommunicationsLing Bai Chongqing Univ. of Posts and TelecommunicationsYinguo Li Mingchi Feng

Chongqing Univ. of Posts and TelecommunicationsChongqing Univ. of Posts and Telecommunications

Image segmentation is a key step from image processing to image analysis. The classical multi-threshold Otsu algorithm has achieved good results in image segmentation, but it is very time-consuming to use exhaustive search methods to find the optimal threshold. To solve this problem, this paper proposes an improved Otsu multi-threshold image segmentation method based on sailfish optimization (SFO). In order to reduce the time complexity of the algorithm, the heuristic search of sailfish biota is simulated to find the optimal threshold of image segmentation. The inter-class variance of multi-threshold is used as the fitness function of SFO, and the fitness value of each iteration is calculated. The final maximum fitness value is the optimal threshold of image segmentation. The experimental results show that the proposed algorithm in this paper not only improves the segmentation quality, but also shortens the optimization time, which demonstrates the correctness and efficiency of the algorithm.

SunB04 Room04 Connected Vehicle and Future Smart Transportation (Special Session) 10:00-12:00 Chair: Songhua Huang Nanjing Research Institute of Ele

ctronics EngineeringCO-Chair: Weida Wang Beijing Institute of Tech.

10:00-10:15 SunB04-1 575 Fault-tolerant Path Tracking Control of Distributed Electric Unmanned Vehicle Based on Differential Steering Ce Yang Tongji Univ.Bo Leng Tongji Univ.Lu Xiong Tongji Univ.Xing Yang Tongji Univ.Fault-tolerant control is significant to achieve steering fail-operational for

unmanned vehicle equipped with active steering system. Distributed electric vehicle can steer by differential steering, which is realized by applying differential torque on front wheels to drive the steering system. In this Paper, a hierarchical controller is designed for path tracking of distributed electric unmanned vehicle in case of active steering system failure, based on differential steering. Model Predictive Control is applied for path tracking and an Anti-integral-windup Sliding Mode Controller is employed for differential steering control to track the desired steering angle. The proposed approach is evaluated by CarMakerSimulink simulation, and the results demonstrate the availability and effectiveness of the controller.

10:15-10:30 SunB04-2 617 Steering Angle Control of SBW System Based on Internal Model Control Zhuoping Yu Tongji Univ.Yang Yu Tongji Univ.Xing Yang Tongji Univ.Bo Leng Tongji Univ.Lu Xiong Tongji Univ.Guirong Zhuo Tongji Univ.The steering angle control is significant for the lateral motion control in the trajectory tracking control of autonomous vehicle. This research proposed a steering tracking control method of the steering-by-wire system based on Internal Model Control (IMC) to solve the uncertainty of steering system model and aligning torque on tracking control. This paper simplified the steer-by-wire system, and established the torque equation to identify the parameters of the steering system. Based on the principle of the Expanded State Observer (ESO) in the Active Disturbance Rejection Control (ADRC), from the perspective of the steering system model, the aligning torque is regarded as the external disturbance of the system, and the disturbance is dynamically estimated in real time. Based on the IMC theory, an angle tracking controller combining an ideal controller and a second-order low-pass filter is designed for the steer-by-wire system. Finally, the simulation test proved that the designed estimator of the aligning torque can accurately obtain the aligning torque value in real time. The designed internal model controller can track the desired angle quickly and accurately, and it has good robustness against model mismatch and external disturbance.

10:30-10:45 SunB04-3 673 An Active Session Handoff Method for Mobile Services Based on Context Prediction Songhua Huang Nanjing Research Institute of Electronics Engin

eeringZhaoChen Zhang Nanjing Research Institute of Electronics Engin

eeringShuangling Wang Nanjing Research Institute of Electronics Engin

eeringThe stability of service which is grounded on the continuity of session is one of the major challenges in providing reliable service for the platforms which are always moving, such as vehicles, ships, and aircrafts. This paper analyzes the research status of quality assurance for service sessions in a mobile environment, and proposes a context prediction based active handoff method for mobile service sessions, including an access model based on incremental session state backup and QoS perception, and an active handoff model for session based on context prediction and QoS available threshold, to solve the problem of QoS fluctuation and failure in a mobile environment.

10:45-11:00 SunB04-4 720 Implementation of Vehicle Management System Based on Spring Boot and VUE Xinwen Zhang Shandong Univ. of Finance and EconomicsSiyuan Wen Shandong Univ. of Finance and EconomicsRui Wang Shandong Univ. of Finance and EconomicsWith the improvement of the national economy,cars ,as a convenient means of transportation, have entered the homes of ordinary people and become an important part of daily commuting. At the same time, enterprises, communities and universities , all of them are facing the problem of managing the increasing number of vehicles. Faced with different organizational structures, networked and information Tech. is necessary to design a set of flexible vehicle management system back-end. Spring boot and VUE framework are employed in this paper to establish vehicle management system, aiming to achieve efficient and standardized backstage.

11:00-11:15 SunB04-5 728 Mechanism-based Modeling and Estimation of Optimal Energy Consumption in Traffic Flow for Electric Vehicles Zihong Yang Beijing Institute of Tech.Xingyu Zhou Beijing Institute of Tech.Fuxing Yao Beijing Institute of Tech.Fei Wang Beijing Institute of Tech.Chao Sun Beijing Institute of Tech.How much energy at least would be consumed driving through a given route ahead conditioned on the current and possible future traffic states,

Technical Programmes CCDC 2021 and which factor would contribute most to the energy consumption? Answers to these problems are necessary for route and velocity planning for automated and connected vehicles. In this paper, considering the efficiency of powertrain components and the restriction of control strategy on their operation points, the mechanismbased tank-to-traffic energy consumption model is developed by integrating the energy dissipation within powertrains and the macroscopic traffic states. With Sobol global sensitivity analysis, the acceleration is identified as the most significant contributor to energy consumption within road segments rather than the control variable. Therefore, the summation of optimal segmental energy consumption (OSEC) is utilized as the estimator of the global optimal accumulative energy consumption (GOAEC) over the entire route, which is validated by correlation analysis between the sequences of OSEC and GOAEC. The validation result suggests that the maximum COR is as high as 0.97 and 0.90 for free and congested traffic condition, respectively, while even in the case of minimum COR, the sequences share a similar shape. The effective estimator for GOAEC provides the quantified evidence supporting decisions on route and velocity planning.

11:15-11:30 SunB04-6 817 An Adaptive Model Predictive Control Strategy for Path Following of Autonomous Vehicles Based on Tire Cornering Stiffness Estimation Yuhang Zhang Beijing Institute of Tech.Weida Wang Beijing Institute of Tech.Chao Yang Beijing Institute of Tech.Mingyue Ma Ministry of Public Security of the People’s

Republic of ChinaPath following performance is a crucial issue for autonomous vehicles. Due to the influence of parameter uncertainties, inevitable deviation occurs during path following process. To solve this problem, an adaptive model predictive control strategy based on tire cornering stiffness estimation is proposed for path following. Firstly, the recursive-least-square (RLS) method is applied to estimate the uncertainty of tire cornering stiffness in real time. Secondly, based on the real-time update system model, a model predictive control (MPC) scheme is proposed to achieve path following. In this way, the proposed strategy can adapt to the changes of driving conditions. Finally, lane change maneuver is performed in simulation to verify the effectiveness of the proposed strategy. The results show that the lateral offset using the proposed strategy is reduced by 10% compared with the traditional MPC.

11:30-11:45 SunB04-7 916 Multi-objective Optimization of Layout of Detectors and Floating Car Datum Requirement for Higher Efficiency of Traffic State Prediction Xingyu Zhou Beijing Institute of Tech.Fei Wang Beijing Institute of Tech.Fuxing Yao Beijing Institute of Tech.Zihong Yang Beijing Institute of Tech.Chao Sun Beijing Institute of Tech.To optimize the prediction error of speed field and the efficiency of traffic state prediction, a multi-objective optimization method considering the different physical and statistical properties of static detector data (SDD) and floating car data (FCD) is proposed to optimize the layout (the number and the corresponding location) of static detectors and the percentage of connected automated vehicles (CAVs) simultaneously. The optimization result is a set of Pareto optimal solutions providing the best trade-off between the layout of detectors and the percentage of the CAVs with reasonable prediction accuracy for different situations. In the detailed analysis, the comparative results of the predicted speed field before and after optimization indicates that: (1) the proposed optimization method improves the prediction accuracy by optimizing the layout of the detectors and the percentage of CAVs, (2) contradicting to intuitive knowledge, the increasing of the percentage of the CAVs may lead to the deterioration of the prediction accuracy as the FCD is lack of statistical representativeness.

11:45-12:00 SunB04-8 967 Application of leapfrog improved A* algorithm in path planning of surface unmanned craft Yi-han Wang Navigation College of Dalian Maritime Univ.Wen-jun Zhang Navigation College of Dalian Maritime Univ.Tian-xin Zhou Navigation College of Dalian Maritime Univ.Unmanned ships have shifted from short-distance and small-tonnage applications such as water reconnaissance and island replenishment to long-distance ocean navigation and large-tonnage commercial transportation. In order to further optimize the safety of the navigation path of the surface unmanned boat in the obstacle area, the article first analyzes the Dijkstra algorithm, A algorithm, A* algorithm, sparse A* algorithm and other path planning methods, characteristics and existing shortcomings, and then adopts the leap forward The formula A* algorithm improves the cost function, search direction and search length in the original algorithm. Simulate the ship entering port and berth, conduct navigation simulation experiments on the rasterized chart of a certain sea area of Zhoushan Islands, and generate a 1 nautical mile dangerous avoidance circle around all obstacles during the path exploration process, ensuring the safety of surface unmanned boats. Sex. The results show that the leapfrog improved A* algorithm shortens the search time by 90%, and has a shorter path and better robustness.

SunB05 Room06 Adaptive Control and Learning Control (II) 10:20-12:20 Chair: Chao Sun Beijing Inst. of Tech.CO-Chair: Guanyu Lai Guangdong Univ. of Tech.

10:20-10:40 SunB05-1 585 Adaptive Neural Control of Uncertain Nonlinear Systems With Input Hysteresis Modeled By Preisach Operator Weiwen Qiu Guangdong Univ. of Tech.Guanyu Lai Guangdong Univ. of Tech.Yun Zhang Guangdong Univ. of Tech.In this paper, we propose a new and novel adaptive neural control scheme for uncertain unparametrizable nonlinear systems with actuator hysteresis modeled by Preisach operator. Since the hysteresis operator couples with uncertain nonlinear dynamics, identifying the hysteresis parameter (or density function) is rather difficult so that the traditional idea of constructing an inverse model of the Preisach operator to compensate the hysteresis effect may not be feasible. To overcome this problem, we used the factorization of the Preisach operator and successfully fused it with our backstepping recursive design, based on which an adaptive neural control scheme was developed to ensure that all signals of the closed-loop system are uniformly ultimately bounded and the tracking error goes into the adjustable neighborhood of the origin. The results are also verified by simulation study.

10:40-11:00 SunB05-2 983 Adaptive Sliding Mode Control for Fast Steering Mirror Based on RBF Neural Network Self-Learning Junyu Chen BeiHang Univ.Qinglei Hu BeiHang Univ.Zhe Lin Beijing Inst. of Space Mechanics & ElectricityHaiyan He Beijing inst. of Space Mechanics & ElectricityThe fast steering mirror (FSM) is a key element in electro-optical systems. For designed fast steering mirror driven by voice coil motors, considering the plant parameter uncertainty due to the unmodeled dynamics, an adaptive sliding mode controller with a robust term constructed based on the RBF neural network self-learning is proposed. The system identification indicates that RBF neural network (RBFNN) can achieve good approximation to the system parameters. Considering the output limitation, the input signal range is determined through the joint simulation of Adams and Matlab. The numerical simulation verifies that the proposed control scheme can achieve high-precision tracking performance. At the same time, the tests in two cases without input constraint and with input constraint are conducted. The test data illustrates that the developed control scheme with input constraints can better protect the FSM system.

11:00-11:20 SunB05-3 1062 Intial Alignment of Simu with Disturbances Fei Luo Beijing Aerospace Automatic Control Inst.Xinmin Zhang Beijing Aerospace Automatic Control Inst.Bing Li Beihang UniversityGang Hu Beijing Aerospace Automatic Control Inst.Considering the complex circumstances and disturbances when SIMU is processing the initial alignment, a new method could restrain the interference is studied .Facing the disturbances caused by the carrier shaking, the engine, the generator,etc. the method add a first order low-pass filter to filter the horizontal velocity observations to reduce the impact of interference. Meanwhile, taking an improved Sage-Husa adaptive kalman filter to estimates R on line to modified filter parameters in order to advance the precision and stability of the filter. The experimental results show that this method can improve the initial alignment of SIMU with disturbances.

11:20-11:40 SunB05-4 1316 ADRC-based Control of Pan-tilt System for Automated Vehicle Sensors Chao Sun Beijing Inst. of Tech.Jianghao Leng Beijing Inst. of Tech.Sifan Wang Beijing Inst. of Tech.Qi Li Beijing Inst. of Tech.The environmental perception ability of automated vehicles is highly dependent on the measurement precision of on-board sensors, which may be influenced by vehicle vibration on rough roads. Thus, a pan-tilt servo system of the sensors is essential for disturbances compensation. This paper analyzed external disturbances of pan-tilt servo system via a road roughness model and a seven-DOF vehicle model. An Active Disturbance Rejection Control (ADRC) framework, which consists of extended state observer (ESO), tracking differentiator (TD), and nonlinear state error feedback (NLSEF) law, is established for the system control. In addition, the control parameters of ADRC are determined via experience and particle swarm optimization (PSO) jointly. Simulation results demonstrate that the proposed control method can effectively reduce the servo system response time while enhancing the system stability, compared with a traditional proportion control approach.

11:40-12:00 SunB05-5

Technical Programmes CCDC 2021 1394 Model Recovery Anti-windup Control for Marginally Stable Plants Based on Characteristic Modeling Ruike Guo Beijing inst. of control engineeringJun Hu Beijing inst. of control engineeringIn view of the input saturation of a class of marginally stable plant, this paper proposes a combination of golden-section adaptive controller based on characteristic modeling and model recovery anti-windup (MRAW) strategies. The main idea is that the low-order characteristic model in the form of second-order difference is equivalent to the output of the original plant. Considering the actuator input saturation, this low-order characteristic model is used to replace the original marginally stable plant in the anti-windup design. To design the MRAW compensator, it can simplify the solution of the MRAW compensator for a class of marginally stable plant. The design of the nominal controller is to establish the characteristic model of the plant based on the characteristic modeling theory, and we design an adaptive golden-section controller that can stabilize the closed-loop system and meet the control requirements. Numerical simulation verifies that the designed anti-windup compensator can well recover the tracking performance with input saturation.

12:00-12:20 SunB05-6 1653 An initial condition-free method for prescribed performance control Zongcheng Liu Air Force Engineering Univ.Haoming Feng Northwestern Polytechnical Univ.This paper presents a scheme which can make the prescribed performance control (PPC) or barrier Lyapunov function (BLF)-based control be completely free from the initial system conditions. A state transformation is introduced to map the unbounded states to bounded ones and thus skillfully achieve the initial condition-free (ICF) purpose. Applying this scheme, an ICF low-complexity PPC controller is proposed for uncertain nonlinear systems. This controller not only can guarantee the system stability and even the prescribed performance without any knowledge of initial conditions of system, but also has the characters of easy implementation, global stabilization, high universality and strong robustness, with simple and approximation-free structure. Simulation results are provided to demonstrate the effectiveness of our scheme and controller.

SunB06 Room06 IntelliSense and Advanced Sensing, Detection Technology (Special Session) 10:00-12:20 Chair: Yong Zhao Northeastern Univ.

10:00-10:20 SunB06-1 656 Anomaly MFL Signal Recovery based on Denoising Sparse Autoencoder Lin Jiang Northeastern Univ.Jinhai Liu Northeastern Univ.Xiangkai Shen Northeastern Univ.Jiarui Liu Northeastern Univ.Xiaoyuan Liu Northeastern Univ.Baojin Zhang Ansteel Group Mining Co.Hang Xu Northeastern Univ.Abnormal signals in Magnetic Flux Leakage (MFL) signals seriously affect accurate assessment of pipeline health. To overcome this problem, this paper proposes a novel anomaly MFL signal recovery method based on denoise sparse autoencoder (DSAE). First, similar to the denoise autoencoder, the input of DSAE is the signals with anomalies, and the target signals are the complete signals. Second, relative entropy is added in the loss function as a penalty factor to improve the sparsity of the network. Finally, the proposed method is verified by comparison experiments. The results indicate that the method proposed in this paper is effective.

10:20-10:40 SunB06-2 737 Blind Source Separation for Intelligent Vehicles Based on Microphone Array in Road Environment Chao Sun Beijing Inst. of Tech.Sifan Wang Beijing Inst. of Tech.Qi Li Beijing Inst. of Tech.Compared with optical signal, sound signal is endowed with advantages of cheaper sensor, less blind area and non-visual field perception. The application of sound perception in intelligent vehicles can enhance the reliability of environment perception, but the problem of blind signal separation in traffic environment should be solved first. In this paper, an improved Fast Independent Component Correlation (Fast-ICA) algorithm is applied to the scene of road delay signal mixing to realize blind source separation of sound signal in the road environment. Firstly, Fast-ICA algorithm is extended to the complex domain to process the sound signal in time and frequency domain. Then, the pre-processing and post-processing methods are proposed based on the road environment. The results of the experiments and simulation show that the extended Fast-ICA algorithm has good adaptability to the time-delay characteristics of road environment, and can effectively separate the sound sources of main sound signals, and provide high-precision sound source signal input for acoustic-based positioning method.

10:40-11:00 SunB06-3

780 Text removal network based on comprehensive loss evaluation Zhangdao Huang Beijing Univ. of Chemical Tech.Jinglin Zhou Beijing Univ. of Chemical Tech.This paper proposes a text removal model, Text Remove Network (TRNet), which achieves an unprecedented clearing effect of picture text. The network uses a jump-connected U-net structure to encode and decode the generator, so as to obtain clearer and sharper texture details of the original image. To solve the problem of color distortion, the generator removes the batch normalization layer and uses ELUs as the activation layer of all convolutional layers. Through the comprehensive loss, which include reconstruction, content, style, total variation, and structural similarity (SSIM) loss, we can clear the image text and preserve the image information of the background, which solves the problem of incomplete text removal and loss of background texture. The local discriminator is used to evaluate the local consistency of the text erasure area. In a text elimination experiment on a synthetic dataset and the ICDAR 2013 dataset, this method had a good effect on foreground text erasure and background authenticity restoration. Experiments on a comprehensive dataset of real documents also showed good results. To achieve targeted removal of sensitive text information on pictures, we collected datasets based on real and synthetic documents, and experimental results were satisfactory. Compared to current and classic algorithms, our text removal algorithm performs best in Image Quality Assessment (IQA).

11:00-11:20 SunB06-4 792 Research on Intelligent Identification and Quantitative Detection of Gas Leakage in Open Space Liang Zhu SINOPEC Research Inst. of Safety EngineeringAnshan Xiao SINOPEC Research Inst. of Safety EngineeringXiaoming Chi SINOPEC Research Inst. of Safety EngineeringGuolong Wang SINOPEC Research Inst. of Safety EngineeringMingjun Li SINOPEC Research Inst. of Safety EngineeringShaohua Gao SINOPEC Research Inst. of Safety EngineeringMing Ma SINOPEC Research Inst. of Safety EngineeringHe Zhang SINOPEC Research Inst. of Safety EngineeringShengjie Zhu SINOPEC Research Inst. of Safety EngineeringMing Jiang SINOPEC Research Inst. of Safety EngineeringLeakage is accident, leakage is emission. The accurate detection of gas leakage in open space will help enterprises to detect the leakage as early as possible, reducing the loss and impact of safety and environmental protection. Firstly, this paper introduces the current situation of gas leakage detection technology in the open space, and the principle of quantum cascade lasers detection technology, comparing the different technologies of open path Fourier transform infrared spectrometry, solar occultation flux, tunable diode laser absorption spectroscopy, optic gas image and so on. Then, according to the technical advantages of open path quantum cascade lasers, combined with the characterization analysis of gas type, a three-layer neural network intelligent recognition model is designed by taking the specific wavelength spectrum absorption detection value as the input and the material type as the output, 2480 groups of sample data were used to train and verify the detection accuracy. The results show that the accuracy rate of leakage gas identification is more than 98%, and the quantitative detection error is less than 8%. Finally, the paper describes an open space application test based on an unmanned aerial vehicle platform, with a horizontal distance of 150 meters and a vertical height of 30 meters, which provides a new method for rapid identification and quantitative detection of gas leakage in open space.

11:20-11:40 SunB06-5 1059 Recent Progress of the Third Generation Semiconductor Applications in Sensors Longsan Si Northeastern Univ.Jihong Liu Northeastern Univ.Qihang Sun Northeastern Univ.Shibo Pei Northeastern Univ.Siheng Chen Northeastern Univ.The development of semiconductor industry has gone through three stages. The first generation of semiconductor materials is represented by silicon. The second generation semiconductor material gallium arsenide has also been widely used. The third generation semiconductor materials, represented by gallium nitride(GaN) and silicon carbide(SiC), zinc oxide(ZnO), alumina and diamond, have significant performance advantages compared with the first two generations of products. This paper mainly introduces the application of the third generation semiconductor materials in sensors, so as to show the outstanding advantages of the third generation semiconductor, and briefly enumerates some of the technical challenges encountered in the development of the third generation semiconductor. On the whole, the application prospect of the third generation semiconductor in the sensor field is very broad and desirable.

11:40-12:00 SunB06-6 1095 Structural dimension measurement and error analysis of power cable based on image computing Fanwu Chu China Electric Power Research Inst.Chao Peng China Electric Power Research Inst.

Technical Programmes CCDC 2021 Chao Fu China Electric Power Research Inst.Weijie Shen State Grid Shanghai Electric Power CompanyShize Zhang China Electric Power Research Inst.Structural dimension measurement of power cables is a key test item to check the quality of cable products. The traditional cable structure dimension measurement method using projector has some shortcomings, such as time-consuming, low precision and poor repeatability, strong dependence on personnel experience and so on. Therefore, a cable structure dimension measurement method based on image computing has been studied and the measurement system based on this method has been also established. The precision of the measurement system are verified by the annular standard instrument measurement experiment. The structure dimension measurement and error analysis of the cable sample are carried out. The experimental data of annular standard instrument measurement shows that the maximum absolute deviation of outer diameter parameters is 0.042mm, and that of thickness parameters is 0.008mm. The measured data of cable sample structure dimension shows that the limit errors of out diameter parameters are less than 0.1mm, and that of thickness parameters are less than 0.01mm. The measurement accuracy of the out diameter and thickness parameters of this measurement system based on the method can meet the measurement requirements, which provides a new idea for the automatic measurement of power cable structure dimension.

12:00-12:20 SunB06-7 1450 Privacy-Preserving Automatic Slipping Detection Method for Elderly in Bathroom Using Depth Sensors Hengshan Zong China Aerospace Academy of Systems

Science and EngineeringHuan Lei Inst. of Intelligent Manufacturing,Guangd

ong Academy of SciencesZeyu Jiao Inst. of Intelligent Manufacturing,Guangd

ong Academy of SciencesZhengyu Zhong Inst. of Intelligent Manufacturing,Guangd

ong Academy of SciencesSlipping is one of the main factors affecting the health of the elderly, especially in the humid bathroom environment. Failure to detect the elderly slipping in time may lead to more serious consequences. However, due to the privacy of the bathroom, it is not feasible to use ordinary video surveillance methods to monitor the elderly slipping in real time. In this research, we propose a slipping detection method based on depth sensors, which gets rid of the dependence on the video surveillance, can automatically detect the elderly slips and issue an alarm. First, based on the data collected by the depth sensors, we constructed a 3D scene point cloud representation without privacy information. Then, a pre-trained three-dimensional human body detection model is used to detect the accurate position of the human body. Finally, according to the coordinates of the 3D bounding box of the human body, it is judged whether the elderly has slipped. After verification on actual data sets, we achieved an accuracy rate of 98.4%, which not only ensures the privacy of the elderly, but also realizes timely slip alarm.

SunB07 Room07 Fractional Calculus and Fractional-order System (Special Session) 10:00-12:20 Chair: Dingyu Xue Northeastern Univ.

10:00-10:20 SunB07-1 470 Stock price forecasting based on fractional grey model with convolution and BP neural network Wenhua Dong Yunnan Univ.Chunna Zhao Yunnan Univ.The stock price reflects the economic trend of a stock's corresponding industry, and the stock price forecast can provide a reference for economic departments to formulate different policies. The fractional grey model with convolution(FGMC(1, m)) can solve linear problems with memory, and it will be very meaningful to predict the linear part of stock prices. Because BP neural network has excellent nonlinear fitting ability, it can be used to predict the nonlinear part of stock prices. This paper proposes a combination model of FGMC(1, m)-BP to predict the future price of stocks. Since the stock price is affected by many factors, the principal component analysis method is used to reduce the dimensionality of high-dimensional stock data during modeling. The experimental results show that the FGMC(1, m)-BP model is more accurate than the prediction results of a single FGMC(1, m) or BP neural network, and it has better prediction performance than the commonly used ARIMA-BP prediction model.

10:20-10:40 SunB07-2 484 H∞ Performance Robustness Analysis of Fractional-Order Systems with Structured Perturbations Qing-Hao Zhang Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing

Dong Yang China Academy of Space Tech.Yan-Long Wu China Academy of Space Tech.Jun-Guo Lu Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing

This paper considers the H ∞ performance robustness analysis of fractional-order systems with structured perturbations. Based on the assumption that the nominal fractional-order systems satisfies H ∞ performance, innovative conditions for checking whether the fractional-order systems with structured perturbations preserve the H∞ performance are given. Then, the explicit robustness bounds of preserving the H ∞ performance of fractional-order systems with structured perturbations are established. The results are derived by using µ-analysis. The final two numerical examples are provided to illustrate the obtained methods.

10:40-11:00 SunB07-3 681 A Novel Fractional-order Discrete Grey Model with Initial Condition Optimization and Its Application Yitong Liu Northeastern Univ.Feng Pan Northeastern Univ.Dingyu Xue Northeastern Univ.Jiwei Nie Northeastern Univ.Estimating the optimal order of fractional accumulating operator and the optimal initial value are two inherent problems in studies of fractional grey models. To find the optimal order of fractional accumulating operator, numerical iteration algorithm is applied in this paper. It is used in applications compared with the other two existing optimization algorithm (genetic algorithm and particle swarm optimization algorithm), which proves that the numerical iteration algorithm is simpler than the other two algorithms and has the same accuracy. At the same time, this paper proposes a numerical method to find the optimal initial condition and gives a proof. And based on methods mentioned previously, a novel fractional-order grey discrete model with initial value optimization is proposed. In order to test the new model, per capita power consumption in southern Jiangsu province is employed. Results show that the new fractional-order discrete grey model proposed in this paper is better than the competitive grey model in predicting and fitting periods.

11:00-11:20 SunB07-4 701 Feature Analysis of Snore Signals and Other Sound Signals Based on Complex Order Derivative Processing Jiangbo Zhao SINOPEC Research Inst. of Safety EngineeringXiaodong Wang SINOPEC Research Inst. of Safety EngineeringJunzheng Wang SINOPEC Research Inst. of Safety EngineeringThe key to snore recognition is to process snore signals and acquire snore features. There is often interference from other sounds during sleep, such as cough sounds and tapping sounds. The snore features extracted by the existing snore processing methods are not significantly different from the environmental noise during sleep, which leads to the complicated algorithm and low accuracy of snore recognition when there is environmental noise interference. In this paper, the complex order derivative was used to process snore signals and extract snore features. The experimental results showed that the snore signals processed by the complex order derivative were obviously different from the ambient noise, and it can be applied to snore recognition.

11:20-11:40 SunB07-5 1243 H∞ Model Reduction for Fractional-Order Linear Systems Guanyou Mo Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing

Junguo Lu Shanghai Jiao Tong Univ.Key Laboratory of System Control and Inform

ation ProcessingDong Yang China Academy of Space Tech.Yanlong Wu China Academy of Space Tech.This paper focuses on the H ∞ model reduction problem of fractional-order linear systems with commensurate fractional order 0 < α < 1. Firstly, resorting to the H∞ bounded real lemma, a design method based on the linear matrix inequalities is proposed to construct an asymptotically stable reduced-order model for the given stable fractional-order linear system, such that the H∞ norm of the error between the transfer function of the reduced-order model and the transfer function of the given stable fractional-order system is less than the prescribed H∞ performance. Secondly, by introducing a free-weighting matrix and congruent transformation, the system parameters of the reduced-order model are decoupled with the complex matrix variable and parameterized by another free-weighting matrix variable. Finally, one numerical example are given to illustrate the effectiveness of the proposed theoretical results.

11:40-12:00 SunB07-6 1244 Novel Robust H ∞ Stability and Stabilization Conditions for Fractional-order Systems with Convex Polytopic Uncertainties Hanru Tang Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing

Junguo Lu Shanghai Jiao Tong Univ.Key Laboratory of System Control and Inform

ation ProcessingDong Yang China Academy of Space Tech.

Technical Programmes CCDC 2021 This paper investigates the problems of robust stability and stabilization for fractional-order systems with polytopic uncertainties. It is assumed that the fractional-order α is a known constant and belongs to 0 < α < 1. Firstly, based on the H ∞ bounded real lemma for commensurate fractional-order control systems, a sufficient condition for the above stability problem is established in terms of linear matrix inequalities (LMIs). Secondly, on the foundation of this condition, sufficient LMI methods for the design of stabilizing controller are obtained for two cases where the polytopic coefficients are known and unknown. In the case of unknown polytopic coefficients, by introducing the additional matrices, the state matrix and the positive-definite Hermitian matrices are decoupled. In the case of known polytopic coefficients, by introducing parameter-dependent matrices, a less-conservative robust H ∞ stabilization condition is obtained. Finally, two different numerical examples are provided to compare the conservatism between the existing results and the results in this paper.

SunB08 Room08 Analysis and Synthesis of Cyber-Physical Systems 10:20-12:20 Chair: Hongli Li Tiangong Univ.CO-Chair: Duanjin Zhang Zhengzhou Univ.

10:20-10:40 SunB08-1 1030 Research on Comfort Measuring Device and Evaluation Algorithm of Clothing Hongli Li Tiangong Univ.Wei Guo Tiangong Univ.Boyu Yao Tiangong Univ.Guowei Chen Tiangong Univ.Ronghua Zhang Tiangong Univ.Lixiang Ma Tianjin JFOD Automation Tech. Co. LTDSmart clothing can monitor the wearer's physical condition in real time or protect the human body during special work. Clothing pressure, temperature, humidity, and the wearer's heart rate are important indexes of clothing comfort evaluation. Based on the low-power chip MSP430F149, a measurement system capable of simultaneously collecting these four parameters and a clothing comfort evaluation method based on these four parameters was developed. Thirty subjects were selected to measure four parameters under different postures. The collected ECG signal was filtered by the empirical mode decomposition, and the adaptive threshold detection algorithm improved the R wave detection efficiency. The fuzzy C-means clustering based on improved kernel function algorithm was used to intelligently analyze the average clothing pressure, extreme pressure difference, extreme temperature difference, extreme humidity difference and change rate of wearer's heart. The research results show that the system has the characteristics of low power consumption and portability. The accuracy of temperature, humidity, and heart rate is high. The proposed evaluation method can objectively and accurately evaluate the comfort of clothing and direct the clothing design.

10:40-11:00 SunB08-2 166 H-infinity Filtering for Cyber-Physical Systems with Random Multi-Step Transmission Delays via Delta Operator Mengkai Liu Zhengzhou Univ.Zheng Du Zhengzhou Univ.Jianxun Zhou Zhengzhou Univ.Duanjin Zhang Zhengzhou Univ.The H-infinity filtering problem for Cyber-physical systems (CPSs) with random multi-step transmission delays is investigated. The delta operator approach is used to solve the problem of numerical instability. A mathematical expression with stochastic processes is built to express random multi-step transmission delays. Sufficient conditions of stochastic stability for the filtering error system with H-infinity performance are given by linear matrix inequalities (LMIs) and Lyapunov-Krasovskii functional in delta domain. The explicit expression of the desired H-infinity filter is also obtained. At last, a target tracking system as the example is shown to indicate the effectiveness of the proposed approach.

11:00-11:20 SunB08-3 1662 Multiple-Relaxation-Time Lattice Boltzmann method for thermosolutal convection in Czchralski silicon crystal growth Huang Weichao Xi’an Univ. of Tech.Wang Jing Xi’an Univ. of Tech.A multiple-relaxation-time lattice Boltzmann Method (MRT-LBM) is established to study the thermosolutal mixed convection with multi-physical parameters in the process of silicon crystal growth. The experimental results show that the proposed model for solving the thermosolutal convection has correctness and rapidity in Czchralski (Cz) crystal growth. The melt temperature distribution and the oxygen concentration distribution under different Re numbers are studied in this paper, which come to the conclusion that the high Reynolds (Re) numbers are helpful to improve the uniformity of the thermosolutal distribution, but it cannot reduce the oxygen concentration effectively. Analysis of the oxygen concentration distributions under the different Cusp magnetic field intensities show that the external magnetic field can force the oxygen concentration gathered at the crucible wall and inhibit the content of oxygen impurity. The oxygen concentration decreases with the increase of Hartmann (Ha) numbers at the solid-liquid interface,

which indicates that the strong magnetic field can reduce the oxygen content of the crystal.

11:20-11:40 SunB08-4 521 A Priority Load-Aware Weighted Round Robin Scheduling Algorithm for Data Transmission Xukun Su Wuhan Univ. of Science and Tech.Xiaohui Li Wuhan Univ. of Science and Tech.Yuemin Ding Tianjin Univ. of Tech.Min Zhao Wuhan Univ. of Science and Tech.Zhenxing Liu Wuhan Univ. of Science and Tech.During data transmission, uneven distribution of network communication resources occurs frequently due to competition for limited network communication resources, which results in low efficiency of high priority data transmission. To solve the above problems, this paper proposes the priority load-aware scheduling round-robin (PLAWRR) scheduling algorithm. It firstly introduces the parameters of average data packet size and remaining queue length to weight calculation in order to allocate network communication resources in a balanced manner. Secondly, it dynamically adjusts the weight by quantifying the load priority to ensure that the high priority queue can transmit more efficiently. The simulation results show that the proposed algorithm reduces the scheduling delay and the packet loss rate of data transmission as well as increasing the throughput.

11:40-12:00 SunB08-5 929 Turing Instability of Malware Spreading Model with Reaction-diffusion in Cyber-physical System Shi Chen Nanjing Univ. of Posts and Telecommunicatio

nsMin Xiao Nanjing Univ. of Posts and Telecommunicatio

nsYunxiang Lu Nanjing Univ. of Posts and Telecommunicatio

nsShuai Zhou Nanjing Univ. of Posts and Telecommunicatio

nsGong Chen Nanjing Univ. of Posts and Telecommunicatio

nsConsidering the nonlinear saturated incidence rate and reaction-diffusion, this paper proposes a SIRS model to characterize the spread of malware in cyber-physical system (CPS). Firstly, the equilibriums and locally asymptotically stability conditions are given by the stability theorem of ordinary differential equation. After introducing diffusion, the Turing instability of the system is examined and the conditions for the appearance of Turing patterns are constructed. Finally, the theoretical analysis is verified by numerical simulation, which reveals that the introduction of reaction-diffusion may induce the Turing instability in CPS.

12:00-12:20 SunB08-6 1165 Secure State Estimation for Discrete CPSs with State Delay and Adversarial Attacks Man Zhang Qingdao Univ.Chong Lin Qingdao Univ.Bing Chen Qingdao Univ.In this paper, an observer is designed for cyber-physical systems (CPSs) modeled by a class of discretetime systems with state delay and sparse actuator and sensor attacks. In the design, the original system is rewritten into a new form, which provides the basis for estimating the actuator and sensor attacks. And then a Projection Operator combined with the notion of orthogonal complement matrix is utilized to determine the correct attack sets. In addition, based on the Krasovskii Stability Theorem, a Lyapunov-Krasovskii functional is chosen and a sufficient condition for the existence of the observer together with its design method is determined. Finally, a numerical example is given to verify the effectiveness of the designed observer.

SunB09 Room09 Fault Diagnosis and Predictive Maintenance (III) 10:20-12:20 Chair: Xin Huo Harbin Inst. of Tech.CO-Chair: Rui Yang Xi’an Jiaotong-Liverpool Univ.

10:20-10:40 SunB09-1 1388 LESO-based Fault Estimation for Actuators of Quadrotor UAV Changchun He Harbin Inst. of Tech.Xin Huo Harbin Inst. of Tech.Chao Wang Harbin Inst. of Tech.Songlin Chen Harbin Inst. of Tech.This paper proposes a fault estimation algorithm based on linear extended state observer (LESO) for the actuator fault model of a quadrotor UAV, where the altitude channel coupling is considered. The fault model of quadrotor UAV with loss of control effectiveness (LoCE) fault is transformed to facilitate decoupling and observer design. In addition, the extended state observer is designed for the altitude, roll, pitch, and yaw channel. The actuator fault is equivalent to the internal disturbance of the system, and the fault information is extracted from the estimated value of the system state and the total disturbance by the four channel extended state observers. Therefore, the LoCE fault coefficients

Technical Programmes CCDC 2021 of the four actuators are obtained. Finally, compared with the existing algorithm, the algorithm has the advantages of faster convergence speed and higher estimation accuracy by simulation.

10:40-11:00 SunB09-2 1545 A Novel Fault Diagnosis method for Rotating Machinery of Imbalanced Data Qi Han Shandong Univ. of Science and Tech.Xianghua Wang Shandong Univ. of Science and Tech.Rui Yang Xi’an Jiaotong-Liverpool Univ.In this paper, a novel classification approach for imbalanced data with high-dimensional and intra-class imbal- ance is proposed, and they applied to fault diagnosis of rotating machinery. It is noted that the most of existed work on imbalanced learning focus on the inter-class imbalance, and ignore the intra-class imbalance. To solve the classification of imbalanced data with high-dimensional and intra-class imbalance, we proposed an integrated data-based and feature- based algorithm, which combines hybrid feature dimensionality reduction with a varied density based safe level synthetic minority oversampling technique (VDB-SLSMOTE), transforming the imbalanced data into balanced data. The balanced data is classified by random forest, and the final experimental result verified the effectiveness of the algorithm.

11:00-11:20 SunB09-3 413 Fault Diagnosis Method for an Aircraft Attitude Control System Based on Deep Learning Xiaoli Luo Huazhong Univ. of Science and Tech.Wei Wang Beijing Aerospace Automatic Control Inst.Hui Hu Huazhong Univ. of Science and Tech.Zhongtao Cheng Huazhong Univ. of Science and Tech.Maoqin Tang Huazhong Univ. of Science and Tech.Bo Wang Huazhong Univ. of Science and Tech.Lei Liu Huazhong Univ. of Science and Tech.This article proposes a deep-learning-based fault diagnosis method for an aircraft attitude control system. The core idea is to construct two deep neural networks to approximate the mapping between actuator fault information and the real-time attitude generated by the dynamics. The proposed fault-diagnosis network consists of an input layer, a convolutional neural network (CNN) layer, a two-layer long and short-term memory network (LSTM), and a fully connected layer in series. The attitude quaternion and the attitude angular velocity are taken as the inputs of the networks, and the estimations of the bias fault size of each actuator are the outputs. Finally, numerical simulations are carried out to verify the proposed method and to make a comparison with the existing diagnose method. The simulation results show the effectiveness of the proposed method and the superiority over the compared method.

11:20-11:40 SunB09-4 436 Fault Modeling of Electro Hydraulic Actuator in Gas Turbine Control System Based on Matlab / Simulink Yanquan Liu North China Electric Power Univ.Lele Tian North China Electric Power Univ.Zhaoya Li North China Electric Power Univ.Wenguang Zhang North China Electric Power Univ.As a practical tool, gas turbine will inevitably be affected by various factors in normal operation. As the most important part of gas turbine control system, actuator has higher requirements for fault diagnosis model. However, the research on fault diagnosis of hydraulic actuator of gas turbine control system is relatively lacking at home and abroad. This paper is a fault model of electro-hydraulic actuator of gas turbine control system. First of all, through the working principle of the electro-hydraulic actuator mechanism modeling, and then compare and analyze the model to establish the electro-hydraulic actuator fault diagnosis model, Finally, in the MATLAB / Simulink platform to simulate the approximation of the mathematical model, get the dynamic response results.

11:40-12:00 SunB09-5 459 Fault Diagnosis of Diaphragm Pump Check Valve Based on Impulse and Cyclostationary Analysis Zezhong Feng Kunming Univ.of Science and Tech.

Yunnan Key Laboratory of Artificial IntelligenceXin Xiong Kunming Univ.of Science and Tech.

Yunnan Key Laboratory of Artificial IntelligenceXiaodong Wang Kunming Univ.of Science and Tech.

Yunnan Key Laboratory of Artificial IntelligenceExtracting sensitive features from vibration signals is still a challenge for intelligent fault diagnosis. Although the fault diagnosis methods have achieved great success in the past decade. However, most of the methods are not able to combine domain knowledge effectively, which leads to inadequate ability fault diagnosis in the actual working environment. To address this issue, we propose a novel fault diagnosis method for the diaphragm pump check valve based on impulse and cyclostationary analysis. Firstly, the wavelet packet transform is used to decompose each intercepted signal to enhance the hidden specific signal structure in the transform domain; then, the impulse and cyclostationary features of each frequency band are extracted separately to capture the inherent properties of the check valve in different working states; finally,

the extracted sample set is used as input to the classifier for pattern recognition. The results show that the proposed method can accurately identify the fault classes of the check valve.

12:00-12:20 SunB09-6 475 Learning the Multi-Grained Process Attributes for Industrial Fault Classification Han Zhou Chongqing Univ.Yanxia Li Chongqing Univ.Dandan Zhao Chongqing Univ.Hongpeng Yin Chongqing Univ.

State Key Laboratory of Power TransmissionEquipment and System Security and New

Tech.Yi Chai Chongqing Univ.

State Key Laboratory of Power TransmissionEquipment and System Security and New

Tech.The variability of abnormality not only stems from abnormal patterns, but also from process attributes, such as operation mode, material fluctuation. Leveraging these attributes can enhance diagnostic performance and be helpful at fault characteristic understanding. This paper proposes a novel fault diagnosis method which automatically ably captures the multi-grained attributes of the industrial process when learning fault features. Further, this paper also extends it into a semi-supervised manner. By incorporating limited prior knowledge, our model gives a more meaningful explanation of the diagnostic procedure. The experimental results of a multiphase flow process demonstrate the effectiveness of the proposed methods.

SunB10 Room10 Optimal Control and Optimization (III) 10:20-12:20 Chair: Hongqian Lu Qilu Univ. of Tech.CO-Chair: Jing Chu Xi’an Univ. of Posts and Telecommunications

Xi’an Key Laboratory of Advanced Controland Intelligent Process

10:20-10:40 SunB10-1 103 Stability Analysis of Event-Triggered Delayed Networked Control Systems Yao Xu Qilu Univ. of Tech.Renren Wang Qilu Univ. of Tech.Hongqian Lu Qilu Univ. of Tech.Xingxing Song Qilu Univ. of Tech.Hongwei Chen Ji Nan Building Source Cement Products Co.LTDThis paper discusses the problem of stability analysis of delayed networked control systems (NCSs). First of all, an event-triggered scheme (ETS) is introduced to save the limited network resources. Second, by constructing a Lyapunov-Krasovskii functional (LKF), which can make use of extended reciprocally convex matrix inequality (ERCMI), a new criterion for stability analysis and control synthesis of event-triggered NCSs is obtained. Finally, the feasibility of the scheme is verified by a numerical example.

10:40-11:00 SunB10-2 1186 Distributed Trajectory Optimization for Time-Optimal Reconfiguration of Multi-Agent Formation Jing Chu Xi’an Univ. of Posts and Telecommunications

Xi’an Key Laboratory of Advanced Controland Intelligent Process

Ruixia Liu Xi’an Univ. of Posts and TelecommunicationsXi’an Key Laboratory of Advanced Control

and Intelligent ProcessThis paper presents a distributed algorithm for the minimum-time reconfiguration of multi-agent formation. The reconfiguration problem is formulated as an optimal control problem where the manifold of terminal states includes parameters to be optimized. The proposed algorithm lays its foundation on the control vector parametrization (CVP) method, the sequential convex programming (SCP) method, and the distributed alternating direction method of multipliers (D-ADMM). By using the CVP method, the original optimal control problem is firstly transcribed into a nonlinear programming problem, which can then be solved by the SCP method. To achieve the distributed implementation the SCP method is integrated with D-ADMM. The effectiveness of the algorithm is demonstrated in a case study of the time-optimal formation reconfiguration of four satellites. A comparison with the global optimization technique shows that the algorithm outputs the global minimum for the studied case.

11:00-11:20 SunB10-3 70 Ascent Phase Trajectory Optimization for Missile with Booster Based on the Gauss Pseudospectral Method Chengchao Li Zhejiang Univ.Zhanglin Chen Zhejiang Univ.In this paper, the ascent phase trajectory optimization problem for missile with two-stage booster is studied. Firstly, in consideration of the coupling relationship between the aerodynamic and the state of the missile, such

Technical Programmes CCDC 2021 as speed, altitude, and center of mass, the effect of rudder is introduced to establish a modified model of aerodynamic parameters. Secondly, in view of the discontinuous thrust when dispensing, a strategy is designed to select the switch-flight-state. In this regard, considering the constraints such as overload, dynamic pressure, and terminal trajectory, the trajectory optimization design of the missile ascending phase is carried out based on the adaptive pseudo-spectrum method. Finally, the simulation analysis verifies the effectiveness of the method.

11:20-11:40 SunB10-4 694 An accelerated distributed optimization algorithm over time-varying digraphs with column-stochastic matrices Xiasheng Shi China Univ. of Mining and Tech.Hanlin Liu China Univ. of Mining and Tech.Jiahao Chen China Univ. of Mining and Tech.Xuesong Wang China Univ. of Mining and Tech.In this paper, the unconstrained distributed convex optimization problem over time-varying unbalanced directed graphs with column matrices is considered. To accelerate the existing distributed algorithm, a heavy-ball based convex optimization algorithm is proposed and its convergence proof is provided by the small gain theorem. Moreover, a nesterov acceleration method is added in the previous algorithm for further accelerating the convergence rate. Finally, some simulations are presented for illustrating the effectiveness.

11:40-12:00 SunB10-5 832 A Novel DPS Control of Dual Active Bridge DC-DC Converters to Minimize Current Stress and Improve Transient Response Jian Ding Xi’an Jiaotong Univ.Geng Li Xi’an Univ. of Tech.Hang Zhang Xi’an Jiaotong Univ.Aiming Zhang Xi’an Jiaotong Univ.Jingjing Huang Xi’an Jiaotong Univ.Dual-Active-Bridge (DAB) DC-DC converter are widely used in energy conversion for its attractive features such as reduced weight, high power density and bidirectional energy flow. The traditional control strategy of DAB faces the problems of large current stress, resulting in low efficiency and slow transient response. This paper proposes an optimal control strategy based on dual-phase-shift (DPS) control, which can minimize the current stress of DAB with wide range of input and output voltages by calculating the optimal phase shift angles. In this way, the current stress will be decreased significantly, especially when the system works in light-load state. Meanwhile, to improve transient response, the direct power control strategy is adopted, which takes capacitance current into consideration. Finally, the simulation results validate the superiority of the proposed DPS control strategy.

12:00-12:20 SunB10-6 889 Bicriteria Exploratory Mean-Variance Portfolio Selection Problem in Continuous Time Heng Zhang Shandong Univ.This article studies a continuous-time mean-variance (MV) portfolio selection problem with reinforcement learning (RL). The MV problem is described by an exploratory, relaxed stochastic control problem, which is first proposed and developed by Wang et al. (2020) and Wang and Zhou (2020). Different from Wang and Zhou (2020), we minimize the variance and maximize the expected terminal return at the same time in the context of reinforcement learning, which is a generalization of Wang and Zhou (2020). By constructing an auxiliary problem, the optimal feedback control (or “policy” or “law”) for our problem is obtained, which is Gaussian with time-decaying variance.

SunB11 Room11 Intelligent Control, Computation and Optimization (III) 10:20-12:20 Chair: Xiuling Zhang Yanshan Univ.

Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

CO-Chair: Fudong Nian Hefei Univ.

10:20-10:40 SunB11-1 657 Reinforcement Learning Combined with Heuristic Search for Solving Discrete Space Path Planning Problems Xiuling Zhang Yanshan Univ.

Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

Xuenan Kang Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

Kailun Wei Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

Jinxiang Li Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

Kai Ma Key Laboratory of Industrial ComputerControl Engineering of Hebei Province

Reinforcement learning (RL) has been successfully applied to solve path planning problems, but learning is generally slow. The main reason is not making full use of information collected during interaction with the environment. This paper proposes a novel method to solve the discrete

space path planning problem in an environment without prior knowledge with intensive obstacles based on RL and heuristic search. Firstly, we apply Dyna-Q algorithm of RL to explore the map and search for the target point and optimize its policy with upper confidence bound (UCB). Then, when the target point is found, we use heuristic search to plan the path from the starting point to the target point and narrow the path to a small range. Finally, we combine Dyna-Q algorithm with the heuristic search recommended path for path planning. We evaluate our algorithm using maze navigation problem. The results verify that heuristic search accelerates Dyan-Q convergence.

10:40-11:00 SunB11-2 1024 Conditional Generative Adversarial Defogging Algorithm Based on Polarization Characteristics Jingjing Zhang Anhui univ.

Key Laboratory of Polarized Light Imaging andDetection Tech. in Anhui Province

Kangsheng Bao Hefei No.6 High SchoolXin Zhang Anhui univ.Fudong Nian Hefei Univ.Teng Li Anhui univ.

Key Laboratory of Polarized Light Imaging andDetection Tech. in Anhui Province

Yuzhou Zeng Anhui univ.To overcome image degradation under the conditions of haze, and fog, a method using conditional generative adversarial defogging algorithm based on polarization characteristic is proposed. Four original images with different polarization angles were obtained from the original image, and then the polarization images were characterized using Stokes vectors. From the relationship between Stokes vector and polarization image, each polarization image with different angles is input into the same network to extract features. At the same time, the enhanced network is used to extract the characteristics of the fog area in the polarization image, and the polarization information is extracted by layer jump connection, which is fused with the image features of different angles. By constructing the loss function and obtaining the optimal solution, the fog-free image is finally reconstructed. Experimental results show that a clear image can be reconstructed in fog situations by using conditional generative adversarial network-based on polarization characteristics, and the structural similarity is improved by about 10%, the peak signal to noise ratio is increased by about 0.5.

11:00-11:20 SunB11-3 1014 Neural Optimal Control for Discrete-Time Asymmetric Constrained Nonlinear Systems via DHP Technique Jin Ren Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Beijing Inst. of Artificial IntelligenceDing Wang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Beijing Inst. of Artificial IntelligenceMingming Zhao

Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemBeijing Inst. of Artificial Intelligence

For discrete-time nonlinear affine systems with asymmetric constraints, an adaptive optimal control algorithm is established in this paper, which is implemented through dual heuristic dynamic programming (DHP). A novel nonquadratic performance functional is presented to overcome the problem caused by asymmetric constrained inputs. Then, under the adaptive critic framework, three neural networks are constructed for implementing the DHP algorithm, which are designed to approximate the nonlinear system, the costate function, and the control law, respectively. Furthermore, by conducting a simulation example with randomly given initial state vectors, the excellent applicability of the present method is demonstrated.

11:20-11:40 SunB11-4 346 Group consensus of heterogeneous multi-agent systems with leader and time delay in switching networks based on delta operator method Xingcheng Pu Chongqing Univ. of Posts and TelecommunicationsHua Yang Chongqing Univ. of Posts and TelecommunicationsChunlian Peng Chongqing Univ. of Posts and Telecommunications

Center for systems theory and applicationsLi Ren Chongqing Univ. of Posts and TelecommunicationsShiyu Li Chongqing Univ. of Posts and Telecommunications

Center for systems theory and applicationsQi Wu Chongqing Univ. of Posts and Telecommunications

Center for systems theory and applicationsJinfeng Wan Chongqing Univ. of Posts and Telecommunications

Center for systems theory and applicationsIn this paper, leader-following group consensus of multi-agent systems with cooperative- competitive interaction and time delay in switching topologies networks is investigated. A new leader-following group consensus protocol is proposed for the heterogeneous multi-agent system which is composed of first-order and second-order agents. Based

Technical Programmes CCDC 2021 on the leader-following group consensus protocol and delta operator theory, the group consensus of the continuous multi-agent systems can be transformed into determining the stability of a system with time-delay. By constructing Lyapunov functional and using LMI(Linear matrix inequality) technique, some sufficient conditions are given to realize the group consensus of this multi-agent systems. Several numeric simulations are given to illustrate the effectiveness of the theoretical results.

11:40-12:00 SunB11-5 354 Study on Characteristic Model based Linear Active Disturbance Rejection Controller for a Class of Nonlinear Systems Xun Chen eijing Univ. of Chemical Tech.Dazi Li eijing Univ. of Chemical Tech.The control problem of high-order nonlinear systems has always been a research hotspot in the field of control science. Linear active disturbance rejection controller (LADRC) regards the part which is different from the canonical form as the total disturbance, and estimates and compensates in real time, which has good control performance for nonlinear systems. However, for nonlinear systems with high-order modes, it is difficult to tune the parameters of high-order LADRC. It is just that characteristic model can compress the high-order information into the time-varying parameters of the low-order model without loss of high-order information, and LADRC based on low-order characteristic model is more convenient in implementation and parameter tuning. Therefore, in this paper, a first-order characteristic model is established first for a class of nonlinear high-order systems, then forgetting factor recursive least-squares (FFRLS) algorithm is used to obtain the parameters. Finally, based on the identified parameters and the first-order characteristic model, a first-order LADRC is designed. In order to verify the performance of the proposed method, comparative simulation has been done for a class of nonlinear system. The result shows that proposed method has faster tracking ability and stronger disturbance rejection ability than PID and golden section adaptive control (GSAC).

12:00-12:20 SunB11-6 109 Prediction of Dioxin Emission Concentration from Municipal Solid Waste Incineration Process Based on PSO and Equispaced Interpolation Dandan Wang Beijing University of Technology

Beijing Key Laboratory Computational Intelligenceand Intelligent System

Jian Tang Beijing University of TechnologyBeijing Key Laboratory Computational Intelligence

and Intelligent SystemZihao Guo Beijing University of Technology

Beijing Key Laboratory Computational Intelligenceand Intelligent System

Junfei Qiao Beijing University of TechnologyBeijing Key Laboratory Computational Intelligence

and Intelligent SystemAs currently widely used municipal solid waste resource treatment method, the municipal solid waste incineration (MSWI) process emit dioxins (DXN) compounds with high toxicity and persistent pollution characteristics. It is one of the main reasons that cause incineration power plants to have a "Not in my back yard". At present, the long-period, high-cost offline detection method used in industrial sites cannot achieve real-time monitoring of DXN emission concentration. Moreover, the number of samples used to build a DXN emission concentration prediction model is extremely scarce. Aim at the above problems, a method for predicting DXN emission concentration in MSWI process based on PSO and equispaced interpolation is proposed. At first, the domain of original small sample input and output is expanded based on the improved mega-trend-diffusion (MTD) technology. Then, the equal interval interpolation method is used to generate the virtual sample inputs, which are used to obtain the virtual sample outputs by combining the mapping model. The above results are combined with the expansion space to delete the bad virtual samples. Thirdly, the PSO algorithm is used to optimize and select the reduced virtual sample set. Finally, a mixed sample set composed of the optimized selected virtual sample set and the original small sample set is used to construct a DXN emission concentration prediction model. The effectiveness of the proposed method is verified with the actual detection data of DXN for many years in a MSWI plant.

SunBIS Room12 Interactive Session 10:20-12:20

SunBIS-01 176 Amazon Chess Based on UCT-PVS Hybrid Algorithm Zhuoxuan Li Southeast Univ.Chenhui Ning Southeast Univ.Jinde Cao Southeast Univ.

Yonsei Univ.Zhengyu Li Big Fish Tournaments (Beijing) Network

Technology Co., Ltd.Amazon Chess is a game with complexity between Go and Chinese Chess. Its complexity is mainly due to its extremely large branching factor, which makes it difficult to reach a high depth in the search process.

This paper adopts the hybrid search algorithm combines with Upper Confidence bounds Trees search algorithm (UCT) and Principal Variation Search (PVS), which is called UCT-PVS hybrid search algorithm. The UCT algorithm is used at the opening stage to enable the game player to obtain a more advantageous opening.The PVS combines with Historical Heuristics and Transposition Table, by reducing the pruning window, the pruning efficiency is effectively increased and greatly improves the search depth. The Amazon Chess software is developed by using this technology and is effectively improved its game level.

SunBIS-02 369 Research on improvement of UCT based on static evaluation Jiajun Wang Shenyang Aerospace Univ.Yuxia Sun Shenyang Aerospace Univ.Qian Feng Shenyang Aerospace Univ.NoGo was born in the National University Student Machine Game Competition. It is a new chess that has only emerged from recent years. At present, there are relatively few studies on it, and the existing methods are not perfect. There are three main methods currently used which are Monte Carlo method, UCT algorithm and static evaluation method. But it is usually a single algorithm application, which has shortcomings. In this article, the author uses the static evaluation method to optimize the program of the UCT algorithm based on the rules and characteristics of NoGo. The experimental results show that this method has higher accuracy and shorter search time than either method alone.

SunBIS-03 1000 The Total Number Calculation of States of Chinese Chess Based on the Dynamic Programming of Computer Games Gui Wu Jianghan Univ.The space complexity of Chinese chess is a primary index for analyzing the complexity of Chinese chess, which is a counting problem of calculating the number of states of Chinese chess. Given the features of Chinese chess, this problem can be divided into several subproblems that can be solved by dynamic programming to obtain a precise solution of the total number of states of Chinese chess. Our results show that the total number of states of Chinese chess mentioned in previous papers is inaccurate and much higher than the actual number of states. Finally, the main idea of the counting method was summarized based on dynamic programming, and illustrations for some uses of the method were provided.

SunBIS-04 1001 Random placement algorithm of Military game based on Fisher-Yates Kai Tong Shenyang Aerospace Univ.Jiehong Wu Shenyang Aerospace Univ.Jiawei Yang Shenyang Aerospace Univ.Jing Li Shenyang Aerospace Univ.Junyi Zhang Shenyang Aerospace Univ.Linyue Wang Shenyang Aerospace Univ.Computer game [1] is a research field, an important research direction in the field of artificial intelligence, and an important scientific research foundation in the field of machine intelligence, wargame deduction, intelligent decision system and other artificial intelligence. Machine game is considered to be one of the most challenging research directions in the field of artificial intelligence. As a member of computer game, military game is a game with incomplete and asymmetric information. One of the important links is that both sides need to play according to their own wishes in advance. It can be said that in the computer Military game, the layout affects the outcome of the whole game to a certain extent. In order to solve the problem of layout choice in practice, this paper proposes a random layout algorithm based on Fisher Yates shuffle algorithm, which can be used to generate absolutely random military chess layout, test the existing layout and program strength, and find the most suitable layout for the determined combat strategy and style. Compared with the previous method of generating a large amount of test data randomly, it can solve the problem. The new algorithm optimizes the original 25! times to 50 times, and optimizes the algorithm from O (n2) of Fisher algorithm to O(n), so researchers can have more energy to find the best way for different strategies, which objectively strengthens the combat capability of the program. And it weakens the influence of human factors in the test and reduces the dependence on human intelligence in computer game.

SunBIS-05 1004 Innovative Application of Genetic Algorithms in the Computer Games Jun Tao Jianghan Univ.

Rowan Univ.Gui Wu Jianghan Univ.Zhentong Yi Jianghan Univ.Peng Zeng Jianghan Univ.The traditional methods to solve the computer games problem are to use various search algorithms on the game search tree and to combine the situation evaluation to generate the corresponding methods. The paper introduces the innovative genetic algorithms to the computer games based on the traditional methods. Through the operations for the search

Technical Programmes CCDC 2021 tree such as selection, crossover and mutation, the better search tree can be gotten from the better solutions. At the same time, the application of the evaluation functions can produce the best steps in the current situation. The advanced and modified genetic algorithms are proved to be practical and applicative by experimentations and tests of computer games.

SunBIS-06 1498 The parallel optimization based on the PVS algorithm and research on the evaluation function in the Game of the Amazons Haoyu Wang Shenyang Aerospace Univ.Hongkun Qiu Shenyang Aerospace Univ.The PVS search function, as a current mainstream and efficient algorithm, has been widely used in various kinds of chess program. We applied the parallel search function based on the PVS and improved the running speed of the program. At the same time, we also did some research and experiments on the evaluation function of Amazon chess which provided a set of available Amazon evaluation functions and parameter adjustment results for reference. Key Words: Computer Game, Amazon Game, PVS, Parallel Programming, Evaluation Function.

SunBIS-07 1536 Research on Thread Optimization and Opening Library Based on Parallel PVS Algorithm in Amazon Chess Jingyuan Ju Shenyang Aerospace Univ.Hongkun Qiu Shenyang Aerospace Univ.FuShuai Wang Shenyang Aerospace Univ.XueJie Wang Shenyang Aerospace Univ.YaJie Wang Shenyang Aerospace Univ.This paper introduces the main algorithms used in Amazon game system,and mainly studies the number of threads in parallel PVS algorithm based on OpenMp.In view of the low utilization of computer hardware by the current algorithm, this paper improves the efficiency of the algorithm by changing the number of parallel algorithm threads to be equal to the number of CPU cores, and draws a conclusion that the multi-core resources can be maximized when the number of parallel algorithm threads is equal to the number of CPU cores.This paper studies the design of the opening Library of Amazon chess, puts forward the design idea of the opening Library of Amazon chess and the necessity of the opening library system.

SunBIS-08 1651 The optimization and improvement of bridge game system Yifan Song Shenyang Aerospace Univ.Hongkun Qiu Shenyang Aerospace Univ.Yajie Wang Shenyang Aerospace Univ.Xiaodong Zheng Shenyang Aerospace Univ.This article from the background of the bridge, improving the system analysis, experimental test three perspective, based on the idea of reinforcement learning, from two aspects of contract and scoring ability, through a large number of calculations and the original program and every time a new system of winning IMP value level to achieve the agreed order, accumulate experience, design a set of good rewards and punishment mechanism, greatly improve the efficiency of the bridge to play CARDS and intelligence, thus the bridge game system was optimized and improved. In addition, the traditional methods need to manually extract the features of poor expansibility, this paper combined with reinforcement learning algorithm, the ideas of game devised a new system, under the condition of different effective play the computer, the program has also reached a higher level of the game structure design, for the incomplete information game theory provides a reasonable method, application creates opportunities to people living in the future.

SunBIS-09 7 Named entity recognition in the food field based on BERT and Adversarial training Zhe Dong North China Univ. of Tech.Ruo-Qi Shao North China Univ. of Tech.Yu-Liang Chen North China Univ. of Tech.Jia-Wei Chen North China Univ. of Tech.Aiming at extracting effective entity information from unstructured corpus in the food field, a named entity recognition (NER) method based on BERT (Bidirectional Encoder Representations from Transformers) and Adversarial training is proposed. The task of NER requires both the identification of entity boundaries and entity types. In order to improve the precision of identifying entity boundaries, we use the BERT word embedding method to enhance the feature extraction ability of input information. To optimize the NER task, adversarial training is introduced, which not only use the shared information obtained from task training of Chinese word segmentation (CWS) and NER, but also prevent the private information of CWS task from generating noise. The experiment is based on the corpus of two categories which are Chinese food safety cases and People's Daily news, respectively. Among them, the Chinese food safety cases data set is used to train the NER task, and People's Daily news data set is used to train the CWS task. We use adversarial training to improve the precision of the NER task for entity recognition (including person, location, organization, food and toxic substance). The

Precision rate, Recall rate and F1 score are 95.46%, 89.50% and 92.38% respectively. Experimental results show that this method has a high precision rate for Chinese NER task where the boundary of a specific domain is indistinct.

SunBIS-10 71 A dynamic outlier ensemble for databases in wind tunnel experiments Hongyan Zhao Northeastern Univ.Dong Yu Shenyang Inst. of Computing Tech.In wind tunnel experiments, a portion of abnormal samples, which are usually referred to as outliers, often contaminates databases. These outliers are detrimental when applying these databases to system identification or process control. To this end, an efficient outlier detection model dedicated to wind tunnel becomes thus necessary. In literature, several outlier ensembles have been proposed and proved more effective than single detectors. However, all these outlier ensembles are static. Sometimes, they are too robust and conservative to discover all outliers. In order to improve the detecting performance, in this paper, we propose a dynamic outlier ensemble. In our dynamic model, we assign the most competent base detector to each test pattern. To accomplish such assignments, we estimate competences of all base detectors by computing their performance on a local region of the corresponding test pattern. Several datasets from real-life wind tunnel experiments are used to validate the proposed outlier detector. The superiority has been approved by comparing our detector with several competitors.

SunBIS-11 76 Change Trend Mutation Point Analysis of Operational Effectiveness of Equipment Systems Hongfa Ke Space Engineering Univ.Jiahui Chen Space Engineering Univ.Change trend law of operational effectiveness is an important basis of weapon equipment system. Research on the trend mutation point of the operational effectiveness data sequence of the equipment system was studied based on the two-dimensional regression mutation analysis method. Combined with data analysis on interconnection and interoperability of equipment system, reconnaissance ability, and command and control levels, the response relationship was found out between the trend mutation in the operational effectiveness of the equipment system and interconnection and interoperability of equipment systems, reconnaissance ability, and command and control levels etc. The analysis results not only show that the operational effectiveness of the weapon equipment system is related to interconnection and interoperability of equipment systems, reconnaissance ability, and command and control levels closely, but also quantify the position of the trend break in the degree of influence. This method provides a new way for the quantitative analysis of the operational effectiveness of the weapon equipment system and its influencing factors.

SunBIS-12 217 A Learning Efficiency Evaluation Model for E-Learning Platforms Based on Analytic Hierarchy Process (AHP) Jinxin Sun Univ. of Chinese Academy of SciencesLijun Fu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Junming Liu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Jingkai Wu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Yongze Chen Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.With the rapid development of education informatization, the number of students taking E-learning courses is growing gradually. The evaluation for students’ E-learning efficiency and ability is a significant direction of education informatization. Based on the data of students' E-learning behaviors generated on the platform, this paper establishes a comprehensive evaluation indicator of E-learning efficiency by integrating 4-aspect E-learning behaviors. Upon this requirement, a comprehensive learning efficiency evaluation model based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE) methods is proposed. An AHP method is used to determine the weight value of E-learning behavior indicator to learning efficiency evaluation objective, and then to establish a hierarchical learning efficiency evaluation model. Moreover, a FCE method is used to calculate the learners' comprehensive learning efficiency from different evaluation perspectives. Through cases of comprehensive learning efficiency calculation, the general steps of learning efficiency evaluation are explained, thus verifying the feasibility and effectiveness of the method in student learning efficiency evaluation.

SunBIS-13 451 FToA-mine:An Algorithm for Frequent Trajectory Discovery of Aircraft Target Biying Zhang Navy Engineering Univ.Yuanguo Cheng Navy Engineering Univ.A novel algorithm named FToA-mine was proposed in this paper to discover frequent trajectory of non-cooperative targets such as opponent

Technical Programmes CCDC 2021 aircrafts in military reconnaissance information system. The presented FToA-mine algorithm is based on a tree data structure (named FToA-tree) and a conditional search function. The FToA-tree was designed to efficiently store initial trajectory patterns and facilitate succedent processing. Simulations demonstrate efficiency and validity of the proposed algorithm.

SunBIS-14 582 An Improved Capsule Network-based Embedding Model for Knowledge Graph Completion Jun Li Chongqing Univ. of Posts and TelecommunicationsJie Hou Chongqing Univ. of Posts and Telecommunications Chunyu Zhou Chongqing Univ. of Posts and TelecommunicationsKnowledge graphs are structured representations of information in the world. However, the current knowledge graphs suffer from incompleteness, which can be inferred from existing information. A popular approach of knowledge graphs (KGs) completion involves learning a low-dimensional representation for the semantics of entities and relations and using them to predict new facts. Recently, several proposed models suggest that convolutional neural network (CNN) based models gain more expressive feature embedding and thus achieve state-of-the-art performance on KGs completion, such as ConvE. Nevertheless, some feature of the embedding of entities and relations are easily lost in these models. In this paper, we introduce an embedding model, named HCapsE, utilizing an improved capsule network(CapsNet) to predict missing links knowledge graphs. Experiments demonstrate that HCapsE achieves better prediction performance than previous embedding models on three commonly used datasets WN18RR, FB15k-237, and YAGO3-10.

SunBIS-15 651 An improved evolutionary density peak clustering algorithm Peng Li National Univ. of Defense Tech.Haibin Xie National Univ. of Defense Tech. Tianrui Jiang National Univ. of Defense Tech.Jiading Yan National Univ. of Defense Tech.Aiming at the problem that traditional density peak clustering algorithm can’t adapt to discover knowledge in the database with dynamic growth of data volume, in this paper, we propose an improved evolutionary density peak clustering algorithm (EDPC). The EDPC algorithm does not need any prior knowledge, and can select the control parameters according to the comprehensive index of the sample itself, which overcomes the defect that there is no reference in the selection of prior parameters in the current incremental algorithm. EDPC algorithm is based on incremental data, which effectively solves the problem that traditional DPC algorithm can not adapt to the dynamic change of data. At the same time, with the addition of new samples, EDPC algorithm can accurately find the cluster center, and adjust the control parameters according to the generated graph, so as to decide whether to generate new clusters or merge old clusters. Finally, experiments on UCI and Synthetic datasets show that EDPC algorithm is able to outperform incremental K-means and incremental DBSCAN algorithm.

SunBIS-16 761 Multi-view Spectral Clustering Based on Low-rank Tensor Decomposition Qingjiang Xiao Northwest Minzu Univ.Shiqiang Du Northwest Minzu Univ. Yixuan Huang Northwest Minzu Univ.Recently, graph-based multi-view clustering has received widespread attention due to its simplicity and efficiency. As we all know, clustering results are highly dependent on data similarity learning. If the constructed similarity graph is poor-quality, the generated clustering results may also be poor-quality. Traditional multi-view learning algorithms only focus on extracting common information from multi views and ignore the unique information and high-level correlations of each view. To overcome these shortcomings, in this paper，we propose a multi-view spectral clustering based on dynamic nearest neighbor learning and low-rank tensor decomposition (DNLTSC) . Specifically, on the one hand, our model learns view-specific affinity matrix by assigning the adaptive and optimal neighbors for each data point based on the local distances. On the other hand, our model constructs a tensor containing the affinity matrix of each view, thereby retaining the information of each view. A tensor nuclear norm based on tensor singular value decomposition (t-SVD) is used to constrain the rank of the target tensor. By minimizing the tensor nuclear norm, a tensor that contains the shared information of each view and has high-level correlation is learned. An alternation direction method of multipliers (ADMM) is used to optimize our proposed method. Extensive experiments on six datasets corresponding to four different types and compared with several state-of-the-art multi-view clustering algorithms, the proposed algorithm can effectively enhance the complementarity and high-order correlation between views, and has good accuracy and robustness.

SunBIS-17 771 GS-SVR: Analysis and Prediction of Henan Province Grain Production Using Support Vector Regression

Jiadi Wu Hefei Inst. of Physical ScienceUniv. of Science and Tech. of China

Yuanyuan Wei Anhui Univ. He Huang Hefei Inst. of Physical ScienceChinese government always attaches great importance to food security. The grain output of Henan Province is of great significance to guarantee China's food security. We select the Henan Province Food Production Data Set from 1990 to 2018 in the Henan Statistical Yearbook, including total grain output, sown area, irrigation area, etc. We propose a GS-SVR model to analyze and predict grain production in Henan Province. At the same time, the traditional support vector regression (SVR), Random Forest (RF), Gradient Boosting Decision Tree (GBDT) three methods are compared in this experiment, the experiment shows: The accuracy of the GS-SVR model for predicting grain output in Henan Province exceeds 96%. The GS-SVR model performs better than the other three models.

SunBIS-18 843 A Data-driven Message-based Coverage Estimation Framework for Air to Ground Downlink Communication Yougen Zhang National Univ. of Defense Tech.Jian Huang National Univ. of Defense Tech. Xiaoshuang Wang National Univ. of Defense Tech.Kai Yan National Univ. of Defense Tech.It is essential for a flying aircraft to report its position, movement trend and status to the command-and-control station on the ground in real time by means of air-ground communication. However, the coverage quality of air-ground communication is very complex and changeable because it may be affected by many factors. Comprehensive study and accurate estimation of aircraft’s coverage performance is helpful to ensure smooth communication, and provide guidelines for the deployment, operation, optimization or even improvement of the air-ground communication system. This work presents a data-driven message-based coverage estimation framework, it utilizes the data records of messages transmission by the aircraft and that of message reception at each receiving node to estimate the communication coverage.

SunBIS-19 949 A Method for Measurement Data Modeling and High-Dimensional Outlier Detection Based on Large Dimensional Matrix Gang Chen China Academy of Engineering PhysicsHuanhuan Fan China Academy of Engineering Physics Baoran An China Academy of Engineering PhysicsIn the research of Large Scale Networked Control Systems, the real-time analysis of the big data from the measurement of thousands of light, machinery, electrical components and sensors is the inevitable requirements of Networked Control Systems. How to analyze outliers from massive and high-speed measurement data among thousands of nodes in the whole network, and how to find outliers from mass data is an important research topic of scientific big data mining. The Curse of Dimensionality makes many existing methods of outlier detection no longer valid for high-dimensional dataset. In this paper, we propose a local outlier detection factor based on weighted subspace and further propose an effective outlier detection method for high-dimensional data. The method firstly recognizes the local neighbor-space of each data point according to its KNN, and then calculate the sparse factor and subspace weighted vector, which can effectively reflect the local outlier factor and outlier-correlated subspace. After that, an effective outlier detection algorithm for high-dimensional dataset is proposed. we conduct extensive experiments to validate the correctness and evaluate the effectiveness of the proposed algorithm on the real-world dataset.

SunBIS-20 965 Application on small sample classification of ER rule classifier Xiaoyan Wang National Univ. of Defense Tech.Jianbin Sun National Univ. of Defense Tech.Qingsong Zhao National Univ. of Defense Tech.Most classical classification methods require a large number of samples to be trained, so the classification accuracy will decline when the sample size is not large enough. Considering the parameters of the recently proposed Evidential Reasoning rule (ER rule) classifier can be set according to experience and expert knowledge, a small sample classification method based on the ER rule classifier is proposed in this study. The classification steps of ER rule classifier contain evidence acquisition, reliability calculation, aggregation of activated evidence, and optimization of the adjustable parameters. And the case study about sleep/wake classification is conducted, including experiments of classification by small sample and 10-fold cross-validation based on ER rule classifier, and classification by small sample based on six classical classification methods. Finally, the feasibility and effectiveness of the ER Rule classifier to classify small samples with high accuracy are verified by analyzing the results.

SunBIS-21 966 Pairwise comparison-based approach for predicting the ranking of global innovation capability Ruijing Cui National Univ. of Defense Tech.Jianbin Sun National Univ. of Defense Tech.

Technical Programmes CCDC 2021 Jiajun Cheng PLA Academy of Military ScienceKewei Yang National Univ. of Defense Tech.The Global Innovation Index (GII) is employed to rank the innovation capability for all countries and economies in the world, which was proposed by Cornell University, the INSEAD, and the World Intellectual Property Organization. To analyze the development status of innovation capability of a country or economy at any time, this paper proposes a pairwise comparison-based global innovation capability ranking prediction approach named PairwiseGIIRank. The experimental results show that: (1) The ranking of innovation capability can be predicted with PairwiseGIIRank effectively. (2) Compared with other classifiers, the approach performs the best when the k-nearest neighbors algorithm is used as its internal classifier. (3) To achieve the desired predictive effect, at least 5 indicators are needed. The ranking of innovation capability of countries and economies around the world can be predicted with PairwiseGIIRank before the GII report is released. It provides decision-making support for decision-makers to adjust their innovation capability development policies.

SunBIS-22 996 Event Representation and Research on Formalizing Representation Model Based on Ontology Bihui Yu Shenyang Inst. of Computing Tech.Chang Liu Shenyang Inst. of Computing Tech.

Univ. of Chinese Academy of SciencesXuezhen Zhang Shenyang Inst. of Computing Tech.

Univ. of Chinese Academy of SciencesDongsheng Yang Shenyang Inst. of Computing Tech.Based on the knowledge graph to describe the process knowledge, there are deficiencies. Based on the theoretical basis of the knowledge graph and the affair map, the author proposes a new ontology for the industrial Internet of Things field—the Internet of Things perception and control ontology to solve the large number of temporal and spatial characteristics and states in domain knowledge. Semantic modeling of the X-ray single crystal diffractometer using t the Internet of Things (IoT) perception and control ontology (IoT-PCO) to describe its automatic operation mechanism. The research results show that the ontology can effectively solve the problem of semantic representation of perception and control data, and provide support for complex semantic flow processing driven by affair knowledge.

SunBIS-23 998 Construction of Military Knowledge Graph Based on Paper Bibliographic Data Dandan Song Beijing Inst. of Tech.Yuan Li Beijing Inst. of Tech.Qinglin Wang Beijing Inst. of Tech.In order to solve the problems of sparse data distribution, weak correlation between data, and difficulty in efficient use of data in the retrieval process of academic research content in the military industry, a method for constructing military knowledge graph based on bibliographic data of military disciplines is proposed. This method designs the structure of the knowledge graph according to the characteristics of the bibliographic information of the paper, which uses information-rich paper titles and keywords to study entity recognition, entity classification, knowledge storage and display. Finally, based on the idea of distributed storage, a knowledge graph service system is built, which improves the intuitiveness and relevance of knowledge, and provides certain significance for academic research in the military field.

SunBIS-24 1100 Automatic generation of 3D models based on architectural drawings Zizhao Wang Shenyang Jianzhu Univ.Hui Zhong Shenyang Jianzhu Univ.Due to the high complexity of architectural drawings and the uncertainty of the drawing situation, there is no perfect method to build 3D models based on architectural drawings to meet the needs of all applications, and there are many problems in the generation of 3D model data and the integration of existing models. In this paper, for the first time, the geometric characteristics and geometric relationships of line types in construction structure drawings (CSDs) are analyzed and studied, and a method for identifying architectural elements in floor plans based on line types is proposed, and the construction of 3D models is completed. Using the feature that space is the smallest unit in the building space information model, the constitutive relationship between each building element that constitutes the space can be found. The algorithm uses graph theory to construct undirected weighted graph, describes the 3D spatial location of the building by identifying the geometric information of the lines in the construction structure drawing(CSD), and generates the corresponding 3D model based on the stored geometric data. The experimental results prove that the 3D building model can be generated quickly and accurately after effective construction structure drawing detection.

SunBIS-25 1111 A classification based on random forest for partial discharge sources

Senlin Pu Wuhan Univ. of Tech.Huajun Zhang Wuhan Univ. of Tech.Cuimin Mao Hubei Electric Power Survey and Design Inst.

Co. LTDGuang Yang Hubei Electric Power Survey and Design Inst.

Co. LTDThe identification of Partial Discharge Sources (PD) is an important task in the monitoring and diagnosis of high voltage components, and the classification of their discharge sources is extremely important. In this paper, three major features of Partial Discharge Sources have been extracted and various machine learning algorithms are applied to classify them. The final experiments in implementing the classification of partial discharge sources show that Random Forest is more robust to noise compared to decision trees and AdaBoost, and runs at a speed comparable to AdaBoost.

SunBIS-26 1177 A Biological Test Questions Naming Entity Recognition Method for Fusion Triggers Mengliang An Univ. of Chinese Academy of SciencesZhijun Chang Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Lijun Fu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Junming Liu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.MengFei An Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech.Training a neural model for named entity recognition (NER) in a new field often requires additional human annotations (for example, a large number of tagged instances), which are often expensive and time-consuming to collect. Therefore, one of the key research problems is how to obtain the supervision effect in an economical and effective way. In this article, we use "entity triggers" for the biological domain, which can help explain how to effectively learn the label of NER's model. Entity triggers are defined as words or words in a sentence, which helps explain why people recognize entities in a sentence. For biological disciplines, according to the characteristics of the high school biology field test data, in a small, small amounts of annotation and trigger a small entity tagging of self-built corpora in the biology one experiment, the experimental results show that using "triggers the matching network can automatically learn" trigger said and soft matching module, which can be easily generalization for the tag will be invisible to the sentence, then use the two-way coding BiLSTM, finally with the airport marking label according to conditions. The experimental results show that this model has a better recognition effect than other algorithms, and can effectively solve the problems of entity recognition difficulty and low accuracy in the task of named entity recognition in the field of biological science, such as insufficient labeling data, and the special ranking of biological science.

SunBIS-27 1383 The Research on Classification of Small Sample Data Set Image Based on Convolutional Neural Network Gen Li Northeastern Univ.Tiancheng Zhang Northeastern Univ.Fangling Leng Northeastern Univ.This paper is aimed to study the problem of small sample image recognition. We use the initialization method of dictionary filter to replace the traditional Convolutional Neural Network (CNN) initial filter method so that the dictionary filter can learn its model structure during the CNN training process and the CNN model can extract a more Multi-effective feature map. For the image classification stage, using GRNN to replace BP neural network can enhance the ability of model classification processing, accelerating the speed of model convergence, and comprehensively improving the recognition accuracy of the model. This article extracted a small amount of data in large data set for experiments, and use the ORL face database as verification simultaneously, the experimental results and analysis are given. Compared with the fine-tuning through migration learning, it has increased by 3.84%, compared with the use of fully connected classifiers, it has increased by4.58%, which proves the accuracy of the proposed method.

SunBIS-28 1467 A tactical maneuver trajectory prediction method using gate recurrent unit based on triangle search optimization with AdaBoost Zhenglei Wei Air Force Engineering Univ.

China Aerodynamics Research & Development Center

Shangqin Tang Air Force Engineering Univ.Xiaofei Wang Air Force Inst. of ResearchYongbo Xuan Air Force Inst. of ResearchYintong Li Air Force Engineering Univ.Lei Xie Air Force Engineering Univ.Andi Tang Air Force Engineering Univ.Peng Zhang Air Force Engineering Univ.In order to obtain accuracy trajectory of target aircraft in UCAV air combat process, a maneuver trajectory prediction method is designed in this paper. To describe the intention knowledge, the tactical maneuver trajectory units are defined according to trajectory characteristics

Technical Programmes CCDC 2021 parameters. Based on the definition of units, the tactical maneuver trajectory prediction model is divided into tactical maneuver trajectory unit prediction module and maneuver trajectory point prediction. To solve prediction model, gate recurrent unit based on triangle search optimization with AdaBoost is proposed. Though fusing the two nodules, the tactical maneuver trajectory prediction method using TSO-GRU-Ada is designed. The simulation results for two case air combat data show that the proposed method is effective and meets the real time of air combat.

SunBIS-29 1487 Modeling Method of Small Sample Data and Optimization in Ultrasonic Extraction Process of Botanicals Qingyu Ma Beijing Univ. of Chemical Tech.Ruihan Luo Systems Engineering Research Inst.Juan Chen Beijing Univ. of Chemical Tech.Aiming at the small sample problem in the dual-frequency ultrasonic extraction process of liquorice, this paper proposes a virtual sample generation method to expand small sample data. This method uses Box-Behnken Design (BBD) to design experiments, in order to collect original small sample data for modeling. Based on this forecasting model, data is generated. Moreover, by adding the Firework-Differential Evolution Algorithm with Lévy Flight (L-FW-DE), reasonable and effective virtual samples are obtained and selected in the iterative process to expand the sample set. Synthetic samples, consist of virtual samples and original small samples, are applied to establish a prediction model for dual-frequency ultrasonic extraction of liquiritin from liquorice. Then, the optimal process parameters are obtained by an optimization algorithm. Finally, the experiment of extracting liquiritin by dual-frequency ultrasound verifies that under these optimized parameters, the extraction rate is the highest. Results of the experiments show that this method can efficiently generate reasonable virtual samples, and the synthetic sample composed of small samples and virtual samples can enhance the accuracy of the prediction model. The reliable optimal process parameters which are obtained by the forecasting model can provide guidance for the extraction process under the small sample condition.

SunBIS-30 1489 ECUL-Miner: Efficiently mining high utility closed itemsets Yue Zhai Dalian Inst. of Science and Tech.Qiyun Xu Dalian Inst. of Science and Tech.Lin Li Dalian Inst. of Science and Tech.Lijuan Wang Dalian Inst. of Science and Tech.High utility itemset mining (HUIM) provides more information, which can help manager to make better decisions. However, the results of HUIM are often huge sets, especially in dense datasets or small threshold. To solve this problem, this paper proposed an approach for mining high utility closed itemsets (HUCIs) efficiently with no loss of information, whose number of elements is significantly smaller than HUIM. This approach includes two phases: (1) proposing an extended utility list structure for storing itemset information in a compact form. (2) The proposed algorithms called ECUL-Miner utilize efficient data structures and backward and forward checking techniques to combine candidates. It also makes use of merging similar transactions to reduce storage space. We carry out experiments to show the efficiency of the proposed method. The experimental results show that this new approach is more efficient in than existing methods for both dense and sparse datasets.

SunBIS-31 1496 A novel oversampling method for imbalanced classification Liyun Zhang Sichuan Univ.Yiting Luo Sichuan Univ.Imbalanced data classification is a topic which continues to garner attention in the field of machine learning. SMOTE algorithm has proven to improve the performance of imbalanced classification in many real-world applications, and it is also considered as one of the most influential sampling methods in the field of machine learning. When there are noise samples or class overlaps in the data set, SMOTE algorithm is easy to introduce synthetic noisy instances to further amplify the influence of noisy instances. To address this problem, many researchers have proposed variants of SMOTE to optimize this algorithm. Unlike most improved algorithms which use the nearest neighbor rule to filter noise, this paper proposes a noise filtering mechanism based on classifier ensemble, which is combined with SMOTE algorithm. In this paper, ten real datasets and noisy datasets are used for experiments, and six improved algorithms of SMOTE are selected for comparison. The experimental results show that the proposed method outperforms the original SMOTE algorithm and other comparative algorithms, and can show better results in noisy datasets.

SunBIS-32 1563 A Metaphor Recognition Model based on LSTM and Keyword Similarity Computation Zhiheng Chen Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing technologyLijun Fu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing technology

Hongjun Wang Univ. of Chinese Academy of SciencesShenyang Inst. of computing technology

YuJiang Liu Univ. of Chinese Academy of SciencesShenyang Inst. of computing technology

Metaphor, as a common figure of speech, can reflect literary grace and profoundness to a certain extent, and is an important indicator in article appreciation. However, at present, there is no perfect application model in automatic recognition. According to the three structures and basic principles of metaphorical sentences, this paper designs a metaphorical sentence recognition model based on LSTM and keyword similarity from the perspective of the sentence itself and its syntactic structure. It solves the problems that LSTM model cannot quickly extract the feature structure of metaphorical sentences and keyword similarity computation ignores the context semantics. The word vector and part of speech tagging are input into the multi-input cyclic neural network, and a recognition method based on LSTM model is designed. On the basis of the metaphorical comparison theory, by using the extracted metaphor tenor and vehicle, this paper improves the computation method of word similarity in CNKI, and designs a recognition method based on keyword similarity. The two, LSTM and keyword similarity, are integrated, with the comprehensive consideration of metaphorical sentences in structure, semantics and context. Finally, the model is verified in the collected corpus files, thus achieving high accuracy.

SunBIS-33 62 Intelligent Disease Diagnosis Only Based on Symptom Data Fangfang Luo Zunyi Medical Univ.Xu Luo Zunyi Medical Univ.Jiechao Lu Zunyi Medical Univ.People concern relationships between symptoms and diseases intuitively when seeking medical advices. To facilitate medical triage and common disease diagnosis, according to relationships between symptoms and diseases in medical records, intelligent disease diagnosis methods based on two machine learning methods, the neural network and the support vector machine (SVM) are given. In proposed methods disease identifications are carried on in three layers, which are main disease category identification, subclass disease type identification and specific disease identification. Performances of the algorithms are tested and the effectiveness is proved in experiments.

SunBIS-34 130 A Neural Network Based on WXLNet and Multi-Task Lable Embedding for Sentiment Analysis Chenxi Xie Nanjing Univ. of Posts and TelecommunicationsZhongyi Meng Nanjing Univ. of Posts and TelecommunicationsBo Song Nanjing Univ. of Posts and TelecommunicationsGuoping Jiang Nanjing Univ. of Posts and TelecommunicationsYurong Song Nanjing Univ. of Posts and TelecommunicationsSentiment analysis is an important field in natural language processing, the development of which has been greatly promoted by deep learning recently. However, previous studies focused on the structure of the model, and did not make full use of text semantics and label information. Moreover, the traditional model does not perform well in complex fine-grained sentiment classification. In this paper, we propose a new deep neural network model WXLNet-MTLE. By modifying XLNet language model, the utilization of text information and the language expression ability have been greatly improved. At the same time, we add Multi-Task Lable Embedding to improve the generalization ability of our model in its downstream sentiment analysis tasks. Comparative analysis is carried out on the data sets of 3 different tasks and 8 different scenarios. The experimental results show that WXLNet-MTLE performs better in sentiment analysis of multiple scenes than the other models.

SunBIS-35 191 Enhanced Unsupervised Data Augmentation for Emergency Events Detection and Classification Xiaomeng Liu Chinaso Inc.

State Key Laboratory of Media Convergence Production Tech. and Systems Xinhua News Agency

Fei Long Chinaso Inc.State Key Laboratory of Media Convergence Production Tech. and Systems Xinhua News

AgencysKun Huang Chinaso Inc.

State Key Laboratory of Media Convergence Production Tech. and Systems Xinhua News Agency

Qiang Ling Univ. of Science and Tech. of ChinaWe propose to jointly detect and classify emergency events using a multi-class text classifier, which is a typical deep learning architecture with transformer modules and particularly employs Bidirectional Encoder Representations from Transformers (BERT). Deep learning requires a large number of labeled data to work. Meanwhile, deep learning often implements the semi-supervised learning (SSL) method, which is able to use massive unlabeled data to improve performance of supervised deep learning. As an effective SSL variant, unsupervised data augmentation (UDA) focuses on data augmentation techniques to improve the performance of deep learning. We present an enhanced version of UDA (EUDA) by mixing more data augmentation strategies and using a problem related prefilter. Our EUDA targets at emergency event detection

Technical Programmes CCDC 2021 and classification. Considering that emergency events always have time and location elements, text can be filtered based on this semantic feature. We propose to use semantic feature aided enhanced unsupervised data augmentation to solve the concerned problem. Empirical studies on the dataset prepared for the task validates that the proposed EUDA can achieve significantly better performance than supervised learning with a limited size of labeled data. Experiments are also carried out on a text classification task, which confirms that EUDA improves performance for BERT neural network.

SunBIS-36 338 PredictingWater Quality Based On EEMD And LSTM Networks Dingyuan Zhang Univ. of Science and Tech. of ChinaRenkai Chang China Water Sunny Data Tech. Company LimitedHaisheng Wang China Water Sunny Data Tech. Company LimitedYong Wang Univ. of Science and Tech. of ChinaHao Wang China Inst. of Water Resources and Hydropower

ResearchShaoqing Chen Information Science Laboratory Center of USTCWater is crucial for all types of life. The security of water quality genuinely influences human wellbeing, fishery economy and agrarian. Considering the characteristics of water quality are dynamic, nonlinear and complex, a novel water quality prediction combined model based on Ensemble Empirical Mode Decomposition (EEMD) and Long Short-Term Memory (LSTM) network is proposed in this paper. In the pactical work, the proposed model was trained by using the Yangtze River water quality history data which was measured daily from September 2009 to November 2017. The dataset of water quality was pretreated by ensemble empirical mode decomposition. For several imf subsequences, a LSTM submodel is established for each subsequence, and the prediction result of each submodel was added to compose the final prediction result. Finally, to obatain the illustration and verification, the method is compared with other traditional network models on practical experiments. The experimental results demonstrated that the proposed model has the best performance.

SunBIS-37 497 Research on Intelligent Analysis Approach of Waterflooding for Mature Fields Deli Jia Petro ChinaJiqun Zhang Petro ChinaQuanbin Wang Petro ChinaLuo Xin Daqing OilfieldXuan Wu Technical Univ. of BerlinWater flooding development for mature fields is faced with serious issues, such as complex injection-production relationship, frequent changes of the displacement field and serious ineffective water circulation. In order to address these issues, the fourth-generation zonal water injection technology is developed to realize real-time monitoring and optimal control. A large amount of data has been obtained, and thus it is urgent to carry out detailed intelligent analysis of mature-field water flooding development to better tap the remaining oil. An intelligent optimization system driven by dynamic and static reservoir data to control zonal water injection operations is constructed in this paper. Data mining and artificial intelligence algorithms are combined to establish a reservoir latent variable system. Then big data and physical laws are integrated within a “data-physics” dynamic model to obtain fluid saturation and pressure field. Automated history matching methods assisted with big data technology continuously deepens the understanding of reservoir heterogeneous physical properties and complex geological features, accordingly realizing lifetime prediction of mature field development, analyzing real-time effectiveness of zonal water injection and adjusting the reservoir development scheme.

SunBIS-38 774 Research on Oversampling Algorithm for Imbalanced Datasets Based On ARIMA Model Gang Chen Dalian Maritime Univ.Xiaomei Guo Dalian Maritime Univ.Imbalanced data classification is an important research topic in machine learning field. Conventional classification algorithms are not ideal for imbalanced datasets. In this paper, we propose an oversampling algorithm based on time series forecasting model. Based on the randomness of the data, the minority class data are transformed into time series. Then according to the particularity of time series forecasting, a series of tests before modeling are performed on the minority class data, which ensure that the conversion sequences conform to the principle of time series modeling. After that, the minority class data are oversampled through the fitted ARIMA model, so that the dataset is balanced. Finally, selecting eight datasets from UCI and KEEL repositories, the proposed algorithm is compared with other oversampling algorithms and the decision tree classifier is used to perform classification experiments. Experimental results show that the proposed algorithm is more effective than other algorithms.

SunBIS-39 789

Driver Drowsiness Detection Based On ResNet-18 And Transfer Learning Shuailei Ma Northeastern Univ.

Key Laboratory of Medical Imaging Calculation of the Ministry of Education

Tingxuan Huang Northeastern Univ.Key Laboratory of Medical Imaging Calculation of the

Ministry of EducationXiaojie Sun Northeastern Univ.

Key Laboratory of Medical Imaging Calculation of the Ministry of Education

Ying Wei Northeastern Univ.Key Laboratory of Medical Imaging Calculation of the

Ministry of EducationTo solve a common problem that the recognizing accuracy of vehicle drivers’ tiredness system depends too much on a large number of training datasets, which are hard to acquire, we present a model that introduces the transfer learning theory and the Resnet-18 model. Firstly, after profoundly analyzing the drivers’ head and facial expressions in a drowsy situation, we define three drivers’ essential fatiguing characteristics and construct a system to collect them. Afterwards, we focus on setting up datasets and building a system to recognize and classify fatiguing characteristics based on the Resnet-18 transfer learning neural network model. Finally, the experiment’s result verifies the effectiveness of proposed algorithms. It shows that the system’s accuracy reaches 98.05%, which meets the standard of high accuracy.

SunBIS-40 984 Macrosomia Prediction Based on Deep Learning Algorithm Tairan Tang Beijing Univ. of Aeronautics and AstronauticsWeihong Wang Beijing Univ. of Aeronautics and AstronauticsNewborns weighing more than 4000g after birth are collectively called macrosomia. Macrosomia can cause many adverse effects on the mother and the fetus itself. If it is possible to complete the prenatal predictive assessment of a macrosomia before the fetus is born, especially in the early pregnancy, has an important meaning. On the other hand, the combination of traditional clinical medicine and deep learning can reduce the burden of doctors and pregnant women to a certain extent, so as to achieve better diagnosis and intervention. This article applies the method of deep learning to realize the prediction of macrosomia based on pregnancy data. First, using the database provided by the partner with maternal medical indicators, through the database screening and data preprocessing, including digitization, processing of missing values, vectorization and standardization to obtain training datasets. Then we experiment on different machine learning algorithm for comparison, and build a deep neural network dense fully connected layer model, choose the appropriate loss function, activation function and optimization algorithm to achieve higher prediction accuracy. The final goal is to form a macrosomia forecast algorithm to assist the doctors make a diagnose.

SunBIS-41 1056 Discrete-Time Zero-Gradient-Sum Algorithm for Distributed Optimization over Directed Networks Xinyi Zhao Xidian Univ.Weifeng Gao Xidian Univ.Jin Xie Xidian Univ.This paper proposes a discrete-time Zero-Gradient-Sum (ZGS) algorithm over directed networks. It is used to solve the distributed convex optimization problem, which is involved in distributed learning algorithms over directed networks under the background of big data. The proposed ZGS algorithm converges to the globally optimal solution and achieves exponential convergence, whose validity is proved by both theory and numerical simulations.

SunBIS-42 1114 Chinese Electronic Medical Record Named Entity Recognition based on FastBERT method Jianyong Tuo Beijing Univ. of Chemical Tech.Zhanzhan Liu Beijing Univ. of Chemical Tech.Qing Chen Beijing Univ. of Chemical Tech.Xin Ma Beijing Univ. of Chemical Tech.Youqing Wang Shandong Univ. of Science and Tech.Chinese Electronic Medical Record Named Entity Recognition (CNER) is to identify and extract the entities related to medical and clinical practice from electronic medical records and classify them into pre-defined categories. In the past few years, deep learning methods have been applied to CNER and have achieved remarkable results, especially the BERT pre-training model. the BERT model can achieve good results, but the high model’s training cost and slow inference speed are unbearable. In order to solve these problems, scholars use various methods to compress the BERT model, such as knowledge distillation and architecture adjustment. In this article, FastBERT is improved and applied to CNER. The sample adaptation mechanism of this model is used to pick up the inference speed. It is learned from experiments that this method can not only improve the reasoning speed of entity recognition, but also maintains good performance.

SunBIS-43

Technical Programmes CCDC 2021 1141 "Financial Automation, Intelligentization, Digitalization" Empowers the Informationization of Our Military's Financial at High-Quality Beibei Zhuang National Defense Univ.Shuang Liu Equipment Department of Air ForceSenlu Peng National Defense Univ.With the development of intelligent financial technology, the construction of military financial informatization continues to accelerate. Financial automation, intellectualization, and digitalization are the future development directions of financial informatization, which have a major impact on promoting the high-quality development of our military’s financial information. This article expounds the basic content of military financial informatization construction, analyzes in detail the development status and existing problems of military financial informatization construction, and proposes the development direction of “automation, intellectualization, and digitalization” of military finance in the next ten years.

SunBIS-44 1219 An Auto-encoding model for 3D object surface reconstruction Chengliang Dang Wuhan Univ. of Science and Tech.Yongli Yang Wuhan Univ. of Science and Tech.Bin Chen Wuhan Univ. of Science and Tech.As the development of deep learning continues to mature, automatically generating object surface shapes from 3D point cloud data has gradually become a research hotspot in the field of 3D reconstruction. Recent methods rely on the encoder-decoder structure to create a point cloud feature representation from the input data, and then learn the mapping of the 2D parameter space to the 3D surface to decode the features. AtlasNet network is representative of this method, which can generate high-resolution surfaces. Based on the principle of parametric 3D surface reconstruction of Auto-encoding model, this paper proposes a new Auto-encoding network for 3D surface reconstruction. First, PointNet++ as the new encoder is used to extract the local features of the point cloud. After the local features are grouped into larger units, higher-dimensional features are obtained through the network, and combined with the local features of each point, the global feature of point cloud is obtained after pooling. The sampling points of the 2D square are then embedded on the global features as input to the decoder, and finally a surface with high resolution can be generated.

SunBIS-45 1375 A Semantic Trajectory Mining System Based on Deep Neural Network Xinyuan Zhang Univ. of Science and Tech. LiaoningJiying Peng Univ. of Science and Tech. LiaoningXinqi Zhang Univ. of Science and Tech. LiaoningAt the beginning of 2020, epidemic of COrona VIrus Disease 19 (COVID-19) broke out. During the epidemic prevention and control, artificial intelligence, big data and other technologies have become powerful weapons against the epidemic, and have been widely used in the fields of epidemic tracing, confirming virus transmission path, resource allocation and so on. In this study, BiLSTM-CRF model, Bootstrap and Tornado frameworks are used to implement a neural network-based semantic trajectory mining system for the COVID-19 patients. On the basis of collecting the data published by the health committees of various provinces and cities, the semantic trajectories of the patients are extracted to ensure the accuracy of the data and then establish mapping relationship between the real space and the text description of the trajectories of the patients, while taking the time and space factors into account and excavating the dynamic changes of the patients.

SunBIS-46 1504 FPNet: Fusion Attention Instance Segmentation Network Based On Pose Estimation Lei Pi Wuhan Univ. of Science and Tech.Jin Wu Wuhan Univ. of Science and Tech.Instance segmentation is a very challenging task. The standard approach to image instance segmentation is based on object detection, such as Mask R-CNN. It can’t be good at handling the occlusion between people. Moreover, the edge information is not rich enough, and the capture of useful information in the feature extraction process is not efficient. In order to solve these problems, we propose FPNet: a fusion attention instance segmentation network based on pose estimation. We add the Split-Attention module to the backbone, so in the feature extraction process, the focus position can be automatically selected according to our needs, and a more distinguishable feature representation can be generated, which can improve the performance of the entire network. In order to obtain richer edge information, we added the Point-based Rendering module to the segmentation module. The module can efficiently calculate high-resolution segmentation images, so the edge contours of the final output can be clearer. To verify the performance of the network, we tested it on COCOPerson(the person category of COCO) and OCHuman(Occluded Human). On COCOPerson validation set, FPNet reached 0.555 AP. On OCHuman validation set, our method reached 0.549 AP. On OCHuman test set, FPNet reacheded 0.544 AP.

SunBIS-47

1638 Analysis of Progress in Research on Community Mining Based on Bibliometrics Shuai Hou National Univ. of Defense Tech.Jichao Li National Univ. of Defense Tech.Jiang Jiang National Univ. of Defense Tech.Mengjun Li National Univ. of Defense Tech.Xueming Xu National Univ. of Defense Tech.In recent years, with the rapid development of science and technology, network science research has entered a new stage. Community mining, as an important aspect of network science research, is playing an increasingly important role. It is of great significance for beginners, relevant researchers, and even policy makers to grasp the whole context of community mining and keep up with the latest developments. Based on Web of Science data, this paper first presents the community mining field as a whole by means of bibliometrics, from the perspectives of time distribution, geographical distribution, and discipline distribution. Then, seven indexes for the published scientific papers are selected from the four aspects of quantity, quality, timeliness, and balance to explore the core research institutions and core authors in the field of community mining. The results show that the core research institutions are Indiana University, the University of New South Wales, the Chinese Academy of Sciences, etc., and the core authors are Olaf Sporns, Santo Fortunato, Santo Fortunato, and so on. Finally, based on the word frequency analysis and topic clustering, this study conducted a topic analysis in the community mining field. The main research topics in this field are applications in the fields of biology and social networks, and research on the theories and methods, along with their application in emerging fields. The analysis results could be used as significant references for further research in the field of community mining.

SunBIS-48 105 Equilibrium and welfare analysis under the intertemporal personal carbon trading scheme Zhimiao Tao Sichuan Univ.Personal carbon trading (PCT) is a hypothetical mechanism that commits to carbon reduction in the con- sumption sector. This paper introduces the intertemporal trading mechanism into the PCT. Based on the consumer utility theory, optimization models are employed to describe the consumers’ choices. The equilibrium price is obtained by using the Lagrangian multiplier method. The influences of the trading parameters on the equilibrium price are analyzed. These results provide advisable insights for the policy-makers to build the potential PCT market in the future.

SunBIS-49 260 Evaluation of cultivated land ecological security in Changchun based on set pair analysis model Gaohang Li Aviation Univ. of Air ForceYaozong Dai Aviation Univ. of Air ForceZhongbin Tang Aviation Univ. of Air ForceThis paper takes Changchun City as the empirical object, takes the land use change survey data and statistical yearbook as the data support, refers to the relevant planning and standards, adopts the methods of set pair analysis model, obstacle degree model and grey prediction model, and makes use of ArcGIS, MATLAB and other software technology platforms. This paper makes qualitative and quantitative evaluation on the historical evolution of cultivated land ecological security level in Changchun City from 2011 to 2020 and the development trend in the following five years, and analyzes and judges the obstacle factors that hinder the ecological security of cultivated land in Changchun City Based on relevant data.

SunBIS-50 340 Research on Prize-Collecting Vehicle Routing Problem Considering Dynamic Pickup Demands Xiong Zhao Shenyang Aerospace Univ.Lin Li Shenyang Aerospace Univ.Ying Chen Shenyang Aerospace Univ.Considering the current orders could not be fully served in a day time and dynamic pickup demands may appear randomly, this paper discussed the prize-collecting vehicle routing problem(PCVRP) with soft time windows, simultaneous pickup and delivery, and dynamic pickup demands (DPCVRP). It established a mathematical model and proposed a heuristic algorithm to solve it. The algorithm was based on the adaptive large neighborhood search algorithm, which embed tabu search algorithm. Simulation experiments results proved the model's rationality and the algorithm's effectiveness.

SunBIS-51 445 Analysis of Regional Innovation and Entrepreneurship Activity Based on IE-MAI Wei Wang Harbin Univ. of CommerceYanan Yang Harbin Univ. of CommerceTaking 31 provinces in China as the research object, this paper constructs the index system of regional innovation and entrepreneurship activity based on the perspective of environment, input, output and

Technical Programmes CCDC 2021 effectiveness. Select the relevant indicators of 16 dimensions of innovation and entrepreneurship in different regions in 2018. This paper introduces the double critical value theory of Alkire and Foster into the measurement of regional innovation and entrepreneurship activity, and constructs the multi-dimensional innovation and entrepreneurship activity index (IE-MAI). The contribution rate of each dimension index to activity was analyzed. The results show that: each dimension index has a significant effect on the regional innovation and entrepreneurship activity. At the same time, according to the weight sensitivity analysis, R&D investment intensity has a significant role in promoting regional innovation and entrepreneurship activity. Therefore, while improving the input of relevant indicators. It can be considered to further enhance the intensity of R&D investment. And then promote the balanced development of innovation and entrepreneurship in all regions of the country.

SunBIS-52 507 Credit Risk Assessment of Chinese Listed Companies Based on SVM Improved by Shuffled Frog Leaping Algorithm Xiaoqiang Ma Northwest Univ.Chong Wei Northwest Univ.Jinmian Han Northwest Univ.In the previous studies, when using Support Vector Machine Model to predict corporate credit risk, a problem of improper parameter values was often existed that lead to low classification accuracy. This article introduces the Shuffled frog leaping algorithm to improve the parameters of the SVM, and selects the relevant data of listed companies in China to build the model. The results show that the SVM improved by the Shuffled Frog Leaping Algorithm has higher accuracy and better effects on the credit risk assessment of listed companies.

SunBIS-53 1610 The Systematic Construction of Global Science and Technology Innovation Center in China: Strategy and Path MingLei Ding China Academy of Science and Tech. for

DevelopmentNing Huang China Academy of Science and Tech. for

DevelopmentPlanning and building science and technology innovation(STI) centers is an important measure to deal with the challenges of the future technological revolution and enhance China's national competitiveness. Based on the research on the development trend of science and technology and innovation in the future, this paper analyzes the characteristics of the development of the STI centers driven by the new round of technological revolution and industrial transformation; and according to the new requirements imposed on the STI centers by China's efforts to rapidly turn itself into an innovative country and a scientific and technological power, puts forward the ideas and path China needs to build STI centers by 2035; and provide some suggestions on making strategic arrangements for enhancing regional innovation with a focus on the STI centers.

SunBIS-54 1611 Study on the Decoupling Relationship between Economic Growth and Ecological Environment in Tianjin Xin Tong Northeastern Univ.

Central Univ. of Finance and EconomicsXuesen Li Shenyang Polytechnic CollegeLin Tong Dalian Maple Leaf College of Tech.This paper uses the decoupling relationship model to study the relationship between economic growth and carbon emissions in Tianjin from 2001 to 2017. The results show that economic growth has been in a state of oerall growth in Tianjin. The change of carbon emission intensity has been in a state of fluctuation, and the average value of economic growth and carbon emissions in Tianjin is significantly higher From the specific situation of each year, Tianjin's economic growth and carbon emission intensity are mainly in the weak decoupling and strong decoupling state, which proves that Tianjin has given enough attention to the sustainable development of economy. Finally, according to the results of the empirical analysis, the paper puts forward the corresponding policy suggestions for Tianjin's economic growth and carbon emission governance.

SunBIS-55 1630 Research on the Application of Blockchain Technology in the Collaborative Governance of Beijing-Tianjin-Hebei Ecological Environment Xin Tong Northeastern Univ.

Central Univ. of Finance and EconomicsXuesen Li Shenyang Polytechnic CollegeLin Tong Dalian Maple Leaf College of Tech.The problems of ecological environment restrict the high-quality integrated development of Beijing-Tianjin- Hebei region. At present, blockchain is setting off an upsurge of innovation in the world, and China has also proposed the development strategy of blockchain power. Under this background, the construction of Beijing-Tianjin-Hebei ecological environment collaborative governance mechanism based on blockchain

technology is of great significance to create a good environment for blockchain innovation, promote the high-quality development of the real economy and promote the sustainable development of the real economy It is of great significance to promote the coordinated development of regional economy. The government should give full play to its guiding role in technological innovation, make overall planning and reasonable layout for the collaborative governance mechanism of Beijing Tianjin Hebei ecological environment based on blockchain technology, start from the path of environmental protection and innovation drive, realize the construction of the collaborative governance mechanism of Beijing Tianjin Hebei ecological environment based on blockchain innovation, and improve the contribution of the collaborative development of Beijing-Tianjin-Hebei ecological environment.

SunBIS-56 320 Evolutionary game of tripartite subjects in Chinese stock market driven by the input of regulatory resources Kun Zhai Shandong Univ. of Finance and EconomicsGuoqing Zhao Shandong Univ. of Finance and Economics

Shandong Key Laboratory of Blockchain FinanceFangliang Huang Shandong Univ. of Finance and EconomicsSi Yan Shandong Univ. of Finance and EconomicsIn order to explore the influence of the difference of regulatory resources on the environment of the stock market. This paper constructs an evolutionary game model of the three players in the stock market, namely, the regulators, the manipulators and the followers, and the evolutionary paths of the tripartite agent behavior in different complex scenarios are also analyzed. The results show that there are three kinds of asymptotically stable states. Further simulation shows that there is a chaotic effect in the investment of regulatory resources; the improper investment of resource makes the stock market fall into the dilemma of ”good times and bad”; and the pursuit of regulatory resources should be persisted for a long time. The simulation results provide a reference for regulatory authorities to make effective policy decisions.

SunBIS-57 729 Evolution of fairness subjected to circle interaction on directed networks Wei Chen Anhui Univ.This study aims to investigate how symmetry breaking of roles affects the evolution of fairness. Apart from bidirectional interaction, we study the evolution of fairness by using directed networks. In this sense, one player suggests how to split a resource with the other one with the connection of outgoing edge, meanwhile receives a offer proposed by a third player. We construct three kinds of circle interactions to explore the origin of evolutionary fairness, including bidirectional interaction, triangle circle interaction and long circle interaction. Our work demonstrates that role symmetry induced by indirect interaction suppresses the evolution of fairness. It is found that increasing circle distance will inhibit the evolution of fairness. Our investigation may shield light on the origin of fairness in human evolution.

SunBIS-58 1152 Military Game Analysis Based on the Graph Model for Conflict Resolution Bin Zhao National Univ. of Defense Tech.Bingfeng Ge National Univ. of Defense Tech.Yuming Huang National Univ. of Defense Tech.Zeqiang Hou National Univ. of Defense Tech.Kewei Yang National Univ. of Defense Tech.The purpose of military game is to get strategic advantages through military negotiation even war. Maintaining the initiative and achieving strategic goals in a military game require scientific analysis and informed decision-making. However, due to the limitation of clear and complete information available for military game (especially at the strategic level), the most existing methods for the analysis of military game relying on the experience of commanders or experts are too subjective. Therefore, the graph model for conflict resolution is presented to perform the modeling and analysis of military game. More specifically, the mappings of key elements between the graph model and military game are first explored. Then, a stability analysis process and an evolution analysis process are applied to analyze the potential resulting outcomes, the possible evolutions of game, and the key situations through the game by considering various solution concepts. Finally, the Battle of Baghdad is applied as an example to verify the feasibility and practicability of the proposed method, which proves that it can provide strategic insights and constructive decision-makings for the military game.

SunBIS-59 1175 Disagreement and Polarization for Extended DeGroot Opinion Dynamics Model in Social Networks Shun Huang Wuhan Univ. of Science and Tech.Qingsong Liu Wuhan Univ. of Science and Tech.Li Chai Wuhan Univ. of Science and Tech.In the real world, the individuals are not easy to fully accept new knowledge from the other individuals since the the cognitive ability or learning ability of the individuals. In this paper, an extended DeGroot

Technical Programmes CCDC 2021 model is proposed to study the evolution of opinions in the social networks. It is shown that the opinion is antagonistic if the network is strongly connected, and sufficient conditions for the opinion reaching disagreement and polarization are obtained in terms of the structure of the social networks. Moreover, we apply the extended model to address the public opinion event heat problem. Finally, a case study that learning scenario is worked out to illustrate the effectiveness of the opinion dynamics model.

SunBIS-60 1211 Accounting information comparability and earnings conservatism Yanghui Liu East China Jiaotong Univ.Wei Tang Shaanxi Open Univ.Xuan Ouyang East China Jiaotong Univ.Hong Chen East China Jiaotong Univ.An important goal of the development of accounting standards is to make accounting information more comparable. In order to study the relationship between the comparability of accounting information and the earnings conservatism, this paper takes Chinese A-share listed companies as a research sample. The empirical research results show that: (1) The comparability of accounting information has an effect on improving the earnings conservatism; (2) And in high-risk enterprises, this improvement effect is more obvious; (3) Further research shows that companies with relatively low accounting information comparability are more inclined to choose low-quality auditors, and the frequency of auditor changes is relatively high. This will not only reduce audit quality and increase audit costs, but also It is also easier to obtain non-standard opinions, and opinions are more likely to be purchased; (4) Meanwhile, the study found that the accounting information comparability and inversely proportional to the positive earnings management, but in direct proportion with the real earnings management.

SunBIS-61 1535 Parallel Analysis on Novel Peer Review System for Academic Journals Li Liu Beijing Univ. of Posts and TelecommunicationsZong-Yuan Tan Donghua Univ.Chen Diao Ningxia Univ.Ning Cai Beijing Univ. of Posts and Telecommunications

Northwest Minzu Univ.For improving the performance and effectiveness of peer review, a novel review system is proposed, based on analysis of peer review process for academic journals under a parallel model built via Monte Carlo method. The model can simulate the review, application and acceptance activities of the review systems, in a distributed manner. According to simulation experiments on two distinct review systems respectively, significant advantages manifest for the novel one.

SunBIS-62 1537 Joint Behaviors of Submission and Citation-Based Study on Temporal Variation of Journal Impact Factor Li Liu Beijing Univ. of Posts and TelecommunicationsJian Zhou Dalian Univ. of Tech.Chen Diao Ningxia Univ.Ning Cai Beijing Univ. of Posts and Telecommunications

Northwest Minzu Univ.Impact Factor is the most important indicator in SCI, which is a quantitative tool for evaluating the ranks and the grades of various scientific journals in the Journal Citation Report (JCR) database. In some sense, impact factor has been perhaps viewed as the most commonly used indicator to measure the quality of journals. The main purpose of this paper is to build a parallel model for exploring the variation of journal impact factors, in a distributed manner. In combination with social computing methods, our model endeavors to model and simulate the behaviors of manuscript submission and paper citation. Based on simulation experiment results to understand the cause of certain phenomena such as the relation between JIF and the academic quality of the journals. The simulation results demonstrate that the behaviors of submission and citation would be influenced and driven by JIF, and the academic quality of journals is consistent with the level of JIF.

SunBIS-63 199 Detection of false data injection attacks in smart grids based on cubature Kalman filtering Zhiwen Wang Lanzhou Univ. of Tech.Qi Zhang Lanzhou Univ. of Tech.Hongtao Sun Qufu Normal Univ.Jiqiang Hu Lanzhou Univ. of Tech.The false data injection attacks (FDIAs) in smart grids can offset the power measurement data and it can bypass the traditional bad data detection mechanism. To solve this problem, a new detection mechanism called cosine similarity ratio which is based on the dynamic estimation algorithm of square root cubature Kalman filter (SRCKF) is proposed in this paper. That is, the detection basis is the change of the cosine similarity between the actual measurement and the predictive measurement before and after the attack. When the system is suddenly attacked, the actual measurement will have an abrupt change. However,

the predictive measurement will not vary promptly with it owing to the delay of Kalman filter estimation. Consequently, the cosine similarity between the two at this moment has undergone a change. This causes the ratio of the cosine similarity at this moment and that at the initial moment to fluctuate considerably compared to safe operation. If the detection threshold is triggered, the system will be judged to be under attack. Finally, the standard IEEE-14bus test system is used for simulation experiments to verify the effectiveness of the proposed detection method.

SunBIS-64 403 Network Intrusion Detection Model Based on Space-time Fusion Features and Attention Mechanism Yali Wu Xi'an Univ. of Tech.Liting Huang Xi'an Univ. of Tech.

Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information

ProcessingJinjin Qi Shaanxi Province Key Laboratory of Complex

System Control and Intelligent Information Processing

Xiaoxiao Quan Shaanxi Province Key Laboratory of Complex System Control and Intelligent Information

ProcessingTo solve the problem of low performance of network intrusion detection, a deep learning intrusion detection model based on space-time fusion features and attention mechanism—CLT-net is proposed. In this model, space-time fusion features are obtained by integrating convolutional neural network and long short-time memory network, and attention module is added to calculate the importance of the input features, and softmax function is used for classification. Through a large number of simulation experiments on NSL-KDD data sets, CLT-net has significantly improved the convergence of the training set and the accuracy of the test set. Compared with the traditional CNN model with similar structure and the space-time fusion CLSTM the accuracy of the model increased by 11.8% and 10.9% respectively. Research shows that this model has great potential in the application field of network intrusion detection.

SunBIS-65 546 Design of a Hybrid Robust Controller for Attacked CPS Yan Pang State Key Laboratory of Structural Analysis for

Industrial EquipmentKey Laboratory of Advanced Tech. for Aerospace

VehiclesShouyuan Chen State Key Laboratory of Structural Analysis for

Industrial EquipmentKey Laboratory of Advanced Tech. for Aerospace

VehiclesHao Xia Dalian Univ. of Tech.This paper presents a high-secure hybrid robust controller for Cyber-Physical-Systems (CPS). The controller contains multiple sub-controllers, and each sub-controller targets a specific type of attack strategy. Under the control of the switching logic, the autonomous switching between different sub-controllers is carried out according to the change of the attack type suffered by the system. The aim is to deal with possible time-varying attack strategies in the system. In this paper, an H2-H∞ robust controller is designed based on the unmanned helicopter dynamics model to verify the effectiveness of the concept. The simulation results demonstrate that the effectiveness of the proposed control strategy has better performance than the single optimal control scheme.

SunBIS-66 593 A data aggregation and real-time electricity billing scheme based on homomorphic encryption in smart grid Yingzhuo Zhao Xi’an Jiaotong Univ.Qingyu Yang Xi’an Jiaotong Univ.Donghe Li Xi’an Jiaotong Univ.Dou An Xi’an Jiaotong Univ.To realize more efficient load distribution, smart grid introduces a real-time electricity price billing strategy. In this kind of market, due to the interaction of a large amount of real-time data between the smart grid and users, the privacy of users has become a key issue. In the existing billing scheme based on homomorphic encryption, it usually introduces a third party to calculate the electricity bill, which leads to huge computing cost. We propose a data aggregation and realtime electricity price billing scheme on the basis of homomorphic encryption without a third party or electricity suppliers to participate in to reduce the computing cost. In our scheme, the local gateway uses hybrid multiplicative homomorphic algorithm for billing, which greatly reduces the system cost. Users’ privacy is protected in the process of meter billing and bill verification in the meantime. Through experiments and performance analysis, the program has good security and better performance.

SunBIS-67 618 Defense Optimization in Power Systems against False Data Injection Attacks Feiyang Hong Zhejiang Univ.

Technical Programmes CCDC 2021 Zhejing Bao Zhejiang Univ.Miao Yu Zhejiang Univ.Recent studies show that the cyber-attacks such as false data injection attack (FDIA) are capable of severely threatening power system security, in such a way that the undetected errors are introduced into the states estimations by evading the conventional bad data detection (BDD) methods. This paper proposes a tri-level optimization model against the FDIA by formulating the attack actions and identifying the optimal defense strategies constrained by the limited resources. In the lower level, an economic dispatching is implemented to seek the operational strategy aiming at minimizing the operating costs under the given attack. The middle level formulates the behaviors of the attacker to discover the most destructive attack strategy intended by the attacker based on the defense strategy determined by the upper level. The upper level represents the actions of the planner and determines the optimal defense allocation on the measuring meters. The min-max-min tri-level model can be solved by the column-and-constraint generation (C&CG) algorithm. Simulations are implemented to identify the components which should be protected under the limited defense resources and severe attacks.

SunBIS-68 648 An Improved Temporal Convolutional Network for Non-intrusive Load Monitoring Yingchen Qian Xi’an Jiaotong Univ.Qingyu Yang Xi’an Jiaotong Univ.Donghe Li Xi’an Jiaotong Univ.Dou An Xi’an Jiaotong Univ.Shouqin Zhou CIMI INTELLIGENT Tech. Co. LtdNon-intrusive Load Monitoring(NILM), also known as load disaggregation or energy disaggregation, estimates the energy consumed by the individual appliance from aggregate power consumption of the entire house. Recently, NILM is used to provide users with reasonable energy saving solutions, optimize energy scheduling and fault diagnosis for the appliances. Deep learning is widely used for NILM because its remarkable achievements in neighbouring fields such as natural language processing. In this paper, an improved temporal convolutional network(TCN) in the form of sequence-to-sequence (seq2sqe) model is proposed for NILM to further improve the efficiency of load disaggregation. Specifically, the problem of gradient disappearance and gradient explosion in deep learning model of NILM is solved by the residual network. The dilated convolution reduces the number of the hidden layer of deep learning model and improves the training speed. In addition, our method retains the sequentiality and time dependence of the input, which is beneficial to improve the disaggregation accuracy. Experimental results show that the proposed model can achieve better disaggregation performance than the current state of the art.

SunBIS-69 726 An Ensemble Learning Method with Feature Fusion for Industrial Control System Anomaly Detection Jianyou Xu Northeastern Univ.Wei Shi Northeastern Univ.Shuo Zhang Northeastern Univ.With the deepening of the integration of industrialization and informatization, information technology brings great risks to the safe operation of industrial control system. How to effectively detect the network anomaly behavior in the industrial control system is the key problem of industrial control security research. This paper proposes an ensemble learning method with feature fusion to solve the problem of anomaly classification of network data. Computational results illustrate that the method proposed in this paper has excellent detection effect on different kinds of network attacks in the experiment and improves the detection accuracy.

SunBIS-70 1565 An Optimization Method of Image Loading Performance Based on Web Lulu Cheng Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Lijun Fu Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Zhijun Chang Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Hongjun Wang Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Mengfei An Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Qingdong Deng Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.With the rapid growth of web applications, website layout has evolved from a simple page to a variety of complex applications. At the same time, the consumption habit based on vision is gradually enhanced. As the most direct way to convey human emotions, the utilization rate of pictures is increasing. Therefore, the loading speed of pictures plays a crucial role in the performance and loading time of the system. Aiming at the problem of image optimization, this paper proposes an optimization method of image loading performance based on the integration of CSS sprites, Pre-loading, Lazy Loading and other technologies. Using CSS sprites, Lazy Loading, Pre-loading and fusion technology, in the college entrance

examination filling system with a large number of image data, through the design of Google browser to obtain the TimeLine experiment of page rendering, comparative analysis, and verification of the integration of CSS Sprites, Lazy Loading and Pre-loading technology improves the performance by 91.67%, the image rendering speed and user experience are improved.

SunBIS-71 33 A Least Square Parameter Identification Method for Nonlinear Motion Model of Unmanned Surface Vehicle Based on Euler Discrete Difference Yangliu Xie Systems Engineering Research Inst. of CSSCWei Wang Systems Engineering Research Inst. of CSSCXu Liang Systems Engineering Research Inst. of CSSCWei Han Systems Engineering Research Inst. of CSSCParameter identification method for the nonlinear motion model of unmanned surface vehicle (USV) is addressed in this paper. A least square (LS) based parameter identification algorithm is proposed combining with Euler discrete difference, which greatly reduces the difficulty of identification system designing and improves the accuracy and running speed of the algorithm. A kind of more accurate nonlinear USV model is studied in this paper compared with traditional simple linear model, and model transformation of the USV model is carefully designed in order to facilitate the design process of the identification system. Some computer simulation experiments are carried out, and simulation data are analyzed. Compared with the original USV model parameters, parameters calculated by the proposed algorithm are basically the same as the original data, which shows the validity and reliability of the least square parameter identification method designed based on Euler discrete difference in this paper.

SunBIS-72 39 Research on Evaluation Method of UAV's Contribution Degree to Army Aviation Combat System Based on Simulation Deduction Liangdi Duan Troop 63871Min Wang Beihang Univ.Zhichao Lin Troop 32200Jialiang Li Troop 63871Hanyue Shi Beihang Univ.Yaoming Zhou Beihang Univ.In order to support the application and development of unmanned aerial vehicles(UAVs), it is necessary to evaluate the system contribution rate of cooperative UAVs in combat system. However, the dynamic confrontation process of system operations was always overlooked in traditional evaluation methods. In this paper, a method of evaluating UAV contribution rate in the combat mission based on simulation deduction is proposed, which can test the improvement degree of UAV's operational effectiveness to combat system under the practical condition of attack and defense. The software AnyLogic was used to build simulation environment and agent model under the specific case of helicopters attacking armored targets. After 50 rounds of simulation, the results can be concluded that: the battle damage ratio of scheme with UAVs was 11.933:1, which was 18% higher than that without UAVs. The simulation result is consistent with the actual situation, and the reference value and effectiveness of the method are verified.

SunBIS-73 565 Probabilistic Ensembles Neural Networks Model for Long-Term Dynamic Behavior Prediction of a Robot Xu Wang Harbin Inst. of Tech.Yanfang Liu Harbin Inst. of Tech.Naiming Qi Harbin Inst. of Tech.Zhihao Tang Harbin Inst. of Tech.For complicated dynamic systems, it is still a great challenge to build a sufficiently precise model to predict their long-term behavior, as there are always nonlinearities too complicated to model. In this paper, a probabilistic ensembles neural networks (PENN) is proposed to predict the long-term dynamic behavior. PENN combines the probabilistic deep network dynamics model with sampling-based uncertainty propagation to deal with the aleatoric and epsitemic uncertainties in dynamic system estimation. Simulation demonstrates the effectiveness of the PENN. The prediction error is about 0.05 deg for one step prediction and 10 deg for 100 steps propagation.

SunBIS-74 935 Helicopter Flight Dynamics Modeling Using Simulink Shanyong Zhao China Helicopter Research and Development Inst.Ke Lu China Helicopter Research and Development Inst.Shangjing Wu China Helicopter Research and Development Inst.Dacheng Su China Helicopter Research and Development Inst.In this paper, a mathematical model for conventional helicopter flight dynamics simulation based on the module of Simulink-Function is developed. The model does not need to call external programs or data, and can be directly compiled to form a dynamic link library, laying a foundation for the joint use of flight control systems of LABVIEW and other software. In the process of modeling, the main aerodynamic components of helicopter are considered comprehensively. Main rotor

Technical Programmes CCDC 2021 model is the key part of helicopter modeling, and the associated model of airfoil aerodynamics, inflow dynamics and blade flapping motion are all considered and discussed. Based on the modeling principle of modularity, the entire flight dynamics model is established in Simulink. The trim calculation and response analysis are carried out and compared with the helicopter flight data. The simulation results validate the rationality and accuracy of modeling methods. Finally, the simulation of an application of mission task is carried out to further verify the validity and usability of the model.

SunBIS-75 1078 Cooperative optimization of pod repositioning and AGV task allocation in Robotic Mobile Fulfillment Systems Yuelong Bao Northeastern Univ.Guoshuai Jiao Northeastern Univ.Min Huang Northeastern Univ.The Robotic Mobile Fulfillment Systems (RMFS) is a new kind of parts-to-picker picking system. In RMFS, Automated Guided Vehicles (AGV) transports the mobile shelf (called “pod”) to the picking station. After picking the goods, AGV sends the pod back to the storage area for storage. This research focuses on the cooperative optimization of pod repositioning and AGV task allocation in RMFS. A heuristic method based on auction is designed to solve the problem, and an experimental study is conducted to demonstrate the effectiveness of the presented heuristic method. The results show that the mix assignment rule is a good choice, compared to the sequence assignment of pods and tasks respectively.

SunBIS-76 1112 Modeling and Dynamic Control of Continuum Robots with Improved State Parameterization Yuqi Zhu Sichuan Univ.Songyi Dian Sichuan Univ.Guofei Xiang Sichuan Univ.Bin Guo Sichuan Univ.Haipeng Wang State Grid Intelligent Tech. Co. LtdXu Zhang State Grid Intelligent Tech. Co. LtdPiecewise Constant Curvature (PCC) hypothesis has been proved to be efficient for describing the kinematics and dynamics of continuum robots. But its conventional parameterization method suffers from many issues that limit its application in dynamic control as singularities -mainly about the straight configuration of the robots. In this paper, an alternative improved state parameterization has been introduced to solve the discussed problems. Numerical simulations are provided to support the theoretical results. The system can perform tasks without any pathological behaviors when applying the improved state parameterization.

SunBIS-77 1217 Research on Modeling Method of Industrial Robots Optimum Design Based on Energy Flow Minghai Yuan Hohai Univ.Yadong Li Hohai Univ.Ke Ma Hohai Univ.Kaiwen Zhou Hohai Univ.Fengque Pei Hohai Univ.Song Wang Hohai Univ.In order to meet the needs of saving energy and optimal design of industrial robots, energy flow modeling for industrial robots is proposed. By analyzing the performance of the industrial robot, a performance constrained domain is established. Energy flow elementary units of energy flow analysis are introduced, and energy flow elements under the energy flow modeling of industrial robots are defined, then the energy flow element is divided, the energy flow element interface is expressed, the energy change is calculated, and the energy flow analysis model is established. Finally, the energy flow modeling of the certain type of the industrial robot is analyzed to verify the feasibility of energy flow modeling in the optimum design of energy conservation of the industrial robot.

SunBIS-78 8 Robust adaptive neural network consensus tracking control of multi-robot systems Huijun Guo Xi'an Univ. of Tech.

Key Laboratory of Shaanxi Province for Complex System Control and Intelligent Information Processing

Jintao Liang Guilin Univ. of Electronic Tech.This paper studies the problem of distributed consensus tracking control for multi-robot systems with different friction coefficients and external disturbances based on the multi-agent theory. For the case where the communication topology is a weighted directed graph and leader node is the neighbor of a small portion of the follower nodes. Each follower nodes are modeled as two second-order nonlinear dynamical system with coupling parts. A distributed robust adaptive control law based on the neural networks (NN) is designed for each follower node, where the controller requires only relative state information from its adjacent neighbors. With such control scheme, the states of all follower nodes ultimately synchronize to the leader node with bounded residual error. Simulation results are provided to validate the effectiveness of the

algorithm.

SunBIS-79 53 Dynamical Systems based Obstacle Avoidance with Workspace Constraint for Manipulators Dake Zheng Shenzhen Inst. of Advanced Tech.

UBTECH Robotics Corp.Xinyu Wu Shenzhen Inst. of Advanced Tech.Jianxin Pang UBTECH Robotics Corp.In this paper, based on Dynamical Systems (DS), we present an obstacle avoidance method that take into account workspace constraint for serial manipulators. Two modulation matrices that consider the effect of an obstacle and the workspace of a manipulator are determined when the obstacle does not intersect the workspace boundary and when the obstacle intersects the workspace boundary respectively. Using the modulation matrices, an original DS is deformed. The proposed approach can ensure that the trajectory of the manipulator computed according to the deformed DS neither penetrate the obstacle nor go out of the workspace. We validate the effectiveness of the approach in the simulations and experiments on the left arm of the UBTECH humanoid robot.

SunBIS-80 54 Real-time Whole-body Obstacle Avoidance for 7-DOF Redundant Manipulators Dake Zheng Shenzhen Inst. of Advanced Tech.

UBTECH Robotics Corp.Xinyu Wu Shenzhen Inst. of Advanced Tech.Jianxin Pang UBTECH Robotics Corp.Mainly because of the heavy computational costs, the real-time whole-body obstacle avoidance for the redundant manipulators has not been well implemented. This paper presents an approach that can ensure that the whole-body of a redundant manipulator can avoid moving obstacles in real-time during the execution of a task. The manipulator is divided into end-effector and non-end-effector portion. Based on dynamical systems (DS), the real-time end-effector obstacle avoidance is obtained. Besides, the end-effector can reach the given target. By using null-space velocity control, the real-time non-endeffector obstacle avoidance is achieved. Finally, a controller is designed to ensure the whole-body obstacle avoidance. We validate the effectiveness of the method in the simulations and experiments on the 7-DOF arm of the UBTECH humanoid robot.

SunC02 Room02 AI-Driven Operational Optimization and Control of Metallurgical Process (Special Session) 13:30-15:30 Chair: Ping Zhou Northeastern Univ.

13:30-13:50 SunC02-1 734 An Interval Neural Network Based on Improved Differential Evolution Algorithm Dapeng Niu Northeastern Univ.Zicheng Zhao Northeastern Univ.Mingxing Jia Northeastern Univ.To solve the problems of slow convergence speed, low precision and easily trapped into local extremum for traditional interval neural network, the evolutionary operation in JADE algorithm is extended to interval operation, and an improved interval differential evolution algorithm is proposed to express uncertain data in metallurgical process. This algorithm adopts interval midpoint radius interval to perform arithmetic operation instead of traditional interval arithmetic operation to ensure the tracking performance of population individuals. This interval neural network is used to perform nonlinear interval functions fitting. Simulation results indicate that compared with gradient descent algorithm and traditional interval differential evolution algorithm, the interval neural network trained by the improved interval differential evolution algorithm has better performance.

13:50-14:10 SunC02-2 736 A Hardware-in-the-loop Simulation system for The Flotation Reagent Control Process Liping Zhou Liaoning Univ. of Tech.Gang Yu State Key Laboratory of Process Automation i

n Mining & MetallurgyRui Bai Liaoning Univ. of Tech.Flotation is a critical and important stage where the purpose is to separate the relatively higher grade ore from the tailing by using flotation columns. The purpose of the optimization control of flotation is to ensure that the concentrate grade and the tailing grade are controlled within their target ranges. An optimal control method for flotation process was proposed, which consists of two layers: the optimal setting layer and the loop control layer. And the reagent feeding computational model will generate the reagent feeding set points according to the unit reagent and the the supply ore mass. PID control method is adopted in the loop control layer, whose function is to make the output of control loop tracking the set-point with the satisfactory performance. Aiming at the flotation process, a hardware-in-the-loop simulation system is developed, which is

Technical Programmes CCDC 2021 composed of the optimal setting computer, model computer, monitoring computer, Rockwell PLC and so on. The results show that the proposed method can control the concentrate grade and tailing grade within the target value range, and effectively improve the product quality index of flotation process.

14:10-14:30 SunC02-3 1124 Unscented Kalman filter based Nonlinear Control for the Forced-circulation Evaporation Process Yonggang Wang Shenyang Agricultural Univ.Nannan Zhang Shenyang Agricultural Univ.Shaowen Lu Northeastern Univ.Yajun Zhang Northeastern Univ.Tan Liu Shenyang Agricultural Univ.The forced-circulation evaporation process plays an important role in the Bayer alumina production. Since the process has a complex dynamics, such as strong coupling, serious nonlinearity, where the conventional controller cannot meet the actual demand. Moreover, parameter uncertainties further increase degree of difficult for the above process. To address above challenge problem, an unscented Kalman filter (UKF) observer with the ability of predicting liquor heat capacity is proposed aiming at track changes of the forced-circulation process. The globally linearizing control (GLC) strategy combined with UKF is used to eliminate the coupling between the two control loops and reduce the effects of the nonlinearities on the system simultaneity. The experimental results show that the UKF observer can track the changes of the parameter uncertainty more quickly compared with the extend Kalman filter (EKF) and the proposed approach has good decoupling control performances, which provide theoretical guidance for actual production.

14:30-14:50 SunC02-4 1253 Prediction Model of Oxygen-Enrichment-Related Gas Utilization Rate Based on Multiple Time Scales Jiajia Peng China Univ. of Geosciences

Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Engineering Research Center of Intelligent Tech. for Geo-Exploration

Jianqi An China Univ. of GeosciencesHubei Key Laboratory of Advanced Control and I

ntelligent Automation for Complex SystemsEngineering Research Center of Intelligent Tech.

for Geo-ExplorationXin Chen China Univ. of Geosciences

Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

Engineering Research Center of Intelligent Tech. for Geo-Exploration

Min Wu China Univ. of GeosciencesHubei Key Laboratory of Advanced Control and I

ntelligent Automation for Complex SystemsEngineering Research Center of Intelligent Tech.

for Geo-ExplorationIn the smelting process of a blast furnace (BF), the oxygen enrichment operation is an important operation to control the gas utilization rate (GUR). It affects the development of GUR by changing the BF states on multiple time scales. Aiming at the problem that oxygen enrichment has multi-time-scale effects on GUR, this paper establishes a multi-time-scale prediction model based on mechanism analysis and data decomposition. First, the influence of oxygen enrichment on BF state is analyzed through the ironmaking mechanism, and three mechanism chains of oxygen enrichment affecting GUR are obtained. Next, the GUR data is decomposed to obtain the multi-time-scale components and the frequency distribution of each component. Based on the above analysis, the multi-time scale relationship between the three mechanism chains and the GUR components is obtained. Finally, a multi-time-scale prediction model of oxygen enrichment on GUR based on BP neural network is established by combining the relationship between the mechanism chain and GUR.

14:50-15:10 SunC02-5 1414 Throat Temperature Estimation for Blast Furnace based on Multi Layer Ore Coke Ratio Distribution Modelling Wangzhen Jin Zhejiang Univ.Xiao-Yu Tang Zhejiang Univ.Zheng Hao Zhejiang Univ.Xin Wang Zhejiang Univ.Chunjie Yang Zhejiang Univ.Effective monitoring of the gas flow distribution at the top of the blast furnace (BF) is of importance for operation, control and optimization of iron-making process. Throat temperature measurement is one of the key indicators to directly reflect the gas flow distribution at the top of the blast furnace. In this paper, a novel method for furnace throat temperature estimation based on multi layer ore coke ratio (OCR) distribution modelling is proposed. The burden layer distributions inside the BF, based on the motion modelling of the ore/coke material flow, and further the OCR distribution of each burden layer is deduced. The temperature at the throat are estimated using generalized regression neural network (GRNN) algorithm with the multi layer OCR distributions and the BF main

state parameters as inputs. The case study in an practical BF with the data in 2019 validates the effectiveness of the proposed method. The accuracy of the estimation is significantly improved, compare with the classical method using only the top layer OCR.

15:10-15:30 SunC02-6 1343 Attack Modeling for Industrial Process Operational Optimization Cyber-physical System Chunyu Yang China Univ. of Mining and Tech.Zhong Chu China Univ. of Mining and Tech.Modern industrial process operational optimization system usually consists of perception layer, network layer and control layer, as an important part of the connection between the perception layer and the control layer, the network layer is vulnerable to various network attacks. Common network attacks usually include denial of service attacks, replay attacks, false data injection attacks etc. Attacking spatial model includes prior system model knowledge, disclosing resources and destroying resources. By analyzing the basic features of typical network attacks and studying the math expression of the model of the corresponding attack space with the method of control theory, the industrial process model under three kinds of network attack, attackers attack Spaces and attackers attack models in cyber-industrial system and process operational optimization model under different types of attack are established. Finally, network attack simulation experiment of mixed separation thickening process is used to verify the performance of destructiveness and concealment, the results show that the attack space model and the attacker attack model can effectively describe the attack characteristics of network attacks.

SunC03 Room03 Adaptive Control for Nonlinear Mechanical Systems (Invited Session) 13:30-15:15 Chair: Shubo Wang Qingdao Univ.

13:30-13:45 SunC03-1 719 Career Decision Making Based on Graduate’s Information Analysis Model QuanQiu Jia ShanDong Univ. of Science and Tech.LongRi Wen ShanDong Univ. of Science and Tech.JiMin Liu ShanDong Univ. of Science and Tech.ChuangSen Xie ShanDong Univ. of Science and Tech.MingHao Song YanBian No 1 High SchoolIn recent years, the number of Chinese university graduates is increasing, which not only leads to the problem of supply and demand of talents, but also make it difficult for graduates to make future career decision. In the prior study, experts use Goal Programming, Intrinsic Estimator and other models to analyze the engagement of graduates, but these models have the problems of time consuming and low accuracy and so on. In order to solve the problems mentioned above, that a proposed graduate s information analysis model based on Age Period Cohort algorithm was proposed. It is found that the traditional Age Period Cohort algorithm very suitable for graduate s engagement trend analysis. For the test, sample data model used graduate s information of Shan Dong University of Science and Technology from 2011 to 2019. In order to improve the timeliness and prediction accuracy, the influence factors such as policy and gender are added in Age Period Cohort algorithm and modified algorithm applied to proposed analysis model. As the test results show, that the proposed model can analyze the graduate s engagement trend more timely and accurately than traditional data analysis model, and provide reliable data analysis basis for predicting future engagement trend of graduates.

13:45-14:00 SunC03-2 755 Global Output Feedback Control of Strict-Feedback Nonlinear Systems with Prescribed Performance and Applications Shubo Wang Qingdao Univ.Qiang Chen Zhejiang Univ. of Tech.Haisheng Yu Qingdao Univ.In this brief, a global output feedback control is represented for nonlinear strict-feedback systems with unknown dynamics. A modified prescribed performance function (PPF) that does not need the accurately initial error is designed, and incorporated into control design. The unmeasured system states are estimated via using high-order sliding mode observer. Then, the global output feedback controller is proposed for nonlinear strict-feedback systems without using function approximators to reject the unknown system dynamics. Finally, the experiments show effectiveness of the suggested approach.

14:00-14:15 SunC03-3 909 Adaptive output feedback control for a multi-motor driving system with completely tracking errors constraint Minlin Wang Changcheng Inst. of Metrology & Measurement

Beijing Inst. of Tech.Xueming Dong Changcheng Inst. of Metrology & MeasurementXuemei Ren Beijing Inst. of Tech.This paper proposes an adaptive output feedback controller for the

Technical Programmes CCDC 2021 multi-motor driving system (MDS) to achieve the precision motion control with completely tracking errors constraint. By adopting a K-filter observer to estimate the unknown system states, a modified barrier Lyapunov function (MBLF) is integrated into the adaptive output feedback control to make all the tracking errors constrained within the prescribed bounds. Since the MBLF is suitable for both constrained and unconstrained conditions, it expands the application filed of the classical Lyapunov function. Moreover, minimize learning parameter technique is utilized into the adaptive law design, which improves the adaptive learning process greatly. The system stability is proven by Lyapunov theory. The simulations are conducted on a four-motor driving system to illustrate the efficiency of the proposed controller.

14:15-14:30 SunC03-4 1070 Research on Early Warning of Driver Fatigue Status Based on Image Processing Guoliang Liu Shandong Univ. of Science and Tech.Dongwen Yan Shandong Univ. of Science and Tech.Ziyu Chen Shandong Univ. of Science and Tech.As a major factor of traffic safety, driving fatigue has a great impact on life and property. A non-contact, real-time and high-precision fatigue detection method is studied in this paper by using the bi-directional projection integral for eye positioning based on the characteristics of the different reflectivity of light in different areas of the face, utilizing the PERCLOS method to detect the frequency of the driver s eye opening and closing according to the change curve of pupil radius, so ad to determine whether it is in a fatigued driving state. The accuracy of the method has been approved by the simulation results, therefore, the method can be applied to fatigue detection effectively.

14:30-14:45 SunC03-5 1071 Model Reference Adaptive Control for Mine Paste Backfill Process Hao Lv Shandong Univ. of Science and Tech.Xuehui Gao Shandong Univ. of Science and Tech.YuanQi Li Shandong Univ. of Science and Tech.Dongwen Yan Shandong Univ. of Science and Tech.In the actual paste backfill process, there are many uncertain factors to degrade the backfill efficiency. Frequent production changes and working conditions caused the controlled object to be difficult described by an accurate mathematical model. Then, a lot of conventional control effects are unsatisfactory. In this paper, based on the established paste filling process model of our previous work, a model reference adaptive control is proposed, and its stability is verified by the Lyapunov stability theory. The proposed method can adjust the controller parameters to adapt to the parameter changes online, so that the tracking error of the system can gradually converge to zero by the proposed model reference adaptive control with the condition of model uncertainty or external disturbance. The proposed scheme is simulated by MATLAB, and the simulation results show that the proposed control strategy has good control performance.

14:45-15:00 SunC03-6 1089 Early Diabetes Prediction Based on Stacking Ensemble Learning Model JiMin Liu Shandong Univ. of Science and Tech.LuHao Fan Shandong Univ. of Science and Tech.QuanQiu Jia Shandong Univ. of Science and Tech.LongRi Wen Shandong Univ. of Science and Tech.ChengFeng Shi Maternal and Child Health HospitalAs one of the chronic diseases threatening human health, early detection and early treatment of diabetes plays an important role in the control and development of the disease. With the increasing development of artificial intelligence technology, machine learning models are widely used in disease prediction. The generalization ability of a single machine learning model is not strong, and it often has limitations in diagnostic applications. Accordingly, this paper proposes an early diabetes prediction model based on Stacking ensemble learning. In this model, Gradient Boosting Decision Tree, Adaboost and Random Forest were used as primary learners and logistic regression was used as secondary learners. The results show that the ensemble model proposed in this paper can predict early diabetes more effectively than a single model.

15:00-15:15 SunC03-7 1359 Adaptive Control for Nonlinear Uncertain Systems with Enhanced Transient Performance Jun Yang Kunming Univ. of Science & Tech.Jing Na Kunming Univ. of Science & Tech.Xi Kang National Univ. of SingaporeYingbo Huang Kunming Univ. of Science & Tech.Although adaptive control has been widely used to cope with unknown system parameters, a critical issue limiting its practical application is that the fast adaptation with a high learning rate for addressing uncertainties and obtaining rapid transient convergence can trigger high-frequency oscillations in the system and even cause system instability. To address this issue, we present two schemes to enhance the transient response of model reference adaptive control (MRAC) in this paper. The first idea is to incorporate a simple compensator into the control action, which can

extract and eliminate the undesired residual dynamics in the transient process. The second idea is to introduce a modified adaptive law with a new leakage term, which contains the parameter estimation error, such that both the tracking error and estimation error will converge to zero exponentially. Apart from theoretical analysis, comparative simulation results based on a benchmark wing rock aircraft model verify the effectiveness of the proposed methods for addressing the system uncertainties and improving the transient responses.

SunC04 Room04 Connected Vehicle and Future Smart Transportation 13:30-15:15 Chair: Rong Wang Nanjing Univ. of Aeronautics and AstronauticsCO-Chair: Jun Gao Jianghan Univ.

13:30-13:45 SunC04-1 1047 Signal control strategy based on deep time division method Xuxin Dong Nanjing Univ. of Science and Tech.Weibin Zhang Nanjing Univ. of Science and Tech.In order to make up for the shortcomings of the existing time division methods in complexity and time division effect, this paper proposes a signal control strategy based on deep time division method. Firstly, the ordered clustering algorithm based on Fisher optimal segmentation is used to segment the period. Secondly, to compensate for the dense distribution of segmentation points in the period with low saturation caused by data volatility, deep segmentation is performed in the period with the largest segmentation result. At last, the state critical points of traffic data are obtained by K-means algorithm, and the segmentation points in the same state are merged. Finally, the division of time periods and the corresponding traffic status of each period can be obtained. The VISSIM simulation results show that the proposed method can effectively reduce the queue length and average delay.

13:45-14:00 SunC04-2 1109 Probability Model of Time to Collision Considering Sensor Accuracy Yang Lei Chongqing Univ. of Posts and Telec.Zhiling Huang Chongqing Univ. of Posts and Telec.Ming Cen Chongqing Univ. of Posts and Telec.The intelligent vehicle anti-collision system can effectively slow down or avoid the occurrence of traffic accidents and improve the driving safety of the vehicle. The time to collision (TTC) algorithm is not sufficient in anti-collision algorithm when the accuracy characteristics of different sensors are ignored. Therefore, this paper proposes an improved TTC threshold calculation method considering the influence of sensor detection error. In view of the influence of sensor detection error, a TTC probability model is constructed. In this model, the host vehicle (HV) speed, the target vehicle (TV) speed and the relative distance between them are regarded as a random process, and their distribution functions are established. Subsequently, these parameter distribution functions are used to construct the TTC probability model that approximates the normal distribution by Geary-Hinkley transformation. This model uses the collision probability index to calculate more reasonable TTC threshold. Experimental results were produced and comparative analyses were conducted for different cases. The obtained results confirm the effectiveness of the proposed TTC probability model.

14:00-14:15 SunC04-3 1283 An Adaptive 3D Panoramic Vision System for Intelligent Vehicle Yi Zhang Chongqing Univ. of Posts and Telec.Dong Zhou Chongqing Univ. of Posts and Telec.Yibin Xing Chongqing Univ. of Posts and Telec.Ming Cen Chongqing Univ. of Posts and Telec.Advanced driver assistance systems (ADAS) improves the driving safety of vehicles greatly, and the vehicle-mounted panoramic vision system is one of the most important type of ADAS. Because of small observation range and fixed area of interest, the existing panoramic vision system can’t provide satisfied performance. In order to solve the defects above, an adaptive 3D panoramic vision system for intelligent vehicle is proposed. By the method, multiple sets of cameras are installed around the vehicle to get driving conditions around the vehicle firstly. Then the image distortion is amended by image preprocessing, and the front and back frames of same camera are fusioned and spliced to further eliminate the visual blind area of the vehicle. By the approach of target detecting and anti-collision warning, different levels of dangerous targets in the driving process are given corresponding warning prompts. Finally, to the three-dimensional panoramic vision is generated by the three-dimensional reconstruction and texture mapping. In order to satisfy the drivers' different areas of interest to the vehicle environment at different driving speeds, this paper proposes three-dimensional model adaptive and observation angle adaptive transformation methods to provide different three-dimensional models and observation angles at different driving speeds. The simulation results show that the proposed system solves the problem of large image distortion and small observable area in the 2D panoramic vision, and the problem of poor adaptability of the existing 3D panoramic vision system model and observation angle is updated also. Then the driver's sense of observation can be improved and the vehicle driving safety can be enhanced correspondingly.

14:15-14:30 SunC04-4

Technical Programmes CCDC 2021 1385 Dynamic Arterial Coordinated Control Based on Multi-agent Reinforcement Learning Liangliang Fang Nanjing Univ. of Science and Tech.Weibin zhang Nanjing Univ. of Science and Tech.In order to effectively reduce the average travel time of vehicles on urban traffic arterial roads, multi-agent reinforcement learning (MARL) is introduced into the arterial coordinated control. Multi-agent RL (MARL) overcomes the scalability issue by distributing the global control to each local RL agent, but it introduces new challenges: now the environment becomes partially observable from the viewpoint of each local agent due to limited communication among agents. Inspired by independent Q-learning, we proposed multi-agent asynchronous advantage-critic (MA2C) algorithm for arterial traffic signals control (ATSC). Moreover, we use the decentralized parameter sharing training protocol to mitigates the slowing of convergence due to nonstationarity. In order to improve the safety of MA2C arterial road coordination control on the actual road and ensure the flexibility of coordination, a new action definition is proposed in combination with NEMA phase. We compares the performance of agents using different reward functions in a simulation of a synthetic arterial road with 5 intersections. The experimental results show that the performance of the MA2C algorithm with different reward functions all outperform the traditional green wave method, in which minimizing waiting time results in the lowest average travel time. Another set of experiments shows that our action definition based on NEMA phase is more effective than the traditional “four stage” scheme in arterial traffic signals control.

14:30-14:45 SunC04-5 1395 Collaborative navigation in V2I network based on Chan-Taylor joint iterative algorithm Lina Zhong Jiangsu Second Normal Univ.Rong Wang Nanjing Univ. of Aeronautics and AstronauticsHuiling Chen Jiangsu Second Normal Univ.The positioning accuracy of moving vehicle based on GNSS will be affected significantly in urban valley. The traditional INS/GNSS integrated navigation system collaborating with vehicle to infrastructure(V2I) is a new way to improve the positioning performance of vehicles under the environment of crowed building groups. In this paper, UWB is integrated with the INS/GNSS system. Then the Chan-Taylor algorithm is introduced to provide higher relative positioning results and fused with the integrated navigation system. Simulation results show that the proposed INS/GNSS/UWB collaboration system based on Chan-Taylor algorithm improves the positioning precision significantly in the situation that GNSS lose a lock in crowed environments.

14:45-15:00 SunC04-6 1482 Prediction of Angle Deflection of Smart Car Based on Neural Network Quanjin Huang North Minzu Univ.Jiandong Li North Minzu Univ.Chunyang Mu North Minzu Univ.Xing Ma North Minzu Univ.This paper designs a model based on convolutional neural network to simulate the deflection angle of a driverless car while running. The model is first trained by extracting the features in the images of the training set, and then the simulated car sends the images taken by the on-board camera into the trained model, so that the driverless car can predict the direction and angle according to the real-time road conditions. After testing, the model's judgment loss on the input road condition images during the training process is reduced to 0.3, which enables the driverless car to predict the direction in which it should deflect based on the actual road conditions in the virtual environment.

15:00-15:15 SunC04-7 1668 A Lane-Changing Detection Model Using Span-based Transformer Jun Gao Jianghan Univ.Jiangang Yi Jianghan Univ.Yi Lu Murphey Jianghan Univ.Lane-changing is an important driving behavior and unreasonable lane changes can potentially result in traffic accidents. Therefore, it is urgent to develop lane change detection systems, especially which could work in the initial phase of lane change. In this paper, a novel Transformer-based model, Lane-changing Span-based Transformer (LS-Transformer) is proposed for lane-changing behavior detection from front view videos. Firstly, with Span-based Dynamic Convolution (SDConv), LS-Transformer is capable of integrating the convolution and self-attention to efficiently capture both global and local dependencies with decreased redundancy. Secondly, the lane boundary based distance features and visual scene features can be generated by the LS-Transformer simultaneously. Finally, a Long Short-Term Memory (LSTM) neural network which models temporal dependencies is designed to learn the co-occurrence features and detect lane-changing behaviors. The model is evaluated on a challenging self-collected data set from real driving scenarios, and the experimental results reveal that the proposed LS-Transformer outperforms the other advanced models with faster speed.

SunC05 Room05 Control and Management on Smart City (Building) (Special Session) 13:30-15:40 Chair: Yanhui Wang Beijing Univ. of Civil Engineering and

Architecture

13:30-13:43 SunC05-1 81 Application of grid management in spatio-temporal prediction of crime Tianyi Zhang Beijing Univ. of Civil Engineering and ArchitectureYibing Ran Beijing Univ. of Civil Engineering and ArchitectureDong Wei Beijing Univ. of Civil Engineering and ArchitectureFor traditional data-driven modeling method is not applicable to predict possibility of a crime under the complex inner link problems, put forward a kind of based on grid management concept, a new method of crime prediction system optimization modeling, the method focuses on the grid of geographical features, implementation of crime time, location, weather data, the network platform of data characteristics, such as the existence of the internal correlation analysis. This article in the Chicago area as the research object, by using the BP neural network algorithm modeling, and crime in the pretreatment process data into three-dimensional space-time grid model, establish units within the grid and relations between adjacent grid data, according to the grid position and distance to determine the influence weight, according to the weight integrate the input data set as a model. The experimental results show that compared with the traditional meshless modeling effect, the MAE error of the four types of cases on the 50*50 grid is reduced by about 0.03 on average, and the RMSE error on the 50*50 grid is reduced by about 0.09 on average. The research proves that the selection of appropriate algorithm on the basis of reasonable application of meshless management has a better effect.

13:43-13:56 SunC05-2 224 A Student Action Recognition Algorithm Based on Adjusted Variational Auto Encoder Simin Li Beijing Institute of Tech.Yaping Dai Beijing Institute of Tech.Ye Ji Beijing Institute of Tech.Kaoru Hirota Beijing Institute of Tech.Wei Dai Beijing Institute of Tech.Student action recognition plays an important role in detecting students learning behaivor in online courses. In order to improve the accuracy of student action recognition in classroom, an algorithm based on Adjusted Variational Auto Encoder (AVAE) is proposed. By adjusting the traditional Variational Auto Encoder (VAE) method, the proposed algorithm can effectively extract the characteristic parameters of students from classroom video images, and then recognize the students’ action. Experiments show that the proposed algorithm for student action recognition preforms better than traditional VAE algorithms with higher accuracy and convergence speed, and improves the recognition accuracy by 5.13% compared with traditional Convolutional Neural Network (CNN) method.

13:56-14:09 SunC05-3 268 Zero Cost Improvements for General Object Detection Network Shaohua Wang Beijing Institute of Tech.Yaping Dai Beijing Institute of Tech.Kaoru Hirota Beijing Institute of Tech.Wei Dai Beijing Institute of Tech.To solve the contradiction of increasing computational cost along with the precision improvement in modern object detection networks, it is necessary to research precision improvement without extra cost. In this work, two modules are proposed to improve detection precision with zero cost, which are focus on FPN and detection head improvement for general object detection networks. The scale attention mechanism is employed to efficiently fuse multi-level feature maps with less parameters, which is called SA-FPN module. For the sake of the correlation between classification head and regression head, sequential head is used to take the place of widely-used parallel head, which is called Seq-HEAD module. To evaluate the effectiveness, the two modules are applied to some modern state-of-art object detection networks, including anchor-based and anchor-free. Experiment results on coco dataset show that the networks with the two modules can surpass original networks by 1.1 AP and 0.8 AP with zero cost for anchor-based and anchor-free networks, respectively.

14:09-14:22 SunC05-4 292 Research on partitioning guide strategy of evacuation in public buildings Tang Jian Beijing Univ. of Civil Engineering and ArchitectureHu Yuling Beijing Univ. of Civil Engineering and ArchitectureIn order to decrease the damage in emergencies and improve the efficiency of evacuation in the space of public buildings, an evacuation strategy with guiders is proposed. The guiders are set within equal areas in evacuation space. Social force model is used to describe evacuees. Based on the multi-agent simulation platform Anylogic, plenty of simulation experiments are performed. Non-guided evacuation processes and equal area partitioning guiding processes are compared with double

Technical Programmes CCDC 2021 and three exits. The research results show the guider strategy with equal area partitioning can improve efficiency of evacuation obviously. In addition, the relationship between the number of guiders and evacuation areas are also studied in order to optimize the evacuation time. We also compared and analyzed the evacuation density map of double exits and triple exits to observe the evacuation and congestion analysis.

14:22-14:35 SunC05-5 499 Residential energy demand response management algorithm considering consumer usage patterns Zhixuan Pi Wuhan Univ. of Science and Tech.Xiaohui Li Wuhan Univ. of Science and Tech.Yuemin Ding Wuhan Univ. of Science and Tech.Min Zhao Wuhan Univ. of Science and Tech.Zhenxing Liu Wuhan Univ. of Science and Tech.Demand Response (DR) is a key attribute to enhance the operation of smart grid. It aims to schedule the various appliances in residence to let their energy consumption match the real-time electricity price provided by smart grid. A residential energy DR management algorithm is presented on the basis of real-time home energy management systems (RHEMS). It defines the user target price (UTP) to indicate different user requirements, learns the comfortable working time (CWT) of the appliances in the house by logistic regression and learns the comfortable working power (CWP) of the appliances in the house by difference exponential smoothing. Thus, the presented algorithm dynamically adjusts the maximum power threshold (MPT), and then automatically arranges the appliances operation to achieve buildings economic energy consumption. Simulation results show that the presented algorithm can significantly reduce the power demand during peak price period while ensuring the user’s comfort, and the total energy cost can be reduced by 16% compared with the user’s expectation.

14:35-14:48 SunC05-6 664 Management of Indoor Gas Safety based on the NB-IoT Gas Meter Jinfeng Liang Beijing Gas Energy Science and Tech. Co,.LtdChen Cai Fifth Branch of Beijing Gas Group Co., LtdXiaowei Cheng Beijing Gas Energy Science and Tech. Co,.LtdWei Wang Beijing Gas Group Co., LtdResidents attach great importance to indoor gas safety, gas safety alarm device can detect gas leakage in the first time to ensure the safety of daily gas use.With the appearance of “smart kitchen”, gas alarms are getting more intelligent and internet-based. A safety alarm device based on NB-IoT gas meter and alarm is proposed in this paper, which can realize leakage alarm by wireless connection, and combined with gas safety monitoring and management platform system, a safety guarantee system with monitoring, analysis and remote transmission functions is established.

14:48-15:01 SunC05-7 795 Application of BIM Tech. in Comprehensive Development of Overseas Smart City- Take Colombo Port City, Sri Lanka As an Example Chengguang Xu China Harbour Engineering Co.,Ltd.Ran Tao China Harbour Engineering Co.,Ltd.Rong Wang China Harbour Engineering Co.,Ltd.Dan Jiang Beijing Univ. of Civil Engineering and ArchitectureWith the continuous development of global urbanization process and the constant improvement of urban informatization application, smart city has become the key to the development of new urbanization and urban transformation in many countries around the world. Based on this,the maturity of BIM Tech. can provide power for the development of smart city. In view of the huge scale of the project, involving many professional fields, we have established a unified BIM working platform, applied BIM Tech. to carry out the design work in various professional fields, promoted the communication and cooperation between Chinese and foreign units, benchmarked international engineering standards, and explored and innovated overseas engineering management Tech. eventually.

15:01-15:14 SunC05-8 804 Research on Short-term load forecasting on Elman Network Yun An National Institute of MetrologyDingzhong Sun National Institute of MetrologyXi Zhang National Institute of MetrologyZengbiao He National Institute of MetrologyZhigang Pang National Institute of MetrologyWang Jian National Institute of MetrologyWith the rapid development of electric power market transaction, short-term load forecasting for general industrial and commercial users was put forward that need to be more rapid and more accurate. In the past the research process was faced with the problem of poor stability of data volume and less, this paper proposed a short-term load forecasting model based on Elman network, and compared with the traditional Back Propagation(BP) load forecasting model. The simulation result showed that Elman prediction model could improve the prediction accuracy and reduce the prediction time. In the current application of short-term load forecasting for general commercial and industrial users, Elman prediction

model has strong practicability.

15:14-15:27 SunC05-9 814 Comparison of PCA and LDA Dimensionality Reduction Algorithms based on Wine Dataset Siyi Feng Beijing Univ. of Civil Engineering and ArchitectureHuaixiu Wang Beijing Univ. of Civil Engineering and ArchitectureIn the construction of machine learning model, when the input data dimension is too large and the data characteristics are particularly complex, the complexity of the model will increase, especially when some sample data is insufficient, which will lead to the poor generalization of the training model. Therefore, it is necessary to remove some redundant features by dimensionality reduction to reduce the dimension of data, so as to facilitate the observation and mining of information. This paper uses the red wine data set in Python to reduce the dimension of PCA and LDA, and on the basis of the existing research, compares the dimension reduction of red wine data set before and after standardization, puts forward the characteristics of PCA dimension reduction and LDA dimension reduction, and the similarities and differences between the two linear dimension reduction methods, so as to provide ideas for the subsequent data processing.

15:27-15:40 SunC05-10 867 GAN-based Intrusion Detection Data Enhancement Wei Fu Beijing Univ. of Civil Engineering and ArchitectureLiping Qian Beijing Univ. of Civil Engineering and ArchitectureXiaohui Zhu Beijing Univ. of Civil Engineering and ArchitectureIn view of the lack of intrusion detection data and the slow update of mainstream detection methods, an intrusion detection data generation method based on a generative adversarial network is proposed. First, the overall data is digitized and normalized to maintain the integrity of the data; Then use the ACGAN model to learn the hidden features of the data and generate new data; Finally, evaluate the similarity and validity of the generated data from multiple perspectives. Experimental results show that the data generated by this method has similar characteristics to the original data, and can be used to enhance the original data set to meet the needs of intrusion detection systems.

SunC06 Room06 Data-driven Intelligent Optimization and Decision for Industrial Processe (Special Session) 13:30-15:30 Chair: Jing Na Kunming Univ. of Science and Tech.

13:30-13:50 SunC06-1 652 Data-Driven Nonlinear Robust Optimization for Gold Cyanidation Leaching Process Shulei Zhang Northeastern Univ.Runda Jia Northeastern Univ.

State Key Laboratory of Synthetical Automation for Process Industries

Weimin Zhang Northeastern Univ.Hongru Chen Northeastern Univ.In order to operate the gold cyanidation leaching process under certainty, a data-driven nonlinear robust optimization method is proposed. By using the T2 statistic limit based on PCA model, the correlations between the uncertain variables can be efficiently captured and a tighter uncertainty set can be defined. The first-order Taylor approximation is employed to linearize the inequality constraints. The equality constraints with state variables are also considered by using implicit function theorem, and the robust counterpart of the nonlinear robust optimization problem is formulated. The efficiency of the proposed data-driven nonlinear robust optimization method is verified through a simulated industrial GCLP. Compared with the nonlinear robust optimization methods with box and ellipsoid uncertainty set, the proposed method can effectively reduce the economic cost.

13:50-14:10 SunC06-2 750 Multivariate statistical modelling and monitoring for nonstationary industrial process with slow feature analysis and temporal working pattern partition Xiaoyu Zou China Univ. of Mining and Tech.

State Key Laboratory of Process Automation in Mining & Metallurgy

Jie Pan China Univ. of Mining and Tech.Due to change of working condition, nonstationarity widely exists in modern industry, leading to time-variant statistical properties and frustrating the modelling techniques for stationary process. In this research, slow feature analysis and working pattern partition based method is proposed for nonstationary industrial process modelling. Slow feature analysis is employed as a filter to separate the signals with different frequencies. Next, stationarity test is applied to the ordered slow features. Then, four categories of features can be obtained with specific significance. Among them, the slow nonstationary features represent the inner change of working condition and thus can be adopted to partition the working condition into multiple patterns, temporally. Hence, the nonstationarity can be decomposed into piece-wise stationarity in the feature space. Each working pattern can then be modelled separately,

Technical Programmes CCDC 2021 using the normal data based on stationary techniques. Finally, the proposed method is validated on a real copper flotation process.

14:10-14:30 SunC06-3 758 Two-Step Partial Least Squares for Monitoring Dynamic Processes Zeyi Yuan Beijing Univ. of Chemical Tech.Xin Ma Beijing Univ. of Chemical Tech.Yihao Qin Beijing Univ. of Chemical Tech.Youqing Wang Shandong Univ. of Science and Tech.The dynamic partial least squares (DPLS) method is widely used in dynamic industrial process monitoring. In this method, an autoregressive model is employed to describe the dynamic characteristics of a system, and traditional partial least squares (PLS) is applied to analyze correlations among data synchronously. However, DPLS only expands the dimensions of the original data; it cannot express the characteristics of the dynamic model concretely, and its monitoring performance is often usatisfactory. The present study uses a moving average autoregressive model to describe dynamic processes and then proposes a two-step PLS (TS-PLS) algorithm to solve this problem. Testing on numerical examples and the continuous stirred-tank reactor, cases reveal that the performance of TS-PLS is much better than that of traditional DPLS.

14:30-14:50 SunC06-4 872 Prediction of Silicon Content of Molten Iron in Blast Furnace Based on Particle Swarm - Random Forest Kecheng Zhou Northeastern Univ.Fei Hu Northeastern Univ.Jun Gong Northeastern Univ.The silicon content of blast furnace hot metal is not only the key index in the steel preparation process, but also is often used to characterize the furnace temperature and the running state of blast furnace. Therefore, the prediction method of molten iron silicon content is an important topic in the research of blast furnace smelting. In this paper, the classical stochastic forest model theory is improved and an inter-tree weighted stochastic forest model is proposed. At the same time, particle swarm optimization (PSO) algorithm is used to optimize the model parameters. Finally, through experimental verification, PSOTWB-RF model proposed in this paper has high prediction accuracy and hit ratio.

14:50-15:10 SunC06-5 1132 A safe control scheme for the dense medium coal separation process based on Bayesian network and Active Learning Wenchao Bao China Univ. of Mining and Tech.Fei Chu China Univ. of Mining and Tech.Chao Shang Tsinghua Univ.Tao Chen Univ. of SurreyFuli Wang Northeastern Univ.Furong Gao Hong Kong Univ. of Science and Tech.The paper presents a safe control method for the operation of dense medium coal preparation process based on Bayesian network (BN) and active learning. The dense medium coal preparation process fluctuates significantly in operating conditions and abnormal operating conditions occur frequently due to the uncertainty in the environment. To address this issue, we establish the BN model of safety control by integrating qualitative expert knowledge and quantitative data information based on the analysis of causes of abnormal conditions and corresponding operation schemes, which acts as the basis for the safety control. Because the previous BN structure learning method based on observation data information can only get Markov equivalence class and needs a large amount of data, it is difficult to establish an accurate BN. To address this issue, we propose a BN structure learning method based on active learning to improve its efficiency and accuracy. Simulation results show that the proposed method can effectively provide safety control decisions for abnormal conditions in the dense medium coal preparation process.

15:10-15:30 SunC06-6 1240 Data-driven Prediction of Heat Flux Distribution in Boiler Based on Computational Fluid Dynamics Data Luyin Pan School of Automation Engineering Northeast Elect

ric Power Univ.Zhenhao Tang School of Automation Engineering Northeast Elect

ric Power Univ.Shengxian Cao School of Automation Engineering Northeast Elect

ric Power Univ.Tao Shen Harbin Boiler Company LimitedThe heat flux reflects the heat distribution inside the boiler. In order to predict the heat flux distribution in the power station boiler, a data-driven model of heat flux distribution is proposed based on computational fluid dynamics (CFD) simulation data via a multi-layer perceptron (MLP). The boundary conditions are determined to get combustion products and other parameters simulation data based on mechanism analysis. The simulation data obtained from CFD are normalized. Pearson correlation coefficient method is adopted to select simulation variables highly related with heat flux as the input. The model of heat flux distribution is established based on MLP. The experimental results show that, compared with other data modeling methods, the average absolute error

of MLP model is reduced by 35.5%, 20.6% and 37.1%, respectively. The MLP combined with Pearson correlation coefficient method model (PMLP) get promising result in the prediction of the distribution of heat flux.

SunC07 Room07 Fractional Calculus and Fractional-order System (Special Session) 13:30-15:30 Chair: Dingyu Xue Northeastern Univ.

13:30-13:50 SunC07-1 1263 A CAD-based Approach for Stabilization of Fractional-Order Control Systems with Multi-Parameter Jing Yang Univ. of Electronic Science and Tech. of ChinaXiaorong Hou Univ. of Electronic Science and Tech. of ChinaXiaoxue Li Univ. of Electronic Science and Tech. of ChinaMin Luo Southwest Petroleum Univ.Based on Cylindrical Algebraic Decomposition (CAD) technique, an approach for stabilizing fractional-order control systems with multi-parameter is presented in this paper. The method transforms the problem for stabilization of control systems to the problem for solving the range of parameters which satisfy the condition of stabilization. The stable parameters region in the parameters space is established, necessary and sufficient conditions about system stabilization is given. Compared to existing methods which may be failed, our method is non-conservative and universal for several fractional-order control systems. Furthermore, the relationship between parameters can be obtained. Two examples are given to show the effectiveness of the proposed method.

13:50-14:10 SunC07-2 1265 Robust Stability and Stabilization of Commensurate Fractional Multi-Order Systems with Norm-bounded Uncertainties Xin-Yu Sha Shanghai Jiao Tong Univ.Jun-Guo Lu Shanghai Jiao Tong Univ.

Key Laboratory of System Control and Information Processing

This paper considers robust stability and stabilization problems of commensurate fractional multi-order systems with norm-bounded uncertainties. With the lowest fractional order 0 < α < 1, robust sufficient stability conditions are presented. Then depending on the accessibility of the systems’ state vector and its fractional derivatives, a robust state feedback controller and a robust static output feedback controller are designed for stabilizing. All results are presented in the form of linear matrix inequality to take advantages of the LMI mathematical tools. Finally, numerical examples are implemented to demonstrate the effectiveness of the proposed results.

14:10-14:30 SunC07-3 1554 Non-fragile consensus for uncertain fractional-order nonlinear multi-agent systems with state time delay Xiaomin Li Hefei Univ. of Tech.

Anhui Engineering Tech. Research Center of Industrial Automation

Liping Chen Hefei Univ. of Tech.Anhui Engineering Tech. Research Center of

Industrial AutomationYu Chen Hefei Univ. of Tech.Wenliang Guo Hefei Univ. of Tech.Changcheng Xu Hefei Univ. of Tech.The problem of non-fragile consensus for nonlinear fractional-order multi-agent systems (FOMAS) with state time delay is investigated in this paper. The structured uncertainties occur in both the plant and the controller. By using the linear matrix inequality approach and the fractional-order Razumikhin theorem, a delay- and order-dependent consensus protocol is obtained. The proposed results can be applied to linear FOMASs, and the proposed analysis can be extended to different kinds of consensus problems for various FOMAS.

14:30-14:50 SunC07-4 1614 New approach to output feedback control of fractional-order linear systems with positive real uncertainty Jie Li Shenyang Aerospace Univ.He Li Shenyang Aerospace Univ.Yue Wu Southwest Jiaotong Univ.This paper concerns with the static output feedback stabilisation of uncertain fractional-order linear systems. To solve the static output problem via linear matrix inequality approach, a new separation technique is adopted to separate the controller gain matrices from Lyapunov variable. Based on this method, the sufficient conditions for robust asymptotical stability of the closed-loop systems are established. Unlike previous methods, the proposed methods do not need equality constraints and consider an iterative search of the controller. A numerical example is provided to illustrate the validity of the proposed methods.

14:50-15:10 SunC07-5 1669

Technical Programmes CCDC 2021 Stabilization of a class of fractional-order systems with both model uncertainty and external disturbance Runlong Peng Qilu Univ. of Tech. (Shandong Academy of S

ciences)Zuosheng Sun Qilu Univ. of Tech. (Shandong Academy of S

ciences)Rongwei Guo Qilu Univ. of Tech. (Shandong Academy of S

ciences)This paper investigates the stabilization problem of a class of fractional-order systems with both model uncertainty and disturbance. By combining the the dynamic feedback control method and the uncertainty and disturbance estimator (UDE)-based control method, a new control method is proposed to stabilize the fractional-order system by two steps. In the first step, the integral order dynamic feedback is designed to stabilize the nominal fractional-order system. An UDE-based fractional-order controller is presented to estimate the whole of uncertainty with external disturbance in the second step. Finally, one numerical example is given to verify the accuracy and validity of the proposed methods.

15:10-15:30 SunC07-6 1671 A Robust and Adaptive FOPI Control Strategy for Balance Recovery of Neutral Point Voltage in Three-Lever NPC Inverter Lingzhi Zhang Shandong Univ.Chenghui Zhang Shandong Univ.Xiangyang Xing Shandong Univ.Xiaoyan Li Shandong Univ.Yan Li Shandong Univ.In three-level neutral point clamped (NPC) inverter, non-zero neutral point current leads to unbalanced neutral point voltage, which eventually causes overvoltage damage to the switching device and increases the content of low order harmonics. In order to overcome this fatal and transmittable problem, this paper introduces the flat phase constraint into the parametric tuning of fractional-order PI controller (FOPI), so that the phase-frequency response curve of the openloop transfer function remains in an ideal bound near the crossover frequency. Thus, the adaptability, robustness and dynamic performance are simultaneously improved to a great extent. Moreover, the proposed FOPI control strategy is implemented through space vector pulse width modulation (SVPWM) for the balance recovery of neutral point voltage in three-lever NPC inverter. When the system is disturbed, this method turns out to be exceedingly efficient for balance recovery. Finally, typical applicable examples are given to validate the above theories and methods.

SunC08 Room08 Theory and application of nonlinear systems (I) 13:30-15:30 Chair: Zhen Wang Yunnan Minzu Univ.CO-Chair: Jindong Shen Northeastern Univ.

13:30-13:50 SunC08-1 786 Reduced-order state estimation of complex-valued Markov jump neural networks with time-varying delay and generally uncertain transition rates Zhen Wang Yunnan Minzu Univ.Lianglin Xiong Yunnan Minzu Univ.In this work, without dividing the complex-valued neutral networks (CVNNs) into a real component and an imaginary component, the partial states of CVNNs with time-varying delay and Markov jump are obtained by a reduced-order state estimator for the first time. And the stochastic stability for the error system is investigated by a state feedback controller. Compared with the full-order state estimation, the computational complexity for the designs of reduced-order state estimation is greatly reduced. And then, the validity and correctness of our theorem is proved by an example.

13:50-14:10 SunC08-2 183 Design of TCP/AQM congestion tracking controller with finite time and nonlinear disturbance observer Jindong Shen Northeastern Univ.Yun Bai Northeastern Univ.Yuanwei Jing Northeastern Univ.This paper studies the finite time tracking control problem for TCP/AQM system with nonresponsive UDP flows. A novel congestion control algorithm is proposed based on the barrier Lyapunov function, backstepping and nonlinear disturbance observer. A modified barrier Lyapunov function is used to limit the queue tracking error of the TCP/AQM system, which improves the dynamic performance of the closed-loop system and all signals converge in finite time. In order to suppress the influence of external disturbance on the system, a nonlinear disturbance observer is designed to approach external disturbance, and a good compensation effect is achieved. The feasibility and superiority of the proposed method are proved by simulation verification and analysis.

14:10-14:30 SunC08-3 34 Iterative learning control for one-sided Lipschtiz nonlinear multi-agent systems with Time-delay Zhiheng Guo South China Univ. of Tech.

Senping Tian South China Univ. of Tech.This paper considers a tracking problem based on iterative learning control for a class of one-sided Lipschitz nonlinear multi-agent systems with time-delay. Here, communication between followers is described in a directed graph, and only a portion of followers can receive information from the leader. An iterative learning control algorithm is proposed for this kind of multi-agent systems. On this basis, the convergence conditions of the algorithm are given and analyzed. The main contribution of this study is to extend the iterative learning control theory to one-sided Lipschitz multi-agent systems. The results show that the algorithm can ensure that the follower's output converges to the leader's path along the iterative axis in the finite time interval. Finally, a simulation example is given to illustrate the effectiveness of the proposed learning algorithm.

14:30-14:50 SunC08-4 111 Causality between Air Quality Index and Influenza-Like Illness: Based on Nonlinear Dynamics Method Yiru Liu Zhejiang Univ.Sicheng Dai Zhejiang Univ.Jun Meng Zhejiang Univ.Exploring the causality between urban air pollution and human influenza infection can effectively help people to carry out effective and accurate prevention and control measures, especially today when influenza diseases continue to break out. In this paper, with the help of the big data platform, we investigate the non-linear characteristics of air quality index (AQI) and influenza-like illness (ILI%) and used the Convergent Cross Mapping (CCM) method to investigate the two variables’ causality for the first time. Based on the CCM test results, the average AQI and the concentration of the five main air pollutants in the 15 sample cities in the south China and ILI% show obvious nonlinear and weak coupling characteristics and a one-way causal relationship. We also use data to prove that correlation is a necessary but insufficient condition for causality. The research in this article has enriched the interactive effects between air pollution and human influenza in experience and has important practical significance for urban managers to control air pollution and allocate medical resources.

14:50-15:10 SunC08-5 114 Sunspot Forecast Using Temporal Convolutional Neural (TCN) Network Based on Phase Space Reconstruction Sicheng Dai Zhejiang Univ.Yiru Liu Zhejiang Univ.Jun Meng Zhejiang Univ.The long-term prediction of sunspot is of great significance to spaceflight, satellite and communication. Based on the chaotic characteristics of sunspot, a prediction model of TCN network based on phase space reconstruction (PSR) is established. PSR is a technology that can map a one-dimensional sunspot sequence to high-dimensional space and reconstruct the original sunspot system. Embedding dimension m and delay time τ are the most important parameters in PSR. In order to verify the effectiveness of the proposed model, four models with different τ and m are studied in the experiment. The results show that the PSR-TCN model with m=8, τ=37 is superior to all other competitors in terms of RMSE. This study predicts 13-month smoothed monthly sunspot number of the Solar Cycle 25 using this model, making the forecast of sunspot number from January 2020 to December 2030. The maximum sunspot number is 139.55 that will occur in April 2024. This indicates that the cycle would be slightly stronger than Solar Cycle 24.

15:10-15:30 SunC08-6 107 Backstepping-based control for a manipulator system with disturbances and input quantization Wenhui Liu Nanjing Normal Univ.Guobao Liu Nanjing Normal Univ.This paper deals with the quantized control for a manipulator system with mismatched disturbances. First, a disturbance observer is designed to track the unavailable disturbances. The system input is quantized by a hysteretic quantizer to reduce chattering. Then, a backstepping methodbased controller with input quantization is designed to ensure the uniform boundedness of the closed-loop system. Finally, the simulation results for the manipulator system show the effectiveness of the proposed method.

SunC09 Room09 Fault Diagnosis and Predictive Maintenance (IV) 13:30-15:30 Chair: Rui Yang Xi’an Jiaotong-Liverpool Univ.CO-Chair: Fan Long China Shipbuilding Industry Systems

Engineering Research Inst.

13:30-13:50 SunC09-1 1562 Fault Diagnosis of Bearings under Different Working Conditions based on MMD-GAN Zhimin Li Shandong Univ. of Science and Tech.Xianghua Wang Shandong Univ. of Science and Tech.Rui Yang Xi’an Jiaotong-Liverpool Univ.In the actual work of rolling bearings, the probability distribution of output

Technical Programmes CCDC 2021 data will change due to changes in load and speed, which will lead to a decrease in the accuracy of the diagnostic model, or even failure. To solve this problem, this paper proposes a fault diagnosis model based on the combination of maximum mean discrepancy (MMD) and Generative adversarial network (GAN), which is called MMD-GAN. The proposed method extracts data features through a convolutional neural network, and then MMD and GAN are combined to reduce the distribution difference between the source and target domain dataset, result in more accurate fault diagnosis results. Finally, experiments were conducted through the CRWU rolling bearing data set, and the effectiveness of the proposed scheme has been verified.

13:50-14:10 SunC09-2 727 Design of Fault Diagnosis and Condition Assessment for the Information and Control System of Ship Long Fan China Shipbuilding Industry Systems

Engineering Research Inst.Cheng Guo China Shipbuilding Industry Systems

Engineering Research Inst.Yuan Qu China Shipbuilding Industry Systems

Engineering Research Inst.Zhikun Liu China Shipbuilding Industry Systems

Engineering Research Inst.Chao Ma China Shipbuilding Industry Systems

Engineering Research Inst.The main function of the Information and Control System depends on the software system. It is different from the pure hardware system, like a mechanical system. These characteristics make it difficult to meet the needs of users for fault diagnosis, maintenance, and repair, such as lacking fault diagnosis, highly specialized diagnosis technology, operate difficultly,. Based on the Integrated Platform Management System (IPMS) of ship as the research object, the author puts forward a comprehensive evaluation method with the general performance parameters and characteristic function parameters. The comprehensive evaluation method uses a variety of monitoring rules to realize the monitoring of system state and fault diagnosis, and supports the combination of automatic monitoring data and user-imported data to realize the real-time and global condition assessment of IPMS. It could help the users who are engaged in the maintenance to locate system faults and get system condition. As the result, the decision-making ability of users is improved.

14:10-14:30 SunC09-3 498 A Data-based Expert System for Aero-Engine Gas Path Fault Diagnosis Ang Sun Dalian Univ. of Tech.Di Guo Dalian Univ. of Tech.Rui Wang Dalian Univ. of Tech.In response to the daily maintenance and repair requirements of domestic military aviation engines, a databased expert system for aero-engine gas path fault diagnosis is proposed. The system is dedicated to the unified management and processing of flight data of various types of domestic engines. In addition to the basic functions of the general aero-engine condition monitoring system, the system has also done in-depth research on issues such as aero-engine parameter denoising, aero-engine parameter prediction, and automatic acquisition of expert knowledge base. Additionally, wavelet domain denoising, LSTM network prediction and rough set attribute reduction are investigated and applied in the proposed system. Finally, the normalized real flight data is applied to simulation verification, and good denizations and prediction results are obtained.

14:30-14:50 SunC09-4 519 Reliability Modeling of Dependent Competitive Failure with Continuous Degradation and Random Shocks Jialin Ma Xi'an Univ. of Tech.Guo Xie Xi'an Univ. of Tech.Lingxia Mu Xi'an Univ. of Tech.Jing Xin Xi'an Univ. of Tech.Wenbin Chen Xi'an Univ. of Tech.Anqi Shangguan Xi'an Univ. of Tech.Haitao Duan Xi'an Univ. of Tech.Based on the reliability analysis of correlated competitive risk systems with degradation processes and stochastic shocks, a dependent competitive failure model considering time-varying soft failure threshold is proposed. The failure process includes soft failure caused by continuous degradation and hard failure caused by random impact. In complex equipment, when the system degrades faster due to performance degradation, the soft failure threshold of the system will decrease with the degradation of system performance.In this study, a time-varying soft failure threshold dependent competitive failure model is proposed. Through the numerical analysis of a micromotor, the reliability of the system is overestimated to a certain extent by comparing the models without considering the change of soft failure threshold.

14:50-15:10 SunC09-5 523 Remaining Useful Life Prediction of Nonlinear Degradation Process Based on EKF

Yubing Wang Xi'an Univ. of Tech.Guo Xie Xi'an Univ. of Tech.Jing Yang Xi'an Univ. of Tech.Yu Liu Xi'an Univ. of Tech.Xinhong Hei Xi'an Univ. of Tech.Huan Gao Xi'an Univ. of Tech.Dan Wang Xi'an Univ. of Tech.Remaining useful life prediction has always been the core issue of prediction and health management (PHM), and has become a research hotspot in the field of health management. Aiming at the problem of engine life prediction, based on the data set provided by NASA to simulate the degradation process of aircraft turbofan engine, the Extended Kalman Filter (EKF) algorithm is applied to the engine life prediction process, combined with condition monitoring. The remaining life of the engine is predicted by fitting the model. The experimental results show that the proposed prediction method can be effectively used in engine life prediction, and has good prediction effect.

15:10-15:30 SunC09-6 525 Fault Diagnosis of Brake Train based on Multi-Source Information Fusion Yongze Jin Xi'an Univ. of Tech.Guo Xie Xi'an Univ. of Tech.Xinhong Hei Xi'an Univ. of Tech.Haitao Duan Xi'an Univ. of Tech.Wenbin Chen Xi'an Univ. of Tech.Jialin Ma Xi'an Univ. of Tech.Qianbo Zang Beijing Univ. of Posts and TelecommunicationsIn this paper, the pure air emergency braking mechanism of high-speed train is analyzed, and the emergency braking model of high-speed train is established. Considering that the actual train operation monitoring data is interfered by external complex factors, the fusion filtering of train multi-source braking monitoring data based on Federal Kalman filter is constructed. Based on the recursive expectation maximization identification, the data fusion results are analyzed, and the gradient descent method is selected to optimize the mathematical expectation. The identification results of the brake disc friction coefficient are obtained, and the diagnosis results and maintenance suggestions are given. The simulation results show that the multi-source monitoring data of the train can be fused effectively and the different levels of faults of brake disc can be diagnosed accurately, the effectiveness of the proposed method is verified.

SunC10 Room10 Optimal Control and Optimization (IV) 13:30-15:30 Chair: Wei Pan Army Academy of Artillery and Air DefenseCO-Chair: Juling Cai Academy of Military Science

13:30-13:50 SunC10-1 44 Radar Threat Assessment based on Improved Chaotic Genetic Algorithm Lichao Ding Army Academy of Artillery and Air DefenseWei Pan Army Academy of Artillery and Air DefenseYu Xia Army Academy of Artillery and Air DefenseSili Liu Army Academy of Artillery and Air DefenseShaolin Xu Army Academy of Artillery and Air DefenseIn this paper, a radiation-source threat assessment algorithm based on adaptive chaotic genetic algorithm is proposed to evaluate radiation-source threat in the command and operation of radar countermeasures. Firstly, the threat factors of radar against radiation sources are analyzed from technical, tactical, dynamic and static aspects. Secondly, a new ideal decision-making model based on matter-element matrix is proposed by using the theory of multi-source optimization and the principle and method of ideal decision-making method. Then, adaptive chaotic genetic algorithm is used to find the optimal solution of the decision model. Finally, an example shows that the method is feasible and effective, which is convenient for computer-aided decision-making. It provides a new way for radar to evaluate the threat of radiation sources, and provides support for commanders to make correct threat judgment.

13:50-14:10 SunC10-2 580 Mixed integer nonlinear programming for the three-dimensional aircraft conflict avoidance problem Juling Cai Academy of Military ScienceNing Zhang Beihang Univ.Yingli Wang Academy of Military ScienceIn this paper, the problem of aircraft conflict avoidance is studied arising in Air Traffic Management systems. The initial configuration of all aircraft including position, velocities, heading angles, altitude and flight trajectories are known, and they are assumed to fly within a shared three-dimensional airspace. The aim of the problem is to find an optimal conflict avoidance strategy for the aircraft so that various conflicts situations can be avoided. A conflict occurs when the relative distance between any pairs of aircraft is less than the minimal safe separation during their flights. In order to ensure the safety of aircraft flight, a Mixed integer Nonlinear Programming model (MINLP) for solving the three-dimensional conflict avoidance problem involving multiple aircrafts is proposed in this paper, where the aircraft are allowed to change simultaneously heading angle and velocity to achieve the separation. The

Technical Programmes CCDC 2021 illustration verifies the effectiveness of the method. The optimal solution of the problem can be obtained easily in a small computational time by using a standard global optimization solver to solve the proposed MINLP model.

14:10-14:30 SunC10-3 40 Study on Parameters of Deception Jamming False Target based on Improved Chaotic Genetic Algorithm Yang Long Army Academy of Artillery and Air DefenseLichao Ding Army Academy of Artillery and Air DefenseFeng Huang Army Academy of Artillery and Air DefenseHuixiang Xie Army Academy of Artillery and Air DefenseBased on analytic hierarchy process theory, a radar target threat assessment model is established. According to the background of jamming application, the model of radar target threat assessment is simplified. And then, it is proposed that an improved chaos genetic algorithm for chaotic perturbation by the same individual after genetic operation. False target parameters with higher threat degree than real target is obtained by the improved chaos genetic algorithm. The simulation results show that the improved chaotic genetic algorithm is faster and more accurate.

14:30-14:50 SunC10-4 690 Trajectory optimization for multiple asteroid rendezvous mission through hybrid propulsion Enmi Yong China Aerodynamics Research and

Development CenterXiao Wang China Aerodynamics Research and

Development CenterIn this paper we investigate optimal trajectories for Near-Earth Asteroid (NEA) rendezvous missions using a hybrid propulsion system, combining solar sailing and solar electric propulsion (SEP). An approach based on physical programming and Gauss pseudo-spectral method is proposed for the rendezvous trajectory multi-objective optimization, aiming at minimizing both total propellant consumption and mission duration. For missions to one NEA, results show that hybrid propulsion is capable of producing different trajectories according to the designer’s preference on saving propellant consumption or flight time. The results also show that the hybrid propulsion makes more possibilities on rendezvous trajectory in varied NEA exploration missions.

14:50-15:10 SunC10-5 933 A Kind of Linear-Quadratic Pareto Cooperative Differential Game for Mean-Field Type Backward Stochastic System Yu Wang Shandong Univ.In this paper, we study a kind of linear-quadratic (LQ) Pareto cooperative differential game problem of mean-field backward stochastic differential equation (MF-BSDE). First, based on a weighted sum technique, we reformulate the Pareto cooperative differential game problem as an optimal control problem. Then, according to the theory of stochastic optimal control and a decoupling technique, we give a feedback form of all Pareto efficient strategies. Finally, an economic problem is given to enhance the realizability of our results.

15:10-15:30 SunC10-6 1471 Cabin topology modeling and its application research under damage control scenario Bing San Naval Univ. of EngineeringYue Hou Naval Univ. of EngineeringJinyun Pu Naval Univ. of EngineeringKangbo Wang Naval Univ. of EngineeringCabin topological structure has great influence on the organization and implementation of damage control action, based on the Graph Theory, the cabin topology structure was established, and the algorithm design of four command elements under damage control scenario was realized, namely, the setting of protection boundary, the division of damage control zoning and the planning of damage control support path. The case analysis verified feasibility and reliability of the related algorithm. This study can improve the effectiveness of damage control command under the existing cabin layout and provides a new idea for the optimal design of cabin topology.

SunC11 Room11 Intelligent Control, Computation and Optimization (IV) 13:30-15:30 Chair: Wei Chen Jiangsu Univ. of Science and Tech.CO-Chair: Shixi Hou Hohai Univ.

13:30-13:50 SunC11-1 564 Design of ROV Adaptive Sliding Mode Control System for Underwater Vehicle Based on RBF Neural Network Wei Chen Jiangsu Univ. of Science and Tech.Shilin Hu Jiangsu Univ. of Science and Tech.Qingyu Wei Jiangsu Univ. of Science and Tech.The dynamic positioning control of ROV near the water surface under wave disturbance is still a challenging problem. The principle of sliding mode control and the method of approximating unknown function by RBF

neural network are studied. The adaptive sliding mode controller of RBF neural network is designed. The stability and convergence of the proposed algorithm are deduced and verified, and compared with the simulation results of traditional adaptive sliding mode control methods. The simulation results show that the ROV's trajectory tracking effect is good in the wave disturbance environment. The experimental results prove the effectiveness of the method and achieved satisfactory performance.

13:50-14:10 SunC11-2 1474 Robust Intelligent Control of Active Power Filter Using Neural Learning Mechanism Shixi Hou Hohai Univ.Shili Fu Electric Power Research Inst. of Yunnan

Power Grid Co. LtdSuwei Zhai Hohai Univ.Yundi Chu Hohai Univ.The problem of harmonic distortion becomes more and more serious with the extensive application of power electronic equipments. This paper proposes a meta-cognitive fuzzy-neural-network controller based on secondorder sliding mode for active power filter (APF) to suppress harmonics. First, the method of second-order sliding mode control (SOSMC) is used to keep tracking of the APF reference current. Next, the meta-cognitive fuzzy-neural-network (MCFNN) with exact approximation ability is further studied to approximate the unknown part in SOSMC. Subsequently, the stability analysis is carried out through Lyapunov theory to ensure the tracking performance of the closed-loop system. Finally, simulation studies have demonstrated the excellent performance of the proposed MCFNN scheme.

14:10-14:30 SunC11-3 644 Improved Disturbance Rejection Performance for Descriptor Systems Based on Adaptive Equivalent-Input-Disturbance Chengye Lin Wenzhou Univ.Gengjiao Yang Wenzhou Univ.Lixin Gao Wenzhou Univ.The improved equivalent-input-disturbance (EID) approach is presented for descriptor systems is this paper. The adaptive law to enhance the disturbance rejection performance. The system is divided into two subsystems by separation theorem. For the one subsystem, sufficient condition for the admissibility of the descriptor networked control system with time-delay is given in terms of linear matrix inequality (LMI), which is used to determine the gain of statefeedback controller. The sufficient condition for the admissibility of the other subsystem, is given to obtain the gain of the observer. The adaptive law is presented by Lyapunov function. A numerical example is carried out to verify the effectiveness of the modified method.

14:30-14:50 SunC11-4 1486 Design and Validation of New Discrete-Time Zeroing Neural Network for Dynamic Matrix Inversion Qingping Liu Huaqiao Univ.Laicheng Yan Huaqiao Univ.Jianhuang Cai Huaqiao Univ.Qingshan Feng Huaqiao Univ.Dongsheng Guo Huaqiao Univ.In the previous work, the zeroing neural network (ZNN) with continuous-time and discrete-time formulations has been studied for dynamic matrix inversion. In this paper, the discrete-time formulation of ZNN is further investigated for computing the inverse of dynamic matrix. Specifically, a special numerical difference rule is established on the basis of Taylor series expansion. By using such a difference rule to discretize the continuous-time ZNN model, the new discrete-time ZNN (DTZNN) model is thus proposed for dynamic matrix inversion. Comparative numerical results are provided to validate the effectiveness of the proposed DTZNN model compared with the existing DTZNN models.

14:50-15:10 SunC11-5 463 Food Safety Crime Risk Assessment Based on Set Pair Analysis Yupeng Zhai Beijing Univ. of Tech.Xiaoli Li Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Engineering Research Center of Digital CommunityKang Wang Beijing Univ. of Tech.Yang Li Communication Univ. of ChinaWith the development of economy and society, major activities such as conferences and sports events held in China are becoming more frequent. It is of great significance to ensure food safety for major activities. This paper focuses on the risk of man-made food safety crime, analyzes the possibility of food crime of suspicious person from four aspects: social text sentiment classification, personnel profile, trajectory analysis, and food crime incident related element analysis, and evaluates the risk of food safety crime by means of Set Pair Analysis. The result shows that Set Pair Analysis method is easy to calculate and the evaluation result is reliable.

Technical Programmes CCDC 2021 15:10-15:30 SunC11-6 708 An Adaptive Dynamic Programming Algorithm Based on ITF-OELM for Discrete-Time Systems Xiaofei Zhang Beijing Inst. of Tech.Hongbin Ma Beijing Inst. of Tech.

State Key Laboratory of Intelligent Control andDecision of Complex Systems

Junyong Chen Beijing Inst. of Tech.Weixue Li Beijing Inst. of Tech.Adaptive dynamic programming (ADP) is a kind of intelligent control method, and it is a non-model-based method that can directly approximate the optimal control policy via online learning. The gradient algorithm is usually used to update weights of action networks and critic networks, however it is clear that gradient descent-based learning methods are generally very slow due to improper learning steps or may easily converge to local minimum. In this paper, in order to overcome those disadvantages of gradient descent-based learning methods, a novel ADP algorithm based on initial-training-free online extreme learning machine (ITF-OELM), in which the critic network link weights of hidden nodes to output nodes can be obtained by least squares instead of gradient algorithm, is introduced. Finally, the ADP algorithm based on ITF-OELM is tested on a discrete time torsional pendulum system, and simulation results indicate that this algorithm makes the system converge in a shorter time compared with the ADP based on gradient algorithm.

SunCIS Room12 Interactive Session 13:30-15:30

SunCIS-01 198 Trajectory Tracking Control of Heterogenous UGV-MMS system Chao-Shuai Li Huazhong Univ. of Science and Tech.Long Chen Huazhong Univ. of Science and Tech.Bin Hu Huazhong Univ. of Science and Tech.Ding-Xue Zhang Yangtze Univ.Xin-Ming Cheng Central South Univ.A new control strategy is presented for trajectory tracking of heterogeneous Unmanned Ground Vehicle (UGV)-Mobile Manipulators (MMS) system in this paper. The heterogenous UGV-MMS system is comprised of the UGV and the MMS. The main innovation of this paper is that the situation that the manipulator compensates the dynamic interactions produced by the UGV is considered, while the UGV doesn’t compensate the dynamic interactions produced by the manipulator. In this situation, we do low calculation and the tracking precision is excellent. Firstly, a kinematic and dynamic controller is designed for the UGV to ensure the UGV to track the desired trajectory. Secondly, a control law based on the sliding mode control is designed for the manipulator to track the desired trajectory. Finally, the Lyapunov function approach and the simualtion results in MATLAB are used to demonstrate the effectiveness and feasibility of the proposed control approach of both the UGV and the manipulator.

SunCIS-02 221 Path Planning and Evaluation for Obstacle Avoidance of Manipulator Based on Improved Artificial Potential Field and Danger Field Jiangbo Zhao Beijing Inst. of Tech.Qiang Zhao Beijing Inst. of Tech.Junzheng Wang Beijing Inst. of Tech.Xin Zhang Xinxing Cathay International Group Co., Ltd.Yanlong Wang Beijing North Vehicle Group Co., Ltd.This paper takes 6-DOF manipulator as the research object, proposes the improved Artificial Potential Field (APF) method to plan the obstacle avoidance path of the manipulator, and combines the Danger Field (DF) method to evaluate the safety of the path planned using APF. According to the characteristics of the manipulator, the kinematics model of the manipulator is analyzed, and the ball envelope algorithm is applied to simplify the physical model of the obstacle. Compared with the traditional APF method, the improved APF searches in the joint space and introduces the joint attraction potential to improve the search speed and accuracy. The problem of local minimum is dealt with by the combination of adding virtual obstacles and increasing joint attraction potential. The improved APF not only has the advantages of good real-time performance and smooth path, but also solves the problem of the traditional APF falling into a local minimum, and combines the danger field method to judge the path rationality to ensure no collision with obstacles. Through simulation verification, the proposed method realizes the obstacle avoidance path planning of the manipulator and the safety evaluation of the planned path.

SunCIS-03 230 6-DOF grasp planning of manipulator combined with self-supervised learning Zhenyu Ren Huazhong Univ. of Science and Tech.

Key Laboratory of Image Processing and Intelligent Control

Gang Peng Huazhong Univ. of Science and Tech.Key Laboratory of Image Processing and Intelligent

ControlJin Yang Huazhong Univ. of Science and Tech.

Key Laboratory of Image Processing and Intelligent Control

Hao Wang Huazhong Univ. of Science and Tech.Key Laboratory of Image Processing and Intelligent

ControlTo realize a robust robotic grasping system for unknown objects in an unstructured environment, large amounts of grasp data and 3D model data for the object are required. To reduce the time cost of data acquisition and labeling and increase the rate of successful grasps, we developed a self-supervised learning mechanism to control grasp tasks performed by manipulators. First, a manipulator automatically collects the point cloud for the objects from multiple perspectives to increase the efficiency of data acquisition. The complete point cloud for the objects is obtained by utilizing the hand-eye vision of the manipulator, and the TSDF algorithm. Then, the point cloud data for the objects is used to generate a series of six-degrees-of-freedom grasp poses, and the force-closure decision algorithm is used to add the grasp quality label to each grasp pose to realize the automatic labeling of grasp data. Finally, the point cloud in the gripper closing area corresponding to each grasp pose is obtained; it is then used to train the grasp-quality classification model for the manipulator. The results of performing actual grasping experiments demonstrate that the proposed self-supervised learning method can increase the rate of successful grasps for the manipulator.

SunCIS-04 306 Adaptive tracking control of robotic manipulator with uncertain dynamics based on a new neural approximator Bing Zhou Univ. of Electronic Science and Tech. of ChinaLiang Yang Univ. of Electronic Science and Tech. of ChinaChengdong Wang Univ. of Electronic Science and Tech. of ChinaYong Chen Univ. of Electronic Science and Tech. of ChinaGenping Fu Zhongkai Univ. of Agriculture and EngineeringIn controlling robotic manipulator, compensating for uncertain dynamics based on the well-known neural network (NNs) approximator is a long-standing, yet well documented open problem in the field of adaptive control. In fact, the existing research and simulation examples have illustrated the effectiveness of the NNs adaptive controller on the manipulator system. However, the online update of the entire NNs idealized weight matrix (IWM) will lead to burdensome computation to the system as the NNs nodes increase. So far, there is still no suitable solution to this problem. In this paper, we propose a novel adaptive scheme based on optimized NNs approximating mechanism. Technically, by using the NNs to compensate the uncertain dynamics and unknown torques online, and by estimating and updating the square of the maximum singular value of the IWM, the burdensome online computation is successfully avoided. Furthermore, it is shown that the asymptotic convergence of the tracking error and the NNs parameters estimation error to arbitrarily small neighborhood of the origin can be guaranteed simultaneously. The illustrative examples shows the effectiveness and superiority of the proposed control scheme.

SunCIS-05 348 Fault-Tolerant Control For Robotic Manipulator With Uncertain Dynamics And Disturbance Changgui Xiong Univ. of Electronic Science and Tech. of ChinaLiang Yang Univ. of Electronic Science and Tech. of ChinaYong Chen Univ. of Electronic Science and Tech. of ChinaGenping Fu Zhongkai Univ. of Agriculture and EngineeringIn this paper, a fault-tolerant tracking control approach is proposed for robotic manipulator with uncertain dynamics and actuator failure. Firstly, a nominal dynamics model considers modeling discrepancies and load changes of the manipulator is established. Secondly, to effectively deal with the actuator failure, an unknown input observer (UIO) is introduced. Own to deem actuator failure as a system unknown inputs (UIs), the complicated estimation of the fault-related parameters are successfully avoided. Moreover, through the state transform, a robust UIO is introduced to estimate the lumped UIs of the system. By accurately observing and compensating the compound UIs, the stability of the system is guaranteed, and the transient performance of the system post-failure is effectively improved. The Lyapunov’s direct method is employed to ensure the validity of the proposed scheme, simulation experiment is provided to verify the effectiveness of the presented algorithm.

SunCIS-06 417 Repetitive control scheme for industrial robotic manipulator using B-spline function Xingyu Wang Northeastern Univ.Anna Wang Northeastern Univ.Xinghua Wang Northeastern Univ.Dazhi Wang Northeastern Univ.ZhiGuo He State Grid Hengyang Power Supply CompanyIndustrial robotic manipulator is a nonlinear, strong coupling, time-varying system, which is affected by various uncertain factors such as unmodeled dynamics, parameter changes, external interference, friction and so on. Therefore, it is very important to improve the trajectory tracking accuracy, convergence speed and trajectory smoothness of the manipulator. A new

Technical Programmes CCDC 2021 controller is designed in this paper. By generating uniform B-spline trajectories, the trajectory planning stage has been integrated into a repetitive control scheme that can modify the reference signal in real time, so as to eliminate the tracking error at the desired path point. In theory, the control scheme is very suitable for the application of industrial equipment with non-open source controller. The simulation results show that the method is effective.

SunCIS-07 481 Dynamics Modeling and Control of Humanoid Robot Arm with 7DOF Actuated by Pneumatic Artificial Muscles Mengyao Pei Beijing Univ. of Tech.

Beijing Key Lab of the Computational Intelligence and Intelligent System

Daoxiong Gong Beijing Univ. of Tech.Beijing Key Lab of the Computational Intelligence

and Intelligent SystemThe robotic arm driven by pneumatic artificial muscle has a configuration similar to that of a human arm, and has essential compliant characteristics, which has important research significance for the development of Tri-Co (coexistence-collaboration-cognitive) robots. In order to realize the real-time motion control of the humanoid robotic arm, it is necessary to model the kinematics and dynamics of the robotic arm. In this paper, the Modified Denavit-Hartenberg (MDH) method is used to model the forward kinematics of the humanoid robotic arm, and the Newton-Euler Dynamics method is used to model the dynamics of the humanoid robotic arm. Research shows that the trajectory of the human arm in free space satisfies the Logistic function, and the Logistic function is used as the curve of each joint of the humanoid robotic arm. According to the established model, the computed torque control scheme is used to realize the control of the humanoid robotic arm. Simulation experiments show that each joint of the humanoid robotic arm can accurately track the desired joint angle curve.

SunCIS-08 566 Research on Gridding Robots Based on Compliant Device Force Control Peiwen Li Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsYang Li Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsTianjun Zha Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsLei Sun Nankai Univ.

Tianjin Key Laboratory of Intelligent RoboticsIn order to improve the precision of complex curved surfaces manufacturing and prevent workers from metal dust pollution, a force-controlled based compliant device is modeled, which is mounted between the end of industry robot and polishing tools. Due to force measuring device is not available in the system, it is necessary to calibrate the output force of compliant device. A cylinder force calibration method divided into static and dynamic two parts is provided. Meanwhile, a ‘planning+control’ framework is proposed and a feedback linearization based robust control method is applied to guarantee the stability of the system. Both simulation experiment and actual grinding experiment are performed to verify the effectiveness of proposed algorithm.

SunCIS-09 974 Observer-based finite time robust stabilization of mechanical arm systems Xin Shi Shandong Jiaotong Univ.Renming Yang Shandong Jiaotong Univ.Jiankuo Cui Shandong Jiaotong Univ.Haiying Zhang Shandong Jiaotong Univ.Haolin Yang Shandong Jiaotong Univ.In this paper, by applying the Hamiltonian function method, we study the robust stabilization problem for a class of two degree of freedom mechanical arm based on finite time observer. Firstly, the two degree of freedom mechanical arm system is transformed into a Port Controlled Hamiltonian (PCH) model, based on which we design its finite time observer system. Secondly, a robust stabilization controller based on finite time observer is designed by using dimension expansion technique and Lyapunov stability theory and some new robust stabilization results based on finite time observer are given. Different from existing stabilization results on the infinite-time observer, under the finite-time observer designed, the closed-loop system can converge quickly and has good robustness when it is disturbed by external environment.

SunCIS-10 1128 Design and Development of Teaching System for Manipulator Simulation Control Yunduo Jiang Northeastern Univ.Yihe Yang Northeastern Univ.Chunfeng Shao Northeastern Univ.Shuying Zhao Northeastern Univ.The Manipulator is widely used in the current industrial field, and its teaching has a strong theoretical and practical nature. However,

traditional theoretical teaching is often out of touch with practice, resulting in poor teaching effects. This paper develops a teaching software system for Manipulator simulation control, provides an interactive and visual learning platform for Manipulator principles, and deepens its understanding of teaching content through PBL teaching, reduces the difficulty of course learning, and improves students Interest in learning.

SunCIS-11 1284 Gaussian Process Based Tracking Control for Robot Manipulators with Dynamical Uncertainties Guannan Lv Peking Univ.Yunxiao Ren Peking Univ.Zhao Zhang Peking Univ.Zhisheng Duan Peking Univ.In this paper, we present a novel approach for tracking control of robot manipulators with dynamical uncertainties. Based on the Gaussian Process (GP), the unknown robotic dynamics are precisely modeled and estimated. And the robust tracking controller is designed in the existence of approximation error and external perturbations. The stability of the closed-loop system is proved with the Lyapunov theory. Furthermore, the effectiveness of the proposed controller is verified through simulations on a 2-DOF robot manipulator and experiments on a 6-DOF Kinova robotic arm.

SunCIS-12 1502 Design and Implementation of Control System for Four-axis Picking Manipulator Chunyang Mu North Minzu Univ.Wenxiao Gao North Minzu Univ.Haiping An North Minzu Univ.Xing Ma North Minzu Univ.Manual fruit picking has the disadvantages of high labor intensity, low efficiency and high cost. In order to solve these problems, a fruit picking robot based on machine vision was designed. By using the steering gear as the actuator of each axis rotation and the Arduino as the control module to realize the motion control of four axis manipulator. Through binocular vision positioning, the spatial point coordinate data of fruits were obtained. Combine the data with the motion control algorithm of four axis manipulator, we can get the angle that each axis needs to rotate. The system transmits data through Bluetooth module, and then controls the rotation of the mechanical arm to realize the fruit picking. In experiments, accuracy is compensated by software programming, and the smooth rotation of the manipulator has been solved.

SunCIS-13 312 A YOLOv4-based Vehicle Detection Method from UAV Videos Hansong Sun Qingdao Univ. of Science and Tech.Guansheng Xing Qingdao Univ. of Science and Tech.In order to solve the positioning problem and missed detection caused by occlusion or small target when the UAV detects the target vehicle, this paper proposes an end-to-end vehicle detection method based on UAV video. The shallow fine-grained information and high-level semantic information are extracted through the YOLOv4 detection network and feature fusion is performed, the loss function is partially replaced with Mish to optimize the flow of gradient information. The initial anchor box obtained by K-means clustering is used to realize a multi-scale detection strategy. Through testing in different environments, the proposed method has high robustness in terms of different vehicle scale and texture changes caused by UAV camera angle movement or weather changes. The mean Average Precision (mAP) index of the algorithm reaches 90.3%, which has good detection effect and positioning accuracy.

SunCIS-14 384 Research and Implementation of Tire Tracking Algorithm Based on Multi-Feature Fusion Panpan Yang Northeastern Univ.Huicheng Chen Northeastern Univ.Jiayun Yang Northeastern Univ.Yongming Yan Shenyang Dixin Artificial Intelligence Industry

Research Inst. Co., Ltd.Lan Yao ZXtech(Shanghai) Co., Ltd.Zhibin Zhao Northeastern Univ.In order to improve highway utility, the national government is vigorously promoting automatic toll at highway entrances. Cameras, as indispensable facilities, have been installed on sides of toll lanes. When vehicles gradually drive through the video area, wheels are detected through target detection and tracking technologies. The number of axles is accordingly detected and it is a factor mattering toll for large trucks. This is turned out an essential methodology for axle counting. Multiple Object Tracking (MOT) is the key point for it, while most existing algorithms fail for the proximity of tire positions and the similarity of tire appearance. For the challenge of proximate positions, a relative position relation model is proposed. It defines adjacent tires as a group according to their proximity. Furthermore, Kalman filter is introduced to calibrate preliminary results by reducing matching range. To conquer the challenge in reference to similar appearance, factors in terms of relative position, background, texture and color of grouped tires are involved. These

Technical Programmes CCDC 2021 factors are synthetically integrated with weighted convolution features to improve association in tires. Based on the real video data collected from toll highway stations, it is tested and evaluated that the proposed algorithm can effectively improve the accuracy of tire tracking in adjacent positions and similar appearance.

SunCIS-15 409 Design of verification platform for FPGA shape selection algorithm and its application in grain classification Jiawei Wang Hangzhou Dianzi Univ.

Zhejiang Provincial Key Lab of Equipment ElectronicsJiye Huang Hangzhou Dianzi Univ.

Zhejiang Provincial Key Lab of Equipment ElectronicsZhekang Dong Hangzhou Dianzi Univ.

Zhejiang Provincial Key Lab of Equipment ElectronicsHong Kong Polytechnic Univ.

With the rapid development of FPGA image algorithm, the verification of FPGA image algorithm is more and more important. This paper presents a fast verification platform for shape selection. Different from other methods, the verification platform can generate testbench files quickly and verify simulation results intuitively. In this paper, through the verification of FPGA shape selection algorithm in grain classification, it proves its superiority. The result shows that the verification platform can effectively verify the defects and shorten the development time of FPGA shape selection algorithm. Therefore, the verification platform can be used as a powerful tool to optimize and upgrade the FPGA shape selection algorithm.

SunCIS-16 837 Image Stitching Method Based on Adaptive Weighted Fusion Chunbo Xiu Tiangong Univ.Jingyao Fang Tiangong Univ.Jiang Zhang Tiangong Univ.Aiming at the problem of stitching seams and uneven brightness caused by illumination and noise during the image stitching process, a stitching method based on adaptive weighted fusion is proposed. The SURF algorithm is used to extract the feature points of the image to be stitched, the Euclidean distance is used to calculate the similarity of the feature point pairs, and complete the image registration. After that, the adaptive weighted method is applied to fuse the brightness difference of the overlapping area to achieve the smooth transition of the spliced image and eliminate stitching seams. Compared with the traditional gradual algorithm and the nonlinear fusion algorithm, the experiment results show that the adaptive fusion algorithm can obtain better image fusion effect and effectively improve the quality of image stitching.

SunCIS-17 976 Stereovision-based Noncooperative Spacecraft Pose Measurement via Circle and Planar Points Cuicui Jiang Beihang Univ.Yizhong Fang The State Key Laboratory of Experiment Physics &

Computational MathematicBeijing Inst. of Space Long March Vehicle

Guopeng Ding Key Lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Xin Chen Key Lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Qingyun Mao Key Lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Qinglei Hu Beihang Univ.In this paper, a novel approach based on the feature circle and planar points is proposed for the relative pose measurement of the noncooperative spacecraft. Specifically, the approach uses information from the stereovisionbased navigation system to provide multiple perspective projections of the circle and planar points on the noncooperative spacecraft. Then the circle center position is recovered and the normal direction is determined by duality disambiguation. Furthermore, a point depth of recovery from the stereovision is used to define the direction from the circle center to the point, which is regarded as the initial attitude for the stereovision-based orthogonal iteration pose estimation to avoid the local minimal value of the iteration method. Therefore, by utilizing the initial attitude and the orthogonal iteration algorithm based on planar points, the pose ambiguity from the planar points is eliminated and the roll angle around the circle normal direction is available. Finally, numerical simulations are conducted to validate the effectiveness of the proposed algorithm.

SunCIS-18 977 Pose Measurement Method of Non-cooperative Target Based on Monocular Vision Chenrong Long Beihang Univ.Zhining Bai Beijing Inst. of Space Mechanics and ElectricityShuai Zhi Key lab of Microsatellite

Innovation Academy for Microsatellites of CASChengbo Qiu Key lab of Microsatellite

Innovation Academy for Microsatellites of CASYamin Wang Key lab of Microsatellite

Innovation Academy for Microsatellites of CASQinglei Hu Beihang Univ.A method based on monocular vision is presented in this paper to estimate the pose of the non-cooperative target spacecraft. It directly takes the image features of the docking ring and solar panel as the recognized object. The circular feature is used to restore two solutions of the relative position and orientation, the vanishing points formed by the two parallel lines of the solar panel are applied to solve the duality problem and restore the roll angle. This algorithm provides a closed-form solution using simple mathematics, therefore, it is suitable to space applications where the computation capability of the on-board processor is very limited. Numerical simulations show that the proposed algorithm can effectively estimate the pose of the non-cooperative target spacecraft.

SunCIS-19 981 Adaptive Multiple Features Spatially Regularized Correlation Filters for Visual Tracking Shanbin Li South China Univ. of Tech.Jiajia Wang South China Univ. of Tech.Correlation filters have reached astonishing achievements in the field of tracking in rencent years due to their speedability and accuracy. However, it will be affected by boundary effect and hardly cope with variation and distraction. In addition, multi-feature with fixed proportion or single-feature has limitations when it comes to different scenes. To address these problems, this paper presents a model named Adaptive Multiple Features Spatially Regularized Correlation Filters (AMFSR). For each kind of feature, the difference map of adjacent frames is used to solve the spatially regularized weights, and then solve the correlation filters respectively. By considering penalty score, the response maps with different features are adaptively combined, and the drift problem caused by interference can be dealt with at the same time. Experimentally, our tracker outperforms other up-to-date trackers with the average of 25FPS, which meets the real-time requirements.

SunCIS-20 987 A Protective Equipment Detection Algorithm Fused with Apparel Check in Electricity Construction Kun Yu Northeastern Univ.Huimin Liu Northeastern Univ.Tengfei Li Northeastern Univ.Xiumei Liu Northeastern Univ.Lan Yao ZXtech(Shanghai) Co., Ltd.Yongming Yan Shenyang Dixin Artificial Intelligence Industry

Research Inst. Co., Ltd.Wearing safety protective equipment appropriately is a necessary routine to ensure the workers0 safety. In this paper, we propose an algorithm of safety protective equipment detection which fuses apparel check. Firstly, we introduce a target detection algorithm to label a human body part in an image. In a body part area, we detect two key classifications of head or hand, which are associated with protective equipment, through the fusion of global color feature and SIFT feature. Further, according to the centroid distance and IoU between a protective equipment and a body part, a normative development of its abidance is verified. The detection of insulating gloves is particularly derived with skin detection as an auxiliary factor. Experimental results on real video data library of on-site electricity construction show the efficiency of our method.

SunCIS-21 1286 Safety Rope State Detection in Electricity Field Construction Zhen-Bo Pan State Grid East Inner Mongolia Electric Power

Supply Co., Ltd.Dan Wang State Grid Liaoning Electric Power Supply Co.,

Ltd.Kun Yu Northeastern Univ.Zhibin Zhao Northeastern Univ.Lan Yao ZXtech(Shanghai) Co., Ltd.In the high-altitude operation in electric power field construction, safety rope abidance is an important prevention to practitioners. It is an intensive enforcement of on-site inspection as well. In recent years, computer vision technology has been applied to industrial safety supervision and has become a research hotspot. However, in the scenario of high-altitude operation in an electric construction field, cameras are generally at a far distance from practitioners and the similarity in the appearance of safety rope and cable poses the challenge to target detection. This paper proposes a novel algorithm for safety rope state check. Firstly, horizontal and vertical poles are identified in the image as the associated target that safety rope should be locked on; secondly, the mobile object detection and Canny edge detection are carried out through the Gaussian Mixture Model to localize practitioners as candidate locations of horizontal/vertical poles; Eight-Neighbor algorithm is introduced to extract lines from an image, and folding times, length and curvature are serving as auxiliaries for precise recognition. Finally, safety rope abidance is determinate according to the spatial position of horizontal/vertical poles and the safety rope. The efficiency of our method is evaluated and analyzed in carefully designed experiments with on-site practical data from a field aerial work video data set.

SunCIS-22

Technical Programmes CCDC 2021 1310 A RCDNet for outdoor unmanned sweeping vehicles based on graph convolutional network Dianyong Yu Harbin Inst. of Tech.Saiyingnan Bian Harbin Inst. of Tech.Changan Hu Harbin Inst. of Tech.Jieming Lou Harbin Inst. of Tech.This paper proposes a graph convolutional network for road curb point detection (RCDNet) which converts road curb point detection into a point cloud segmentation problem. The input of the network comes from the point cloud below the segmented road edge. First, the point cloud enters a permutation network to ensure that the spatial permutability of the point cloud remains unchanged. Then a neighbor number k is given, and k neighbor points of each point are calculated through the xyz attribute of the point cloud. These k neighbor points used as the local features of the point are fused with the xyz attributes of the point to construct the edge feature. Then convolving the edge features to extract higher-level point cloud spatial information. Through this method of fusion of local features and global features, more local information of the point cloud can be obtained, so that the network can segment the three-dimensional point cloud with higher accuracy.

SunCIS-23 1318 Composite pattern separation with CNN for multi-view structured light 3D reconstruction Shulin Zhang Wuhan Univ. of Science and Tech.

Engineering Research Center of Metallurgical Automation and Measurement Tech.

Sen Xiang Wuhan Univ. of Science and Tech.Engineering Research Center of Metallurgical

Automation and Measurement Tech.Huiping Deng Wuhan Univ. of Science and Tech.

Engineering Research Center of Metallurgical Automation and Measurement Tech.

Jin Wu Wuhan Univ. of Science and Tech.Engineering Research Center of Metallurgical

Automation and Measurement Tech.Structured light(SL) is a fundamental and important method in 3D reconstruction. Multi-view SL can provide broader and more complete scene geometry than single SL, but the overlap of projected patterns is a fatal problem, and thus it is very much desired to separate signals from the composite pattern. In this paper, we propose a novel learningbased method to decompose patterns for phase-coding SL. The network has a backbone to extract common features and two branches to get the unique features and reconstruct the two components. In addition, by analyzing the process of the pattern overlapping, we propose a novel loss function and ground truth label data by guaranteeing correct phase. Moreover, we also propose a synthetic dataset for training. Experiment results demonstrate that the proposed method successfully separate the composite patterns on our data with noise and light attenuation, and outperforms traditional method such as band pass filter and principal component analysis.

SunCIS-24 73 Robot Localization Using Laser Positioning of Reflectors Based on ICP Jingge Yu Huazhong Univ. of Science and Tech.Anwen Shen Huazhong Univ. of Science and Tech.Xin Luo Huazhong Univ. of Science and Tech.Tianzouzi Xiao Huazhong Univ. of Science and Tech.In this paper, the global localization for a mobile robot in the environment with reflectors arranged in advance is studied. The robot is equipped with a laser that allows to acquire the range and bearing information of the robot with respect to reflectors. After reflector matching, the global pose of the robot can be calculated by localization methods. Focusing on the localization method, a novel laser positioning method based on ICP (Iterative Nearest Point) is proposed, which realizes global localizations by constructing the rigid transformation between reflector sets for two continuous scanning time. In addition, compared with the original ICP algorithm that dealing with point cloud sets, the proposed method has less computation and needs no iteration. The implementation process of the proposed method is derived, and the proposed method is compared with the widely used trilateral localization methods. Experimental results show that our method has higher accuracy and better stability than trilateral positioning method.

SunCIS-25 322 Research progress of fiber Bragg grating flexible sensor：A Review Xiaoqiang Xu Wuhan Univ. of Tech.Ziqi Song Wuhan Univ. of Tech.Yan Mao Wuhan Univ. of Tech.

Rizhao Biomedicine and New Materials Research Inst. of Wuhan Univ. of Tech.

Yang Du Wuhan Univ. of Tech.Fiber Bragg Grating (FBG) sensor has the advantages of a lightweight, small size, low transmission loss, high sensitivity, fast response speed, strong anti-electromagnetic interference ability, etc., especially has good flexibility and compatibility, and can be combined by a special preparation process FBG and flexible materials are made into a flexible sensor with

high-density distributed sensing. This article reviews the sensing principles of fiber gratings, flexible sensors of different materials, preparation technology, future research focus, and development trends. The silicone rubber flexible sensor, textile woven flexible sensor and other polymer flexible sensors are analyzed. The respective characteristics of the preparation technology are introduced in detail. Finally, main problems, application fields and future research directions of sensors and analyzed.

SunCIS-26 378 An Improved Calibration Method for Stereo Fisheye Vision Chengtao Cai Harbin Engineering Univ.Renjie Qiao Harbin Engineering Univ.The fisheye stereo vision system has been widely studied as primary vision sensors in the field of robot vision because of its wide view of scene. However, currently, serious image distortion has delayed its further development. To remedy this, the paper focuses on the calibration and distortion correction for stereo fisheye system. And then an improved, specialized and systematic calibration method named Stereo Fisheye Calibration Method (SFCM) will be proposed to calibrate the parameters and correct the image distortion of the fisheye stereo vision. The SFCM is introduced by the following two aspects. Firstly, a precise mathematical model based on the law of fisheye lens imaging is established, which assumes that the imaging function can be described by a general polynomial approximation. Secondly, a simple five-point calibration algorithm was implemented to find the possible solutions for relative camera pose between two calibrated views. And then a nonlinear refinement based on the maximum likelihood for internal and external parameters was carried out with Levenberg-Marquarat (LM) algorithm. Finally, using real data captured by equipment, we performed experiments covering all the necessary stages to obtain a high-performance stereo fisheye vision system. The correctness and effectiveness of the proposed method are demonstrated with the statistical analyses of the experimental results.

SunCIS-27 581 Costmap Construction and Pseudo-Lidar Conversion Method of Mobile Robot Based on Monocular Camera Haoran Fu Northeastern Univ.Qibin Chen Northeastern Univ.Zhenguo Chen Northeastern Univ.Shiguang Wen Northeastern Univ.With the development of robotics and computer vision technology, monocular cameras with low cost advantage are widely used in mobile robots. In the research of mobile robot obstacle avoidance competition based on ROS and monocular RGB camera, this paper does not directly use monocular camera for navigation and obstacle avoidance, but proposes a novel costmap creation and pseudo-Lidar transformation method based on monocular vision. The main work of this paper is to process monocular vision images, convert the image coordinate system to map coordinate system through camera calibration, display obstacle information on the costmap, extract obstacle edges and publish them in Lidar data format. This paper realizes the conversion of monocular vision to map and Lidar, completes the environment perception and mapping of mobile robots, and makes it possible to use Lidar-based navigation algorithms and toolkits.

SunCIS-28 707 Design of vehicle-mounted illuminance detection system based on ROS Xiaoyang He Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech. Chinese Academy of Sciences

Dalian Polytechnic Univ.Chuanxi Wang Dalian Polytechnic Univ.Chenyu Zhao Dalian Polytechnic Univ.Bin Hao Dalian Polytechnic Univ.Haoyu Hu Dalian Polytechnic Univ.Yuting Tong Dalian Polytechnic Univ.Min Jiang Dalian Polytechnic Univ.Li Shao Dalian Polytechnic Univ.Illuminance is closely related to our lives, and indoor light has become an indispensable part of our lives. However, excessive light or darkness indoors can greatly increase the fatigue of the human body and even trigger an accident. For this reason, illumination measurement becomes particularly important. Traditional indoor illumination measurement methods use full manual measurement. When there are many measurement points, the manual measurement workload is large and time-consuming, and when the surveyor approaches the measuring instrument, it will cause interference to the light environment. The on-board illumination measurement method proposed in recent years requires manual remote control due to its own system, so automatic detection cannot be achieved. In order to improve the efficiency of indoor manual field measurement of lighting quality, an intelligent on-board illumination detection system is designed according to actual requirements combined with the ROS robot operating system and laser SLAM technology. The system uses STM32 single-chip microcomputer to drive cars, carries lidars, models the indoor environment with laser SLAM technology, processes the illuminance sensor data through raspberry pi,

Technical Programmes CCDC 2021 and finally sends them to the upper computer, displays illuminance information, and achieves the purpose of automatically measuring illuminance. Compared with manual measurement methods, its measurement efficiency and accuracy have been greatly improved, providing an effective means for in situ indoor measurement.

SunCIS-29 1411 Flexible tactile sensor with high sensitivity based on dual-porous dielectric layer Xin Zheng Nanjing Univ. of Posts and TelecommunicationsShifeng Zhang Nanjing Univ. of Posts and TelecommunicationsShuxing Bao Nanjing Univ. of Posts and TelecommunicationsJingjin Shen Nanjing Univ. of Posts and TelecommunicationsIn this paper, the design and manufacturing of a highly sensitive capacitive-based soft pressure sensor for wearable electronic devices are presented, critical factors need to be considered including low manufacturing cost and a wide range of application scenarios. In this work, a facile approach is developed for a novel dual-porous dielectric layer is produced by incorporating the fermentation processing during sugar bulk modification. The fabricated sensors exhibit fast response and high sensitivity in the low pressure range to be able to detect weak pressure down to the weight of a mung bean. The sensor also exhibit fast response and relaxation. As a practical demonstration, the capacitive sensor is embedded into a glove for grasp motion monitoring during activities of daily living.

SunCIS-30 172 Fault-tolerant Consensus of Leaderless Multi-AUV System with Partial Actuator Breakdown Xiaogong Lin Harbin Engineering Univ.Weida Tian Harbin Engineering Univ.Wei Zhang Harbin Engineering Univ.Jia Zeng Harbin Engineering Univ.Yeye Liu Harbin Engineering Univ.In this paper, a fault-tolerant consensus problem of the leaderless second-order multiple autonomous underwater vehicle systems with partial actuator breakdown is considered. The dynamic model of AUV is linearized by the feedback linear method. The communication graph is any strongly connection directed graph, and the effectiveness of the actuator can be obtained by a physical device or analysis algorithm. The fault-tolerant consensus protocol is proposed. The stability of the fault-tolerant multi-AUV system is derived by Lyapunov functional. A numerical example illustrates the effectiveness of the fault-tolerant consensus protocol method.

SunCIS-31 465 Research on Multi-AUV Cooperative Obstacle Avoidance Method During Formation Trajectory Tracking Zheping Yan Harbin Engineering Univ.Chao Zhang Harbin Engineering Univ.Weida Tian Harbin Engineering Univ.Yeye Liu Harbin Engineering Univ.This research is oriented to the obstacle avoidance problem of multi-AUV formations in three-dimensional space. The four AUVs form and maintain a line-shaped formation on the vertical plane based on the virtual leader method to track the trajectory and achieve stable convergence. When the AUV detects the obstacle in real-time, it will carry out autonomous and coordinated obstacle avoidance operations according to the proposed improved artificial potential field method. Then, the convergence of the way is demonstrated through the generalized energy function. Finally, the simulation verifies the usability of the vertical plane formation and the effectiveness of the improved artificial potential field method. Research proves that method we proposed can achieve stable convergence of formation trajectory tracking.

SunCIS-32 541 A Fast Generation Algorithm of Communication Topology for Three-dimensional Optimally Persistent Formation Guoqiang Wang Hefei Univ. of Tech.

Key Laboratory of Process Optimization and Intelligent Decision-Making

Engineering Research Center for Intelligent Decision-making & Information Systems

TechnologiesQianwei Lv Hefei Univ. of Tech.

Key Laboratory of Process Optimization and Intelligent Decision-Making

Xiaoduo Li Hefei Univ. of Tech.Key Laboratory of Process Optimization and

Intelligent Decision-MakingXin Cao Hefei Univ. of Tech.

Key Laboratory of Process Optimization and Intelligent Decision-Making

He Luo Hefei Univ. of Tech.Key Laboratory of Process Optimization and

Intelligent Decision-MakingKey Laboratory of Urban ITS Tech. Optimization and

IntegrationTo solve the communication topology generation problem of three-dimensional optimally persistent formation with leader constraint, a fast generation algorithm based on minimum cost arborescence, arc addition and path reverse operation is proposed. Compared with the existing algorithms, this algorithm has a shorter calculation time. Finally, the simulation results further verify the correctness and effectiveness of the algorithm.

SunCIS-33 1214 An Improved Multi-robot Distributed Formation Tracking Control Based on Sensor Self-calibration: System Design and Experimental Study Ming Zhang Huazhong Univ. of Science and Tech.Liangliang Lu Huazhong Univ. of Science and Tech.Qiang Gu PS-MicroYueyue Chen Huazhong Univ. of Science and Tech.Dingxin He Huazhong Univ. of Science and Tech.In recent years, the research on formation control of multi-robot has become a hot topic. Most of methods are dependent on external positioning equipment, such as optical motion capture system. An improved distributed multi-robot formation tracking control is proposed in this paper. A distance-based approach is given, where lidars are self-calibrated to eliminate the effect of measurement inconsistencies. A robot with global positioning is set as the multi-robot leader to observe and control the formation pose and movement. A trajectory tracking controller is designed based on Lyapunov theory. The experimental results show that the proposed method can generate prescribed formations and track target trajectories accurately using global pose information of only one robot.

SunCIS-34 1374 Indoor and Outdoor Seamless Positioning Method for Open Environment Yinming Liu Chongqing Univ. of Posts and TelecommunicationsSong Wang Chongqing Univ. of Posts and TelecommunicationsWei Zuo Chongqing Univ. of Posts and TelecommunicationsMing Cen Chongqing Univ. of Posts and TelecommunicationsPositioning technology is the key of various fields such as mobile robots and intelligent vehicles. Lots of applications need to obtain indoor and outdoor positioning data continuously when the environment of themselves changes. This paper proposes an indoor and outdoor seamless positioning method for open environment based on the adaptive Federated Filter (FF). Rather than most of the existing indoor positioning methods which need to add extra equipment to the positioning target, this method uses target tracking algorithm to provide multi-target indoor positioning and uses data association method to confirm the target identity. In addition, two sub-filter built by Inertial Measurement Unit (IMU) and Global Navigation Satellite System (GNSS), IMU and indoor positioning respectively are integrated to construct an adaptive FF to realize the indoor and outdoor seamless positioning. An indoor and outdoor seamless positioning system consisting of multiple mobile units and indoor positioning units is also designed to implement the method expediently. The experiment results show that the indoor positioning method proposed in this paper is greatly improves the applicability of indoor positioning services and lightly better than the ultrawideband (UWB) and IMU fusion positioning method, and the indoor and outdoor seamless positioning method can obtain stable and smooth positioning data in the whole positioning process.

SunCIS-35 1379 Dynamic Hunting Method for Multi-USVs Based on Improved Game Theory Model Yuanhui Wang Harbin Engineering Univ.Yang Liu Harbin Engineering Univ.Kechao Xie Harbin Engineering Univ.For multi-USVs hunting problem, a muti-USVs dynamic hunting method based on improved game theory model was designed, and the traditional grid model was improved, the hunting USVs got a smoother hunting path, and the ability to avoid collisions between hunting boats and obstacles was gained. Firstly, mathematical description was made for the hunting, and a Environmental model was established, the termination conditions for hunting were designed. Combined with the actual hunting behavior, the grid strategy set of the hunting USVs was improved. After constructing the profit function for evaluating the pros and cons of the strategy, the dynamic hunting model based on improved game theory was established. The single-step optimal strategy was gained by solving the Nash equilibrium, and then expanded to get the complete optimal dynamic hunting strategy. Finally, the effectiveness of the designed dynamic hunting method was verified by simulation. The effects of different number of hunting USVs and the speed of escaper were compared and verified.

SunCIS-36 1401 The Resource Optimization Scheduling Algorithm of Ad-hoc Cloud Robotics for Complex Collaborative Tasks Jingqi Jiang Northeastern Univ.

Technical Programmes CCDC 2021 Peng Ji Northeastern Univ.Xianxin Wang Northeastern Univ.Qifeng Liu Northeastern Univ.Multi-robotics cooperation to complete specific complex tasks is the key problem in the field of robotics research, and ad-hoc cloud technology provides a good computing power carrier for multi robot cooperation. In this paper, an ad-hoc cloud resource scheduling algorithm based on reinforcement learning is proposed. Firstly, the task scheduling process is modeled, and the resource scheduling model and resource scheduling performance index are designed. Furthermore, a resource prescheduling algorithm based on Q-Learning is designed to pre schedule the simulated task set. On this basis, a resource optimal scheduling model based on DQN is proposed, which uses neural network instead of Q-table to realize the evaluation and learning of ad-hoc cloud resources. It can be seen from the experimental results that the algorithm proposed in this paper effectively realizes the resource scheduling task of ad-hoc cloud platform, and can provide powerful technical support for multi robot collaborative sharing computing.

SunCIS-37 1601 Dynamic Deploying among Multi-scout based on Two Optimal Objectives Leigang Wang Key Laboratory of Complex Electromagnetic

Environment Effects on Electronics and Information System

Limin Xu Zhengzhou Univ. of AeronauticsXinlong Wei Zhengzhou Univ. of AeronauticsThe relative deployment among multi-scout and target affects the information value of the relative measurement for estimating the target’s position. For the scouts whose positions are dynamic and undetermined, to optimize their deployment for the best relative measurement on the target and obtain the optimal position estimation on themselves at the same time, this paper takes the determinant of Fisher information matrix as the index for valuing the relative measurement information. The first objective is established by maximizing the utilization of relative measurement information for estimating the position of the target and the scout. Meanwhile, regarding the position of the scouts and the target as the variables to be optimized, the second objective is established by minimizing the error variance of the overall position estimation of them. The two objectives are mutually conditional and iterating each other. The simulation results show that, compared with the traditional method, the proposed method can achieve more objective spatial deploying. The proposed method not only improves the accuracy of target position estimation, but also helps to estimate the position of scouts.

SunCIS-38 479 A Mobile Robot Tracking Controller Design and Implementation on ROS-MATLAB based Experiment System Zhenning Yu Beijing Normal Univ.

Univ. of MacauSeng Fat Wong Univ. of MacauThe aim of this paper is solving the problem of an autonomous mobile robot indoor trajectory tracking based on Simultaneous Location And Mapping (SLAM) technique. Faced with the high-cost experiment equipment, this paper introduces a solution via lower-cost hardware. In addition, the system improves mobile robot tracking rate of accuracy by an adaptive controller. Therefore, it can help to rapid development for self-driving industry regarding cost-effectiveness. Similar with a human driver, the adaptive controller optimizes the mobile robot movement based on Lyapunov direct method. In addition, the controller implementation task was finished on a practical low-cost mobile robot, Turtlebot2. In future, the research will focus on a path-planning algorithm when the mobile robot moving on the way and facing with coming obstacles. This technology can be applied to the self-driving car in industry, and improves the feasibility of outdoor automatic logistics.

SunCIS-39 206 Autonomous Cognition and Correction System of Robot Service Based on Emotional Information and Case-Based Reasoning Fei Lu Shandong Univ.Wenjia Si Shandong Univ.Guohui Tian Shandong Univ.In the human-robot coexisting environment, robots can not only autonomous cognition tasks and provide intelligent services, but also focus on the warmness of services. In this paper, human emotion and the real-time scene information are utilized to realize the intelligence and emotional intelligence of the service. Emotional information and case reasoning are combined to build an autonomous cognition and correction system of robot service. Case database based on smart space scene data containing user emotional information was established. Using nearest neighbor strategy for case retrieval, autonomous cognition by reusing source case results is achieved. The user preference model is combined with emotional feedback information to correct the service content. Simulation results show that the method can significantly improve the service quality of the service robot and the harmony of human-robot interaction.

SunCIS-40

329 Augmented Reality and Deep Learning Guided Task Oriented Robot Qiang Duan Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Xiangyu Zhu Shandong Inst. of New Generation Information Industry Tech. Co. Ltd

Inspur Tech. CenterLuoluo Feng Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Xue Li Shandong Inst. of New Generation Information Industry Tech. Co. Ltd

Inspur Tech. CenterQingshan Yin Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Jing Zhao Shandong Inst. of New Generation Information Industry Tech. Co. Ltd

Inspur Tech. CenterMing Gao Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Longlong Wang Shandong Inst. of New Generation Information Industry Tech. Co. Ltd

Inspur Tech. CenterQingcai Luo Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Jianhua Wang Shandong Yingxin Computer Tech. Co., Ltd.Rui Li Shandong Inst. of New Generation Information

Industry Tech. Co. LtdInspur Tech. Center

Robot navigation is one of the key features in robot system, which can be done in several ways. Different approaches show both strength and weakness, and the choice usually relies on the targeted scenario. The current work proposes a robot navigation system that works indoor for a certain specified task. In the system, augmented reality, SLAM (simultaneous localization and mapping) and deep learning are used to guide a robot. Augmented reality acts an interface bridging the physical and virtual world together. SLAM offers the robot the ability to sense the physical world. Deep learning enables the robot to recognize objects. As a result, a robot can follow the expected routine to get a task done without colliding.

SunCIS-41 443 Surgical Assistance System Based on Visual Servoing Ruiqi Rao Northeastern Univ.Fei Wang Northeastern Univ.Sike Luan Northeastern Univ.Qibin Chen Northeastern Univ.Jiaqi Wei Northeastern Univ.With the continuous improvement of medical services, surgical assisted robots have important application prospects, and the development of its technology has high requirements for the coordination of manipulator and doctor’s hand. This paper designs a surgical assistance system based on visual servoing to solve the problem of the manipulator dynamically following the doctor’s hand. The system combines computer vision, automatic control principles, manipulator control and motion planning technology. In this paper, the AprilTag visual positioning algorithm is called to identify the AprilTag's posture, and the posture of the target point relative to the AprilTag is specified to determine the movement of the manipulator from the initial posture to the desired posture, and the Kalman filter algorithm is used to estimate the movement speed of AprilTag , carry out feedforward compensation to the servo system to correct the errors generated during the motion process. In simulation experiments and physical experiments, the performance of the system is verified under different illumination conditions under the conditions of AprilTag static and moving. The experimental results show that the system has lower tracking error and higher stability. It can identify targets under different lighting conditions and different angles, and can dynamically follow the AprilTag on the doctor’s hand to achieve the purpose of dynamically following the doctor’s hand. It has adaptive planning and anti-interference for both static and dynamic scenarios.

SunCIS-42 571 Vision-Based Robotic Manipulation of Intelligent Wheelchair with Human-Computer Shared Control Siyi Du Northeastern Univ.Fei Wang Northeastern Univ.Guilin Zhou Northeastern Univ.Jiaqi Li Northeastern Univ.Lintao Yang Northeastern Univ.Dongxu Wang Northeastern Univ.Based on human-computer shared control, this paper introduces a novel robotic manipulation fashion combining computer vision and brain-computer interface (BCI). Designed for disabled groups, the intelligent wheelchair with our proposed method exhibits the precise robotic manipulation ability but also the human decision-making capabilities, which will bring better life quality for the disabled. The overall

Technical Programmes CCDC 2021 pipeline includes three parts: asynchronous brain-computer interface based on steady-state visual evoked potential (SSVEP), vision detection with deep network and robotic manipulation of UR5 robot. Particularly, first, the user receives the periodic visual stimulation with different frequencies and then electroencephalography (EEG) signals of the user are collected by EEG cap. Second, we preprocess the EEG signals and extract feature embedding. To judge the frequency of the stimulus signals received by the user, the canonical correlation analysis (CCA) algorithm is used to fit and compare it with the standard EEG signal. In our work, the signals with different frequencies corresponds to different types of objects item by item. Third, we apply the off-theshelf vision detection algorithm, Mask-RCNN, to output the position of the object corresponding to the detected EEG in the image frame. UR5 robot arm plan manipulation path according to the position of objects transferred by robot operating system (ROS). Extensive experiments show that our method can achieves performance with more than 90% accuracy and the user can control the robot arm to grab the expected object accurately through BCI.

SunCIS-43 606 Upper Limb Exoskeleton Rehabilitation System Based on Biofeedback Junlin Li Northeastern Univ.Chen Cui Northeastern Univ.Bingxin Bo Northeastern Univ.Yuze Chi Northeastern Univ.Chengdong Wu Northeastern Univ.Fei Wang Northeastern Univ.With the rapid growth of people with dyskinesia and the increasing demand for efficient sports rehabilitation, a set of upper limb exoskeleton rehabilitation system based on biofeedback is designed in this paper. The system uses biofeedback as a reference for exoskeleton movement and uses Generalized Regression Neural Network (GRNN) to establish the relationship between the surface electromyography (sEMG) and joint movement. The experimental results show that compared with Back-Propagation (BP) neural network and Support Vector Machine (SVM), the proposed method takes shorter time, and the root-mean-square-error is reduced by 9.5% and 17.3%, which achieve high-precision motion intention recognition. The introduction of virtual reality technology to achieve real-time motion following and immersive experience has high motion following accuracy, the average error of which is 0.08m.

SunCIS-44 691 Improvement of 5-Point Data Glove and Implementation of Fingertip Mapping Jianjun Yu Beijing Univ. of Tech.Xuchen Li Beijing Univ. of Tech.Guoyu Zuo Beijing Univ. of Tech.Zihao Zhang Beijing Univ. of Tech.Jie Jia Beijing Univ. of Tech.In this paper, because of the low accuracy of traditional 5-point data gloves in detecting finger joint angle, that can only be used for master-slave hand gesture teaching in teleoperation. We design a 10-point data glove improvement method by increasing the bend sensors. The angle information of finger joints can be calculated based on the motion rule of finger. This method improves the accuracy of glove information collection. At the same time, fingertip mapping algorithm is introduced to solve the problem from the angle of hand joints based on fingertip position information. This method makes the glove can be used in the teleoperation of finger motion accuracy requirement of high grasping movement. Raise the application range of glove. Build a slave virtual hand in U3D for simulation experiment. Experimental results show that compared with the traditional 5-point data glove, the improved data glove can more accurately measure the angle of each finger joints. The fingertip mapping algorithm is more accurate than the joint angle mapping, so the glove can be used to grasp the motion in teleoperation.

SunCIS-45 972 Research on Human Motion Data Filtering Based on Unscented Kalman Filter Algorithm Hongtao Yang Changchun Univ. of Tech.Xiaoyuan Li Changchun Univ. of Tech.Xiulan Li Changchun Univ. of Tech.With the continuous development of economy in China, manual production has long been unable to meet the current social needs, and more and more companies have begun to use robots instead of manual operations to carry out changes[1]. In the manufacturing field, human-machine collaborative manufacturing can effectively combine the advantages of industrial robots and workers, reduce production costs, improve product quality, make manufacturing more flexible and individualized, and develop in the direction of high speed, high precision and high efficiency. A key factor in the manufacturing of human-machine collaboration is the safety and security, namely industrial robots operate with a staff job at the same time in the working environment, not harm, directly or indirectly to the staff. Therefore, how to ensure that the industrial robot can accurately obtain the current position of the staff and predict the future position, ensure the safety of the staff, and clarify the intention of the staff is an urgent problem to be solved. In this article, a

method aimed at improving the accuracy of Kinect v2 sensor skeleton data is proposed. This method uses the kinematic filtering method of unscented Kalman filter to try to achieve consistency by combining observation data with the motion prediction data being executed. Finally, the Kinect v2 SDK is corrected by extracting motion information and constraining the length of human bones. Some errors occurred in the measurement data. The data source of this experiment is measured by Kinect v2 SDK. The algorithm is realized by Python, and the result of this experiment is compared with the data of Kinect v2 SDK.

SunCIS-46 1049 Interaction with UAV Formation based on Gesture Recognition with Leap Motion Jiang Jun Air force Engineering Univ.Boxin Zhao Air force Engineering Univ.Peng Zhang Air force Engineering Univ.Fuyao Jia Air force Engineering Univ.Peng Fang Air force Medical Univ.UAV formation can combine different aircraft platforms and mission loads, and it also can give full play to the quantity and coordination advantages of formation. At present, a single UAV generally adopts human-computer interaction modes such as mouse, keyboard and sense of operation. When this interaction mode performs more complex combat tasks for UAV formations, its interaction performance and efficiency are often greatly restricted. In this paper, based on Leap Motion gesture recognition equipment, online recognition methods as well as operation and control strategies of various gesture categories for UAV formation operation and control are designed. Through classifying and redeveloping the recognizable gesture contents, control instructions for UAV formation are generated, which can be used to improve the human-computer interaction efficiency of UAV formation.

SunCIS-47 1052 Research on UAV Control Method Based on Eye Tracking Jiang Jun Air force Engineering Univ.Boxin Zhao Air force Engineering Univ.Peng Zhang Air force Engineering Univ.Zhenkun Chen Air force Engineering Univ.Fang Peng Air force Medical Univ.To a certain extent, eye movement behavior can reflect people's psychological cognitive activities on things, and can be used for auxiliary control of drones. This paper designed an architecture and function of the manipulation intention detection module. The eye movement data was processed and transformed to the command of drones, by detection of the different eye movement pattern of operators. The re experimental results showed that the eye movement behavior pattern detection module can realize the rapid and accurate recognition of the operator's eye movement behavior patter, which can be used to control the external devices alike drones.

SunCIS-48 1054 UAV Formation Flight Control by Using the Surface Electromyography Signals Jiang Jun Air force Engineering Univ.Boxin Zhao Air force Engineering Univ.Peng Zhang Air force Engineering Univ.Wanhui Guo Air force Engineering Univ.Peng Fang Air force Medical Univ.Gesture, an important mode of human natural behavior, has the advantages of rich semantics, natural flexibility, simplicity and convenience, and has become a research focus of new human-machine interaction technique. This paper, take the multi-UAV formation human-machine coordinated operation and control as the background, based on the gesture recognition method of surface electromyography(sEMG), and researches the gesture interaction technique for multi-UAV formations, which makes the operators use gestures to complete the flight operation and control of UAV formations without space constraints.

SunCIS-49 93 A Novel Path Tracking Controller for Magnetic Guided AGVs Zhenghong Jiang Nankai Univ.Yucheng Xu Nankai Univ.Lei Sun Nankai Univ.Due to its low cost, the magnetic guided AGV is widely deployed in the industrial logistics. Since the AGV will follow a track made of an adhesive magnetic tape affixed on the floor, the position followed by the tape could not be measured directly by the magnetic sensor. Thus the traditional trajectory tracking controller would not be used. Therefore a novel path tracking controller is developed in the paper to guarantee the position accuracy of the AGV. According to the description of the kinematics of the AGV and its position and orientation errors, the proposed controller would guarantee the uniformly asymptotical stability by Lyapunov theory. And the effectiveness of the controller is validated by the experimental results.

SunCIS-50 125

Technical Programmes CCDC 2021 A Mobile Robot Path Planning Method Based on Safe Pathfinding Guidance Tong Bie Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Xiaoqing Zhu Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemXiaoli Li Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Xiaogang Ruan Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemThe problem of path planning in the field of robot navigation is now a major research hot spot. On the premise of ensuring that the robot can successfully navigate to the target point, the safety of the path also needs to be considered. In order to find a safer path, a safe pathfinding path planning method is proposed, which introduces two safety parameters that affect the path selection: hazard coefficient and movement coefficient. After defining two security parameters, design an appropriate reward function and use A2C algorithm and PPO algorithm to guide the robot to conduct reinforcement learning. The experiment will be conducted on a two-dimensional grid map containing various obstacles. By conducting comparative experiments, it is verified that the safety pathfinding method proposed in this paper is feasible and reasonable, and can enable the robot to choose a safer road instead of a faster road when planning the path.

SunCIS-51 367 Safe Mobile Robot Path Planning Based on an Improved JPS Algorithm Yiren Cao Univ. of Science and Tech. of ChinaShaoqing Chen Univ. of Science and Tech. of ChinaYong Wang Univ. of Science and Tech. of ChinaJump point search algorithm is an improved A* algorithm aiming at the problem in the computation, where the quality of path is not considered and even shows worse performance in security than traditional A* because of the rules imported to speed up the path-finding procedure. This paper proposed an algorithm to improve the security and smoothness of path planned by jump point search algorithm with the modification of its pruning rules. This improved way is not only based on the conceptions of JPS, but also combined with the method to achieve similar improvements of traditional A*, which was realized by a modified evaluation function and recursive check of nodes. To prove the improvements of path quality and consistent performance in computation time, simulation is carried out in grid map. The result demonstrates that the improved jump point search algorithm could effectively increase the quality of the final path especially in security, while the performance in speed of the proposed method is as good as the original jump point search algorithm and much more effective than A*.

SunCIS-52 428 Research on Multiple Mobile Robots Scheduling Based on Penalized Hybrid A* Algorithm and S-T Graph Ying Li Shanghai Jiao Tong Univ.Nan Xia China Shipbuilding Industry Corporation 708th Inst.Xuhong Wei China Shipbuilding Industry Corporation 708th Inst.Gumin Jin Shanghai Jiao Tong Univ.Jianxun Li Shanghai Jiao Tong Univ.With the wide application of mobile robots, multiple mobile robots scheduling problem has attracted much attention. When parked in a confined space, due to the fixed and single exit, mobile robots are easy to cause conflicts in the outbound process, resulting in low efficiency. In order to improve the overall performance of mobile robots, a scheduling scheme for multiple mobile robots is proposed in this paper. In view of mobile robots with complicated kinematic constraints, penalized hybrid A* algorithm is used to obtain a smooth path. The improved A* algorithm based on S-T graph is adopted to determine velocity of mobile robots assuring collision-free with moving objects. The experimental simulation demonstrates that the scheme can make multiple mobile robots of good performance leave the confined space as soon as possible along the smooth path.

SunCIS-53 684 Artificial Neural Network with Bayes' Rule for Reasoning Task-Oriented Grasp Guoyu Zuo Beijing Univ. of Tech.

Beijing Key Laboratory of Computing Intelligence and Intelligent Systems

Hongxing Liu Beijing Univ. of Tech.Beijing Key Laboratory of Computing Intelligence and

Intelligent SystemsJiayuan Tong Beijing Univ. of Tech.

Beijing Key Laboratory of Computing Intelligence and Intelligent Systems

For the cognitive process of human grasping, the grasp position for a specific object is not only highly related to its inner structural morphology but also with its subsequent functional purpose of the intended

manipulation task. In this paper, we propose a Bayesian reasoning improved neural network (BRNN) to identify the task-oriented grasp positions. In this model, the grasping task is input as a hyperparameter into BRNN to control the feature learning of object. A telescopic translator is learned to transform the task-oriented parameter distribution of the BRNN model to obtain the grasp positions of the current task based on the previous learned task. A task-switching mechanism is realized to switch the BRNN reasoning modes for different tasks. Experimental results show that when there is no task constraint, the model has the grasp detection accuracy of 95.2% on the Cornell Grasp Dataset, a little less than the state-of-the-art performance of 96.0%. However, with the task constraint, the model has the accuracy of 95.1% and 95.6% on the selfbuilt BJUT-Grasp dataset for the reasoning of the non-function-use grasp tasks and function-use grasp tasks, respectively. For the visual 2D images, the BRNN model improves the reasoning ability for grasp detection and shows excellent ability in the task-oriented grasp position reasoning.

SunCIS-54 765 iDRM & Improved MaxiMin NSGA-II-based Motion Planning for a Humanoid Mobile Manipulator System Yan Wei Chang'an Univ.Huangfei Yin Chang'an Univ.Wenzhen Li Chang'an Univ.Autonomic mobile base (MB)’s location & upper-body’s configuration design and control are two of the fundamental issues to realize robotic autonomy. Besides, the human-like behaviors capacity is essential for humanrobot interaction scenarios. Thus, to achieve human-like autonomy, this paper proposes a motion planning and tracking strategy for a redundant dual-arm humanoid mobile manipulator system. Specifically, the inverse dynamic reachability map (iDRM), the improved MaxiMin NSGA-II and the direct-connect bidirectional RRT & gradient descent algorithms are employed. Firstly, iRM is constructed and used to design potential MB’s location area. Then, the improved MaxiMin NSGA-II algorithm is used to determine the desired end-pose based on the selected MB’s location area. After the desired end-pose is designed, the direct-connect bidirectional RRT & gradient descent algorithm will be used to generate the approaching trajectory from the initial pose to the desired end-pose. Finally, the RBFNN controller is used to track the planned trajectory. On the other hand, if there are new obstacles, iDRM will be obtained by updating iRM. iRM and iDRM accelerate the desired end-pose design by narrowing MB’s searching area. Moreover, to achieve human-like motions five criteria including the end-effectors’ (EE) displacement with respect to MB are proposed. Several motion planning simulations are realized that validate the proposed strategy.

SunCIS-55 952 Spatial Representation Model Based on Grid Cell to Place Cell Lingmei Ding Key Laboratory of Advanced Perception and

Intelligent Control of High-end EquipmentAnhui Polytechnic Univ.

Yukun Zhang Key Laboratory of Advanced Perception and Intelligent Control of High-end Equipment

Anhui Polytechnic Univ.Mengyuan Chen Key Laboratory of Advanced Perception and

Intelligent Control of High-end EquipmentAnhui Polytechnic Univ.

Xuechao Yuan Wuhu Googol Automation Tech. Co.,LtdA biologically heuristic map construction and autonomous localization algorithm is proposed inspired by the cognitive mechanism of hippocampal structures to address the problems of limited localization accuracy and environmental noise interference in simultaneous localization and mapping of mobile robots in unknown environments. The self-motion information of the mobile robot and the external visual landmark information were used as inputs to build the location-aware model from the grid cell to the place cell and visual landmark model, respectively. The visual information extracted by the visual landmark model is combined with the location information obtained by the location-aware model through Hebbian network to construct the spatial topology map. A grid cell reset mechanism is added to the system to limit the number of error nodes generated, and reduce the adverse effects of environmental noise on robot positioning. The experimental results show that the proposed algorithm in this paper enables the construction of spatial topology maps with a low number of error nodes generated when there is a grid cell reset mechanism.

SunCIS-56 1679 Research on Extraction Method of Navigation Line of Substation Wheeled Inspection Robot Based on Machine Vision Chen Huang Changchun Univ. of Tech.Dong Qiu Changchun Univ. of Tech.Aiming at the problem of different roads for intelligent inspection robots in substations, a navigation line extraction method suitable for three types of road conditions is proposed. First, extract the region of interest from the acquired image, perform grayscale processing and median filter denoising. Use Otsu method to binarize the denoised image, and morphological processing is used to reduce the noise of the image twice. In order to effectively filter out false edges and extract edge features better, an improved Canny edge detection algorithm is proposed. The

Technical Programmes CCDC 2021 algorithm uses the horizontal, vertical, 45 ° and 135 ° gradient templates to calculate the image gradient; At the same time, the interpolation method in non-maximum suppression is improved, and Otsu method is used to determine the high and low thresholds of the image. The algorithm optimizes the Canny edge extraction effect, and finally uses a line fitting algorithm to extract the navigation line.

SunCIS-57 1102 Cross-overlapping Hierarchical Reinforcement Learning in Humanoid Robots Kuihan Chen Nanjing Univ. of Posts and TelecommunicationsZhiwei Liang Nanjing Univ. of Posts and TelecommunicationsWenzhao Liang Nanjing Univ. of Posts and TelecommunicationsHuijie Zhou Nanjing Univ. of Posts and TelecommunicationsLi Chen Nanjing Univ. of Posts and TelecommunicationsShiyan Qin Nanjing Univ. of Posts and TelecommunicationsIn the RoboCup3D project, how to make the humanoid robot with faster running speed and more accurate kicking action is a popular research direction. In this paper, we extend the Overlapping Layered Learning method by proposing a cross-overlapping hierarchical reinforcement learning method, which is based on overlapping layered learning to smooth the action articulation by cross-learning the articulated action parameters or cross-learning the higherlevel action parameters to obtain better action execution. The article also introduces the baseline-based optimization technique and elaborates the specific optimization strategy and optimization task. Finally, the effectiveness of crossoverlapping hierarchical reinforcement learning and baseline-based optimization techniques is demonstrated experimentally.

SunCIS-58 1143 Dribble Optimization Of Humanoid Soccer Robot Based on Overlapping Layered Learning Wenzhao Liang Nanjing Univ. of Posts and TelecommunicationsZhiwei Liang Nanjing Univ. of Posts and TelecommunicationsKuihan Chen Nanjing Univ. of Posts and TelecommunicationsHuijie Zhou Nanjing Univ. of Posts and TelecommunicationsGuoxiang Zhao Nanjing Univ. of Posts and TelecommunicationsZhan Xuan Nanjing Univ. of Posts and TelecommunicationsIn recent years, the research of humanoid robots has developed rapidly, especially in the RoboCup3D soccer competition. With the reinforcement of each team’s defensive strategy, it’s hard to kick a ball successfully through the surounded opponents sometimes. However, fast and accurate dribbling skill is more effective in such critical situation. This paper mainly proposes an extended Overlapping Layered Learning method to optimize the parameters of dribbling. Firstly, we used parameters of sprint to optimize a fast dribbling skill. Then, the hierarchical optimization tasks with multifarious target points were designed to construct a accurate dribble model. Eventually, we enhanced a training method to coordinate the new dribbling skill with original kicking skills. The training results demonstrate the effectiveness of the proposed method, therefore a fast and accurate robot dribble behavior was obtained.

SunCIS-59 1580 Probabilistic Reward-Based Reinforcement Learning for Multi-Agent Pursuit and Evasion Bo-Kun Zhang Huazhong Univ. of Science and Tech.Bin Hu Huazhong Univ. of Science and Tech.Long Chen Huazhong Univ. of Science and Tech.Ding-Xue Zhang Yangtze Univ.Xin-Ming Cheng Central South Univ.Zhi-Hong Guan Huazhong Univ. of Science and Tech.The reinforcement learning is studied to solve the problem of multi-agent pursuit and evasion games in this article. The main problem of current reinforcement learning for multi-agents is the low learning efficiency of agents. An important factor leading to this problem is that the delay of the Q function is related to the environment changing. To solve this problem, a probabilistic distribution reward value is used to replace the Q function in the multi-agent depth deterministic policy gradient framework (hereinafter referred to as MADDPG). The distribution Bellman equation is proved to be convergent, and can be brought into the framework of reinforcement learning algorithm. The probabilistic distribution reward value is updated in the algorithm, so that the reward value can be more adaptive to the complex environment. In the same time, eliminating the delay of rewards improves the efficiency of the strategy and obtains a better pursuitevasion results. The final simulation and experiment show that the multi-agent algorithm with distribution rewards achieves better results under the setting environment.

SunCIS-60 724 Design and Analysis of a Piezoelectric-Actuated Symmetric Compliant Microgripper Zekui Lyu Univ. of MacauQingsong Xu Univ. of MacauPrecise and stable operations in micro-manipulation and micro-assembly require high-performance microgrippers. To enable predominant static and dynamic characteristics, a novel piezoelectrically actuated compliant

microgripper is designed and analyzed in this paper. The microgripper realizes a large gripping stroke through displacement amplification using the integration of a compliant bridge mechanism, an L-shaped mechanism, and a levered parallelogram mechanism. Optimization technology based on response surface is applied to generate a microgripper with sizeable gripping stroke and high natural frequency. The influence of structural parameters on microgripper performance is studied, and critical structural parameters are determined by the optimization. Finite element analysis method has been applied to verify the performance of the designed microgripper. Results show that the microgripper can grasp micro-objects with the maximum jaw motion stroke of 298.6 μ m and natural frequency of 825.91 Hz. The microgripper proposed in this paper is expected to complete the operation task of micro-manipulation.

SunCIS-61 48 Localization and Navigation System of Patrol Robot Based on 3D Lidar Xuejiao Yan Inst. of Aerospace System Engineering ShanghaiChengpeng Gu Inst. of Aerospace System Engineering ShanghaiWeidi Chen Inst. of Aerospace System Engineering ShanghaiSong Jiang Inst. of Aerospace System Engineering ShanghaiMeng Chen Inst. of Aerospace System Engineering ShanghaiAiming at the large-scale outdoor environment, a localization and navigation method based on 3D lidar is proposed, and a localization and navigation system for patrol robot based on 3D lidar is designed and completed. Firstly, the system records the patrol points and the environment 3D point cloud data traversed by the patrol robot in the remote-control mode. Then the recorded point cloud data is used to establish the environment global point cloud map, and the patrol path is planned according to the environment global map and patrol points. After obtaining the environment global map, the localization information of the robot is calculated by matching the current lidar scanning data with the global map. The experiments in outdoor large scene show that the navigation system can complete the perception of the inspection environment, map building, localization, and path planning, which can realize the high-precision trackless navigation of the robot, and ensure that the robot can accurately reach the designated patrol points to carry out patrol work.

SunCIS-62 162 Research and Development of Vision Based Target System with Somatosensory Control Minrui Hou Heilongjiang Univ.

Key Laboratory of Information Fusion Estimation and Detection

Yongbin Li Harbin Huade Univ.Ding Wang Heilongjiang Univ.

Key Laboratory of Information Fusion Estimation and Detection

At present, the somatosensory target system game equipment in the market is still in its infancy. According to the current market situation and future development trends, this paper designs a home somatosensory target system. This design uses computer vision technology to realize somatosensory operation, discarding the throwing objects in traditional target system games, making it convenient, safe and improving its entertainment. This thesis first analyzes the requirements and performance index of the home somatosensory target, determines the overall design concept, and then designs the recognition algorithm, using computer vision technology to determine the thrower’s throwing posture, and then determine it the accuracy of its throw. Finally, the system is programmed to realize its various functions, and to debug the various functions of the system. Experiments have proved that the system can determine the posture of the body after the throw is completed by the relative positional relationship between the hand and the head in the image, and use this as a basis to determine the accuracy of the throw, and then give the corresponding score. The system functions well and achieve the expected results.

SunCIS-63 662 A Method for Navigation System of Security Robot based on Millimeter Wave Fangdong Li Anhui Normal Univ.

Anhui Province Engineering Laboratory on Information Fusion and Control of Intelligent Robot

Rui Zheng Anhui Normal Univ.Southeast Univ.

Anhui Province Engineering Laboratory on Information Fusion and Control of Intelligent Robot

Xiaoming Liu Anhui Normal Univ.Anhui Province Engineering Laboratory on

Information Fusion and Control of Intelligent RobotZibao Lu Anhui Normal Univ.

Anhui Province Engineering Laboratory on Information Fusion and Control of Intelligent Robot

Chuanchao Zhao Anhui Normal Univ.Anhui Province Engineering Laboratory on

Information Fusion and Control of Intelligent RobotLi Gong Anhui Normal Univ.

Technical Programmes CCDC 2021 Anhui Province Engineering Laboratory on

Information Fusion and Control of Intelligent RobotThe complexity of their working environment increases along with the expansion of the application field of security robots. Navigation system with the style of visual and laser are no longer applicable in special environments such as smoke, dust, and darkness. To solve this problem, based on the research on the principle of detecting distance with millimeter-wave radar, a multi-stage canceller is designed to reject the static clutter in the indoor environment, and then a dynamic threshold detector is designed to stabilize the detection performance of the radar according to the changing noise environment in the indoor environment. Finally, a millimeter wave navigation scheme based on principle of triangulation is proposed. Experimental results show that the robot can run along a set route. The x-axis error is about 0.09 m, the y-axis error is about 0.08 m, and the positioning error is about 0.12 m. Therefore, based on this method, the robot can run autonomously in dark and smoke environment.

SunCIS-64 689 Design of an Odor Search Robot System Based on Open Sampling System Qingfeng Wang Jilin Univ.Shuaiqi Wang Jilin Univ.Huaixu Ni Jilin Univ.An odor source search robot system based on open sampling system is designed in this paper. First, a tracked vehicle equipped with five sensor arrays and cameras which positions can be adjusted arbitrarily is designed for intelligent and fast gas sensing and odor source localization. Then, the circuit system is designed based on MCU of Raspberry Pi. The gases information from the olfactory system is uploaded to the host computer via Wi-Fi and the video information is captured on the web page using OpenCV. If needed the operator can judge the direction of the odor source through the information displayed on the control panel and controls the robot to search for the odor source. Finally, the particle swarm optimization algorithm is proposed for the odor source location of multi-robots and the simulation is realized by Python and Fluent. The robot system proposed in this paper can detect the concentration of gases accurately and find the odor source quickly.

SunCIS-65 883 Design and Development of an Underwater Search and Rescue Vehicle Mingzhi Chen Shanghai Maritime Univ.Daqi Zhu Shanghai Maritime Univ.Wen Pang Shanghai Maritime Univ.Bing Sun Shanghai Maritime Univ.Zhenzhong Chu Shanghai Maritime Univ.Shuxuan Guo Shanghai Maritime Univ.Remotely operating vehicles have been used in many specific regions. Underwater search and rescue is difficult but very important, and ROV can help accomplish this task. In this work, a ROV is designed and developed to assist the underwater search and rescue task. The ROV is an open frame type and loaded with many sensors to scan the underwater areas. It is about 76 kilograms and can meet with the requirement to sail at 4 knots. Through computational fluid dynamics (CFD) calculation, its hydrodynamic performance is tested. After the electrical design is completed, the control and user interface software is written with C and C # languages. Both manual and automatic controls are devised for the ROV. In the manual control mode, the ROV is operated manually by the joysticks. While in the automatic control mode, it can run in fixed depth and direction. From the experiments, the ROV sails smoothly. It has finished the auto-depth and auto-direction tests, which can be helpful for the underwater search and rescue.

SunCIS-66 1673 Numerical simulation analysis and optimization of modular joint temperature field based on ANSYS Tao Zhang Dalian Univ. of Tech.

Inst. for Robotics and Intelligent ManufacturingKey Laboratory of Networked Control Systems

Wenjing Xu CRRC DALIAN R&D Co., Ltd.Zizhao Huang Inst. for Robotics and Intelligent Manufacturing

Key Laboratory of Networked Control SystemsUniv. of Chinese Academy of Sciences

Zhenqiang Yang Dalian Univ. of Tech.Xiyou Chen Dalian Univ. of Tech.Bingjie Zhao Inst. for Robotics and Intelligent Manufacturing

Key Laboratory of Networked Control SystemsHualiang Zhang Inst. for Robotics and Intelligent Manufacturing

Key Laboratory of Networked Control SystemsIn the low-speed state, temperature change has a significant impact on the working efficiency of a harmonic reducer. Therefore, since modular joints used in collaborative robots arbitrarily arrange nodes and meshes through the Finite Element Method (FEM), a temperature field calculation model of the integrated joints was established in this study. By utilizing the ANSYS software, a steady-state temperature field distribution of the modular joint was calculated, and the temperature rise of the harmonic reducer was reduced by changing the thermal resistance. Comparing the results demonstrated that the method was effective, which is very

important for the safe operation and performance improvement of modular joints.

SunCIS-67 116 Optimization Model Analysis for Emergency Medical Rescue System Xiangqian Xu National Univ. of Defense Tech.Zeshui Liu National Univ. of Defense Tech.Zhuoqian Li National Univ. of Defense Tech.Jun Yang National Univ. of Defense Tech.Nan Xiang National Univ. of Defense Tech.Yajie Dou National Univ. of Defense Tech.Battlefield medical rescue and sudden disaster medical rescue are called emergency medical rescue. Predictive care should be immediately given and resuscitation or surgical treatment should be carried out for seriously wounded people as soon as possible to ensure that they can be treated within 1 hour after injury. However, there are a few common problems in the current emergency medical rescue system, which makes the system diffcult to reach the optimum. In this study, optimization model analysis was conducted to solve common problems in the current emergency medical rescue system. To make reasonable medical strategy and allocate medical resources rationally, computer simulation was carried out with data of a medical rescue incident. On this basis, four queuing treatment models were proposed and their performance indices were calculated. The simulation results showed that the optimized rescue model performed well both in the waiting time of the wounded and the work intensity of the rescue unit, which could meet the basic requirements of emergency rescue.

SunCIS-68 224 Schizophrenia Detection from Scalp EEG Using Convolutional Neural Networks and SVM Jing Liu Tianjin Univ. of Tech. and EducationYingmei Qin Tianjin Univ. of Tech. and EducationHailing Zheng Tianjin Univ. of Tech. and EducationQing Qin Tianjin Univ. of Tech. and EducationRuofan Wang Tianjin Univ. of Tech. and EducationYanqiu Che Tianjin Univ. of Tech. and EducationAutomatic detection of schizophrenia using the scalp EEG of patients is of great significance in clinical practice. This paper presents a framework based on conventional convolutional neural network (CNN) and support vector machine (SVM) for schizophrenia detection. First, the raw EEG signals are converted into two-dimensional spectrograms by short-time Fourier transform (STFT). Then, these images are fed into the CNN for automatic feature extraction. Finally, the feature vectors are input to SVM for a binary classification. The hybrid CNN-SVM model takes advantages of CNN and SVM and outperform (with accuracy of 92.86%, sensitivity of 95.19%, and false positive rate of 0.05) than most of the existing works. The proposed framework has potential for schizophrenia diagnosis using scalp EEG.

SunCIS-69 810 Research on Infectious Diseases That Can Be Infected Multiple Times Qiang Zheng Shenyang Jianzhu Univ.Qi-cheng Xu Shenyang Jianzhu Univ.In view of the possibility that cured persons in some infectious diseases may be infected again, on the basis of the original SEIR model, the situation that cured persons can also be re-infected was added to obtain the initial transmission model of infectious diseases. Then use the basic method of the cellular automata model to simulate the spread of infectious diseases when residents are restricted from traveling after the occurrence of infectious diseases. As a result, in the early stage of the outbreak, infectious diseases spread widely throughout the system. After restricting residents' travel, infectious diseases s pread in small areas in the system. Applying the model to the "Diamond Princess" case in the new crown epidemic, the final results obtained are consistent with the actual situation, verifying the rationality and reliability of the model.

SunCIS-70 1597 Study on Face Recognition Based on Improved CCN and Ensemble Learning Gui Wu Jianghan Univ.In order to avoid overfitting of face recognition results in complex Convolutional Neural Network (CNN) on small and medium face situation, this paper provides a face recognition algorithm based on improved CNN and ensemble learning. Combining the characteristics of planar networks and residual networks, the improved CNN replaces the fully connected layer with the average pooling layer to make the network structure simple and highly portable. Based on this improved CNN, the voting-based ensemble learning strategy is used to implement convex combination for results of all individual learners and more accurate face recognition could be realized. The experimental results show that the face recognition accuracy of the proposed algorithm reaches to 99.67% on face databases with a high convergence speed.

SunCIS-71

Technical Programmes CCDC 2021 527 An Improved Bayesian Zero-Velocity Detection Algorithm for Pedestrian Navigation Based on MIMU XiaoYu Zhang Naval Aviation Univ.Shaowu Dai Naval Aviation Univ.Hongde Dai Naval Aviation Univ.WenJie Qua Naval Aviation Univ.Yang Zhao Naval Aviation Univ.In the pedestrian navigation the zero-velocity detection is the prerequisite for zero velocity update, and its accuracy will greatly affect the navigation accuracy. Aiming at the problem that the Bayesian zero-velocity detection algorithm (SHOE) can't provide the accurate zero-velocity interval and that it may judge a zero-velocity interval as multiple zero-velocity intervals, this paper proposes an improved Bayesian zero-velocity detection algorithm with adaptive threshold (ISHOE). In the original algorithm, the threshold is reset when a zero-velocity interval begins. But in the improved algorithm, the threshold won't be reset untill the zero-velocity ends. Considering that the attenuation threshold will cause the acquired zero-velocity interval to last too long, this paper takes the threshold at the time when the non- zero-velocity interval enters the zero-speed interval as a fixed threshold, and collects the zero-velocity interval to obtain a more reasonable zero-velocity interval. The algorithm proposed in this paper can obtain zero-velocity intervals, and effectively avoids judging a zero-velocity interval as multiple zero-velocity intervals. In order to test the detection accuracy of the improved Bayesian zero-velocity detection algorithm with adaptive threshold, this paper compares and evaluates the new algorithm with the original algorithm (SHOE), acceleration variance detection algorithm (MV), acceleration amplitude detection algorithm (MAG) and angular velocity energy detection algorithm (ARE) under two different pace conditions. The results show that, without adjusting the threshold of the improved Bayesian detection algorithm with adaptive threshold, the algorithm can still achieve high accuracy and is suitable for applications in multiple sports modes.

SunCIS-72 13 Development Research and Engineering Application of ship with full electric propulsion Hua Zhang China Waterborne Transport Research Inst.Chang Liu China Waterborne Transport Research Inst.Lipeng Wang China Waterborne Transport Research Inst.Rui Yang China Waterborne Transport Research Inst.Ship’s combustion products will cause great harm to people's health, in order to reduce ship emissions and improve ship energy efficiency, the full electric propulsion technology ship power system is used to supply ship power. Combined with the current application status and development trend of electric ships, through the research and comparison of four kinds of commonly used electric ship technical frameworks, the development technology direction of ship with full electric propulsion technology is obtained. The results show that the optimized energy management system of the ship with full electric propulsion technology has strong practicability and high robustness under different ship load requirements, electric ship technical frameworks plays a significant role in ship energy consuming reduction and ship power output stabilization.

SunCIS-73 99 Analysis on the Knowledge Value-added Mechanism of Military-civilian Fusion Technology Cooperative Innovation Man Wang Qingdao Univ. of Science and Tech.Jie Zhang Qingdao Univ. of Science and Tech.Jiyang Yuan Qingdao Univ. of Science and Tech.Yumei Wang Qingdao Univ. of Science and Tech.First of all, this paper conducts a comprehensive analysis of the influencing factors that affect the value-added of scientific and technological collaborative innovation in order to provide a theoretical basis for the subsequent establishment of a network model.After that, use the VENSIM software to model resource sharing, knowledge value-added and innovative behaviors in military-civilian integrated scientific and technological collaborative innovation and conduct mechanism analysis, so as to visually observe the shortcomings and advantages that the scientific and technological collaborative innovation process will face.

SunCIS-74 120 Executive Heterogeneity, Executive Power and Selection of Equity Incentive Mode: Empirical Evidence from Chinese Listed Companies Miao Li Xi'an International Studies Univ.How to choose the equity incentive mode scientifically and rationally has always been a significant research issue. This study takes the Chinese listed companies that implemented stock option and restricted stock incentive schemes from January 1, 2006 to December 31, 2019 as the sample, and studies the effect of executive heterogeneity on the choice of equity incentive mode, and further reveals the moderating effect of executive power. The results show that the heterogeneity of the incentivized executive, such as age, tenure and educational background, significantly affects the choice of the equity incentive mode of company. For executive with age, long tenure and high academic qualification, the enterprise is more inclined to grant restricted stock; The larger the

proportion of male executive, the more the company prefers giving the stock option, but the impact of executive gender is not significant. Executive power has a significant negative moderating effect on the influence of executive heterogeneity on the choice of equity incentive mode of Chinese listed companies, that is, the greater the executive power, the less the influence of executive heterogeneity on the choice of equity incentive mode. After robustness test, the conclusion of the study is still valid. The conclusions of this paper provide a reference for listed enterprises to choose the equity incentive mode effectively.

SunCIS-75 123 Optimization of Underwater Cluster Operational Effectiveness Evaluation Based on Support Vector Machine Ruixiang Hu Dalian Univ.Yuanming Ding Dalian Univ.Chengzhen Zhang Dalian Univ.In modern naval warfare, the development of underwater combat groups is an inevitable trend of networking, unmanned and intelligent naval warfare. Therefore, it is very important to evaluate the effectiveness of underwater combat cluster accurately and quickly. At present, most of the system effectiveness values are the sum of the effectiveness of the subsystems, ignoring the overall emergence and nonlinearity of the system. From the point of view of system theory, this paper constructs an underwater unmanned cluster combat effectiveness evaluation model based on the improved cuckoo search algorithm and optimizes the support vector machine (SVM), and uses the SVM to solve the problems of small sample, non-linearity, high dimension and so on. The improved cuckoo search (ICS) algorithm is used to find the optimal parameters, which avoids the blindness of artificially setting penalty factors and kernel function parameters. The simulation results show that the model can evaluate the combat effectiveness of underwater unmanned cluster quickly and effectively.

SunCIS-76 138 Research on a Tripartite Model of Behavioural Choice in Innovative Strategic Management Zhenzhen Tian Shandong Univ. of Science and Tech.Xinhua Wang Shandong Univ. of Science and Tech.Jiangyong Sun Shandong Univ. of Science and Tech.Staff from the primary level are the key personnel involved in transforming innovation strategy into social value and are the "link" between innovation strategy and the market. Their behavioural choices play an important role in the effective implementation of innovation strategy. In addition, the supervision and management of the managers from the top level and middle level also provide a guarantee for the effective implementation of innovation strategies. This paper constructs a three-level innovation strategy management system consisting of the top level, the middle level and the primary level by building a tripartite evolutionary game model based on the influence of the behavioural choices of the three levels of personnel on innovation strategy. Then, this paper determines the optimal strategy by stability analysis based on the two aspects of incentive measures and constraint measures.

SunCIS-77 159 Proactive Microgrid Formation Strategy for Resilience Enhancement of Distribution Systems in Extreme Conditions Sheng Cai Nanjing Univ. of Science and Tech.Yunyun Xie Nanjing Univ. of Science and Tech.Yun Zou Nanjing Univ. of Science and Tech.Microgrid formation (MF) is a promising solution to enhance the resilience of distribution systems in extreme conditions. The traditional MF methods follow the post-outage recovery criteria, which divide the on-outage areas into multiple microgrids after the faults occur. However, such passive methods spend much time restoring the large-scale outage area, reducing the abilities of distribution systems in resisting and rapidly recovering from an extreme event. To further enhance the resilience of distribution systems in extreme conditions, this paper proposes a proactive MF method to sectionalize the distribution system into several microgrids (MGs) prior to the extreme events and ensure the power supply to critical loads when the faults occur. Specifically, an ambiguity set is constructed to describe the line failure probability distribution. And a distributionally robust optimization (DRO) model is proposed to minimize the expected amount of load shedding with regard to the worst-case distribution within the ambiguity set. Moreover, the proposed DRO model is recast to facilitate the solution with column-and-constraint generation (C&CG) algorithm. By proactively partitioning the system into multiple MGs, the resilience of the distribution system in extreme conditions is enhanced. The effectiveness of proposed method is validated by numerical simulations with the modified IEEE 37 bus system.

SunCIS-78 184 Performance Analysis of Healthcare Alliances with “Green Channel” Miao Yu Shenyang Jianzhu Univ.Wang Zhou Shenyang Jianzhu Univ.Jian Ma Shenyang Jianzhu Univ.This paper investigates the performance of a healthcare alliance with “green channel”, including general hospital (GH) and community hospital

Technical Programmes CCDC 2021 (CH) with referral patients. By analyzing the behavior of delay-sensitive patients, we mainly explore the influence of CH treatment threshold on the arrival rate of CH and the profit of GH. Firstly, we discuss the expected waiting time of patients using M/M/1 queue model according to the selection behavior of delay-sensitive patients. With the customer perceived value and utility function, we model and analyze the patient's choice behavior. Then we get the relationship between the treatment threshold and the arrival rate of CH. Secondly, by the profit model of GH, we prove that the optimal treat threshold to maximize the profit of GH exists. Finally, simulation study shows numerical observations of theoretical analysis, thereby provides valuable suggestions for enhancing performance of healthcare alliances with “green channel”.

SunCIS-79 264 A Study on the Equilibrium of Spatial Allocation of Medical Resources in Liaoning Province Miao Yu Shenyang Jianzhu Univ.Fenghao Wang Shenyang Jianzhu Univ.Jian Ma Shenyang Jianzhu Univ.This paper analyzed the equilibrium and change of the spatial allocation of medical resources at different levels in Liaoning Province by Theil index method. Based on the 2014-2018 relevant statistical yearbook data, this paper mainly adopted some indicators such as the number of institutions, the number of health technicians, the number of practicing (assistant) physicians, and the number of beds. Research results show that the allocation of medical resources in Liaoning Province presents an unbalanced situation of "rich resources in the center and weak resources in the surrounding areas" centered on Dalian and Shenyang. The balance of medical resources at the primary health service organization level is higher than that at general hospital level. However, the overall medical resources occupied by the primary health service institutions are very limited, so the equilibrium is at a low level.

SunCIS-80 288 Modified Golden Rule Correlation Values for Interval Values in Decision-making Situations Bin Zhang Northwest A&F Univ.Bingyi Kang Northwest A&F Univ.Jianfeng Zhang Northwest A&F Univ.In the process of dealing with multi-criteria decision-making problems, determining the priority of interval values is a common and important issue. When there are multiple interval values to be compared, it is diffcult to determine their priority by directly comparing the endpoints of these interval values. We need to use some processing methods to convert interval values into directly comparable related values that are closely related to some of their properties. Yager proposed in 2016 to use the golden rule to calculate correlation values of interval values, but this method has some problems. For example, when the midpoint is 0.5, the influence of interval width on the correlation value is ignored and the influence of interval width on the correlation value is not uniform. Therefore, we propose a modified golden rule correlation value calculation method. The new method better considers the actual meaning of interval midpoint and interval length in decision-making situations, and reflects it in the calculation of correlation value.

SunD02 Room02 Distributed Optimization over Networks (Invited Session) 15:50-17:10 Chair: Baoyong Zhang Nanjing Univ. of Science and Tech.

15:50-16:10 SunD02-1 741 Distributed Projection Sub-gradient Algorithm over General Directed Graphs with Compressed Communication Jing Fan Chongqing Normal Univ.Jueyou Li Chongqing Normal Univ.Lanlan Chen Chongqing Normal Univ.We consider a distributed convex optimization with the global objective function being distributed over a multi-agent network that can only communicate to their neighbors on a fixed communication digraph with row stochastic weight matrix. As the dimension of local parameter increases, distributed optimization faces a major bottleneck that is the heavy communication load due to each agent transmitting large messages to its neighbors. To tackle this issue, we propose a quantized distributed projection sub-gradient algorithm (Q-DPSG) over general directed graphs. Furthermore, we prove that our algorithm allows us to match the convergence rates of the distributed optimization with perfect-communication for convex objective functions. We also provide numerical simulations that corroborate our main theoretical results and compare the convergence properties of the distributed projection sub-gradient (DPSG) methods with and without quantization.

16:10-16:30 SunD02-2 1017 Quantizer-based distributed mirror descent for multi-agent convex optimization Menghui Xiong Nanjing Univ. of Science and Tech.Baoyong Zhang Nanjing Univ. of Science and Tech.Deming Yuan Nanjing Univ. of Science and Tech.

This paper is concerned with the constrained distributed multi-agent convex optimization problem over a timevarying network. We assume that the bit rate of the considered communication is limited, such that a uniform quantizer is applied in the process of exchanging information over the multi-agent network. Then a quantizer-based distributed mirror descent (QDMD) algorithm, which utilizes the Bregman divergence as the distance-measuring function, is developed for such optimization problem. The convergence result of the developed algorithm is also analyzed. By choosing the iteration step-size ηt =λ/√t and quantization interval vt = λ/t with a prescribed parameter , it is shown that the convergence rate of the QDMD algorithm can achieve O(1/√T), where T is the number of iterations.

16:30-16:50 SunD02-3 1031 Distributed Continuous Time Optimization Algorithms over Unbalanced Graphs Songsong Cheng Anhui Univ.Yuan Fan Anhui Univ.This paper investigates two distributed optimization schemes with or without feasible set constraints over unbalanced graphs, respectively. To overcome the inaccuracy convergence problem of unbalanced graphs, we propose two continuous time algorithms with an inexact gradient tracking dynamic. The first one is a distributed optimization algorithm without feasible set constraints and the second one is improved from the first one with feasible set constraints. Moreover, we provide the convergence analyses for the two optimization algorithms.

16:50-17:10 SunD02-4 1191 A New Algorithm for Containment Control with Nonconvex Constraints Maoyong Tu Central South Univ.Jiahao Xu Central South Univ.This paper addresses the containment control problem with nonconvex constraints and communication delays for discrete-time multiagent systems. A new nonlinear distributed control algorithm is proposed to ensure that the followers move into the convex hull spanned by the leaders. To deal with the nonconvex constraints, two scaling factors are introduced to transform the original constrained system to an unconstrained one. Due to the coupling of the two factors, a model transformation technique is employed to transform the unconstrained system to an equivalent one whose system matrix is stochastic. Based on the convexity analysis, the maximum distance between the followers and the convex hull is proved to be nonincreasing and converge to zero asymptotically. It is shown that containment control with nonconvex velocity and control input contraints can be achieved on condition that spanning trees exist in the union of the interaction topologies during each time interval. Further simulation has been performed to demonstrate the effectiveness of the theoretical results.

SunD03 Room03 Adaptive-based Intelligent Control for Uncertain Nonlinear Systems: Theory & Application (Invited Session) 15:50-17:50 Chair: Yingbo Huang Kunming Univ. of Science & Tech.

15:50-16:10 SunD03-1 878 Excess air factor-oriented modeling and analysis of fuel cell system under variable operating conditions Mingru Zeng Nanchang Univ.Qingpeng Wang Nanchang Univ.Yongfeng Lv Taiyuan Univ. of Tech.Jiawen Yang Nanchang Univ.Yong Xia Nanchang Univ.Yunjun Yu Nanchang Univ.Xiaolong Wu Nanchang Univ.Xi Li Huazhong Univ. of Science and Tech.Fuel cells are widely used in new energy vehicles, which can effectively alleviate environmental pollution and energy shortage. Proton exchange membrane fuel cell system is an important type of fuel cell system. There are still many problems in control that need to be solved urgently. Among them, the excess air ratio is a key indicator. The research object of this paper is the proton exchange membrane fuel cell system, which mainly includes air induction system, heat management system, water management system and energy management system. Finding out the excess air ratio required for a specific power demand is the focus of this article. In order to solve this problem, this paper builds a proton exchange membrane fuel cell system model based on the mechanism model, and finds the appropriate excess air ratio under the premise of setting the basic working conditions and external load requirements. At the same time, according to the change of external load demand, an appropriate excess air ratio is found, which facilitates the development of the later operating condition switching control strategy.

16:10-16:30 SunD03-2 1083 Research on Operation Mode and Control Strategy of Plug-in Hybrid Powertrain System Ziliang Zhao Shandong Univ. of Science & Tech.

Technical Programmes CCDC 2021 Yushan Li Shandong Univ. of Science & Tech.Bin Guo Shandong Univ. of Science & Tech.Yihong Zheng Shanghai Huawei Technology Co., Ltd.Yongyou Zhou Shanghai Huawei Technology Co., Ltd.As the advantage of good fuel efficiency, simple structure and easy modularization, the single motor P2 hybrid powertrain has been widely used by major automobile manufacturers. However, because the hybrid system has only one motor, the control strategy in the vehicle operating process is complex, which is also recognized as a difficult point in the industry. Based on a P2 plug-in hybrid car, the configuration, operation mode and control strategy of the hybrid powertrain are discussed in detail. The important module of the P2 hybrid powertrain configuration is CCM (Clutch Coupling with Motor) module, engine engages or disengages the DCT (Dual Clutch Transmission) by the clutch, with help of the configuration, there are six operation modes were carried out, such as engine idle stop, pure electric drive, engine drive &amp; charging battery, only engine drive, boost, and regenerative break. At the same time, the two kinds of powertrain system (AER (All Electric Range) and Blended) were discussed for PHEV (Plug-in Hybrid Electric Vehicle), according to requirement, the AER powertrain system was adopted in this paper. Based on NEDC driving cycle, the control strategy was studied, also the operation mode and performance of PHEV car were verified, from the results, the expected development goal was achieved.

16:30-16:50 SunD03-3 1142 Validation and Analyses of General DTRNN with Different Values of Selection Parameter for Solving Discrete-Form Time-Variant Complex Division Yang Shi Yangzhou Univ.Jie Wang Yangzhou Univ.Wenhan Zhao Yangzhou Univ.Xiaobing Sun Yangzhou Univ.Jiyun Wang Yangzhou Univ.Dimitrios K. Gerontitis Aristotle Univ. of ThessalonikiIn recent years, many engineering problems have gradually evolved into discrete-form time-variant problems, and the researches show the superior performance for solving above discrete-form time-variant problems in discrete-time environment using recurrent neural network (RNN) method. In this paper, a discrete-time RNN (DTRNN) model is established by using the general single-parameter square-pattern discretization (SPD) formula for solving discrete-form time-variant complex division. Specifically, first of all, according to the traditional Euclid division, the continuous-form time-variant complex division is presented, and the corresponding discrete-form time-variant complex division is established and converted. Then, the presented discretization method and the corresponding DTRNN model are developed. Finally, numerical experiments show that the effective results of general DTRNN model with different values of selection parameter.

16:50-17:10 SunD03-4 1181 Command Filter based Adaptive Neural Control for Dual Arm Robots with Asymmetric Motion Yiming Jiang Hunan Univ.Jingmo Nie Hunan Univ.This paper presents an adaptive control method for dual-arm robot systems to perform bimanual tasks under modelling uncertainties. Different to traditional symmetric bimanual robot control, we study the dual-arm robot control with relative motions between the robotic arms and the grasped object. The robot system is first divided into two sub-systems, a settled manipulator system and a tool used manipulator system. Then a command filtered control technique is developed for the trajectory tracking and contact force control. Additionally, to deal with the inevitable dynamic uncertainties, a radical basis function neural network (RBFNN) is employed for the robot, with a novel composite learning law to update the NN weights. The composite learning is mainly based on an integration of the historic data of NN regression, such that information of the estimate error can be obtained. Moreover, a partial persistent excitation condition is employed to ensure estimation convergence. The stability analysis is performed by using the Lyapunov Theorem. Numerical experimental results demonstrate the validity of the proposed control and learning algorithm.

17:10-17:30 SunD03-5 1192 Distributed Dynamic Event Triggered and Self Triggered Control for General Linear Multi-agent Systems under Directed Graphs Zhihan Wang Beijing Inst. of Tech.Qinghe Wu Beijing Inst. of Tech.This paper proposes a distributed dynamic event triggered control mechanism for general linear multi-agent systems (MASs) under directed graphs to solve the consensus problems, and extends it into a self triggered control mechanism. Different from the existing works, the proposed dynamic event triggered mechanism decreases the calculation of event triggered function and triggering times, also removes the assumption that each agent has to broadcast information continuously. By applying the dynamic event triggered function the novel event triggered mechanism is presented and proof that the agents reach a weighted average state asymptotically, and Zeno behavior is also excluded. Then it is extended to an implementable self triggered mechanism. Numerical simulations are provided to illustrate the

effectiveness of proposed theoretical event-triggered control protocol designs.

17:30-17:50 SunD03-6 1400 Adaptive Optimal Control of Nonlinear Active Suspension Systems with Completely Unknown Dynamics Xin Chen Kunming Univ. of Science and Tech.Yingbo Huang Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Guanbin Gao Kunming Univ. of Science and Tech.Jun Zhao Kunming Univ. of Science and Tech.Ride comfort, tire deflection and suspension stroke are generally conflicting in the active suspension control. To achieve a compromise among the three requirements, a novel online adaptive optimal control method is developed for the nonlinear active suspension systems subject to completely unknown dynamics. Based on the adaptive dynamic programming (ADP) algorithm, this paper proposes an identifier-critic structure with a dual neural network (NN) to realize the active suspension optimal control. To this end, a cost function concerning ride comfort, suspension stroke and control input is constructed. Then, an identifier NN with a novel adaptive law based on the parameter estimation error is used to approximate the unknown nonlinear dynamics. To obtain the optimal solution, a critic NN with the proposed adaptive law is suggested to design the optimal controller. Simulation results are provided to verify the effectiveness of the proposed method.

SunD04 Room04 Future Robotics Technology and Application (Special Session) 15:50-17:50 Chair: Weihai Chen Beihang Univ.Co-Chair: Shiqian Wu Wuhan Univ. of Science and Tech.

15:50-16:05 SunD04-1 146 A Novel Vehicle Merging Model Based on Psychological Stimuli Equilibrium Zheng Zhao Beihang Univ.Caijun Chen Engineering Research Center of Intelligent Transp

ort of Zhejiang ProvinceEnjoyor Co.

Weihai Chen Beihang Univ.Zhongcai Pei Beihang Univ.Vehicle merging is one of the typical scenarios in traffic system, and accidents easily happen when a vehicle merges to target lane from current lane. Research on merging process could benefit traffic control and reduce traffic accidents. As nearly all vehicles are manual-controlled on the road, the human factor inevitably plays a key role in the formation of traffic flow, and it obviously reflect in the interactions between different vehicles in merging process. From the driver's point of view, this paper introduces psychological stimuli equilibrium (PSE) to describe such kind of interactions, and then develops a novel vehicle merging model. Simulations under different situations are implemented to validate the proposed model. As a general analysis method, this model can also be applied in other man-machine hybrid system research, such as multi robot control and human-computer interaction, which will contribute to the development of advanced robot technology.

16:05-16:20 SunD04-2 474 Single image dehazing via combining the prior knowledge and CNNs Yuwen Li Wuhan Univ. of Science and Tech.

Nanchang Inst. of Tech.Chaobing Zheng Wuhan Univ. of Science and Tech.Shiqian Wu Wuhan Univ. of Science and Tech.Wangming Xu Wuhan Univ. of Science and Tech.Aiming at the existing single image haze removal algorithms, which are based on prior knowledge and assumptions, subject to many limitations in practical applications, and could suffer from noise and halo amplification. An end-to-end system is proposed in this paper to reduce defects by combining the prior knowledge and deep learning method. The haze image is decomposed into the base layer and detail layers through a weighted guided image filter (WGIF) firstly, and the airlight is estimated from the base layer. Then, the base layer image is passed to the efficient deep convolutional network for estimating the transmission map. To restore object close to the camera completely without amplifying noise in sky or heavily hazy scene, an adaptive strategy is proposed based on the value of the transmission map. If the transmission map of a pixel is small, the base layer of the haze image is used to recover a haze-free image via atmospheric scattering model, finally. Otherwise, the haze image is used. Experiments show that the proposed method achieves superior performance over existing methods.

16:20-16:35 SunD04-3 645 A Survey of Extrinsic Parameters Calibration Techniques for Autonomous Devices Jiwei Nie Northeastern Univ.Feng Pan Northeastern Univ.Dingyu Xue Northeastern Univ.

Technical Programmes CCDC 2021 Ling Luo Northeastern Univ.In recent years, autonomous driving devices have been widely used in civil traffic, disaster environment, commercial run and many other applications in real world, due to the multi-sensor fusion technique is widely applied to sense surroundings. The aim of this paper is to review the methods of range-image extrinsic parameters calibration, which is a popular choice of sensors fusion for robotics perception in detail. More than 50 existing methods are categorized into two main groups: offline method and online method, and the light of each typical method is introduced in each category. Finally, the discussion is presented. It helps to analyze the advantages and limitations of different approaches for the design and setup in the autonomous driving devices.

16:35-16:50 SunD04-4 1269 Indoor Instance-Aware Semantic Mapping Using Instance Segmentation Yinpeng Jiang Southeast Univ.Xudong Ma Southeast Univ.Fang Fang Southeast Univ.Xuewen Kang Huaibei Normal Univ.In order to accomplish the requirement of scene understanding to complete various kinds of complex tasks in home environment for robots, a novel instance segmentation method is adopted to build an instance-level 3D semantic map and obtain information such as categories, positions and interrelationship of instance objects within the environment. Different from the previous method which focuses on a certain feature in geometry or vision, we synchronously learn the features of geometric and visual information, distinguish instance objects and background areas and create the feature voxel grid of the environment. The proposed 3D-RPN network takes the grid as input and makes use of the cuboid bounding box to predict each instance and the category it represents. With the mask prediction branch, we binarized voxels in each bounding box to determine the exact distribution of the instance object. Our method borrows the idea of Mask R-CNN and the main body is constructed by 3D and 2D convolutional network, making full use of the features of 2D and 3D. We have tested our method on ScanNet and S3DIS, two large-scale indoor scene data sets, and the experiment has verified that our method can find and identify the instance information more accurately than previous methods.

16:50-17:05 SunD04-5 1459 Laplacian Pyramid Based Convolutional Neural Network for Multi-Exposure Fusion Yilun Xu Beihang Univ.Xingming Wu Beihang Univ.Jianhua Wang Beihang Univ.Hui Dong Zhejiang Univ.Qiantong Wang Beihang Univ.Haosong Yue Beihang Univ.Weihai Chen Beihang Univ.Multi-exposure fusion (MEF) fuses a bracket of differently exposed low dynamic range images into one high-quality image. Motivated by the classical pyramid based MEF, a Laplacian pyramid based convolutional neural network (CNN) is proposed in this paper to fuse LDR images. The network integrates the multi-resolution fusion and non-linear inference of CNN in a model, maintaining global contrast and the detail in the fusion results. With a coarseto-fine strategy, we rebuild the results from low-resolution to high-resolution, adding details to coarse fusion results progressively. The proposed network preserves details better than traditional CNN based MEF networks.

17:05-17:20 SunD04-6 1476 Reflector-based Laser-Wheel-Fusion Mapping System Mi Zhang Zhejiang Sineva Intelligent Tech. Co.

Beijing Sineva Tech. Co.Shihan Wang Zhejiang Sineva Intelligent Tech. Co.Xing Liu Zhejiang Sineva Intelligent Tech. Co.Songshan Han Zhejiang Sineva Intelligent Tech. Co.Liang Han Zhejiang Sineva Intelligent Tech. Co.Jiayang Zhao Zhejiang Sineva Intelligent Tech. Co.Google Cartographer is a state-of-art laser-based Simultaneous Localization And Mapping (SLAM) widely used in Automated Guided Vehicles (AGVs). However bad initial pose and long corridor scenes can easily lead to map and trajectory drift because of the scan-to-submap matching algorithm used in Cartographer’s front end: Correlative Scan Match (CSM) . We propose a laser-based mapping algorithm fusing wheel odometry and reflector data. The initial pose estimation for CSM in front end is modeled as a least square estimation problem of a sliding window fusing wheel odometry, the results of historical nodes estimated by CSM and reflectors, which is real-time and constant time complexity when estimating each reflector pose and reflector position in sliding window by batch estimator. The sliding window can ensure the efficiency and accuracy of front-end laser odometry; A reflector-based loop check strategy is used for eliminating the effect of wrong loops in similar scenes on the robustness of system; At the back end, to ensure the global consistency of mapping system, an algorithm combining reflector observation information and Sparse Pose Adjustment (SPA) is used to optimize the reflector and occupancy grid map. Our mapping system is qualitatively compared to Cartographer, then the experiment results prove

the robustness of our mapping system when mapping in long corridors by using a low-accuracy wheel encoder.

17:20-17:35 SunD04-7 1477 Visual-Marker-Inertial Fusion Localization System Using Sliding Window Optimization Mi Zhang Zhejiang Sineva Intelligent Tech. Co.

Beijing Sineva Tech. Co.Songshan Han Zhejiang Sineva Intelligent Tech. Co.Xing Liu Zhejiang Sineva Intelligent Tech. Co.Shihan Wang Zhejiang Sineva Intelligent Tech. Co.Liang Han Zhejiang Sineva Intelligent Tech. Co.Jiayang Zhao Zhejiang Sineva Intelligent Tech. Co.Simultaneous localization and mapping (SLAM) is a fundamental problem for autonomous mobile robots widely used in automated warehouses, factory material transfer systems, flexible assembly systems, and other intelligent transportation systems. Compared to 2D lidar, a visual inertial odometry (VIO) consisting of a low-cost camera and a MEMS inertial measurement unit (IMU) is a popular method for achieving 6-DOF state estimation of robots. However, visual inertial odometry is prone to drift, especially in weak textures scenes, and cannot provide robust and high-precision localization. In this paper, we propose a fiducial-markers visual inertial tight coupling algorithm framework based on sliding window optimization. The framework includes: the preprocessing of fiducial-markers, camera and IMU, sliding window optimization, key frame selection, marginalization, closed-loop detection and optimization. Due to the corner detection noise of single-frame Tag, the pose estimation by PNP (Perspective-n-Point) method has large error, and will cause pose ambiguity problem. In this paper, we propose to use Tag observation in the sliding window optimization, which can effectively suppress the pose ambiguity and improve the global localization accuracy using the reprojection constraint of multi-frames and multi-Tag. Because non-observability, large initial error and wrong Tag detection will lead to the instability of the sliding window optimization, the improved strategy of the sliding window optimization is proposed. Experimental results show that the proposed algorithm framework is effective and robust, and can achieve high precision localization in weak texture conditions.

17:35-17:50 SunD04-8 1519 Object Servoing of Differential-Drive Robots Weibin Jia Zhejiang Univ.Wenjie Zhao Zhejiang Univ.Zhihuan Song Zhejiang Univ.Zhengguo Li SRO Department of Inst. for Infocomm Res

earchDue to possibly changing pose of a movable object and nonholonomic constraint of a differential-drive robot, it is challenging to design an object servoing scheme for the differential-drive robot to asymptotically park at a predefined relative pose to the movable object. In this paper, a novel object servoing scheme is designed for the differential-drive robots. Each on-line relative pose is first estimated by using feature points of the moveable object and it serves as the input of an object servoing friendly parking controller. The linear velocity and angular velocity are then determined by the parking controller. Experimental results validate the performance of the proposed object servoing scheme. Due to its low on-line computational cost, the proposed scheme can be applied for last mile delivery of differential-drive robots to movable objects.

SunD05 Room05 Control and Management on Smart City (Building) (Special Session) 15:40-17:50 Chair: Yanhui Wang Beijing Univ. of Civil Engineering and

Architecture

15:40-15:53 SunD05-1 901 Design and analysis of dynamic ice slurry system in a laboratory building of a research institute Xi Zhang National Institute of MetrologyYun An National Institute of MetrologyZhigang Pang National Institute of MetrologyYu Wang National Institute of MetrologyZengbiao He National Institute of MetrologyJian Wang National Institute of MetrologyThe ice storage air conditioning system has become one of the Techologies with great development potential in recent years. It has been widely applied in various fields due to the advantages of "peak shifting and valley filling", balancing power grid and saving costs. This paper discusses the application advantages of dynamic ice making compared with static ice making, and expounds the principle of dynamic ice slurry storage Tech.. Taking the lab building as an example, this paper introduces the application of dynamic ice slurry storage Tech. in practical engineering, and provides ideas for solving similar problems in the future.

15:53-16:06 SunD05-2 902 Research on Diagnosis of Faulty Working Turbine Gas Flowmeter Ran Zhao Beijing gas Group Company with Limited Liability

Technical Programmes CCDC 2021 Liang Wang Beijing gas Group Company with Limited LiabilityThe construction of smart gas has been widely carried out in China. Many researches on smart gas are focused on the fault diagnosis research of pressure regulator and pipeline, and the diagnosis of faulty metering instrument is rarely studied. Normally, the fault of metering instrument will not directly affect the pressure regulation and pipeline, but some functions in smart pipeline dispatching and smart pressure regulation will based on the data measured by metering instrument. Facing a large number of turbine flowmeter fault diagnosis work, on the one hand, enterprises take professional personnel on-site inspection in fault diagnosis, on the other hand, they diagnose the laws of gas usage curve [16][17]. On the on-site inspection fault diagnosis the accuracy is high, while the timeliness of fault detection is poor. The timeliness of diagnose the laws of gas usage curve is good, while the diagnosis accuracy is low. After research on turbine gas flow meters, it is found that there is a unique functional relationship between the pressure loss and gas usage. In view of the current situation, the data of flow and pressure loss are analyzed in the computer, which can timely and effectively diagnose the fault of turbine gas flow meter. The correctness of the argument is verified by experiments in this paper. Meanwhile turbine gas flow meter is widely used in custody transfer of town gas. So it requires us to spend more energy on the research of working turbine gas flow meter fault diagnosis.

16:06-16:19 SunD05-3 1076 Research on Human Pose Classification Based on Improved SVM Yingda Zhang Beijing Univ. of Civil Engineering and ArchitectureHongyan Ma Beijing Univ. of Civil Engineering and ArchitectureYongxue Ye Beijing Univ. of Civil Engineering and ArchitectureJiaming Dou Beijing Univ. of Civil Engineering and ArchitectureJingjian Yang Beijing Univ. of Civil Engineering and ArchitectureWith the development of intelligent buildings and aging of population, designing a high-accuracy alarm system is essential to prevent the elderly from accidentally falling indoors. To address the problems of poor generalization and low accuracy of the SVM in the application of indoor elderly fall alarm systems, in this paper, the human keypoints detection algorithm, which increases the discrimination of the dataset, is proposed to pre-process the dataset. Based on this, the features of images are extracted from the histogram of the oriented gradient and the gray-level co-occurrence matrix, and the improved SVM algorithm is then used again for image classification. The simulation results show that the classification accuracy of using SVM to detect the dataset is around 91%, while the accuracy of the dataset classified as using improved SVM is around 97%. The method proposed in this paper is effective in increasing the accuracy of computer recognition of accidental falls by the elderly.

16:19-16:32 SunD05-4 1156 Study on the Spatial Heterogeneity and Influencing Factors of the Residential Land Price in Shenyang Rongge Zhang Northeastern Univ.Yanpeng Gao Northeastern Univ.Yanhua Fu Northeastern Univ.Ruiqiu Pang Northeastern Univ.The paper is based on the transfer data of 936 residential land in the main urban area of Shenyang from 2009 to 2019, with the help of ArcGIS 10.2, GS+7.0, Geoda, and SPSS24.0 software using nearest neighbor analysis, ESDA (Exploratory Spatial Data Analysis), Hedonic model and other methods to explore the spatial pattern characteristics and influencing factors of residential land prices in the main urban area of Shenyang. The results show that: ① The price, area, and quantity of residential land sold in various regions are not balanced. Particularly, most of the land for sale is concentrated in the downtown area of Shenyang. ②There is an anisotropic second-order trend in land prices in Shenyang, presenting a central circle structure pattern of "one master and multiple assistants". ③The distribution characteristics of land price clusters are significant, and there is a positive spatial correlation. With the evolution of time, the formation of high-value areas and low-value areas in Shenyang gradually becomes apparent, and the surrounding land-price land blocks such as "village in the city" decreases. ④The land price of residential land is anisotropic. The best theoretical fitting model is a spherical model. The spatial variation of land price in Shenyang is caused by the characteristics of spatial structure. ⑤Through the Hedonic model, it can be concluded that the spatial structure characteristics (the distance from the plot to the district government, the nearest subway station, bus station, key primary and secondary schools, parks, industrial parks, shopping malls, etc., and plot ratios, etc.) are the main factors affecting for the land price.

16:32-16:45 SunD05-5 1159 The Wall Temperature Receptacle System Design with NB-IoT Network Tao Xu Shenyang Aerospace Univ.Manghe Geng Shenyang Aerospace Univ.Huapeng Li Shenyang Aerospace Univ.Jiahui Liu Shenyang Aerospace Univ.Regarding the heat providing company’s demand for home temperature, this paper designs the wall temperature receptacle system with NB-IoT network. SHT30 is used to detect the home temperature and DS1302 is used to get accurate date and time. OLED is used to display temperature and time. STC micro-controlling unit is chosen to design the central unit.

E2PROM in the micro-controlling unit is used to save important working paramters. BC26 is used to design the NB-IoT interface circuits. The Tianyi cloud platform is used to process the NB-IoT networks including downloading commands and uploading information. Therefore, the wall temperature receptacle based on NB-IoT networks provides necessary information for the heating providing companies.

16:45-16:58 SunD05-6 1334 Video action recognition method based on attention residual network and LSTM Yu Zhang Beijing Univ. of Civil Engineering and ArchitecturePengyue Dong Beijing Univ. of Civil Engineering and ArchitectureA video action recognition method based on attention residual network and long-term memory network(LSTM) is proposed, which is to solve the problems that the existing human action recognition methods are prone to overfitting, susceptible to interference information, and lack of feature expression ability. In the beginning, the traditional data preprocessing method and sampling method are improved to enhance the generalization ability of the model. Then, a residual network with attention is proposed to improve the feature extraction ability of the network. At length, LSTM is used to recognize video actions. Experimental results on UCF YouTube dataset show that the proposed method can recognize the actions in video more effectively than other similar methods in this field, and the recognition rate reaches 95.45%.

16:58-17:11 SunD05-7 1407 Online Energy Management Method for User-side Microgrid with Mobile Energy Storage System Guofeng Wang Zhejiang Univ. of Tech.Man Zheng Zhejiang Univ. of Tech.Youbing Zhang Zhejiang Univ. of Tech.As an important part of the energy Internet, the user-side microgrid has some problems such as high complexity, communication congestion, and difficulty in regulation and control, which need to be solved urgently. In order to deal with the above problems, we propose an event-driven online energy management method with better characterization of the user's personality behavior, which can effectively reduce the electricity cost and improve the energy quality and satisfaction of users. Based on the fuzzy evaluation method, a new optimization model is constructed in the Lyapunov optimization theory, and we give a detailed energy pre-allocation strategy. After analyzing the operating characteristics of mobile energy storage systems, including fixed energy storage and electric vehicle clusters, we propose an online energy management algorithm to promote the real-time consumption of renewable energy with energy storage systems, and improve the efficiency of online energy management. Furthermore, by defining the controllable load virtual queue, we transform the complex global stochastic programming problem into a single-slot linear programming problem, which is based on Lyapunov's optimization theory. The simulation of the calculation example verifies that the online optimization algorithm can effectively reduce the electricity cost and ensure the user-comfort and queue delay.

17:11-17:24 SunD05-8 1674 A Fault Detection Method for Gas Pressure Regulators Based on Improved Dynamic Canonical Correlation Analysis Yang Song Beijing Univ. of Civil Engineering and ArchitectureYahui Wang Beijing Univ. of Civil Engineering and ArchitectureThis paper proposes a new data-driven fault detection (FD) approach for a class of gas pressure regulators based on the improved dynamic canonical correlation analysis (DCCA) method. First, the general principle of gas pressure regulators is introduced, and several typical faulty cases are presented accordingly. Using the reference signal, input, and output data, the improved DCCA-based residuals are generated. Based on this, the thresholds are determined using the randomized algorithm to build the FD logic. Compared with existing methods, the proposed approach can extract the fault information from the input and output data instead of only the output signals. To verify the effectiveness of this approach, experiments on the practical data in fault-free and two faulty cases are conducted.

17:24-17:37 SunD05-9 1688 Steel Gas Pipeline Burst Detection Chuan Wen Beijing Gas Group LtdWenlong Hai Beijing Univ. of Civil Engineering and ArchitectureAiming at the problem that it is difficult to detect sudden bursts of steel gas pipelines, this paper proposes a method to monitor bursts based on the pressure drop rate and the duration of exceeding the set value on the basis of previous studies. First, the existing physical model of pipeline leakage simulation and the mathematical model of pipeline leakage are analyzed, and it is found that the known condition data in the model is difficult to collect in actual engineering, so the pressure point analysis method is adopted as the main detection method. After that, it is analyzed by collecting pressure data. The focus of the study is to set the pre-warning pressure setting value of the pipe burst, and obtain the pressure drop rate and duration of the part that exceeds the set value, so as to determine whether the pre-warning of the pipe burst is issued. Finally, a simulation experiment was carried out. Among the five

Technical Programmes CCDC 2021 experiments, the monitoring was four times of tube bursting state, and one time of normal state. This method preliminarily solves the problem of missing the known quantity of the mathematical model of pipeline leakage.

17:37-17:50 SunD05-10 1690 Computer Simulation Study on Local Crack Propagation of Steel Pipe Changqing Zhang

Chinese Academy of Forestry

Jindong Wang Beijing Univ. of Civil Engineering and ArchitectureThe disaster caused by leakage of steel pipelines in cities seriously threatens the safety of people's lives and property. Based on damage theory, the finite element analysis method is used to simulate the damage and failure process of steel pipelines with defects under loading conditions. The results show that the creep damage around the elliptical hole in the steel pipe wall is serious, and a damage zone is formed along the hole, where the crack initiation is most likely to occur. The maximum equivalent stress around the elliptical hole in the steel pipe wall is the origin point of crack initiation, but with the increase of service time and the crack propagation, where the stress relaxes, the equivalent stress value decreases and the crack propagation rate slows down. The maximum stress triaxiality around the elliptical hole of steel pipe wall is the origin point of crack initiation, and the stress triaxiality increases with the increase of service time, and the crack will also expand rapidly. In this paper, a theoretical study on local crack propagation of steel pipeline is carried out, which provides a reliable basis for urban disaster prevention and prediction.

SunD06 Room06 Data-driven Intelligent Optimization and Decision for Industrial Processe (Special Session) 15:30-17:50 Chair: Jing Na Kunming Univ. of Science and Tech.

15:30-15:50 SunD06-1 1391 An IGD+ Performance Indicator Based Particle Swarm Optimizer For Multi-objective Optimization Fei Li Anhui Univ. of Tech.

Ma’anshan Univ.Shijian Dong China Univ. of Mining and Tech.Yuanqu Liu North China Electric Power Univ.Zhengkun Shang Anhui Univ. of Tech.Particle swarm optimizer (PSO) is suitable for solving multi-objective optimization problems (MOPs). However, there are two main issues for any multi-objective particle swarm optimizers (MOPSOs). The first issue is how to balance the convergence and diversity. The second issue is how to enhance the exploitation and exploration during the evolutionary procedure. In order to address these issues, and modified inverted generational distance (IGD+) performance indicator based PSO (IGD+-MOPSO) is proposed. The external archive updating strategies based on the IGD+ indicator and the objective space decomposition method are proposed to select the evenly distributed non-dominated solutions. The leader updated strategy of each particle is based on the IGD+ indicator value which is associated to the corresponding reference vector. The genetic operator is embedded into the evolutionary procedure to reset the position in order to help the particle jump out of the local optimum. We have conducted the simulation on some related benchmark test instances. The experimental results have indicated that the proposed algorithm is competitive with some related algorithms.

15:50-16:10 SunD06 -2 1479 A novel quality-related process monitoring based on multi-block slow feature analysis of mutual information-index Yinghua Yang Northeastern Univ.Weiqi Kang Northeastern Univ.Xiaozhi Liu Northeastern Univ.As an emerging feature extraction method, slow feature analysis (SFA) abstracts the intrinsic properties characterizing the system from the original input signal, and therefore it is considered to have the potential to uncover the true laws of industrial processes. In recent years, slow feature analysis has developed into an important method in the field of quality-related process monitoring. In this paper, a multi-block strategy is combined with slow feature analysis, and mutual information (MI) indicators are used for the division of quality-related subspaces. Besides, statistical indicators are used to further divide the quality-related subspaces. Monitoring statistics and control limits are constructed in the three subspaces for process monitoring. To verify the effectiveness of the method, simulation experiments were conducted in the context of the Tennessee Eastman process.

16:10-16:30 SunD06-3 1503 A Comparative Study for Multilayer Perception versus Convolutional Neural Network Based On the Wind Turbine Benchmark Model Dapeng Zhang Tianjin Univ.Yuqing Yang Tianjin Univ.Hongwei Fang Tianjin Univ.

The condition monitoring and fault detection are vital for the wind turbine to reduce the maintenance costs and improve operation level. In this paper two data-driven models that include a convolutional neural network (CNN) and a multilayer perceptual network (MLP) are built to detect the faults from the observed variables. Both models are trained based on the data from the wind turbine benchmark model and made a comparison in the recognition accuracy. The study shows the training time of CNN is longer than that of MLP based on the back propagation approach and the recognition accuracy of CNN is about 10% better than that of MLP. The simulation also shows both models have a good robustness on the transition process after a fault occurs.

16:30-16:50 SunD06-4 1505 Generalized Policy Iteration-based Reinforcement Learning Algorithm for Optimal Control of Unknown Discrete-time Systems Mingduo Lin Guangdong Univ. of Tech.Bo Zhao Beijing Normal Univ.Derong Liu Guangdong Univ. of Tech.Xi Liu Guangdong Univ. of Tech.Fangchao Luo Guangdong Univ. of Tech.This paper presents a novel generalized policy iteration-based reinforcement learning (RL) algorithm to deal with infinite-horizon optimal control problems of nonlinear discrete-time systems with completely unknown dynamics. In the present iterative algorithm, two iteration procedures are utilized to obtain the iterative Q-function and the iterative control policy. Furthermore, the iterative Q-function is obtained by the temporal difference learning and the policy gradient method is utilized to directly optimize the iterative control policy. Then, the convergence and optimality analysis of the generalized policy iteration-based RL algorithm are provided. To implement this algorithm, two neural networks, including a critic network and an action network, are used to approximate the iterative Q-function and the iterative control policy. Finally, a numerical simulation example is provided to illustrate the effectiveness of the proposed control method.

16:50-17:10 SunD06-5 1549 A Machine Vision-based Fabric Defect Detection Solution for Textile Production Industry Using Object Detection Shuo Xin Zhejiang Univ.Chunhui Zhao Zhejiang Univ.Youxian Sun Zhejiang Univ.Along the process of textile industrial production, fabric defect detection is a critical task to ensure the reliable quality of large-scale products. To overcome the setbacks of conventional detections, there are still two crucial issues that need to be addressed. For the first time, a novel Multi-task Cascade R-CNN model based on two-stage detection method is proposed, which elegantly mitigates the large defect shape changing challenges by introducing three well-designed candidate region selection strategies and combining with cascading network to extract various defect information adaptively. As for another challenge led by fabric materials, a promoted-DSSD model on Resnet framework is proposed, which utilizes a new loss estimation and greatly refines the learning rate of unbalanced defect categories in the optimization iteration module. It overcomes the severely sample amount unbalance issue. Experimental results on the datasets collected from real textile production workshop demonstrate the excellent performance of our proposed methods, which not only meet the inspection requirements of real-time but also significantly reduce the missing rate for fabric detection in high-speed production scenarios.

17:10-17:30 SunD06-6 1609 Two-layer Dynamic EMPC of Organic Ranking Cycle systems with non-Gaussian disturbances Yiqiu Zhang Taiyuan Univ. of Tech.Mifeng Ren Taiyuan Univ. of Tech.Mingyue Gong China Construction Bank Handan BranchLan Cheng Taiyuan Univ. of Tech.Organic Rankine Cycle (ORC) is one of the energy recovery technologies that use low-boiling organics as the working fluid. During the control process of the ORC system, some random disturbances are often encountered, such as the mass flow of flue gas, the inlet temperature of the heat source, etc. The disturbance does not necessarily obey the Gaussian distribution. Based on the generalized correntropy (GC) criterion, this paper proposes a two-layer dynamic economic model predictive control (EMPC) method for Organic Ranking Cycle (ORC) systems with non-Gaussian disturbances. In the upper layer, the ratio of thermal efficiency to total heat transfer area is firstly used to establish an economic performance index function. Then, the real-time economically optimal trajectory, instead of the given fixed set-point, is calculated by dynamic EMPC, which uses the dynamic model of ORC instead of the steady-state model. In the lower layer, a GC-based MPC algorithm is used to make the system output track the reference trajectory obtained from the upper layer. Finally, the simulation results illustrate the effectiveness of the proposed GC-based two-layer EMPC method for ORC systems.

17:30-17:50 SunD06-7 775

Technical Programmes CCDC 2021 Deep latent variable modeling and applications for big data in the process industry Zhiqiang Ge Zhejiang Univ.For a long time, the hidden variable model, characterized by dimensionality reduction, key information extraction and visualization, has played an important role in the modeling, control and optimization of process industrial processes. With the increase of data volume and data dimension in the era of industrial big data, it is becoming more and more difficult to extract the core elements of industrial system, and the traditional hidden variable model is facing great difficulties. From the point of view of machine learning, the traditional hidden variable model structure obviously can not provide enough model complexity, which leads to its description ability is very limited, can not accurately depict the complex industrial system, extract its essential characteristics, and severely limit the application range of the model in the actual process. Many practices have proved that deep learning provides a very efficient framework for model complexity expansion, while modern industrial systems also provide a natural big data environment for the effective performance of deep learning model, greatly reducing the overfit risk brought about by the increase of hidden variable model complexity. Therefore, the deep hidden variable model is an important direction of industrial system modeling and knowledge analysis in the era of big data, and it is also one of the best focus points for the deep integration of artificial intelligence technology and manufacturing industry. Based on the research direction of industrial big data and the background of complex process industry, and facing its typical application scenario, our group puts forward the deep hidden variable model of customized form from the dynamic, time-varying and variable-lage characteristics of big data in process industry. First of all, based on the model architecture of linear dynamic system, we put forward the nonlinear form of dynamic system by introducing the variable self-encoder, and made the deep processing, which greatly improved the effect of dynamic system modeling and application. Secondly, in view of the time-varying industrial big data pattern, we propose a deep hidden variable model based on streaming variational Bayesian method, which effectively solves the problem of model mismatch caused by the time-varying characteristics of big data in the process industry. Third, the process industrial measurement variable has the typical spatial delay characteristics, on the basis of analyzing the variable structure, we put forward a deep hidden variable model based on variable delay reconstruction, using the internal and external double-loop parameter iteration optimization method, greatly improved the efficiency of model solving and application, and achieved good practical industrial application results. In addition, through in-depth analysis of the advantages and disadvantages of the deep hidden variable model, we provide some ideas for the next step of research in this direction for peer exchange and discussions.

SunD07 Room07 Innovative Understanding and Identification Techniques for Sensor Signals and Their Combination with Deep Neural Networks (Invited Session) 15:50-17:30 Chair: Tingli Su Beijing Tech. and Business Univ.

15:50-16:10 SunD07-1 1158 Power Load Forecasting Method of Encoder-Decoder Based on Attention Xuebo Jin Beijing Tech. and Business Univ.Weizhen Zheng Beijing Tech. and Business Univ.Jianlei Kong Beijing Tech. and Business Univ.Tingli Su Beijing Tech. and Business Univ.Yuting Bai Beijing Tech. and Business Univ.Short-term electricity load forecasting plays an important role in the safety, stability, and sustainability of the electricity production and dispatch process. Better forecasting results can help the power industry and power supply companies to make reliable decisions, control operating conditions, manage power systems, facilitate production, reduce costs and prevent pollution. In this paper, we model and forecast electricity load data with non-linear, non-smooth, and complex characteristics. To address the limitations of existing load forecasting methods in handling time series data, poor model stability, and unsatisfactory forecast accuracy. We propose an attention-based encoder-decoder network based on Bayesian optimization for short-term electricity load forecasting. The prediction model makes full use of the powerful feature extraction and learning capabilities of the GRU encoder-decoder neural network for modeling time-series data and incorporates attention layer to improve the prediction accuracy and robustness of the prediction model. Finally, a Bayesian optimization method is used to determine the model parameters and achieve optimal predictions. Validation of the model using AEP's electrical load data. Model stability and model prediction accuracy were verified by RMSE, MAE, and other metrics.

16:10-16:30 SunD07-2 1203 Multi-sensor GM-PHD Fusion Tracking Algorithm Under Decentralized Structure Han SHEN-TU Hangzhou Dianzi Univ.Jian-Bo XU Hangzhou Dianzi Univ.Ji-An LUO Hangzhou Dianzi Univ.Traditional tracking algorithm under the centralized and distributed structure is not suitable for the decentralized network structure. For the

above situation, this paper proposes a decentralized fusion tracking algorithm based on GM-PHD. First, by dividing the Euclidean distance between the target and the sensor, the modeling of target outside the FOV of sensor is solved; then the information owned by the network node is mapping as a matrix, and the communication content is determined by the node information matrix calculation to reduce the information redundancy caused by the structure, a decentralized network multi-hop communication strategy is designed; finally, the confidence index of the Gaussian component is considered in the fusion strategy, which effectively alleviates the error caused by the differences of node information, and the fusion frame under the decentralized network is constructed. Algorithm simulation shows that under the decentralized network structure, the algorithm proposed in this paper can enable a single sensor in the network to effectively track targets in the global scope.

16:30-16:50 SunD07-3 1357 Performance of Rail Vehicles under Different Track Excitations Based on Inerters Zhongcheng Qiu Kunming Univ. of Science and Tech.Shichang Han Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Xian Wang Kunming Univ. of Science and Tech.Chen Wang Kunming Univ. of Science and Tech.This paper investigates the performance of rail vehicle suspensions with inerters under a combination of three track excitations, which are random irregularity, harmonic and impulsive excitations. A 10 degree-of-freedom vertical dynamic model of a rail vehicle including primary and secondary suspensions is established. The particle swarm optimization algorithm is used to optimize the parameters of suspensions containing inerters while the optimization goal is to improve the riding comfort. The vertical and pitch acceleration of the vehicle body as well as the wheel-rail contact force are analyzed at the velocities of 20, 30 and 40m/s. Comparison of different suspension layouts is made in both time domain and frequency domain. Result shows that the suspension layout employing inerters has an overall effect on improving riding comfort.

16:50-17:10 SunD07-4 1363 Suspension Performance Analysis of a Half Car Model with a Fluid Inerter Chen Wang Kunming Univ. of Science and Tech.Shichang Han Kunming Univ. of Science and Tech.Jing Na Kunming Univ. of Science and Tech.Xian Wang Kunming Univ. of Science and Tech.Zhongcheng Qiu Kunming Univ. of Science and Tech.As a new type of vibration isolation component, inerter has great potential in improving the performance of vibration isolation system. In this paper, a half car model is taken as the research object, and three suspension structure models with inerter are studied. Particle swarm optimization algorithm is used to optimize the parameters of inerter suspension. The performances of the three inerter suspensions are compared with those of the traditional suspension in terms of body acceleration and pitching angular acceleration. The simulation results show that compared with the traditional suspension, the performance of the suspension system with inerter has been significantly improved. In addition, the dynamic model of fluid inerter including friction force, parasitic damping force and inertial force is established, and the nonlinearity of fluid inerter is analyzed in frequency domain and time domain.

17:10-17:30 SunD07-5 1439 Human-in-the-Loop Teleoperation of NRS with Event-based Local Communication in A Fully-Distributed Manner Ming-Feng Ge China Univ. of GeosciencesJing-Zhe Xu China Univ. of GeosciencesZhi-Wei Liu Huazhong Univ. of Science and Tech.Teng-Fei Ding China Univ. of GeosciencesYan-Wu Wang Huazhong Univ. of Science and Tech.This paper investigates the teleoperation of the networked robotic system (NRS), which is composed of the multiple slave robots interacted in the event-based local communication, linking in one or several long-distance communication between a human-controlled master robot with multiple slave ones. The control objective of such humanin- the-loop teleoperation system is prioritized to regulate the master robot to track the connected slave robots (i.e., M2S tracking), while actuating all the slave robots follow the master one (i.e., S2M tracking), thus permitting the human operator to take control of the NRS effectively. Another crucial objective is capable of regulating the system in a fullydistributed manner, i.e., the information of the master robot cannot be globally accessible by all the slave ones and can only be obtainable by partial individuals of the NRS. To this end, a novel fully-distributed event-based control (FDEBC) method and a new human-in-the-loop decision (HiLD) protocol are proposed. Based on the Lyapunov stability theory, the sufficient conditions on the control parameters are provided for guaranteeing the tracking performance of the closed-loop dynamics. Simulation examples are given to demonstrate the theoretical results.

SunD08 Room08 Theory and Application of Nonlinear Systems (II) 15:50-17:50 Chair: Guoliang Cai Zhengzhou Shengda Univ.

Technical Programmes CCDC 2021 Jiangsu Univ.

CO-Chair: Jiayue Jiang Key Laboratory of Intelligent Tech. and Application of Marine Equipment

Harbin Engineering Univ.

15:50-16:10 SunD08-1 86 Synchronization Analysis of a New Hyperchaotic Finance System Model via Linear Feedback Control Wenjun Shi Zhengzhou Shengda Univ.Yanfeng Ding Zhengzhou Shengda Univ.Guoliang Cai Zhengzhou Shengda Univ.

Jiangsu Univ.Using linear feedback control strategy, the present paper discusses synchronization analysis of a new hyperchaotic Finance system model. We use four linear control schemes to synchronize this hyperchaotic finance system model via the back-stepping method. Some simpler controllers are used to realize a global asymptotical synchronization. In the four schemes, we use Lyapunov stability theorem to derive sufficient conditions for synchronization of two identical financial hyperchaotic systems using linear feedback control. Finally, these results are verified by four numerical simulation examples.

16:10-16:30 SunD08-2 241 Research on Trajectory Tracking of Surface Vessel with Side Thrusters Based on Dynamic Sliding Mode Control Zhilin Liu Key Laboratory of Intelligent Tech. and A

pplication of Marine EquipmentHarbin Engineering Univ.

Jiayue Jiang Key Laboratory of Intelligent Tech. and Application of Marine Equipment

Harbin Engineering Univ.Zhongxin Wang Key Laboratory of Intelligent Tech. and A

pplication of Marine EquipmentHarbin Engineering Univ.

Shouzheng Yuan Key Laboratory of Intelligent Tech. and Application of Marine Equipment

Harbin Engineering Univ.Side thruster is the auxiliary propeller of the ship, which can improve the control efficiency during the navigation of the ship. The use of side thrusters can improve the steering ability of the ship, thereby reducing the risk of collision, and can also increase the positioning and berthing capabilities of the ship. The use of side thruster combined with rudder improves the performance of the ship significantly. This paper mainly studies the control system of a two-propellers and two-rudders ship with side thrusters. With the goal of trajectory tracking, the mathematical model of the ship's motion with two propellers and two rudders is established, in which the wind, wave, current, and model uncertainties are considered. Moreover, a disturbance observer is designed, and an adaptive dynamic sliding mode observer is used to estimate the upper bound of the error. Finally, simulation is carried out to verify the designed controller in trajectory tracking. The results show the rationality and effectiveness of the design.

16:30-16:50 SunD08-3 252 Angle-Only tracking of Dual-station Based on Uncorrelated Conversion Cubature Kalman Filter Baobao Wang Jiangsu Automation Research Inst.Chen He Jiangsu Automation Research Inst.Shoufeng Wang Jiangsu Automation Research Inst.Lianzhong Zhang Jiangsu Automation Research Inst.Aiming at the problem that Angle-Only tracking technology has low target tracking accuracy in dual-station systems, this paper proposes a tracking algorithm based on uncorrelated conversion cubature Kalman filter algorithm (UCCKF). Firstly, the uncorrelated conversion technology is used to obtain the enhanced measurement. The enhanced measurement is used to supplement the measurement equation to achieve the expansion of the measurement information. Secondly, the state estimation of the target is realized under the framework of cubature Kalman filtering. The enhanced measurement and angle information in this method is uncorrelated. Using this method can effectively reduce the target state update covariance. In the simulation experiment, the tracking performance of the UCCKF algorithm and the CKF algorithm was compared, analyzed and verified. The simulation results show that, under the same conditions, the UCCKF algorithm can reduce the position error by 24.9% and the speed error by 18.7%. The UCCKF algorithm is more effective when applied to a dual-station tracking system.

16:50-17:10 SunD08-4 397 Anti-synchronization Control of Complex-valued Neural Networks with Unbounded Time-varying Delays Changqing Long South-Central Univ. for NationalitiesGuodong Zhang South-Central Univ. for NationalitiesThis article discusses the anti-synchronization control problem of a class of complex-valued neural networks with unbounded time-varying delays. Then, under the Lyapunov stability theory and some inequality techniques, several new criteria are established to ensure the global exponential anti-synchronization of the addressed models via a designed

complex-valued linear feedback controller. Compared with some existing synchronization results, our results indicate that the approach via not separating the real and imaginary parts are more concise and effective. Moreover, some conservative conditions are also released. At last, numerical examples are presented to illustrate the feasibility of the derived results.

17:10-17:30 SunD08-5 440 Robust Cooperative Control for Nonlinear Multi-agent Systems with Input-Disturbances via Adaptive Dynamic Programming Qiuxia Qu Shenyang Jianzhu Univ.Juan Wang Shenyang Jianzhu Univ.Qiong Xia Shenyang Jianzhu Univ.Liangliang Sun Shenyang Jianzhu Univ.Yang Cui Univ. of Science and Tech. LiaoningConsidering the leader-following consensus problem for the nonlinear multi-agent systems with bounded input-disturbances under fixed topology, a novel distributed robust protocol is designed to guarantee all followers synchronize to the leader by investigating the gain of the Nash Equilibrium. The robustness restrictions are given through Lyapunov theory. To get the Nash solution, critic neural networks are trained based on adaptive dynamic programming algorithm in an online and forward-in-time manner to solve the coupled Hamilton-Jacobi equations. An additional term is added to the neural network weight tuning law to avoid the requirement for the initial admissible control law.

17:30-17:50 SunD08-6 213 Research on Synchronization of Bidirectional Coupled Chaotic Systems with Different Dimensions Haibin Yu Chongqing Univ. of Posts and Telecommu

nicationsJiming Zheng Chongqing Univ. of Posts and Telecommu

nicationsThis paper investigates the synchronization behavior in two bidirectional coupled chaotic financial systems with different dimensions. Firstly, using the generalized synchronization definition, scale matrix, direct design method and Lyapunov stability theory, the synchronization controller is designed for two different dimension systems. The dimension of two systems are controlled by selecting scale matrix. Secondly, the stability conditions is proposed by investigating the synchronization behavior of two chaotic systems. Finally, two examples are given to illustrate the effectiveness of the designed controllers.

SunD09 Room9 Fault Diagnosis and Predictive Maintenance (V) 15:50-17:50 Chair: Yanfeng Wang Huzhou Univ.CO-Chair: Yong Zhang Wuhan Univ. of Science and Tech.

15:50-16:10 SunD09-1 1484 Fault Tolerant Control for Networked Markov Jump Systems with Data Packet Dropout Based on Observer Yating Xu Huzhou Univ.Xiaoyue Sun Huzhou Univ.Yanfeng Wang Huzhou Univ.Peiliang Wang Huzhou Univ.Zuxin Li Huzhou Univ.Hongyi Xu Huzhou Univ.The design of fault tolerant controller for a class of observer-based networked Markov jump systems is investigated in this paper. The data packet dropout follows the Markov characteristics, and the mathematical model of the networked Markov jump systems with data packet dropout in S/C channel is established, the sufficient conditions on the stochastic stability of the closed-loop system are obtained, based on the linear matrix inequality, the design method of the fault tolerant controller is proposed. Finally, a numerical simulation example shows the effectiveness of the method.

16:10-16:30 SunD09-2 876 Remaining Useful Life Prediction of Lithium-ion Batteries with Fused Features and Multi-kernel Gaussian Process Regression Runqiu Wang Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech. of

Ministry of EducationZhenxing Liu Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech. of

Ministry of EducationYong Zhang Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech. of

Ministry of EducationQian Su Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech. of

Ministry of EducationXianhe Li Wuhan Univ. of Science and Tech.

Technical Programmes CCDC 2021 Engineering Research Center for Metallurgical

Automation and Measurement Tech. ofMinistry of Education

In this paper, both State of Health estimation and Remaining Useful Lifetime prediction of lithium-ion battery are investigated. In order to accurately predict the RUL of battery, a multi-kernel Gaussian Process Regression (GPR) model which combines an adaptive feature fusion method is proposed. Firstly, the five raw features are extracted from charging and discharging curves. Secondly, an adaptive feature fusion method is used to combine different features and integrate their advantages of the features. In the same time, the optimized multi-kernel GPR model with Fruit-fly Optimization Algorithm is established to solve the GPR kernel function selection problem. The effectiveness of the proposed method is verified with simulation experiments derived from battery dataset of NASA. Simulation results show that the proposed method achieves better prediction accuracy and reliability than the GPR model using single feature or single kernel function.

16:30-16:50 SunD09-3 551 Failure Prediction Analysis of UAV Flight Control System Runtong Dong Shenyang Aerospace Univ.The UAV flight control system is the core of the UAV flight control, with the complex structure, which is prone to malfunction during the execution of missions, resulting in affecting mission execution. In this paper, based on the full understanding of the structure of the flight control system, with the query of the probability of failure of the UAV flight control system in a certain time segment, prediction analysis on the failure rate of the UAV flight control system is conducted using Matlab software, in the BP (back propagation) neural network prediction algorithm, and the results have important theoretical significance and application value for the development of UAV and the setting of its contingency plan.

16:50-17:10 SunD09-4 679 Short-term Load Forecasting based on Kalman Filter and Nonlinear Autoregressive Neural Network Dingguo Liang Peking Univ.Ying Yang Peking Univ.Rongchang Li Peking Univ.This paper focuses on the problem of distributed fault detection (FD) for large-scale dynamic systems which are modeled as the interconnection of several subsystems. A distributed estimator is constructed for each subsystem, in which an adaptive fuzzy approximator is embedded to learn the unknown nonlinear interconnection with the neighboring subsystems. Overlapping decompositions are considered in the FD scheme, thus allowing some state variables to be shared among different subsystems. A consensus filter is designed to help the shared state variable in each subsystem reach a common FD decision as well as improve the property of FD. The stability of the estimation error is analyzed and the sufficient conditions of the fault detectability are also addressed. Simulation results are provided to illustrate the effectiveness of the proposed method.

17:10-17:30 SunD09-5 1589 Short-term Load Forecasting based on Kalman Filter and Nonlinear Autoregressive Neural Network Liang Zhang Kunming Univ. of Science and Tech.Chengyuan Zheng Kunming Univ. of Science and Tech.Zhengang Zhao Kunming Univ. of Science and Tech.Dacheng Zhang Kunming Univ. of Science and Tech.

Yunnan Key Laboratory of Computer Tech.Application

Power load forecasting is significant to research for power supply and strategy departments of electric power companies. It affects the distribution and the demand for electrical energy in different regions. Power load forecasting methods mainly include regression analysis, time series analysis, and other statistical methods combined with machine learning. In this paper, the authors analyzed the load data of a region in Yunnan Province, established a load calculation model by combining environmental factors, week types, special events, and daily load, and obtained the best load estimation data through the Kalman filtering process. Besides, the authors analyzed the statistical characteristics of the load series and established the Nonlinear Autoregressive Neural Network (NARnn) based on the Kalman filtered data. The following two weeks’ load data was predicted by shifting the timeline keeping the total load data constant. The average daily mean absolute precentage error (MAPE) for the two weeks is 7.69%. The MAPE between two-week forecast data and actual load is 8.05%.

17:30-17:50 SunD09-6 749 Research on BIT False Alarm Suppression Technology of Airborne Power System Based on Genetic BP Neural Network Zhilong Zhang Naval Aviation Univ.Xianjun Shi Naval Aviation Univ.Jiapeng Lv Naval Aviation Univ.Yufeng Qin Naval Aviation Univ.Aiming at the fact that the existing fault diagnosis methods cannot effectively suppress the false alarm of the airborne power system BIT, a

method of reducing the false alarm of the multi-electric aircraft power system based on genetic BP neural network technology is proposed. First, analyze the failure mode of the multi-electric aircraft power system. Based on the introduction of the BP neural network technology optimized by the genetic algorithm, combined with the characteristics of the airborne power system BIT, a certain type of multi-electric aircraft power system is selected as the research object. There are 13 typical fault features and 10 typical faults. Finally, Matlab is used for simulation. Experimental results show that this method can accurately locate faults, effectively suppress BIT false alarms, and improve the fault diagnosis capability of the airborne power supply system.

SunD10 Room10 Optimal Control and Optimization (V) 15:50-17:50 Chair: Yuekun Wang Univ. of Pan ZhihuaCO-Chair: Xiaoqian Li Taishan Univ.

15:50-16:10 SunD10-1 154 Application of new internal model control in aeration system Huirong Li Univ. of Pan ZhihuaGuo Xiaoying Univ. of Pan ZhihuaWang Yuekun Univ. of Pan ZhihuaIn order to solve the problem of dissolved oxygen concentration control caused by strong nonlinearity, large time delay, large time-varying, multivariable coupling and serious uncertainty interference in sewage treatment system, according to the mathematical model of aeration system and blower, combined with the advantages of internal model control and cascade PID control, a two degree of freedom internal model PID control strategy is proposed, and the corresponding sewage treatment aeration control system is designed. This strategy solves the problem that traditional PID control method is difficult to obtain good control effect when the mathematical model of aeration system and blower is not accurate. The simulation experiment is carried out in Matlab / Simulink and compared with other common control methods. The simulation results show that the control system has strong adaptability to the change of working conditions, good tracking and adjusting performance, and has a significant improvement effect on the large time delay control of sewage treatment.

16:10-16:30 SunD10-2 301 A Nash Game Approach to Mixed H2/H∞ Problem with Input Delay: The Discrete-time Case Xiaoqian Li Taishan Univ.Peijun Ju Taishan Univ.Zhongjin Guo Taishan Univ.Jing Lei Taishan Univ.Zonglei Jing Taishan Univ.As an important branch of control theory, mixed H2/H∞ control can not only enable the system to obtain excellent regulation characteristics but also maintain robust stability, which has important theoretical significance and extensive application background.This paper studies mixed H2/H∞ problem with input delay. The mixed H2/H∞ control problem can be equivalently described as the LQ non-zero sum game problem, namely, Nash game problem. Based on Nash game strategy, the optimal solution for discrete time system with input delay is given. The maximum principle is established, by solving the delayed forward backward difference equations, sufficient and necessary conditions of solvability are obtained.

16:30-16:50 SunD10-3 1130 An optimal control model for the Lyapunov system of stability problem Xiaolin Xiong Zhanjiang Science and Tech. CollegeZhi Lao Zhanjiang Science and Tech. CollegeZhiguo Feng Guangdong Ocean Univ.Lyapunov theory is the key point of stability problems. In this paper, we consider the stability problem of a nonlinear system. First, we design the control function and prove that the Lyapunov function can be maximally reduced. Next, we treat the Lyapunov function as a state variable and formulate a dynamic system of Lyapunov function, where the control is the magnitude of original control function. After proving that there exist many control functions such that the Lyapunov function can be stabilized in finite time, we generate the optimal control problem to find the control function such that a given objective, which combines stabilize time and control cost, is minimized. Finally, we take the Lorenz system as a numerical example to illustrate the proposed method.

16:50-17:10 SunD10-4 1599 Adaptive Dynamic Programming without Persistence Excitation for Tracking in A Special Class of Linear Systems with Input Constraints Chunbin Qin Henan Univ.Jinguang Wang Henan Univ.Heyang Zhu Henan Univ.Jishi Zhang Henan Univ.Dehua Zhang Henan Univ.In this paper, an approximate optimal control method with input constraints based on adaptive dynamic programming (ADP) is proposed

Technical Programmes CCDC 2021 for a class of special linear systems. The method is based on an actor/critic framework. The critic approximator is used to approximate the optimal cost function, and the actor approximator is used to approximate the bounded control input. In view of the fact that the algorithm requires a persistence of excitation (PE) condition, we use the previous data and the current data to alleviate this requirement. When Lyapunov method is used to prove stability, the error between the optimal control and the bounded control is considered. It is prove that the closed-loop system can be guaranteed to be uniformly ultimately bounded (UUB). On this basis, a robustness term is added to compensate the effect of the approximation error. A simulation example shows the effectiveness.

17:10-17:30 SunD10-5 1023 Improved State Machine Strategy Based on Consumption Minimization for Fuel Cell/Battery/Ultracapacitor Hybrid Electric Vehicles Congcong Wang Nanjing Univ. of Science and Tech.Haoping Wang Nanjing Univ. of Science and Tech.Yang Tian Nanjing Univ. of Science and Tech.In this paper, an improved state machine control strategy based on equivalent consumption minimization for fuel cell/battery/ultra-capacitor hybrid electric vehicles is presented. By combining with an improved state machine control strategy, the power of the three power sources (fuel cell, battery and ultracapacitor) is reasonably allocated to improve the performance of the equivalent consumption minimization strategy (ECMS). In addition, the current is tracked by the non-singular terminal sliding mode controller (NTSMC) to obtain accurate current. The proposed method can effectively reduce the fuel consumption of the system and improve the fuel cell efficiency. The fuel cell hybrid vehicle is modeled in MATLAB/Simulink. The simulation results show that compared with the state machine and equivalent consumption minimization strategy, this method can effectively reduce hydrogen consumption and reduce the consumption cost of fuel cell hybrid electric vehicles.

17:30-17:50 SunD10-6 638 USV optimal obstacle avoidance trajectory planning based on improved adaptive hp-Radau pseudospectral method Hongbin Wang Naval Univ. of EngineeringZhong Liu Naval Univ. of EngineeringYasong Luo Naval Univ. of EngineeringJiao Dong Naval Univ. of EngineeringAiming at the problem of real-time obstacle avoidance trajectory planning for a class of unmanned surface craft under multiple constraints, an optimal obstacle avoidance trajectory planning algorithm based on improved adaptive hp-Radau pseudo-spectrum method is proposed. Firstly, describe the real-time optimal obstacle avoidance trajectory planning problem of USV under the framework of optimal control theory: on the basis of following the International Convention on Regulations for Preventing Collisions at sea, adopt different avoidance strategies according to different obstacle avoidance scenarios; Secondly, when dealing with the non-smooth optimal control problem with the Radau pseudospectral method, it is difficult to capture the discontinuity and non-smoothness of the solution, an improved adaptive hp-Radau pseudo-spectral method discretization solution strategy is proposed, the algorithm divides the obstacle avoidance trajectory into multiple sub-intervals, according to preset error evaluation criteria, the two-layer optimization strategy is adopted to adaptively adjust the order of the interpolation basis function and the number of refined units to meet the requirements of solution efficiency and accuracy. Finally, the improved adaptive hp-Radau pseudospectral method is applied to the USV continuous-time optimal obstacle avoidance trajectory planning model for discretization, theoretical analysis and simulation experiment prove the effectiveness of the algorithm.

SunD11 Room11 Intelligent Control, Computation and Optimization (V) 15:50-17:50 Chair: Liting Fan Shenyang Jianzhu Univ.CO-Chair: Min Zhou Beijing Jiaotong Univ.

15:50-16:10 SunD11-1 1193 Virtual simulation model of portable inverted pendulum Dexin Meng Shenyang Jianzhu Univ.Liting Fan Shenyang Jianzhu Univ.Yan Shi Shenyang Jianzhu Univ.Zhongjiang Cheng Shenyang Jianzhu Univ.As a typical unstable linear system, the inverted pendulum has the characteristics of simple structure and low cost, and has been widely used in robotics and aerospace. A modeling method based on the combination of the Matlab virtual simulation and test data is proposed,so as to solve the problem of deviation between theoretical modeling and engineering application of the portable linear inverted pendulum.The rigid body model of the inverted pendulum is obtained by connecting SolidWorks with Matlab,and the electromechanical part is added in the SimMechanics,so as to obtain the ideal model of the system at the unstable equilibrium point.The pendulum swinging are studied for the inverted pendulum on the basis of the model. The test results show that the virtual simulation model can visually display and record the changes of the system variables.

16:10-16:30 SunD11-2 1116 Automatic Train Regulation with Time Tuning and Holding Control under the Saturated Passenger Demand Condition Zhuopu Hou Beijing Jiaotong Univ.Min Zhou Beijing Jiaotong Univ.Lingbin Ning Beijing Jiaotong Univ.Hairong Dong Beijing Jiaotong Univ.For urban mass transit systems in the saturated passenger flow situation, the normal operation of trains might be disturbed by the unexpected disturbance. A large bunch of passengers might be stranded on platforms due to service gaps and the limited free capacity of trains. In this paper, we develop an automatic train regulation model which integrates two different regulation strategies and considers the pre-set automatic train operation (ATO) recommended speed profile. More specifically, a mixed integer programming (MIP) model is proposed which aims to jointly reduce the total train delay and the number of stranded passengers. We consider a train holding strategy to futher balance the interval of two consecutive trains which can reduce the number of stranded passengers on the platform. Then, a state-of-the-art mathematical solver CPLEX is adopted to solve the problem, which can obtain trade-off solutions in a reasonable time. Finally, two experiments based on the operational data of the Beijing Subway Yizhuang Line are carried out to verify the effectiveness of the proposed approach.

16:30-16:50 SunD11-3 1490 Research on Path Planning of Robot Arm Based on RRT-connect Algorithm Chaoli Zhao North Minzu Univ.Xing Ma North Minzu Univ.

The Key Laboratory of Intelligent Informationand Big Data Processing of Ningxia Province

Chunyang Mu North Minzu Univ.The Key Laboratory of Intelligent Information

and Big Data Processing of Ningxia ProvinceTo address the shortcomings of the RRT-connect algorithm in terms of low efficiency of path planning in sampling space and high randomness of node sampling, this paper proposes a three-source fast extended random tree algorithm (GT-RRT) combined with gravitational field. The algorithm adds a third node as a new extension node between the start point and the target point, so that the algorithm can extend the random tree from the start point, the target point and the third node. At the same time, a gravitational field is superimposed on each of the three nodes to guide the generation of nodes, reducing the search range of the null space. The GT-RRT and RRT-connect algorithms are compared in 30 simulation experiments, and the results show that the improved algorithm has better path planning efficiency than the original algorithm in complex environments. Finally, this paper verifies the effectiveness of the improved algorithm in both the moveit simulation and the real environment by using the robot arm of Baxter robot as an example.

16:50-17:10 SunD11-4 784 New Discrete-Time Zeroing Neural Network for Solving Time-Varying System of Linear Equation and Inequality Jianhuang Cai Huaqiao Univ.Qingshan Feng Huaqiao Univ.Dongsheng Guo Huaqiao Univ.Recently, the zeroing neural network (ZNN) model with continues-time form has been established to solve the time-varying system of linear equation and inequality. For completeness and further investigation, the discrete-time form of such a ZNN is proposed and analyzed in this paper. Specifically, a special difference formula based on Taylor series expansion is constructed. By using the difference formula to discretize the continues-time ZNN model, the new discretetime ZNN (DTZNN) model is thus developed for solving the time-varying system of linear equation and inequality. Numerical results are presented to further validate the effectiveness and superiority of the proposed DTZNN model.

17:10-17:30 SunD11-5 847 FeLU: A Fractional Exponential Linear Unit Ke Zhang Soochow Univ.Xinhao Yang Soochow Univ.Jianhui Zang Soochow Univ.Ze Li Suzhou Univ. of Science and Tech.Appropriate activation functions need to be selected to train the complex neural networks. Considering the dead zone problem of ReLU function. Therefore, an Exponential Linear Unit (eLU) is proposed for improvement. In this paper, by observing the function curve characteristics of different activation function, network training comparison analysis for different functions, on the basis of eLU activation function adds some is advantageous to the features of network performance, such as non monotonicity, limit tends to zero, and keep the landed weight etc., thus put forward a kind of fractional exponential linear unit (FeLU).Experiments show that FeLU function is superior to eLU function in different depth models and training data sets, and it is an activation function that can effectively improve network performance.

17:30-17:50 SunD11-6

Technical Programmes CCDC 2021 848 POP PIANO MUSIC GENERATION WITH THE SIMPLIFIED TRANSFORMER-XL Qing Huang Soochow Univ.Xinhao Yang Soochow Univ.Feiyang Qian Soochow Univ.Ze Li Suzhou Univ. of Science and Tech.Pop piano music is generated by deep learning methods in this paper. The model we used is a simplified Transformer-XL, which removes the mask module from Transformer-XL. The simplified Transformer-XL enables the generated music to have a better integrity. Subsequently, professionals and non-professionals are invited to evaluate the generated music based on a subjective evaluation algorithm. The evaluation results show that the simplified Transformer-XL can generate pop piano music with good effect.

SunDIS Room12 Interactive Session 15:50-17:50

SunDIS-01 291 An Improved Method of Converting Z-number into Classical Fuzzy Number Ruolan Cheng Northwest A&F Univ.Bingyi Kang Northwest A&F Univ.Jianfeng Zhang Northwest A&F Univ.In order to better describe uncertain information, Zadeh first proposed the concept of Z-number in 2011. Znumber has a three-dimensional structure with fuzzy constraints, reliability measures and hidden probability distributions. The complexity of Z-number operation makes it difficult to directly apply to engineering practice. The current fuzzy set theory is relatively mature, so how to reasonably convert Z-numbers into classic fuzzy numbers is very important for engineering applications. Aiming at the problem that the existing conversion method does not consider the influence of hidden probability distribution on Z-number information, we propose an improved method of converting Z-number into fuzzy number. The proposed method is based on the compatibility of hidden probability distributions in Z-numbers. Consider using the compatibility information to correct the reliability component of the Z-number, and then convert it into a classical fuzzy number based on the principle that fuzzy expectations are approximately equal. To illustrate the conversion process of the proposed method in detail, we provide a simple numerical example.

SunDIS-02 297 Research on Evaluation Index System of High-end Chemical Industry Development Based on Principal Component Method Yumei Wang Qingdao Univ. of Science and Tech.Chen Wang Qingdao Univ. of Science and Tech.Xia Zhang Qingdao Univ. of Science and Tech.According to the connotation and development characteristics of the high-end chemical industry, design an indicator framework for the evaluation of the development of the high-end chemical industry, collect big data on the development of the high-end chemical industry through questionnaire surveys, extract the key factors for the development of the high-end chemical industry based on quantitative analysis of principal components, and Construct a scientific evaluation index system for the development of the high-end chemical industry to provide theoretical and realistic basis for the evaluation of the development status of the high-end chemical industry.

SunDIS-03 464 An Improved Dynamic Step Size RRT Algorithm in Complex Environments Yuwei Zhang Beijing Inst. of Tech.Ruirong Wang Beijing Inst. of Tech.Chunlei Song Beijing Inst. of Tech.Jianhua Xu Beijing Inst. of Tech.Rapidly exploring Random Tree (RRT) is an efficient path planning algorithm based on random sampling, which plays an important role in the robot field and autonomous driving field. However, due to the randomness of sampling, its results are usually not optimal. This paper proposes a dynamic step size RRT algorithm, which mainly improves the traditional RRT as follows. First, combined with the Artificial Potential Field (APF), the target makes heuristic guidance for the sampling process. And then, the step size is adaptively changed according to the density of obstacles. After that, a one-shot heuristic strategy is used to speed up the search process. Finally, a bi-directional pruning strategy is adopted to reduce the path length by merging points. The simulation results show that the improved RRT algorithm can find the target faster and better.

SunDIS-04 478 Graph Convolutional Neural Networks with AM-Actor-Critic for Minimum Vertex Cover Problem Hong Tian Beijing Univ. of Chemical Tech.Dazi Li Beijing Univ. of Chemical Tech.At present, deep reinforcement learning (DRL) methods are mostly used to solve the problems in Euclidean space. Typical evaluation environment

of algorithm performance includes Atari pixel game with image as input, or single inverted pendulum system with specific data input shape, etc. However, most of the practical problems exist in non-Euclidean space, and the features of topological data need to be extracted. In this paper, a new framework combining DRL and graph neural network (GNN) is proposed, which aims to reduce the over-estimation problem, and obtain better performance results than before in solving NP-hard problems. NP-hard problem is a problem with high time complexity. The proposed method is used to solve a typical NP-hard problem named the Minimum Vertex Cover problem (MVC) and is compared with the common Actor-Critic-GNN (AC-GNN) method, a heuristic strategy and a random strategy, the proposed algorithm has the best performance. At the same time, the experiment also show the superiority of our new method in generalization ability, which is a feature that the previous DRL methods do not have.

SunDIS-05 512 Identification friend or foe (IFF) of aircraft target based on TS-ANFIS Chunlei Han Xi’an Research Inst. of Navigation Tech.Jianjun Sun Xi’an Research Inst. of Navigation Tech.Yuanna Liu Northwestern Polytechnical Univ.Zihao Zhao Northwestern Polytechnical Univ.The target identification of friend or foe is one of the important means of military confrontation in the modern informationized battlefield. It can greatly enhance the accuracy of combat command and control, and the coordination between combat units, then reduce the probability of false injury. In this paper, a target attribute recognition algorithm is proposed based on the Time Series-Adaptive Neuro-Fuzzy Inference System (TS-ANFIS) with consideration of target motion characteristics. The altitude, speed and flexibility information of tracks detected by radar are preprocessed as time series and act as input data to train the TS-ANFIS networks of corresponding airspace. Then evidence theory is introduced to fuse the recognition confidence. Experiments on the generated track dataset demonstrate the effectiveness of TS-ANFIS to learn the motion features and distinguish target attributes.

SunDIS-06 539 Bank Credit Decision Analysis Based on Fuzzy Analytic Hierarchy Process Di Zhao Shenyang Jianzhu Univ.Fuan Lin Shenyang Jianzhu Univ.Ruonan Gu Shenyang Jianzhu Univ.Haoran Peng Shenyang Jianzhu Univ.Tingting Yan Shenyang Jianzhu Univ.This article starts from the perspective of the bank. The fuzzy analytic hierarchy process is used to establish a risk assessment model, and the bank's credit rating for small, medium and micro enterprises is obtained. Use target optimization methods to establish credit decision models. We perform regression analysis on existing data to get the relationship between bank loan amount, and annual interest rate. Then we established the objective function to maximize the net profit of bank credit and calculated the annual interest rate and loan amount of each level of enterprise. This enables banks to assess the credit risks of different companies when facing loans to small, medium and micro enterprises, and then make loans reasonably.

SunDIS-07 547 Research on the Co-construction and Sharing Mode of Military Data Resources Based on Cloud Service Yujiao Jiang Academy of Military SceineceWenmin Jiang Naval Submarine AcademyZenghua Li Academy of Military SceineceMingxing Yuan Academy of Military SceineceThe co-construction and sharing of military data resource is important for improving the supporting role of data in military decision-making. The paper explains the part cloud service play in the co-construction and sharing of military data resources, analyzes the requirements of military data management platforms construction based on cloud service, gives an idea on planning and design of military data management platforms construction based on cloud service, and proposes the co-construction and sharing strategy of military data resource based on cloud service.

SunDIS-08 572 Research on Weapon Equipment Supply Center Site Selection Based on the Center-of-Gravity Method and the TOPSIS Method Xiaosong Li Military Science Information Research Center

PLA Military ScienceZenghua Li Military Science Information Research Center

PLA Military ScienceXinran Peng Military Science Information Research Center

PLA Military ScienceZhenghua Xiao Military Commission Equipment Development

DepartmentTian Liu Military Science Information Research Cente

rPLA Military ScienceThe weapon equipment supply center site selection is an important part of the weapon equipment supply system. This paper put forward the site

Technical Programmes CCDC 2021 selecting idea of weapon equipment supply center, constructed the site selection model based on the center-of-gravity method, and came up a preliminary site selecting plan for the supply center. On this basis, analyzed the influencing factors of the site selection, constructed a model based on the TOPSIS method, and obtain an optimal plan for the supply security center, and carried out a case analysis. The research conclusions can provide a practical reference for the site selection of weapon equipment supply center.

SunDIS-09 604 An approach of talents evaluation based on multi-expert decision-making Yi-xiao Sun Northwestern Polytechnical Univ.Lin Song Northwestern Polytechnical Univ.

Xi'an Univ. of Architecture and Tech.Peer reviewing of technological talents is generally based on multi-expert voting or scoring, in which the controversial decisions are not effectively processed and the effect of subjective factors on the evaluation results is difficult to be removed. An approach of decision fusion is proposed for peer reviewing based on dynamic evaluation credibility. In the framework of evidence reasoning, a frame of discernment is defined for talents evaluation, and the traditional scoring is converted into qualitative evaluation evidence in terms of belief. The expert relative supporting degree is introduced to acquire dynamic evaluation credibility, which is used for modifying the evidential bodies. Multiple evidences are fused based on the rules of evidence reasoning. A linear utility function is employed to convert the fusion result into the final score, which is used to rank and select the candidates. At last, case study validates the effectiveness of the approach and the evaluation results tend to be more reasonable.

SunDIS-10 703 Evaluation of the Developmental Level of Hazardous Chemicals Transportation Company-Taking Company A's Choice of Hazardous Chemicals Transportation Company as an Example Mingzhi Han Shandong Univ. of Finance and EconomicsRui Wang Shandong Univ. of Finance and EconomicsSiyuan Wen Shandong Univ. of Finance and EconomicsIn recent years, with the vigorous development of the refining and chemical industry, the chemical products derived from it have become more abundant. However, the transportation of hazardous chemicals is different from other industrial products. Due to the characteristics of hazardous chemicals, it is particularly important to choose a good transportation company when transporting hazardous chemicals. This paper studies the problem of choosing a transportation company by analyzing the developmental level of hazardous chemicals transportation companies. It briefly summarizes the methods of evaluating the developmental level of logistics companies in China and foreign countries, and summarizes the factors affecting the development of hazardous chemicals transportation companies around the characteristics of hazardous chemicals. According to the principles and steps of establishing an evaluation index system, it establishes an evaluation index system for the developmental level of hazardous chemicals transportation enterprises. And it takes Company A's choice of a hazardous chemicals transportation company as an example, uses the analytic hierarchy process to determine the weight of the index, and uses the fuzzy comprehensive analysis method to find out the hazardous chemicals transportation company suitable for Company A. The research conclusions of this paper are of great significance for evaluating the developmental level of hazardous chemicals transportation companies.

SunDIS-11 735 Risk assessment of aircraft landing phase based on neural network cloud and Monte Carlo Jiahui Shi Air Force Engineering Univ.Jihui Xu Air Force Engineering Univ.In view of the complex factors affecting the landing risk of military aircraft after the mission, the assessment method is not systematic, and the process is fuzzy, random and uncertain. In this paper, a risk assessment model based on neural network cloud is constructed and implemented by Monte Carlo simulation. Firstly, four parameters, namely ground speed, elevation angle, vertical acceleration and ground distance deviation, are selected to describe the risk situation of landing phase. Roulette algorithm is used to generate 100 groups of cloud data randomly, and the probability membership degree is determined by cloud generator as the training data of GA-BP neural network. The simulation evaluation system is formed after learning and training of neural network, and the landing number of a certain army aircraft is calculated. The rationality and scientificity of the evaluation system is verified by an example, which proves that it has certain practical significance and reference value.

SunDIS-12 790 The Impact Mechanism of Informatization Level on Local Government Contingent Debts Guan-ping Zhu Xi´an Univ. of Tech.Wen-xiu Hu Xi´an Univ. of Tech.Song Shan Jinhua Polytechnic

De-bing Guo Guilin Univ. of Tech.In recent years, local government contingent debts have received more and more attention from entities and scholars. In order to explore the impact factors of local government contingent debts, this paper empirically tests the relationship of informatization level on local government contingent debts by using the data of 31 provinces in china during 2007-2018. The study finds that the informatization level is significantly negatively correlated with local government contingent debts, indicating that informatization level development can inhibit the formation of local government contingent debts. Further research finds that internal agency behavior and external social capital play an intermediary transmission function in the process of informatization level affecting local government contingent debts, indicating that informatization level has an inhibitory effect on local government contingent debts by inhibiting local officials agency behavior and expanding external social capital. The research conclusions of this article not only enrich the impact factors of local government contingent debts, but also provide important reference value for policy makers.

SunDIS-13 811 Research on the Relations of Collaborative Innovation Constraints of Civil-Military Integration with the DEMAT EL　 -ISM Method Ying Qu Hebei Univ. of Science and Tech.Xing Bai Hebei Univ. of Science and Tech.Collaborative innovation is an important foundation and internal motivation for the in-depth development strategy of civil-military integration. The analysis of constraints on collaborative innovation of civil-military integration can help promote the in-depth development of civil-military integration. Through literature review, an indicator system of 14 factors has been constructed in four dimensions: policy bottlenecks, ideological constraints, institutional barriers, and resource element barriers. This paper analyzes the important factors affecting collaborative innovation of civil-military integration with the DEMATEL-ISM method and build a hierarchy model. The results indicates that the most fundamental reasons for the constrains of collaborative innovation of civil-military integration are the profit distribution, insufficient legal and regulatory basis, traditional thought barriers and institutional mechanisms.

SunDIS-14 825 New Method of Energy Charging Station Site Selection based on Fuzzy Evaluation Jing Guo Aostar Information Tech. Co., Ltd.The development of the electric vehicle industry has the problems of difficulty in charging and dislocation of vehicle piles. Before the construction of charging stations, scientific and intelligent site selection is the key to solving the problem. Comprehensively analyze the factors affecting the site selection of new energy charging stations, establish a site selection index model, calculate the index weight according to the analytic hierarchy process, and use the improved fuzzy evaluation method of fuzzy evaluation. The quantifiable index directly maps the evaluation level, based on historical operating data estimates the profitability of the site, and integrates the fuzzy evaluation results of all candidate addresses and profit estimates to obtain the most optimal site selection plan. Finally, an area is used as the research object to apply the method, and the results show that the method can reflect various factors that affect the site selection and the site selection can be determined scientifically and quickly. The method proposed is based on the profit of the station and ensures the income of the charging station from the source, which has certain practical value.

SunDIS-15 852 Sequential three-branch decision method based on Bayesian principle Ke Zeng Univ. of Chinese Academy of Sciences

Xi’an Inst. of Optics and Precision Mechanics of CASJunfeng Han Xi’an Inst. of Optics and Precision Mechanics of CASIn this paper, the sequential three-branch decision method based on Bayesian principle is applied to face image recognition to realize the minimum risk decision. Adding a pending area to the traditional two-branch decision can minimize the decision cost when the misclassification cost is imbalanced and the feature information of the sample is temporarily insufficient. In addition, the acquisition of sample feature information is not unlimited. Although each feature information obtained can reduce the cost of misclassification of decision, it is also necessary to consider the increased test cost of each feature information obtained. This paper used the Bayesian principle to realize the dynamic balance of them to achieve the minimum total decision cost (misclassification cost and test cost). In this paper, 2DPCA was used to obtain the granular features of face images, and experiments were conducted on face databases such as AR and PIE to verify the effectiveness of the sequential three-branch decision method based on Bayesian principle.

SunDIS-16 862 Multi-loitering Munitions Cooperative Interference Resource Allocation Based on Hybrid Ant Colony Algorithm Sheng Luo Xi’an Modern Control Tech. Research Inst.

Technical Programmes CCDC 2021 Zihao Wei ShenYuan Honors College of Beihang Univ.Zhenyu Ma Xi’an Modern Control Tech. Research Inst.Qiang Luo Xi’an Modern Control Tech. Research Inst.Aiming at optimizing the cooperative jamming resource allocation in cooperative operation of loitering munitions, a cooperative jamming resource allocation algorithm based on hybrid ant colony algorithm is proposed. First of all, according to the characteristics of cooperative jamming operation of loitering munitions, the jamming effectiveness evaluation indices are determined, and the jamming benefit decision matrix is constructed. And then a cooperative jamming resource allocation model of multiple loitering munitions is established to solve the problem of asymmetric number of loitering munitions and radars. Finally, a hybrid ant colony algorithm is designed by introducing cross mutation mechanism and metropolis criterion. As the problem that ant colony algorithm tends to fall into local optimal solution is solved, it enhances the global optimization ability of the algorithm. Simulation results show that, compared with the basic ant colony algorithm, the proposed algorithm has good robustness and scalability, and improves the convergence speed of the optimal solution.

SunDIS-17 877 Linguistic multi-attribute group decision-making method based on similarity measurement of cloud model Jinntao Yu Air Force Early Warning AcademyBing Xiao Air Force Early Warning AcademyJiajun Xiong Air Force Early Warning AcademyHongquan Li Air Force Early Warning AcademyQiushi Xi Air Force Early Warning AcademyAs for the defects of the existing cloud similarity, a novel similarity measurement approach based on the location and shape similarity is proposed and applied to linguistic multi-attribute group decision-making in this paper. Firstly, the framework of integrated similarity measurement of cloud model is established by combining the location and shape similarity. The advantages of the new method are analyzed on comparison with other methods. Then by converting the linguistic information to cloud model, the proposed similarity measurement is used to linguistic multi-attribute group decision-making problem. And an improved TOPSIS method is proposed. Finally, the feasibility and effectiveness of the proposed method are verified by numerical example and comparative analysis.

SunDIS-18 938 Research on Cooperative Target Assignment Decision of Aircraft Yameng Cui Beijing Aerospace Automatic Control Inst.Ruiguang Hu Beijing Aerospace Automatic Control Inst.Jiaxin Huang Beijing Aerospace Automatic Control Inst.Chunsheng Zheng Beijing Aerospace Automatic Control Inst.Huixia Wang Beijing Aerospace Automatic Control Inst.Weapon target assignment is one of the core issues of command decision-making. Seeking a target assignment decision can save firepower resources and improve combat capabilities. Aiming at the problems of a slow solution of weapon target assignment problem, low attack efficiency ratio, and insufficient application scenarios, an improved Hungary algorithm and Monte Carlo Tree Search algorithm are proposed. First, collaborative target assignment optimization models were established, and the models have adjusted accordingly for different algorithms. Secondly, for different confrontation scenarios, virtual firepower units were set to improve the Hungary algorithm; and then aiming at the problem of fire resource waste in the Monte Carlo Tree Search algorithm, design the constraints of the weapon requirement, and get the search method with the shortest search time and the highest attack efficiency ratio. Finally, in different scale confrontation scenarios, several algorithms are compared and tested to verify the use of MCTS_ WR algorithm can optimize the assignment of fire resources, greatly improve the attack efficiency ratio of the target, and save the assignment time and its flexibility to adapt to confrontation scenarios.

SunDIS-19 947 Coupling Degree Evaluation and Correlation Game Argumentation Between Cultural Industry and Tourism Industry Based on Grey Decision Model Danhong Chen Shenyang Aerospace Univ.Peng Yi Shenyang Education Research Inst.At present, the coupling development of cultural industry and tourism industry plays an active role in linking industrial chain, prolonging life cycle, stimulating new business forms and amplifying coupling effect on cultural tourism industry. Based on the actual situation of the industry, this paper conducts the coupling degree evaluation and correlation game demonstration of the cultural industry and the tourism industry based on the gray prediction model, and finally proposes the path to improve the coupling degree of the cultural industry and the tourism industry.

SunDIS-20 1040 Identification of Combat Intention of the Carrier-based Fighter Based on MEBN Yiyang Luo National Univ. of Defense Tech.Qingsong Zhao National Univ. of Defense Tech.

Jianbin Sun National Univ. of Defense Tech.Tao Zhang National Univ. of Defense Tech.The maritime battlefield has become one of the main battlefields of modern warfare. As the main means of attack in maritime operations, it is of great significance to identify the combat intention of the carrier-based fighter. In this paper, a combat intention identification model based on multi-entity Bayesian network (MEBN) is proposed. First, the combat process of the carrier-based fighter is analyzed to get the observable key factors that affect combat intentions. Then, combined with the operational knowledge of the carrier-based fighter, the MEBN frag (MFrag) database of carrier-based fighter combat intentions is constructed, and a method is used to synthesize the knowledge of military experts and historical data conclusions to provide conditional probability distribution. More well-rounded and comprehensive information is utilized in the propose MEBN-based model to identify the combat intention of the carrier-based fighter, and it has a fast identification speed based on the complete MFrag base. The experiments in typical cases show that the recognition results of the proposed MEBN-based model are consistent with the analysis results.

SunDIS-21 1063 A New Evaluation Method for Test and Evaluation with Multi-Source Information Tao Zhang National Univ. of Defense Tech.Jianbin Sun National Univ. of Defense Tech.Jiang Jiang National Univ. of Defense Tech.Yiyang Luo National Univ. of Defense Tech.A new evaluation method based on evidential reasoning is proposed for the test and evaluation (T&E) for weaponry in this paper. The proposed method can solve the problem of multiple data sources and diverse data types. The belief rule base (BRB) in the method proposed can aggregate the data with diverse data types which have single sources and the output of the BRB have the same belief structure, the evidential reasoning rule (ER rule) is used to aggregate the result of the BRB. The feasibility of the new evaluation method is demonstrated using a T&E for weaponry case about unmanned aerial vehicle (UAV).

SunDIS-22 1107 Risk Evaluation on Ship Navigation System Based on Analytic Hierarchy Process and Fuzzy Comprehensive Evaluation Method Mi Yan Wuhan Univ. of Tech.Huajun Zhang Wuhan Univ. of Tech.Lishou liu Wuhan Univ. of Tech.Ship navigation system is a necessary factor for the safe navigation of ships. However, the relationship between the factors that affect the ship navigation system is usually very complicated and cannot be directly expressed by mathematical expressions. This paper proposes a risk evaluation method for ship navigation system based on analytic hierarchy process (AHP) and fuzzy comprehensive evaluation (FCE). This paper firstly searched for relevant references to determine the three-level structure of the ship navigation system’s fuzzy evaluation, and determined the final comment set of the ship navigation system and the corresponding ten-point score, then through AHP, the weight of each sub-factor relative to the target layer was determined. Finally, the FCE is used to evaluate the ship navigation equipment at multiple levels, so as to obtain the final evaluation result and the score under the tenth scale. The results show that this method can effectively make risk assessment decisions and has certain practical value.

SunDIS-23 1122 Multi-criteria Group Decision-making Method Based on Expert Trust Network and Cloud Model Xin Luo Wuhan Univ. of Tech.Huajun Zhang Wuhan Univ. of Tech.Aiming at the fuzzy weight of experts, the complexity of objects and the fuzziness of human thinking in the process of multi-criteria group decision-making, this paper proposed to use expert trust network and cloud model operators to realize the comprehensive evaluation decision-making based on linguistic variables. Firstly, the linguistic evaluation information of linguistic term sets are transformed into corresponding numerical information, which are transformed into corresponding cloud models. At the same time, the expert weight is calculated iteratively by using expert trust network. Finally, the comprehensive evaluation index is obtained by tne CWAA operator.

SunDIS-24 1230 An Improved SVDD Algorithm for Fast Real-time Response Wenya Zhai Wuhan Univ. of Science and Tech.Shaowu Lu Wuhan Univ. of Science and Tech.

Dongguan Samsun Optoelectronic Technology Co., Ltd

Wenjing Cai Wuhan Univ. of Science and Tech.Canjun Yuan Wuhan Univ. of Science and Tech.Yunxuan Zhang Wuhan Univ. of Science and Tech.Yuge Zheng Wuhan Univ. of Science and Tech.Support Vector Data Description (SVDD) is a one-class classification algorithm. It can distinguish target samples from non-target samples, and

Technical Programmes CCDC 2021 is usually used in anomaly detection and fault detection. However, by expanding the kernel of the traditional SVDD decision function, it is found that the running time complexity is linearly related to the number of support vectors. Therefore, in order to achieve fast real-time response, it is necessary to speed up the running speed of its decision function. In this paper, by using the direct image searching method we can find the pre-image of the spherical center which is no longer an expansion expression of support vectors. Finally, the decision function of quick SVDD (QSVDD) is obtained, so that the running time complexity is a constant in the support number. Therefore, regardless of the size of the training set, the kernel extension in the traditional SVDD decision function can be naturally reduced obviously, which solves the problem of reducing the SVDD test time complexity to a great extent. We believe that QSVDD can be applied in the process of product quality inspection, which will greatly improve the speed of this process.

SunDIS-25 1298 A Potential Downside Risk Assessment Considering Uncertain Investment Period Dazhi Wang Northeastern Univ.Yanhua Chen Northeastern Univ.Min Huang Northeastern Univ.Yuxin Zhang Northeastern Univ.Chunhui Xu Chiba Inst. of Tech.In the financial market, investors often adopt the downside risk that is associated with investment losses to depict the magnitude of the potential investment failures. Reviewing the literatures, some popular downside risks such as Semi-Variance, Value at Risk (VaR) and Conditional Value at Risk (CVaR) have been proposed for evaluating the risk concerning the extent of losses. Due to the numerous volatile factors in the investment market, the risk measurement of uncertain investment period has gradually attracted the attention of the scholars and has become the focus of the related research field. In this research, we attempt to develop a new risk assessment called Expected Risk (ER) motived from the data fitting for quantifying the potential extent to the investment failures related to the uncertain investment period. Some preliminary computational results are provided to show the rationality of the developed risk assessment.

SunDIS-26 1446 Linguistic interval neutrosophic sets in multi-criteria group decision-making Sang-sang He Central South Univ.Jian-qiang Wang Central South Univ.Jun-hua Hu Central South Univ.Yi-ting Wang Central South Univ.Fei Xiao Central South Univ.Rui-lu Huang Central South Univ.Li Yu Central South Univ.Zi-yu Chen Central South Univ.Min-hui Deng Central South Univ.Ya-nan Wang Central South Univ.Neutrosophic set is an inclusive and powerful set, which can deal with incomplete, inconsistent, and indeterminate information very well. To ease its application in real-world problems, many extensions of NSs have been proposed. In this paper, we presented a linguistic interval neutrosophic set, which integrates the advantages of NSs, IVFSs and linguistic variables together and more applicable to practical problems. In addition, a LINSs based TOPSIS method is established to solve multi-criteria group decision-making problems. A numerical example is presented to illustrate the application of the proposed method. And some discussions are given to show the superiority of the proposed method.

SunDIS-27 1515 Airport resource scheduling optimization based on planning time window Bin Chen Civil Aviation Univ. of ChinaKaifeng Tang Civil Aviation Univ. of ChinaYalei Yang Civil Aviation Univ. of China

Jining Univ.Aiming at the problem of insufficient operation efficiency of airport support, the optimization model of airport support resource scheduling is established based on the support operation time window before and after collaboration. Taking the three aspects of total operation time, total driving distance and actual operation cost as optimization objectives, the model optimization solution method based on integer coding inheritance algorithm is proposed. The simulation results show that the total service time of the two systems is reduced by 7.1% and 7.3% respectively, which can realize the support resource scheduling and path planning based on the planning operation time window, which shows the effectiveness of the optimization model and algorithm of the support resource scheduling, and compares the results before and after the collaboration The analysis proves the superiority of the collaborative planning model of flight transit support under resource constraints, which is of great significance to improve the coordination ability between ground support operation processes, increase the limited time of flight transit and resource utilization efficiency.

SunDIS-28

1553 A Visualization Method for Scientific Research Data of University Teachers Based on Temporal Hierarchical Layout Strategy Qihang Yang Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech., Chinese Academy of Sciences

Bin Xiao Hebei College of Industry and Tech.Lijun Fu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech., Chinese Academy of Sciences

Jin Li Univ. of Chinese Academy of SciencesShenyang Inst. of computing tech., Chines

e Academy of SciencesXiaojuan Liu Univ. of Chinese Academy of Sciences

Shenyang Inst. of computing tech., Chinese Academy of Sciences

Scientific research evaluation has become the core of scientific research and academic management in colleges and universities. According to the scientific research data of university teachers in school, the objective and accurate scientific research evaluation of individual teachers and even the whole university will play a guiding role in the scientific research planning of university teachers and the reasonable allocation of scientific research resources. According to the characteristics of teachers' scientific research data with both complex multidimensional and temporal attributes, a hybrid visualization method (Zoomable Sunburst Time-Line Mix) was proposed based on hierarchical zoom nesting map and temporal representation. The method is applied to teachers' research data, and the visualization of teachers and the whole university is realized. The experimental results show that the proposed ZSTM method can accurately display the scientific research data of teachers, and help users at all levels of universities to effectively and intuitively evaluate the scientific research management work.

SunDIS-29 1603 Where China's Investments Go? An Empirical Study Based on Foreign Aid Contracted Projects Shuhua Guo Yunnan Univ. of Finance and EconomicsQifa Jiang Yunnan Univ. of Finance and EconomicsHaoshuai Chen Yunnan Univ. of Finance and EconomicsThe implementation of China's Belt and Road Initiative (BRI) has strengthened China's ties with the world and made great progress in China's foreign project investments. In general, the implementation of BRI has greatly changed the arenas and regions of China’s investment, and main directions of China’s foreign aid. However, many Chinese people still have many doubts about the effects of China's foreign aid due to lack of relevant information. This study selects relevant data on the turnover of China's contracted projects in Asia, Africa, Latin America and North America during the period of 2010 to 2019, and uses the Difference-indifference (DID) model to compare the size of China's project investments and analyze its main directions. Based on panel data formed before and after the implementation of China's BRI, an empirical analysis shows that China's investments still mainly flow into the Asian region, and the implementation of BRI has a significant effect on Asian investments. Smart policies should focus on the improvement of data transparency and do more research to realize the impacts of BRI for the public.

SunDIS-30 1639 Research on Optimization of Evaluation Index System of National Defense Science and Technology Venture Capital Fund Project Beibei Zhuang National Defense Univ.Jianrong Wang National Defense Univ.Yongtao Wang National Defense Univ.Yalei Guo Academy of Air CommandThe construction of evaluation index system is an important means for project selection. This paper optimizes the evaluation index system of national defense science and technology venture investment projects by means of variance analysis and SPSS quantitative analysis, combining with the investment practice of GD capital, the feasibility of the proposed method is proved.

SunDIS-31 1647 Optimization of the Tactical Disposition Scheme for Ground Air Defense Based on Fuzzy Comprehensive Evaluation Pengsong Guo Air Force Engineering Univ.Weimin Li Air Force Engineering Univ.Longyue Li Air Force Engineering Univ.Ning Li Air Force Engineering Univ.Changan Shang Air Force Engineering Univ.The evaluation of the tactical disposition scheme for ground air defense is a vital link in ground air defense operations. A well-designed tactical disposition scheme serves as an important premise for reasonable employment of armed forces, full use of fire, and effective combat against aerial attackers. By applying the principle and method of fuzzy mathematics in combination with the characteristics of tactical disposition for ground air defense, the evaluation index system of the tactical disposition scheme for ground air defense was established, and a corresponding fuzzy comprehensive evaluation model was constructed. This study realized the evaluation and optimization analysis for the

Technical Programmes CCDC 2021 disposition scheme, and selected the optimized tactical disposition scheme for air defense, which could provide auxiliary decision-making in combat for officers and soldiers.

SunDIS-32 150 Design and Realization of Liner Ship-route Allocation Decision Support System Xiao-jun Li Tianjin Research Inst. for Water Transport

Engineering M.O.T.Ran Zhou Tianjin Research Inst. for Water Transport

Engineering M.O.T.Lequn Zhu Tianjin Research Inst. for Water Transport

Engineering M.O.T.Xin-lian Xie Dalian Maritime Univ.Hong Du Dalian Maritime Univ.In order to improve the practicability of the optimization method, the LSADSS (Liner Ship-route Allocation Decision Support System) is designed and developed based on the mathematical model of multi-port liner shipping line and the mixed programming technology of Visual Basic and Lingo. This paper introduces the overall design of the system and the key technologies of the system implementation, and gives a calculation case. The operation results show that the system is stable and reliable, and can provide good support for shipping enterprises to optimize liner shipping lines.

SunDIS-33 485 A Workstation Solution Based Heuristic Algorithm for Assembly Line Balancing Problem Qidong Yin Northeastern Univ.Xiaochuan Luo Northeastern Univ.Jie Sun Lei Zhang

Northeastern Univ.Northeastern Univ.

In the automotive industry, assembly line balancing work plays an important role in assembly process planning. Assembly line balancing problem is a kind of NP hard problem, especially for Two-sided assembly line balancing problem. It is too difficult to obtain the optimal result using the commercial solvers directly. This paper presents a heuristic algorithm to deal with the two-sided assembly line balancing problem. Based on the mathematical model of the two-sided assembly line balancing problem, we analyze the characteristics of the problem. A workstation oriented strategy is used to generate assignment for each mated-station. Priority rules are created in the process of solution generation. We implement numerical experiments based on the benchmark data sets. Results verified the effectiveness and efficiencies of the proposed algorithm.

SunDIS-34 540 Produce prediction modeling of Industrial production processes using the improved PLS-CM Yongming Han Guizhou Provincial Key Laboratory of

Public Big DataBeijing Univ. of Chemical Tech.

Jintao Liu Guizhou Provincial Key Laboratory of Public Big Data

Beijing Univ. of Chemical Tech.Zhiqiang Geng Guizhou Provincial Key Laboratory of

Public Big DataBeijing Univ. of Chemical Tech

Feng Xie Guizhou Provincial Key Laboratory of Public Big Data

Kai Chen Guizhou Provincial Key Laboratory of Public Big Data

Yajie Wang Guizhou Provincial Key Laboratory of Public Big Data

In the industrial production process, the configuration of raw materials plays an important role in predicting products and improving the efficiency of the production process. Therefore, this paper establishes the produce prediction model of industrial production processes based on the partial least squares(PLS) method integrating the correlation matrix (PLS-CM).The correlation matrix is used to evaluate the correlation among variables, and eliminate the weak correlation variables in ethylene production. Then the PLS method based on other strongly correlated variables is established to predict the ethylene products. Because the initial independent variables have serious correlation and redundant variables, the PLS-CM can simplify the variable system and remove the invalid variable to realize the regression modeling of small samples. Finally, the PLS-CM is used in the produce prediction model of the ethylene industry. The proposed model predicts the ethylene products based on the lightdoil, naphtha, raffinate, hydrogoil, lhydr, c345 in crude oil. Compared with the traditional neural network and the PLS, the PLS-CM achieves the best result which indicates the PLS-CM has more advantages in the model prediction.

SunDIS-35 778 Multi-Objective Robust Optimization for Planning of Mineral Processing under Uncertainty Quan Xu Northeastern Univ.Kesheng Zhang Northeastern Univ.

Mingyu Li Northeastern Univ.Yangang Chu Northeastern Univ.Danwei Zhang Northeastern Univ.The planning of mineral processing is crucial for improving the utilization ratio of nonrenewable raw mineral resources. However, in the optimization process, the uncertainty of raw ore grade poses a very important issue and it directly affects the optimization performance. To address the above-mentioned issues, an improved multi-objective robust optimization algorithm is proposed, which employs NSGA-II as the basic component assisted by the random elite immigrant scheme and robustness evaluation in batches strategy. The proposed strategy aims at realizing the optimization of the planning of mineral processing. Using real data from a mineral processing plant on iron ore beneficiation process has been carried out. Experiment results show that the proposed strategy can efficiently achieve the Pareto front of the multi-objective robust optimization problem under the disturbance.

SunDIS-36 826 Tidal Forecasting Based on ARIMA-LSTM Neural Network Tianxin Zhou Dalian Maritime Univ.Wenjun Zhang Dalian Maritime Univ.Shuangfu Ma Dalian Maritime Univ.In order to improve the accuracy of tidal forecasting, a neural network deep learning method is proposed, which combines LSTM with ARIMA to predict the tidal height. ARIMA-LSTM model also uses the prediction value of harmonic analysis as the factor, and then considers the wind speed and sea water temperature to predict. Firstly, the strong nonlinear relationship ability of LSTM is used to predict, and then the data is linearly fitted and predicted through ARIMA model, so this step can be regarded as the linearization process of error. The experimental results verify the feasibility and effectiveness of the method, and obtain good simulation results, which verify that the prediction accuracy of the model is high and that of the traditional method.

SunDIS-37 1034 Improved Adaptive Feedback Scheduling Algorithm based on LATE in Hadoop Platform Jing Guo Aostar Information Technologies Co., Ltd.Yong Wang Aostar Information Technologies Co., Ltd.Hadoop is the mainstream cloud platform for data analysis and processing. Job scheduling algorithm directly affects job response time and system resource utilization. The research and improvement of scheduling algorithm has always been an important topic. Based on the original LATE scheduling algorithm, this paper proposes an adaptive feedback LATE (AF-LATE) algorithm to improve the autonomous selection and feedback of execution nodes and backup tasks. In the process of scheduling, according to the load type of the task, the idle node with highest ratio of task success rate to node load is selected to back up the backward task. At the same time, the feedback of the task and node working data is obtained to dynamically adjust the fast and slow node set. The algorithm improves the resource utilization and load balance, and improves the reliability of task execution and reduces the running time of scheduling algorithm. In this paper the experimental environment is built to verify the algorithm. The results show that the scheduling algorithm is more reasonable in judging backward tasks and selecting execution nodes in heterogeneous environment, which can shorten the response time of jobs, improve the utilization and efficiency of the cluster, and can adaptively adjust the performance of execution nodes to improve the cluster reliability.

SunDIS-38 1072 An effective evolutionary algorithm for steelmaking and continuous casting scheduling Qing Wang Northeastern Univ.Hehui Wang Northeastern Univ.Min Huang Northeastern Univ.Steelmaking-continuous casting (SCC) problem is the most important problem in steelmaking industry, which directly determines the quality and delivery time of products. Due to the complex production process with loopback in some operations, the production scheduling is very difficult. Manual scheduling method is inefficient and prones to long waiting time. In this paper, a mathematical model is proposed to describe the problem of SCC with reentrant operations. And an evolutionary algorithm with improved search strategy is designed to make the solution escape from the local optimum and enhance the search quality. The heuristic decoding method can make the solutions meet the constraints and balance the objectives of waiting time and makespan. Finally, the effectiveness of the optimization model and the algorithm is testified by computational experiments and comparing with the heuristic algorithm in simulated problem instances of different scales.

SunDIS-39 1335 Intelligent Management of Satellite Ground System Miaomiao Tian Aerospace Information Research Inst., Chinese

Academy of SciencesPeng Huang Aerospace Information Research Inst., Chinese

Academy of Sciences

Technical Programmes CCDC 2021 Guangbin Ma Aerospace Information Research Inst., Chinese

Academy of SciencesWei Li Aerospace Information Research Institute, Chine

se Academy of SciencesXiaomu Li Aerospace Information Research Inst., Chinese

Academy of SciencesUniv. of Chinese Academy of Sciences

In recent years, the number of satellite-ground communication missions has increased sharply, and the structure and information of the satellite ground system became more and more complex. Based on the background of China Remote Sensing Satellite Ground Station, this paper analyzes the application of intelligent management system methods in satellite ground system management, including equipment and system management, mission and ground station resource scheduling, and system realtime optimization. And the way from automation to intelligence of the satellite ground system management system is discussed.

SunDIS-40 238 A Stop-skipping Pattern for Metro Trains using Passenger-oriented optimization strategy Songpo Yang Beijing Univ. of Tech.Liang Chen Beijing Univ. of Tech.Energy-efficient timetable optimization has attracted much attention recently to reduce the energy consumption and the associated costs of metro systems. Compared with all-stop patterns, stop-skipping patterns potentially lead to decreasing energy consumption. This paper develops a passenger-based approach to design energy-efficient metro timetables and speed profiles with a stop-skipping pattern based on smart-card data. First, we develop an algorithm to generate: a set of likely to be skipped stations is identified according to historical passenger demands, and a heuristic rule is adopted to select the specific skipped stations for each train. Second, we reformulate the initial optimization problem as a convex quadratic programming problem and develop a solution algorithm to determine the optimized timetables and speed profiles. A numerical example is conducted by the train operational data of a metro line in Beijing (China). The results show that the developed approach reduces energy consumption by 11.38% in comparison with the current timetable.

SunDIS-41 608 Design and Implementation of Intelligent Management and Control Decision System for Ship Platform Chao Ma Systems Engineering Research Inst.Wei Zhang Systems Engineering Research Inst.Yuan Qu Systems Engineering Research Inst.Long Fan Systems Engineering Research Inst.In order to monitor, manage, make decisions and evaluate the overall situation of the ship platform, we designed and implemented a ship-to-shore application-oriented intelligent platform management control and decision-making system, with a ship-borne intelligent system with network-entity system technology as the core. Shore-based information services based on ship-shore integration, a ship platform with intelligent functions such as perception, analysis, evaluation, prediction, decision-making, management, control, and remote support. The system takes the ship platform informationization as the demand traction. Based on the analysis of the requirements for the intelligent development of ships, the design and research are carried out from the overall framework, functions, and operation logic of the intelligent platform management and control decision-making system. The human-computer interaction interface is based on Visual Studio. Use Visual C# language to develop and package the model library. Designed and developed system application examples, and looked forward to the future application prospects of the system, providing support for the design, research and application of the subsequent military and civilian ship intelligent platform management and control decision-making systems.

SunDIS-42 721 Modeling, Analysis and Simulation on Safety Chain of Railway Bureau Jun Liu Inst. of Computing Tech., Chinese Academy of

Railway SciencesZhongmin Fan Railway Safety Supervision Department, TaiYua

n Railway BureauWei Jing Railway Safety Supervision Department, TaiYua

n Railway BureauXin Li Inst. of Computing Tech., Chinese Academy of

Railway SciencesZhanqiang Yan Railway Safety Supervision Department, TaiYua

n Railway BureauJiqiang Zhu Railway Safety Supervision Department, TaiYua

n Railway BureauRailway safety is the complicated and hot topic in the fields of intelligent railway, big data and artificial intelligent, while it is closely related to the risk point, hidden danger and accident, etc. The safety concepts and models on risk point, hidden danger and accident are provided in this paper, and the corresponding mathematical relationship are also analyzed to better manage the key safety problem. Simulation results discuss the relationship among risk point, hidden danger and accident, and the obtained results can help the safety manager to control the key

safety point and improve the safety management efficiency.

SunDIS-43 1197 A Method Using Clustering and SVDD for Quality Detection Weipeng Huang Wuhan Univ. of Science and Tech.Shaowu Lu Wuhan Univ. of Science and Tech.

Dongguan Samsun Optoelectronic TechnologyCo., Ltd

Xiaoqi Tang Dongguan Samsun Optoelectronic TechnologyCo., Ltd

With the development of industry 4.0, intelligent detection methods for product quality have received widespread attention. Traditional quality control methods are usually based on statistical process control. Process data must meet the requirements of independent and identical distribution, which limits its application in industry. According to the characteristics of current product quality data of manufacturing industry, a product quality detection method based on clustering hypersphere model was proposed. First, the data set is divided into k subsets by k -means clustering. Then describe the data of each subset separately to obtain closed hyperspheres containing most normal data, and use their radius as the control limit. Finally, through the comprehensive detection of the test samples in each hypersphere, the quality of the product is identified. By selecting the k value, a more flexible detection boundary can be obtained and the control limit is more reasonable. It is an effective method to ensure product quality stability and realize intelligent manufacturing for the manufacturing industry.

SunDIS-44 101 Artificial Intelligence And Internal Control˖ A Perspective of Chinese Enterprises Degui Zhu Harbin Univ. of CommerceSiwen Shen Harbin Univ. of CommerceBased on the theories of artificial intelligence and internal control, this paper analyses the important roles of artificial intelligence technology in enterprise internal control. The purpose of the study is to offer a certain reference for the development of enterprise internal control. We want to expound the advantages of artificial intelligence technology so as to study the practical application strategy of artificial intelligence in enterprise internal control. The technology of artificial intelligence is used to optimize some processes in people's production and life. The current trend of AI development has a good application prospect in all walks of life.

SunDIS-45 418 Software Design and Development of a Dynamic Display Platform for Agricultural Information Based on Cloud Services Chunyun Li Southeast Univ.Shixiong Zhai Southeast Univ.Zuding Tang Southeast Univ.Based on the rapid development of agricultural informatization and the increasing demand of Nanjing government for informatization of Baima Modern agricultural high-tech industrial Park in Nanjing, Jiangsu province. Aiming at the demand of informatization promotion in Baima Science and Technology Park agricultural high-tech Zone, this paper designed and developed a dynamic display system of SaaS model based cloud service agricultural information dynamic cloud platform. For the dynamic display system of The Agricultural High-tech Industrial Park in Nanjing, Jiangsu Baima Science and Technology Park, VR panoramic tour display system, online real-time display system and image and text data display system are mainly provided. Cloud platform display will contribute to the vigorous development and promotion of Baima Science and Technology Park Agricultural high-tech zone.

SunDIS-46 533 Overview of DOA / Handle System Development in Industrial Internet Hongfei Li China Control Systems Cyber Emergency

Response TeamYang Zhang China Control Systems Cyber Emergency

Response TeamYong Kong China Beidou Security Testing CenterJun Li China Control Systems Cyber Emergency

Response TeamChongHua Wang China Control Systems Cyber Emergency

Response TeamWith the development of new generation Internet technology, Handle system plays an important role in industrial system. As the core part of the digital object architecture (DOA), Handle system is mainly used for the registration, resolution and administration of digital object identification. Its function is similar to the domain name resolution system (DNS), but the data management function is more powerful and the security mechanism is more perfect. Foundation of DOA/Handle system is introduced in this paper, including the Handle system's namespace, data model, service mode and operation mode. The development process of Handle technology application is described, and focuses on the development status of Industrial Internet. The characteristics and advantages of Industrial Internet based Handle system are summarized.

Technical Programmes CCDC 2021 SunDIS-47 844 The Moderating Effects of Online and Offline Reputation on Service Quality and Online Patient Satisfaction Xianye Cao Hunan Univ. of Tech. and Business

Central South Univ.Yongmei Liu Central South Univ.Guiju Zhu Hunan Univ. of Tech. and BusinessWeiguo Fan Univ. of IowaThis paper studies the impact of service quality (information quality and interaction quality) and reputation (online and offline reputation) on patient satisfaction. By collecting the data from an online medical service site in China, the results show that: service quality and online reputation have positive effects on patient satisfaction; Online and offline reputation moderate the relationship between service quality and satisfaction, but in the opposite direction.

SunDIS-48 1185 Electric Vehicle Charging Data Management System Based on RFID and Socket Communication Ang Li Tianjin Univ. of Tech.Aiming at the problem of accurate calculation of large capacity random load in power grid due to the increase of electric vehicles, a remote management system for charging data of electric vehicles is developed. Firstly, RFID technology is used to identify the information of charging vehicles, so as to obtain the charging capacity information of charging station. Secondly, based on socket communication technology and TCP/IP communication protocol, the identified information data is transferred to SQL Server 2008 r2 database through data analysis, and C# language form program is used to develop the interface of server and client. The system realizes the data monitoring and collection of charging power of each charging station, and verifies the effectiveness and feasibility of RFID technology and socket communication technology in the accurate statistical application of the random loads such as electric vehicles.

SunDIS-49 1348 Multi-factor Statistical Analysis of Tourism Development in China Xinyue Yu Univ. of JinanShaoli Jin Univ. of JinanYucheng Song Univ. of JinanThis paper studies the present situation of tourism in China and the factors that affect the income of tourism in different regions. The tourism data of 2008-2018 are used for research and analysis, including the per capita disposable income of each region, the length of transportation lines, the number of accommodation corporate enterprises, GDP, international tourism revenue, tourism fixed assets and tourism revenue of the provinces, etc. With the help of Eviews software, after eliminating the Multicollinearity, heteroscedasticity and autocorrelation, a multivariate statistical model was established, and the effect of each factor on tourism is analyzed. In addition, the paper puts forward reasonable suggestions on improving the efficiency of tourism service and vigorously promoting the development of linkage industry.

SunDIS-50 1646 Multi-stage TOGAF architecture development method adaption in small-and-medium enterprises-A case study in a start-up logistics service company Jiasen Qi Beijing Foreign Studies Univ.Xiaoyu Ma Lin Li Feng Wang

Beijing Foreign Studies Univ.Peking Univ.

China Academy of Information and Communications Tech

With the increasing business complexity and changing business needs, modern information systems have been a key enabler for responding to business opportunities and proactively managing risk, while also improving operational efficiency. However, it has also created a complex IT landscape. Besides, IT provides limited business value due to ignoring the interdependencies between the business and related IT solutions. Therefore, marginal revenue declines with more and more IT investments. The emerging Enterprise Architecture (EA) discipline provides a powerful tool to improve the value of IT. The Open Group Architecture Framework (TOGAF) is one of the best practice frameworks in developing EA for an organization. Although TOGAF’s popularity and applicability in large companies are widely accepted, the large size and complexity make it difficult to handle for users and may be costly in SMEs, which requires adaption design more suitable for SMEs. In our paper, we research and design a multi-stage adaption version of the TOGAF architecture development method (ADM), taking SMEs’ critical features into consideration and drawing on the idea of minimum viable product (MVP） in the lean start-up approach. Finally, a case study in a start-up logistics service company is used for validating the adapted ADM version’s practical benefits in SMEs.

SunDIS-51 134 A branch-and-cut algorithm for flexible vehicle scheduling problem in scenic areas

Zhujun Liu Northeastern Univ.Ruiyou Zhang Northeastern Univ.This paper studies a flexible vehicle scheduling (FVS) problem in scenic areas. The FVS problem allows a vehicle to carry multiple groups of tourists within the capacity limit and a vehicle can leave the tourists while they are visiting at a spot. This mode has great potential to improve vehicle utilization but has not been extensively studied. We formulate the FVS problem based on graph description as a mixed-integer linear programming model. To further strengthen the model, we propose two families of valid inequalities. Based on valid inequalities, a branch-and-cut algorithm is proposed to solve the FVS problem. The computational results based on randomly generated instances show the superiority of the branch-and-cut algorithm.

SunDIS-52 204 Research on Benefits Distribution Strategy of Edible Mushroom Supply Chain Based on Cooperative Game Yi Zheng Harbin Univ. of CommerceZeguo Qiu Harbin Univ. of CommerceYaoqun Xu Harbin Univ. of CommerceAlthough the cultivation of edible mushrooms has developed rapidly, there are still many problems. The instability of the supply chain and the uneven distribution of benefits have restricted the development of the mushrooms industry. In order to solve this problem, default risk, operating cost, willingness to cooperate, and information symmetry introduced as correction factors for a modified Shapley value, which fills up the shortcomings of the classic Shapley value. The profit distribution model of the supply chain gives a relatively reasonable profit distribution. The results show that: (1) Under decentralized decision-making, the benefits allocation is unreasonable, farmers have low bargaining ability and negative to planting. (2) Farmers in edible mushrooms supply chain are improved through classic Shapley value benefits allocation, while processors not motivated to participate in cooperation. (3) Pareto improvement be achieved under the optimal strategy of benefits distribution of edible mushrooms supply chain based on the modified Shapley value and “farmer + processors + supermarkets” alliance is stable.

SunDIS-53 303 Manufacturer Information Acquisition with Channel Encroachment Qian Xu Nanjing Univ. of Science and Tech.Weiqian He Nanjing Univ. of Science and Tech.Huaming Song Nanjing Univ. of Science and Tech.This paper studies the interaction between manufacturer's information acquisition and channel encroachment in the environment of supply chain. Manufacturer knows the quality of their products, but they don't know the quality preference of customers. The manufacturer may encroach through direct channel, and then consider the manufacturer's strategy to obtain consumer quality preference information. This paper studies the influence of the proportion of high-quality preference consumers and the difference of consumers' quality preference on manufacturer' information acquisition and encroachment decisions. Research shows that if manufacturer do not obtain information, when manufacturer can open direct channel, the situation may be worse than when they do not. Therefore, manufacturer may promise not to encroach retail channel, although they can choose to establish direct channel free of charge. When the basic market demand is medium or small and the product quality is low, the retailer and the manufacturer will achieve a win-win situation. Information acquisition promotes direct channel encroachment. Moreover, the encroachment can alleviate the damage of information acquisition to retailer and make retailer benefit from the encroachment.

SunDIS-54 362 Research for the Multi-trip Vehicle Routing Problem based on Genetic Algorithm Zehao Zhang Shandong Univ. of Finance and EconomicsGuohua Sun Shandong Univ. of Finance and EconomicsThis paper studies the solution of the Multi-trip Vehicle Routing Problem (MVRP), in which a set of customers have to be served by the same type vehicle. Each vehicle needs to service multiple trips in one working day. The objective is to minimize the total cost with vehicle capacity, service time constraints. The MVRP is a typical NP-Hard problem. And the general accurate algorithm can not find the optimal exact solution, therefore, this paper builds a mathematical model and uses Genetic Algorithm to solve the problem. In order to verify the effectiveness of the Genetic Algorithm, it is applied to the company A and is compared with Mileage Saving Method. The results show that compared with Mileage Saving Method, the Genetic Algorithm has better results, including lower cost and more reasonable route.

SunDIS-55 457 Optimization research on remanufacturer’s financing strategy selection in cooperative remanufacturing mode Xiaodong Xia Southeast Univ.Yongqing Nan Nanjing Audit Univ.Aiming at the cooperative remanufacturing mode in the closed loop

Technical Programmes CCDC 2021 supply chain which led by the remanufacturer, the equilibrium solution and the optimal financing strategy of the remanufacturer are studied in the practical example of the remanufacturer facing capital shortage. Firstly, the cooperative decision models were constructed under the sufficient capital scenario, it is easy to find the critical vale of the initial capital for the remanufacturer choosing the debt financing strategy. Secondly, two financing strategies like the debt financing and the equity financing were studied, and the remanufacturer’s selection boundary of the optimal financing strategy was found. This research shows that when remanufacturer’s initial capital is limited, the equilibrium solution will be restricted by the initial capital level, moreover, the remanufacturer’s initial capital level and the venture investor’s equity dividend ratio are the key factors for the selection of the financing strategies. The result indicates that equity financing strategy makes the product prices lower and the total market sales higher, so as to improve the consumers’ purchasing benefits and the social benefits of remanufacturing. Finally, within the range of comparable initial capital and equity dividend ratio, the optimal financing strategy and scientific suggestions are given by numerical analysis method.

SunDIS-56 663 Research on Pricing Strategy of O2O Group Buying Dual Channel Supply Chain -- Based on Hotelling Model Hongchun Wang Beijing Univ. of Civil Engineering and ArchitectureYang Chen Beijing Univ. of Civil Engineering and ArchitectureWith the development of New Media, O2O group buying mode of New Media operation has gradually developed and matured.In order to study the pricing strategy of O2O group buying dual channel supply chain under the New Media operation mode, this paper constructs the consumer utility model of O2O group buying dual channel based on Hotelling model, and obtains the influence of the popularity of New Media operation information and the preferential degree of group purchase on the demand and profit of the dual channel supply chain.The research shows that: the demand of online channel increases with the increase of O2O group buying preference degree, while the demand of offline channel decreases with the increase of O2O group buying preference degree; the demand of online channel increases with the increase of popularity of new media operation information, and the demand of offline channel has no obvious change relationship with information popularity. Thus, when the O2O group purchase commodity is full of price elasticity, the commodity can increase the online and offline channels of merchants total revenue.

SunDIS-57 753 The Choice of Suning Appliance’s Distribution Model Based on AHP and TOPSIS RuFang Ji Shandong Univ. of Finance and EconomicsSiyuan Wen Shandong Univ. of Finance and EconomicsRui Wang Shandong Univ. of Finance and EconomicsUnder the general trend of e-commerce, the field of household appliances has obviously become a promising development direction. Is followed by a rapid rise of all kinds of home appliance chain enterprises, however, home appliance enterprise logistics market share occupied inseparable from the key link, the choice of distribution mode is the key problem of logistics distribution, choose reasonable distribution mode can improve the efficiency of the logistics at the end of the, improve customer satisfaction, promote the further development of enterprises in the logistics distribution link. This paper takes Suning Appliance as an example, adopts analytic hierarchy process (AHP) and Topsis pros and cons solution method, uses analytic hierarchy process to determine each weight of all influencing factors, and then uses Topsis to score each factor to obtain a more suitable distribution model, providing certain guidance on the selection decision of logistics distribution model.

SunDIS-58 770 Receding Horizon Optimization for Dynamic Joint Scheduling of Taxiways and Gates Ping Yan Shenyang Aerospace Univ.Meng-shi Liu Shenyang Aerospace Univ.Ming-hai Jiao Northeastern Univ.In this study, a joint scheduling problem of taxiways and gates are considered where both the flight taxiing time and the gate idle time are optimized in the objective function. The problem is formulated as a nonlinear mathematical programming optimization problem which is NP-hard. A modified version of a particle swarm algorithm base on receding horizon is applied to solve it. A novel matrix coding scheme is designed to convert a particle position vector into the priority sequence of gates for each flight while a taxiing path planning heuristic algorithm based on conflict avoidance is proposed to allocate taxiway paths for all incoming and outgoing flights. In order to improve the search capability of PSO, an opposition-based learning search strategy is introduced to exploit more latitude of search space and solve the global minimum localization problem. The algorithm is tested based on the simulated operational data from the actual airport. Experimental results reveal that the proposed algorithm is effective in solving the problem.

SunDIS-59

772 Research on Invisible Van Based on Spatial Evolution of Urban Logistics Distribution System Yating Luo Yunnan Communication Vocational and

Technical CollegeNeng Wan Univ. of Science and Tech.The invisible van can be defined as a van that undertakes the task of freight transportation. It is a new distribution vehicle without freight qualification because of urban freight control under the evolution of urban logistics distribution system. In this paper, we construct the system dynamics model of invisible van, and verify the model feasibility by comparing with the actual data. On this basis, combined with the actual situation of Kunming City, the existing data is used to forecasts the urban traffic bearing index under different freight traffic control conditions in the next 15 years, and analyzed qualitatively and quantitatively. The results represent that the implementation of freight traffic control measures can not achieve the original intention of improving urban traffic bearing capacity, on the contrary, it will reduce the traffic bearing capacity index and accelerate the urban traffic congestion, which is not conducive to the development of the urban logistics system.

SunDIS-60 788 Research on the Structure of Regional Logistics Network Based on Improved Gravity Model and SNA-A Case of Beijing-Tianjin-Hebei Region Hongchun Wang Beijing Univ. of Civil Engineering and

ArchitectureXinying Ma Beijing Univ. of Civil Engineering and

ArchitectureAt present, various localities are actively promoting the construction of regional integration, and the role of logistics industry in regional economic development is becoming increasingly prominent. Through the study of regional logistics network structure, we can judge the status of regional logistics integration. In order to study the structural characteristics of regional logistics network, this article takes Beijing-Tianjin-Hebei region as an example. Firstly, the article calculates the logistics gravity between every two cities based on the improved gravity model. Then based on the social network analysis method, this paper constructs a quantitative analysis system of directed multivalued network, and analyzes the structure of regional logistics network from the perspective of intensiveness, correlation, centrality and connection mechanism by using correlation analysis and structural hole theory etc. The main conclusions are as follows: the intensity of the Beijing-Tianjin-Hebei regional logistics network is relatively high, but the level of logistics development is unbalanced; the region lacks bridge cities, and the logistics network has no obvious hierarchical structure; the network presents a significant core-edge structure, and the relation intensity between core cities and marginal cities is weak, as well as inside marginal cities. Therefore, it is necessary to break through the administrative restrictions of geographical location and give full play to the leading role of core cities and bridge cities in order to achieve the coordinated development of regional logistics in Beijing, Tianjin and Hebei.

SunDIS-61 879 Dynamic Network Design for Fourth Party Logistics Considering Multi-period Pricing under Stochastic Demand Yuxin Zhang Northeastern Univ.Min Huang Northeastern Univ.Mingqiang Yin Northeastern Univ.With the increasingly competitive market in the logistics industry, it may be crucial to increase market share by adjusting freight prices. The problem of dynamic network design considering multi-period pricing has become a new challenging problem in fourth party logistics (4PL) operation mode. This paper uses demand scenarios to describe stochastic demand. Through the relationship between price and demand, a method to obtain the freight price levels is introduced. The mixed integer programming model is established to maximize network profit under limited investment. The performance and application of the model are investigated through two reasonable scale examples. The numerical results show that the solutions obtained by using CPLEX solver to solve the small-scale example of the mixed integer programming model are acceptable. Moreover, numerical experiments analysis the influence of maximum investment cost and multi-period pricing. The results demonstrate the significance of the proposed model and multi-period pricing.

SunDIS-62 905 Multi-period Location-Routing Problem for Recycling Logistics Based on the Route Reliability Rui Li Liaoning Univ. of Tech.Zhiqiang Tang Liaoning Univ. of Tech.In order to reduce the cost of recycling and improve the reliability of the route in a multi-period environment, the multi-period location-routing problem of recycling logistics based on the reliability of the routes is studied. The location-routing optimization model of recycling logistics is established, which minimizes the total cost of recycling logistics with satisfying the vehicle routes’ reliability constraints in each period. According to the characteristics of the problem, a symbiotic organisms search (SOS) algorithm using real coding method is designed to solve

Technical Programmes CCDC 2021 the problem. Finally, the performance of SOS algorithm is tested by solving examples of different sizes through simulation experiments. Experimental results show that SOS algorithm can effectively solve problems of different sizes and keep stable performance under different reliability levels.

SunDIS-63 926 A route problem with customers’ preferences for a fourth party logistics provider Xinyue Cai Shenzhen Univ.Xiaofeng Wang Shenzhen Univ.Xiaohu Qian Shenzhen Univ.Mingqiang Yin Northeastern Univ.Yunxia Li Shenzhen Univ.In the e-commerce area, a fourth party logistics (4PL) provider is introduced to provide logistics services around the world. Although consumers’ preference is an important factor in promoting the satisfaction level, it has not been explicitly investigated in the existing logistics routing literature. Integrating the most two important preferences of customers by conducting a questionnaire survey, a mathematical model is constructed to minimize the linear combination of the transportation cost and the time cost. For small-scale problems, optimal solutions can be obtained by using the EXCEL solver. Numerical examples are conducted to show the effectiveness of the model, and managerial implications are drawn for the 4PL to run an effective logistics system.

SunDIS-64 968 Research on Benefit Distribution of E-commerce Rice Sales Supply Chain Based on Cooperative Game Xuejie Bai Harbin Finance Univ.E-commerce is perceived a powerful engine for sustainable economic development in the post COVID-19 pandemic. For the agricultural food industry, e-commerce platforms have become a new sales channel for agricultural products. Yet little is known about the rice supply chain on e-commerce platforms. Thus, this study: 1) Synthesizes the behaviors that rice supply chain stakeholders will take to maximize benefits. 2) Based on the impacts of cooperation cost, default risk, response speed and logistics services in the rice supply chain cooperation, an analysis framework is proposed to clarify how to make these decisions. 3) Offers specific examples to illustrate the decision-making process of using Shapley value distribution method to distribute the benefits of the rice supply chain fairly. The results show that: When the benefits of cooperation are greater cooperation will occur. Alliance. The improved distribution plan based on traditional Shapley value distribution will contributes to a stable and cooperative supply chain alliance.

SunDIS-65 990 Pricing strategy of dual-channel supply chain considering cross-channel effects Jie Chen Nanjing Univ. of Science and Tech.Yuanyuan Huang Nanjing Univ. of Science and Tech.In order to study the influence of cross-channel effects on dual-channel supply chain pricing, the paper constructs a dual-channel supply chain system, which composed of a manufacturer and a retailer, the optimal pricing strategy and profits of the manufacturer and the retailer are solved through the stackelberg game model. Research shows that when the online-to-offline cross-channel effect is larger, the retailer will reduce the online channel price and increase the offline channel price compared with not considering cross-channel effects. On the contrary, the retailer will increase the selling price of online channel and reduce the selling price of offline channel compared with not considering the cross-channel effects; and the cross effects will increase the profits of the retailer and the manufacturer; when the offline-to-online cross-channel effect is larger, the price of the online channel is higher than the unified price than the offline channel price..

SunDIS-66 1092 A last-mile delivery problem with alternative delivery options based on prospect theory Hongsen Ma Northeastern Univ.Hanbin Kuang Northeastern Univ.Min Huang Northeastern Univ.The competition of logistics companies for market share is mainly reflected in logistics cost and customer satisfaction in the last-mile delivery process of e-commerce. The traditional vehicle routing problem (VRP) did not take into account the customer behavior factor and alternative delivery options including home delivery and express cabinets. The proposed problem can be treated as an extension of the vehicle routing problem with soft time window (VRPSTW). We consider both transportation cost and customer satisfaction which are described by the prospect theory. The value functions of customer satisfaction with alternative delivery options are introduced, and the two satisfaction functions are converted into penalty costs, respectively. Then, a nonlinear 0-1 integer programming model is established. Our objective function is to minimize the total cost, including the transportation cost and the customer dissatisfaction penalty cost. According to different requirements of logistics companies, the model can balance transportation cost and

customer satisfaction, thereby contributing to improve the competitiveness of the companies. An improved adaptive large neighborhood search algorithm (I-ALNS) is designed to solve the model. The results demonstrated that the I-ALNS can effectively improve the solutions and the efficiency through comparing I-ALNS with the basic adaptive large neighborhood search algorithm (B-ALNS).

SunDIS-67 1147 Multi-depot vehicle routing problem considering customer satisfaction Wentao Li Northeastern Univ.Qihuan Zhang Northeastern Univ.Min Huang Northeastern Univ.Yang Yu Northeastern Univ.Multi-depot vehicle routing problem (MDVRP) is an important branch of vehicle routing problem (VRP). This paper investigates the multi-depot vehicle routing problem considering customer satisfaction (MDVRPCS). The problem is to program the vehicle path of multi-depot with limited resources to maximize the satisfaction of total customers. The prospect theory is used to describe the satisfaction of customer, and limited resource is considered as constraint. Furthermore, a mixed-integer mathematical model with nonlinear objective function is established. Due to the objective function is nonlinear, the improved variable neighborhood algorithm (IVNS) is used to solve this problem. The algorithm uses the improved K-means algorithm to generate the initial solution and an adaptive mechanism is added in the solution process. Finally, the computational results show that the improved variable neighborhood algorithm (IVNS) outperforms the variable neighborhood algorithm (VNS) in solving quality and time, and joint multi-depot service can result in cost savings and higher service satisfaction.

SunDIS-68 1303 The Order Allocation Model of Multi-source Suppliers of Mechanical Parts in Automobile Industry Based on DEA Theory Lijun Zhao Beijing Jiaotong Univ.This paper aims to build a multi-objective planning model for order allocation based on DEA resource allocation theory. The objective is maximum the sum of multi-source suppliers’ scale efficiency. Numerical simulation is carried out to prove that the order allocation model constructed in this paper is reasonable and applicable. The research in this paper can provide a reference plan for the order allocation problem of mechanical parts in the automobile industry, and expand the application of DEA theory in resource allocation problems.

SunDIS-69 1315 One-to-One Stable Vehicle Cargo Matching in the Fourth Party Logistics Hanfei Wang Northeastern Univ.Min Huang Northeastern Univ.Hanbin Kuang Northeastern Univ.This paper proposes an effective solution to the problem of one-to-one vehicle cargo matching in the fourth party logistics (4PL) platform. Firstly, base on the attributes of multiple vehicles and cargoes, both a preference matrix of cargoes to each vehicle considering vehicle type and a preference matrix of vehicles to each cargo considering profit can be obtained. Based on the preference matrices, a stable matching model is established with the objective of maximizing the platform revenue, among which, stability constraints are designed to avoid blocking pairs of the matching. Then, CPLEX is used to solve the proposed problem. After that, numerical experiments are carried out to demonstrate the performance of the proposed stable matching model by comparing it with the traditional matching model without considering stable constraints in the one-to-one vehicle cargo matching cases with either the quantities of vehicles and cargoes balance or imbalance. The results show that compared with the traditional matching model without stability constraints, the stable matching model can achieve higher revenue and higher stability rate for the platform.

SunDIS-70 1437 Green Truck Appointment System at Container Terminals: Overview and Research Opportunities Xin Lin Zhejiang Univ.Ruitong Liu Zhejiang Univ.Fuxin Lin Zhejiang Univ.Zhiqi Qin Zhejiang Univ.

Dalian Univ. of Tech.Beilei Jin China Jiliang Univ.Xisheng Xiao Guizhou Light Industry Polytechnic CollegeThe increase in international trade causes a sharp increase in the business of container terminals, with the growing of traffic congestion and environmental pollution in seaport surroundings. Among numerous research directions, truck appointment system (TAS) has gradually become a research hotspot due to its advantages in reasonable planning of truck arrival and optimization of scheduling. In addition, with the increasing awareness of environmental protection all over the world, the green seaport problem and its subproblem, the green truck appointment problem, gradually get the attention of academics. In this paper, the

Technical Programmes CCDC 2021 current status of research in green truck appointment problem is presented in the form of a comprehensive literature review which divides papers into three aspects: traditional TAS’s contribution to green seaports, green TAS’s methodologies of quantifying environmental costs, and coordinated optimization. By means of analyzing and comparing their strengths and weaknesses, valuable research directions in measurement of environmental cost are proposed, such as enriching pollutant types, more precise measurement of pollution emission, emission trading, etc. In addition, this paper also suggests that future research work should focus on data-driven green TAS modeling method and efficient algorithm design, so as to provide reference for the application transformation of research results.

SunDIS-71 1528 The hybrid vehicle routing problem with backhauls and 3D loading constraints Qing Wang Northeastern Univ.Shuai Peng Northeastern Univ.Min Huang Northeastern Univ.In this paper, we consider a hybrid vehicle routing problem with backhauls and 3D loading constraints (VRPB3DL), which is presented by formulating as an integer linear programming (ILP). The designed problem model is solved by using a Tabu search algorithm. The effectiveness of the algorithm is testified by a standard experiment set and compared it with the solution obtained by CPLEX. Finally, the experimental results are analyzed and discussed.

SunDIS-72 1546 The Influence of Servitization Strategy on Manufacturing Enterprises Performance: Taking Service Innovation as Intermediary Variable Mingjing Gao Dalian Neusoft Univ. of InformationFanwen Meng Hualu Zhida Tech. Co., LtdThis paper constructs a theoretical model between the servitization strategy, service innovation and performance of manufacturing enterprises, and discusses the moderating effect of knowledge cross-border search on the relationship between servitization and service innovation, so as to clarify the mechanism of servitization strategy affecting enterprise performance. This paper takes 149 Chinese manufacturing enterprises as samples to conduct empirical test. The results show that: servitization strategy has a significant positive impact on enterprise performance and service innovation; service innovation has a significant role in promoting enterprise performance; service innovation has a partial mediating effect on the relationship between servitization and enterprise performance; knowledge cross-border search can effectively adjust the relationship between servitization and enterprise performance. This paper extends the study of the relationship between servitization strategy and enterprise performance, and provides theoretical guidance for manufacturing enterprises that are implementing servitization strategy.

SunDIS-73 1576 Analysis of Influencing Factors of China-ASEAN Port Connectivity Based on BP-DANP Method Yuan Ji Dalian Maritime Univ.Jing Lu Dalian Maritime Univ.Port connectivity is the basic and key guarantee for the realization of China-ASEAN (Association of Southeast Asian Nations) community of interests. There are many interrelated factors affecting port connectivity. Identifying key factors and their relationships has become the focus of academic research. This paper improves the shortcomings of DEMATEL (Decision-making Trial and Evaluation Laboratory) method and proposes a BP (Back Propagation)-DANP (the method of combining DEMATEL and Analytic Network Process) method with strong objectivity. The direct influence matrix of DEMATEL method is obtained by using BP neural network, which improves the defect of strong subjectivity of the input indicators. ANP (Analytic Network Process) method is introduced to improve the defect of the same weight of each indicator in DEMATEL method. Based on the empirical data of China-ASEAN and from the perspective of a complete port system, this paper identifies the influencing factors of port connectivity from three aspects, which are international connectivity, port operation capacity and hinterland connectivity. The analysis results confirm the feasibility of the method and provide theoretical support for researches on improving port connectivity. BP-DANP method enriches the theory and method of influencing factors research, and provides the possibility for effective extraction of influencing factors.

SunDIS-74 129 Boundary Control of Air Cushion Vehicle Based on an Improved Multi Power Reaching Law of Sliding Mode Control Mingyu Fu Harbin Engineering Univ.Xudong Hao Harbin Engineering Univ.Yujie Xu Harbin Engineering Univ.In order to improve the Air Cushion Vehicle(ACV) speed and turning rate control convergence speed and accuracy, an improved multi power reaching law of sliding mode control is proposed, which can be adjusted

when the system is in different states. The convergence speed of the system is greatly improved, there is no high-frequency chattering phenomenon, and the arctangent function is used to limit the amplitude of the control input signal to prevent the control input from being oversaturated. Furthermore, the turning rate of the ACV is strictly limited under different speed to avoid unsafe sideslip and roll during the steering process. Finally, the effectiveness of the proposed method is verified by comparing the multi power reaching law of sliding mode control with the simulation experiment of ACV speed and turning rate control.

SunDIS-75 177 Deadbeat Predictive Current Control of Permanent Magnet Synchronous Motors Based on Disturbance Compensation Wei Shen Beijing Inst. of Tech.Huan Liu Beijing Inst. of Tech.Luwei Shao Beijing Inst. of Tech.The deadbeat predictive current control (DPCC) exhibits strong dynamic performance in the current control of permanent magnet synchronous motor (PMSM), whereas the performance of the traditional DPCC depends on the accuracy of model parameters to a great extent. To cope with this problem, parameter sensitivity of the traditional DPCC has been analyzed in detail, with a disturbance observation compensation method proposed to improve the robustness of the current control of PMSM. Furthermore, a method of non-singular terminal sliding mode disturbance observer (NTSMDO) which employs a novel reaching law, combined with the DPCC is put forward to enhance the parameters robustness of the current loop. A harmonic compensation (HC) term has been added to the output of the current control for ignoring the harmonic electromagnetic process in the mathematical model of the PMSM to further promote the performance of the current loop control. Simulation and experimental results demonstrate that the proposed method offers robustness against parameters mismatch and control precision compared with the traditional DPCC.

SunDIS-76 25 Trajectory Tracking for Underactuated Unmanned Surface VesselBased on Limit Segmentation Dongdong Mu Dalian Maritime Univ.Guofeng Wang Dalian Maritime Univ.Yunsheng Fan Dalian Maritime Univ.Xuguo Jiao Qingdao Univ. of Tech.Bingbing Qiu Dalian Maritime Univ.Xiaojie Sun Dalian Maritime Univ.Yutong Sun Dalian Maritime Univ.In this paper, a novel limit segmentation method is proposed for the trajectory tracking control of an underactuated unmanned surface vessel (USV). In practical engineering, actuators (for ordinary ships, actuators are propeller and rudder) are difficult to output accurate force and moment to realize trajectory tracking. Based on the limit segmentation theory, the continuous desired trajectory is divided into a series of straight lines. In other words, the trajectory tracking problem is transformed into a series of linear tracking problems. On the basis of considering the nonlinearity of the model, Lyapunov function and nonlinear Backstepping method are employed to propose a linear tracking feedback control law. The simulation results show that the method proposed in this paper can make USV track the desired straight line and curve, and the control inputs are within a reasonable range, which verifies the correctness of the limit segmentation theory and control law.

SunDIS-77 78 Finite Control Set Model Predictive Position Control of PMSM System Zhiqiang Wang Tianjin Univ. of Tech. and Education.Xiuyun Zhang Tianjin Univ. of Tech. and EducationJie Bian Yunnan Industrial technician college

Servo drivers used for position control require higher dynamic performance in speed control. In order to improve the dynamic response speed and position tracking accuracy of permanent magnet synchronous motor (PMSM) system, this paper proposes a control strategy for servo system based on finite control set. First, the mathematical model of the PMSM system is established; then the reference voltage vector is predicted, and a speed expectation correction method is adopted to take into account system stability and fast followability; then the difference between the reference voltage vector and the candidate voltage vector is used as the improved cost function, and the selection of the candidate voltage vector and the optimization of the cost function are performed, to eliminate the complexity caused by the tuning of the weight coefficient. After that, considering the actual user needs, the current limit is integrated into the selection of the candidate voltage vector to achieve the current limit. Finally, the simulation research prove that the proposed control strategy can meet the requirements of dynamic response speed and position tracking accuracy.

SunDIS-78 88 Iterative Learning and Modify Adaptive Disturbance Compensate Method for Contour Control of X-Y Table Bing Li Bohai Univ.

Technical Programmes CCDC 2021 The servo control system of the biaxial X-Y table has a large contour error mainly owing to the existence of uncertainties, dynamic inconsistency and complex contour processing. This paper uses the integrated contouring control scheme with an iterative learning control and modified disturbance adaptive compensator (MDAC) for the linear motor drives X-Y table. For the repetition of the contouring tracking question, firstly, the tracking error was decoupled into tangential and normal error used transform matrix under task coordinate frame. Then, the normal contour error was compensated by an PID iterative learning control (ILC). The MDAC control was used for offsetting the load disturbances which are taken as one of the main sources of uncertainties. The compensator used the output of virtual plant for feedback signal instead of estimation force of traditional compensated structure. The method eliminated influences of both the measure noise and load disturbances. Simulation results the proposed scheme can ensure the system has the higher contour accuracy than traditional method, the maximum contour error can be reduced by about 90%.

SunDIS-79 317 Sliding Mode Decoupling Control of Hovercraft Heave and Pitch Dynamics Yuanhui Wang Harbin Engineering Univ.Qingyan Ma Harbin Engineering Univ.Xiyun Jiang Harbin Engineering Univ.The motion of hovercraft is composed of heave and pitch in the vertical direction. It is easy to be disturbed by the external environment. The control of heave height can improve the comfort of air cushion vehicle passengers, and the control of pitch angle can effectively avoid the occurrence of pantograph burying accident. In addition, the hovercraft is a two degree of freedom under-actuated system controlled by a buffer fan in the vertical plane, which has strong coupling. It is also a typical nonlinear system. In this paper, a decoupled sliding mode controller for heave and pitch is designed. Based on the reaching law, an improved simulation experiment is carried out.

SunDIS-80 349 Hovercraft Course Control with the Constraints of Yaw States and Drift Angle under System Uncertainty Fuguang Ding Harbin Engineering Univ.Zhaowei Jia Harbin Engineering Univ.Yuanhui Wang Harbin Engineering Univ.Yujie Xu Harbin Engineering Univ.The course control problem of hovercraft with the constraints of yaw states and drift angle is studied under the system uncertainty caused by model parameter uncertainties and external disturbances. Firstly, a fixed-time observer is proposed to estimate the system uncertainty, which effectively compensates the influence of uncertainty in the process of course control. Secondly, based on the symmetric barrier Lyapunov function (SBLF), the speed controller is designed to constrain the drift angle. Thirdly, the course controller of the hovercraft is designed based on the integral barrier Lyapunov function (IBLF), and the bounded constraints of yaw angle and yaw rate are realized. Finally, simulation results verify the effectiveness of the method proposed in the terms of uncertainty estimation and motion state constraints of hovercraft.

Monday, 24 May, 2021

MonA02 Room02 Modeling, Control and Simulations of Biological Systems (II) 08:00-10:00 Chair: Chunli Wu Northeastern Univ.CO-Chair: Dawei Pi Nanjing Univ. of Science and Tech.

08:00-08:20 MonA02-1 1544 An Integrated Tool for RNA 3D Structure Prediction and Analysis Li Yuan Wuhan Textile Univ.Zhi-Hao Guo Wuhan Textile Univ.Wen-Jie Cao Wuhan Textile Univ.Yong Luo Wuhan Textile Univ.

Guangdong Yixun Tech. Company LimitedYa-Zhou Shi Wuhan Textile Univ.Since the knowledge of the 3D structures of RNAs is a fundamental prerequisite to completely understand the functions of RNAs. In this work, we have integrated the existing RNA 3D structure prediction models and structural analysis methods (e.g., the DSSR software and the script of rmsd•PyPI) into a new tool to realize the 3D structure prediction and analysis for RNAs with sequences and secondary structures as input. Although only the models with ready-made web server are integrated in the present version of the tool, our test on 3D structures prediction for different RNAs indicates that integrating the existing models is very necessary to accurately predict RNA 3D structures.

08:20-08:40 MonA02-2 1567 Toxicological experimental data analysis and prediction by machine learning Zhenzhen Xiong Wenjie Cao

Wuhan Textile Univ.Wuhan Textile Univ.

Jian Jiang Wuhan Textile Univ.Ting Wang Wuhan Textile Univ.Bengong Zhang Wuhan Textile Univ.Toxicological experiment is an experiment to detect the toxicity of various substances. Its main purpose is to determine the level of toxic effects and provide important information for safety evaluation or risk evaluation. This paper aims to analyze toxicology experimental data and modeling it by using machine learning algorithms. The final purpose is to give the testing and prediction the toxicology data. The mainly algorithms used in this paper are the random forest, support vector Machine and LightGBM (Light Gradient Boosting Machine) and other Machine learning algorithms. It has following steps. On the one hand, we model and analyze the existing toxicology experimental data, and on the other hand, we optimize the parameters and improve the model to achieve better prediction effect by using the algorithms above. By comparing three indexes， prediction accuracy, time efficiency and memory space usage respectively of the three algorithms above, it is found that LightGBM has outstanding performance in all aspects. It is a relatively excellent algorithm which has emerged in recent years. It can present faster training efficiency, higher accuracy, and lower memory usage. It also supports parallel learning and large-scale data processing, and provides the best predictive model for toxicology experiments and analysis strategy.

08:40-09:00 MonA02-3 1594 An Open-loop Model of Bus Drivers’ Lane Change Behavior Based on Naturalistic Driving Data Xuyin Huang Nanjing Univ. of Science and Tech.Dawei Pi Nanjing Univ. of Science and Tech.Boyuan Xie Nanjing Univ. of Science and Tech.Hongliang Wang Nanjing Univ. of Science and Tech.Accurately describing bus driver’s behavior is crucial for driving safety intervention strategy. Based on naturalistic data collected from 6 bus drivers, a novel open-loop bus driver model is proposed, which describes bus driver’s behavior in starting and ordinary lane change scenarios. 236 lane change segments classified into six groups are extracted, with analysis of variance method being applied to capture maneuver characteristics of bus drivers in typical lane change scenes. 50% cumulative frequency parameter and cluster centers generated by k-means method are used to learn parameters of the proposed model. The results show that the proposed driver model can well simulate behavior of bus drivers under natural starting and ordinary lane change scenes.

09:00-09:20 MonA02-4 1617 Patient-Specific Seizure Prediction Using and Transfer Learning and Support Vector Machine Xiaoling Zhang Tianjin Univ. of Tech. and EducationHuiyan Li Tianjin Univ. of Tech. and EducationJing Liu Tianjin Univ. of Tech. and EducationQing Qin Tianjin Univ. of Tech. and EducationYanqiu Che Tianjin Univ. of Tech. and EducationEpilepsy is a common neurological disorder. Accurate prediction of epileptic seizures will help improve the sense of security and well-being of epilepsy patients and their families. Epileptic seizures vary greatly between different patients. This paper proposes a personalized prediction model for epileptic seizures by combination of transfer learning and support vector machine (SVM). First, we convert each person’s one-dimensional EEG data into two-dimensional spectrograms using the short-time Fourier transform (STFT). Then we input these spectrograms into 5 pre-trained transfer learning models (Alexnet, Resnet18, EfficientNet b0, Googlenet and Vgg16) to extract their features. Finally, we feed the features into the SVM to make patient-specific seizure prediction. The results show that the combined model of Alexnet and SVM provides the best prediction performance with the average area under the curve (AUC) of 95.50%, the accuracy of 90.68%, the precision of 90.07%, the recall of 90.86%, and the specificity of 90.31%.

09:20-09:40 MonA02-5 1637 Total Variation Regularized SENSE Image Reconstruction Based on Improved regularization parameter selection Chunli Wu Northeastern Univ.Zhiming Bai Northeastern Univ.In parallel magnetic resonance imaging (MRI), total variation (TV) regularized sensitivity coding (SENSE) reconstruction is a kind of fast and effective image reconstruction method. However, in the process of TV-SENSE reconstruction, the value of regularization parameter is an important factor that affects the signal-to-noise ratio (SNR) of image and directly affects the quality of image reconstruction. To solve the unbalanced problems of low signal-to-noise ratio and insufficient detail information preservation, this paper presents a regularization parameter selection method based on Monte-Carlo SURE in TV-SENSE image reconstruction. Adaptive iteration is adopted to select the regularization parameter value which minimizes the mean square error (MSE) of reconstructed image, so that achieve the balance between regularization term and data fidelity term. The reconstruction experiments were carried out by collecting the MRI phantom data under different acceleration factors, and the reconstruction results were compared with the generalized cross-validation (GCV) TV-SENSE method and L-curve

Technical Programmes CCDC 2021 TV-SENSE method. The research results show that the TV-SENSE image reconstruction algorithm based on the improved regularization parameter selection can effectively suppress the artifacts, the peak signal-to-noise ratio (PSNR) has also been improved and the artifact power (AP) of reconstructed image is also decreased even in the case of a higher acceleration factor, thus further verifies the feasibility and effectiveness of the proposed TV-SENSE image reconstruction algorithm.

09:40-10:00 MonA02-6 1657 Projective synchronization of a new 4-D quadratic autonomous hyper-chaotic system by a single input controller Zuosheng Sun Qilu Univ. of Tech.Rongwei Guo Qilu Univ. of Tech.This paper investigates the projective synchronization problem of a new 4-D quadratic autonomous hyperchaotic system. Firstly, the existence of the projective synchronization problem of this hyper-chaotic system is proved. Then, the projective synchronization problem of such system is realized by a single input controller which is obtained by the dynamic feedback control method. Finally, numerical simulation is performed to verify the validity and effectiveness of the theoretical results.

MonA03 Room03 Motion Control (I) 08:00-10:00 Chair: Lei Jiang Zhejiang Univ.CO-Chair: Hongyu Li Liaoning Univ. of Tech.

08:00-08:20 MonA03-1 29 Research on Foot Slippage Estimation of Insect Type Hexapod Robot Yufei Liu China North Vehicle Research Inst.Boyang Xing Guangdong University of TechnologyLei Jiang Zhejiang UniversityZhirui Wang China North Vehicle Research Inst.Zhenjie Liang China North Vehicle Research Inst.Jianxin Zhao China North Vehicle Research Inst.Bo Su China North Vehicle Research Inst.The accurate of the velocity and posture data of hexapod robot can not be obtained by body sensors only due to the influence of the sensor error itself and the installation error, as well as the foot slippage or mechanical error during the foot-terrain interaction. In this paper, the data fusion method based on Extended Kalman Filter is adopted, and the data of accurate velocity and posture of trunk body are from the kinematics and IMU information. It is difficult to detect the occurrence of slippage through the body sensors. The foot-terrain slippage model for insect type hexapod robot based on leg dynamics model with foot slip is proposed. And the foot-terrain slippage estimation method based on state estimation is presented. The method can detect the slippage rate of the foot and the state of the slippage can be determined. The estimated value is compared with state data obtained by the external motion capture system (Mocap system), and the experimental results have demonstrated the effectiveness of the proposed method and realize the foot slippage estimation of insect type hexapod robot.

08:20-08:40 MonA03-2 46 Predictive Speed Synchronous Control of Dual Permanent Magnet Motor System Based on Quadratic Cost Function Xiuyun Zhang Tianjin Univ. of Tech. and EducationZhiqiang Wang Tianjin Univ. of Tech. and EducationJie Bian Yunnan Industrial technician collegeIn order to solve the problem of reliance on manual experience and long time consuming for the weight coefficient setting in the predictive speed synchronous control of the dual permanent magnet motor system, this paper proposes a finite control set model predictive control strategy based on quadratic cost function. Firstly, based on the idea of unified modeling, the motion equation and electrical equation of the driving motor, the mathematical model of the two-level voltage source inverter are combined to establish a unified model. Then, the offline solution algorithm is used based on Lyapunov stability analysis to realize the self-tuning of the weight coefficient matrix, ensuring the convergence of each error term in the consecutive period; After that, the adjusted weight coefficient matrix is used for the online rolling optimization process of the predictive speed synchronous controller, and the cost function optimization is finally performed. Simultaneously considering the actual user needs, the current limit is added to the system. Finally, the simulation research prove that the proposed control strategy has fast dynamic response speed and synchronous tracking accuracy.

08:40-09:00 MonA03-3 133 Real-Time Path Generation for UAV Swarms Using Receding Planning Framework and Priority Decoupling Mechanism Guangtong Xu Beijing Inst. of Tech.Yan Cao Beijing Inst. of Tech.Jingliang Sun Beijing Inst. of Tech.Zhexuan Zhang Beijing Inst. of Tech.Teng Long Beijing Inst. of Tech.This paper presents a real-time path planning method combining receding planning framework and priority decoupling mechanism to solve

the large-scale UAV swarms path generation problems. The receding planning framework is employed to decompose the swarm path planning problem into a number of short-horizon planning problems for reducing the problem dimension. In each receding horizon, the priority decoupling mechanism converts the swarm path planning problem as a series of single-UAV path planning problems to further reduce the computational cost. For the short-horizon single-UAV path planning problem, a customized sparse A* search algorithm (SAS) is used to compute the optimal/suboptimal paths efficiently. The obstacle/collision detection method is developed to eliminate inactive obstacle/collision avoidance constraints according to the relative position relation among obstacles and UAVs. The efficiency and effectiveness of the proposed method are verified in simulation with up to 50 UAVs and indoor flight experiments on 8 quadrotors. The experimental results demonstrate that this method can generate safe swarm paths within a few seconds and guide UAVs to fly in confined environments with obstacles.

09:00-09:20 MonA03-4 22 A fractional order composite control method for a class of motion control systems Weijia Zheng Foshan Univ.Xin Li Foshan Univ.Meijin Lin Foshan Univ.Danfeng Chen Foshan Univ.Xiaohong Wang South China University of TechnologyIn this paper, a fractional order composite control scheme for a class of permanent magnet synchronous motor (PMSM) speed servo systems is proposed to fulfil the requirements of tracking performance and disturbance rejection performance in actual applications. An extended state observer is employed to compensate the uncertainties in the current loop and convert the PMSM speed servo plant into a double-integrator model. A fractional order proportional-derivative (FOPD) controller is applied to control the speed of the PMSM. The FOPD controller is tuned by a differential evolution based optimization method, ensuring the control system to achieve optimal step response performance. Simulation results show that the PMSM servo system using the proposed control method achieves the robustness to parametric uncertainties and load disturbance. Besides, it can achieve better step response performance than the systems using some existing control methods.

09:20-09:40 MonA03-5 1226 Active disturbance rejection speed control for unmanned electric vehicle system with mismatched uncertainty and generalized state constraint Bingkuan Yin Northeastern Univ.JIan Feng Northeastern Univ.The mismatched uncertainty and the generalized state constraint make

the controller design more compli cated for the unmanned electric vehicle system. To this end, in this paper, a generalized proportional integral observer (GPIO) based state constraint control is proposed to cope with the mismatched uncertainty and acceleration constraint of the unmanned electric vehicle. The bandwidth parameterization enable GPIO to accurately estimate system states and mismatch uncertainty. The state constraint controller can actively eliminate the mismatch uncertainty, in the meantime satisfy the generalized state constraint condition. The Lyapunov stability analysis shows that the closed-loop system is bounded. Simulation verifies the effectiveness of the proposed method.

09:40-10:00 MonA03-6 156 Speed Control Strategy of PMSM Based on Improved Auto Disturbance Rejection Control Yueling Zhao Liaoning Univ. of Tech.HongYu Li Liaoning Univ. of Tech.Xuhong Gao Beijing Inst. of Space Launch Tech.Dong Guo Liaoning Univ. of Tech.In order to improve the speed control system of permanent magnet synchronous motor (PMSM) for new energy vehicles, a speed controller based on auto disturbance rejection control (ADRC) technology is designed. In ADRC, a nonlinear function based on fixed time convergence is introduced into the extended state observer to solve the global non-finite time convergence problem of the nonlinear function in the extended state observer. The simulation results show that the improved extended state observer has better disturbance estimation ability, and improves the response speed and anti-disturbance ability of the speed control system.

MonA04 Room04 Automatic Control of Unmanned Systems (I) 08:00-10:00 Chair: Haoyu Yang Harbin Engineering Univ.CO-Chair: Haiyang Zheng Guangdong Univ. of Tech.

08:00-08:20 MonA04-1 20 Design of the Anti-disturbance Virtual Model Controller for Quadruped Robot boyang Xing China North Vehicle Research Inst.Yufei Liu China North Vehicle Research Inst.Zhirui Wang China North Vehicle Research Inst.

Technical Programmes CCDC 2021 Zhenjie Liang China North Vehicle Research Inst.Jianxin Zhao China North Vehicle Research Inst.Bo Su China North Vehicle Research Inst.Lei Jiang Zhejiang UniversityThe virtual model control (VMC) framework proposed by Marc Raibert (Boston Dynamics) reduces the dimension of the multi-point support balance control problem in quadruped robot, which decouples the stability control of the quadruped robot into height, attitude and speed. The traditional virtual model is constructed by the PD feedback controller (PD-VMC), which can realizes reliable movement on complex terrain. However, the PD controller cannot overcome the disturbances cause by system’s non-linearity, modeling error and leg swing inertia. The fixed feedback gain of PD controller also makes the poor system adaptive ability on real robot. In this paper, an anti-disturbance virtual model controller (D-VMC) is constructed by LADRC. The federal LESO observer (F-LESO) is proposed to improve the accuracy of state observation by using multiple measurements. Simulations and experiments have verified the effectiveness of the proposed D-VMC controller. It has the highly anti-disturbance ability, which effectively improve the tracking accuracy of the desired attitude, and can effectively guarantee the stable running of the quadruped robot at 1m/s. In addition, the F-LESO observer can estimate the unknown load quality and improve the height control stability.

08:20-08:40 MonA04-2 57 An Exclusive Landing Control System Fusing Vision of Multirotor UAV Yingchun Zhong Guangdong Univ. of Tech.Haiyang Zheng Guangdong Univ. of Tech.Zhenhua Zeng Guangdong Univ. of Tech.Zhiyong Luo Guangzhou Ufly Tech. Co.Bo Wang Guangdong Machinery Technician CollegeThe aim of this investigation is to clarify how the multirotor Unmanned Aerial Vehicle (UAV) can accurately and safely touchdown the parking apron of distributed airport autonomously. This paper proposed an exclusive landing control system fusing vision of multirotor UAV, which is only effective during touchdown period. First, the block diagram of exclusive control system fusing vision is designed. In the exclusive landing system, the pose control from existing control system is the inner control loop, the speed control system is the middle control loop and the position control fusing vision is the outer control loop. Second, the algorithm to acquire the position error from image of landing region is designed. Third, the control laws of position and speed controller are determined respectively. The simulation and field experiments show that: (1) It spends about 77ms by the airborne embedded system to process the image of landing region in order to acquire the target on the apron of distributed airport, which is basically satisfy the real time control requirement. (2) The control system with PI position controller and PD speed controller has obvious less overshoot and transition time than other control system during the simulation of landing period. (3) The average landing position error is about 0.14m while the PI position controller and PD speed controller are employed during the field experiments. Therefore, the exclusive landing control system fusing vision proposed in this paper can significantly improve the autonomously landing accuracy and safety of the multirotor UAV.

08:40-09:00 MonA04-3 115 Autonomous exploration and navigation of mine countermeasures USV in complex unknown environment Quanshun Yang Naval Univ. of EngineeringYang Yin Naval Univ. of EngineeringShuai Chen Naval Univ. of EngineeringYang Liu Naval Univ. of EngineeringWith the development of autonomous control technology for unmanned surface vehicle (USV), its role in the military field has become more and more prominent. The autonomous control technology of mine countermeasures USV (MCM USV) is part of the current research hotspots. Aiming at the application scenario of the MCM USV performing minesweeping tasks in unfamiliar seas, an MCM USV path planner with autonomous exploration, real-time obstacle avoidance, and autonomous mine elimination capabilities is designed, and the training is carried out by layered reinforcement learning. Use the Unity physics engine to build a simulation environment, abstract and model mines, reefs, dynamic obstacles, etc., and build an unmanned boat model with environmental

awareness and autonomous decision making capabilities. Experimental verification shows the effectiveness of the path planner in dealing with dynamic and complex water exploration problems.

09:00-09:20 MonA04-4 193 Research on Path Tracking of Double Closed-loop Integral Sliding Mode Control Based on UUV Under Driven System Wei Zhang Harbin Engineering Univ.Ximing Chen Harbin Engineering Univ.Yu Zhang Harbin Engineering Univ.Aiming at the three-dimensional path tracking problem of the under-driven UUV in the unknown external interference environment, a double closed-loop integral sliding mode controller is designed. Considering that the under-driven UUV only provides control input from the main thruster, rudder and elevator, the model is divided into a driving part and For the undriven part, analyze the tracking error, establish the

path tracking error model, and design the controller on this basis. Different from the traditional sliding mode control, the designed double closed-loop integral sliding mode controller has the inner and outer loops coordinated with each other. The outer loop controller gets the virtual speed command, and the inner loop controller designs the control input of the system according to the virtual speed command, so as to realize path tracking, and uses Lyapunov's theorem to prove its stability. In addition, for the external unknown interference problem, a nonlinear interference observer is designed to estimate the interference and add an interference compensation item to the controller. Finally, through simulation verification, the results show that the nonlinear disturbance observer can accurately estimate the unknown disturbance and the controller control effect is stable, which can realize the desired path track.

09:20-09:40 MonA04-5 205 Bionic fish pose tracking control based on CPG sliding mode Wei Zhang Harbin Engineering Univ.Fantai Lin Harbin Engineering Univ.Haoyu Yang Harbin Engineering Univ.Qingshuo Gong Harbin Engineering Univ.In order to achieve the purpose of bionic fish pose tracking, this paper proposes a bionic fish pose tracking control algorithm based on the combination of CPG (central pattern generator) and sliding mode control. Firstly, this paper establishes a bionic fish CPG motion control network, and the conversion relationship between the CPG control parameters and the speed and angular velocity of the bionic fish is studied; Secondly, the bionic fish pose information obtained by the upper processor is used as the feedback input, Innovatively designed CPG-sliding mode controller with global progressive stability according to the tracking error of the bionic fish. Through real-time adjustment and control of the CPG parameters of the bionic fish’s multi-modal swimming achieve the purpose of tracking the expected pose of the bionic fish; Finally, the effectiveness and feasibility of the control algorithm are verified through simulation and experiment.

09:40-10:00 MonA04-6 208 Research on Navigation and Crawl Underwater Unmanned Vehicle Design and CPG-based Motion Mode Switching method Zheping Yan Harbin Engineering Univ.Haoyu Yang Harbin Engineering Univ.Wei Zhang Harbin Engineering Univ.Qingshuo Gong Harbin Engineering Univ.Fantai Lin Harbin Engineering Univ.Yanbin Wang Harbin Engineering Univ.Maowen Tang Harbin Engineering Univ.In order to improve the efficiency and quality of underwater operations, this paper proposes and designs a new underwater robot, the navigation and crawl underwater unmanned vehicle (NCUUV), it has two motion modes: swimming and crawling. The upper controller selects the CPG network in the middle controller according to the height information by the acoustic rangefinder. The middle controller includes two CPG networks, which control the swimming and crawling of NCUUV respectively. The CPG network selected by the upper controller gradually vibrates, and the vibration of the unselected CPG network is gradually suppressed, thereby completing the smooth switching between swimming and crawling. The experimental result shows that this control mechanism of motion mode switching can enable NCUUV to stably realize motion mode switching and the switching process is smooth and reliable.

MonA05 Room05 Space Vehicle Control (I) 08:00-10:00 Chair: Liang Sun Univ. of Science and Tech. BeijingCO-Chair: Shaoming He Beijing Inst. of Tech.

08:00-08:20 MonA05-1 1518 Finite-Time Relative Pose Tracking Control for Uncertain Spacecraft Rendezvous and Docking Liang Sun Univ. of Science and Tech. BeijingJinwei Wang Univ. of Science and Tech. BeijingJingjing Jiang Loughborough Univ.This study investigates the robust finite-time pose tracking control for spacecraft autonomous rendezvous and docking with parametric uncertainties and bounded external disturbances. Based on the uncertainly coupled relative pose dynamics, a fast terminal sliding mode controller is developed to achieve the finite-time convergence of the pose tracking errors. To reduce the control chattering results from the signum function in the controller, an exponential reaching law is employed to achieve the decreasing of the reaching time towards the sliding surface. The explicit tuning rules for designing parameters are derived based on the stability analysis of the closed-loop system. It is proved in Lyapunov framework that all closed-loop signals are always kept bounded and the pose tracking error converges to small neighborhood of zero in finite time. Simulation results validate the performance of the proposed robust finite-time control strategy.

08:20-08:40 MonA05-2 266

Technical Programmes CCDC 2021 A New Proportional-Integral Guidance Law Shaoming He Beijing Inst. of Tech.Xueyan Zhang Beijing Inst. of Tech.

China Development BankDefu Lin Beijing Inst. of Tech.Chang-Hun Lee Korea Advanced Inst. of Science and Tech.This paper proposes a new proportional-Integral (PI) guidance law for maneuvering targets interception. We first investigate the physical interpretation of the PI guidance law and the analysis results reveal that the PI guidance law is exactly the same as a direct model reference adaptive guidance law: the integral term in PI control plays the same role as the adaptive law in adaptive control. Based on this fact, we suggest a new PI guidance law by leveraging a modified reference model to improve the transient performance. Numerical simulation results are conducted to validate the analytic findings.

08:40-09:00 MonA05-3 63 Design of Drive Controller for Spaceborne Microwave Radar Youbo Wang Beihang Univ.

Shanghai Aerospace Control Tech. Inst.Pengfei WU Shanghai Aerospace Control Tech. Inst.Zhenfeng Fang Jiangsu Jinling Intelligent Manufacturing Research

Inst. CompanyWanliang Zhao Shanghai Aerospace Control Tech. Inst.Fengdan ZU Shanghai Aerospace Control Tech. Inst.In order to realize the control of spaceborne microwave radar with high accuracy, long service term and high reliability, this paper designed a driving controller for direct driven permanent magnet synchronous motor(PMSM) based on aerospace-level anti-fuse FPGA. The mathematical model of the structure of microwave radar is built, and the frequency response characteristics of each ring are analyzed. The least squares method is adopted for calculating the speed, which has increased the accuracy in calculating the speed. The vector control on the PMSM is realized by combining the location information collected by the dual-channel rotary transformer and the current signal collected by AD sensor. Based on the space vector pulse width modulation (SVPWM) strategy, a circular rotating field is acquired, which can increase the controlling performance. The designed airspace searching strategy can realize flexible autonomous switching between dual closed-loop and triple closed-loop controlling. The experiment results indicate that this design can control the PMSM accurately and steadily, enabling the location tracking error of the microwave radar lower than 0.02°and the speed fluctuation between ±0.6%.

09:00-09:20 MonA05-4 143 Finite-time Descent Guidance for Mars Vehicles Peng Zhang China Academy of Space Tech.Ye Feng China Academy of Space Tech.Yue Lin China Academy of Space Tech.Zhiyong Liu China Academy of Space Tech.In this paper, a finite-time control law for a class of second-order systems is proposed. The descent guidance phase is formulated as a second-order dynamic equation. Two kinds of finite-time guidance law are given by applying the proposed finite-time control theory. A fal function is introduced to replace the signum term in guidance laws to avoid the chattering effect. Furthermore, the principle for the determination of lift-to-drag ratio is given to obtain the optimal lift and drag acceleration. Simulation results demonstrate the finite-time convergence property of the proposed guidance laws, and the benefits of guidance law 2 over guidance law 1 are shown.

09:20-09:40 MonA05-5 248 A new duplex design and the control of the 6-DOF simulator Hongyuan Zhang Harbin Inst. of Tech.Mingying Huo Harbin Inst. of Tech.Youfeng Wang Shanghai Aerospace Control Tech. Inst.Yunyi Qiao Harbin Inst. of Tech.Ruhao Jin Harbin Inst. of Tech.Fuyou Zhao Harbin Inst. of Tech.Ze Yu Harbin Inst. of Tech.Naiming Qi Harbin Inst. of Tech.To assess the nature of a spacecraft’s dynamics, it is necessary to develop the simulator of it. However, most of the existing simulators belong to the 3-DOF or the 5-DOF. Until now, the development of the 6-DOF simulator is not satisfying. This paper puts forward a 6-DOF simulator’s design which is based on the 5-DOF and uses two tiers to create the freedom in the vertical direction. The feasibility of the design is verified in the end which is composed of the control of the simulator’s adjusting levelness system and the calculation of the air film’s bearing ability.

09:40-10:00 MonA05-6 412 Endoatmospheric Energy Management Method for Launch Vehicle with Solid Rocket Boosters based on Changeable Launch Plane Strategy Wenzhe Fu Huazhong Univ. of Science and Tech.Wei Wang Beijing Aerospace Automatic Control Inst.

Lei Liu Huazhong Univ. of Science and Tech.Bo Wang Huazhong Univ. of Science and Tech.Zhongtao Cheng Huazhong Univ. of Science and Tech.Since the solid rocket boosters (SRB) can only be extinguished after fuel consumption, the energy management problem for the launch aircraft has received more and more attention nowadays. This paper comes up with a novel endoatmospheric energy management method based onthe changeable launch plane (CLP) strategy for the SRB energy management problem. The transverse maneuvering energy management model is established. Based on the CLP theory, the height in the longitudinal plane and the displacement in the lateral plane are approximated by polynomial spline curves. The angle of attack (AOA) and the sideslip angle is calculated with the inverse of the dynamics subsequently. Finally, a simulation result for a three-stage solid rocket, called Vega, is given to demonstrate the effectiveness and efficiency of our proposed method. Compared with the traditional energy management method, our proposed method has a 75.86% improvement in the capacity of energy consumption.

MonA06 Room06 Space Vehicle Control (II) 08:00-10:00 Chair: Zhaojing Wu Yantai Univ.CO-Chair: Baoyong Zhang Nanjing Univ. of Science and Tech.

08:00-08:20 MonA06-1 1043 Robust steady-state Kalman filter for uncertain descriptor system with missing measurement and uncertain-variance correlated noises Yinfeng Dou Heilongjiang Univ.Jiayi Zheng Heilongjiang Univ.For the linear stochastic descriptor system with missing measurement and uncertain-variance correlated noise, the robust steady-state Kalman filtering problem is addressed. Applying the singular value decomposition (SVD) method and the fictitious noise approach, the original uncertain descriptor system is transformed to new reduced-order standard system only with uncertain-variance fictitious noises. Applying the minimax robust estimation principle and classical Kalman filtering theory, the actual steady-state Kalman filter is presented in the sense that its actual filtering error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. Applying the Lyapunov equation approach, the robustness of the presented actual steady-state Kalman filter is proved, which is also called as the robust steady-state Kalman filter. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.

08:20-08:40 MonA06-2 1021 H∞ sliding mode control problem for uncertain singular Markovian jump system with time-varying delays Yingkang An Nanjing Univ. of Science and Tech.Baoyong Zhang Nanjing Univ. of Science and Tech.In this paper, we deal with the H∞ sliding mode control (SMC) problem for uncertain singular Markovian jump systems with time-varying delays. First, an integral-type switching surface function is constructed. Then, by using a Lyapunov Krasovskii functional which depends on time delays and system modes, we obtain the sufficient conditions for stochastic admissibility of sliding mode dynamics with a given disturbance attenuation level which are expressed in terms of strict linear matrix inequalities (LMIs). Moreover, a SMC law is designed to ensure that system states can reach the predefined sliding surface finally. Finally, a numerical example is given to show the effectiveness of the proposed method.

08:40-09:00 MonA06-3 971 Random trajectory tracking of four Mecanum wheeled mobile platform Qingxi Hu Yantai Univ.Zhaojing Wu Yantai Univ.Dianfeng Zhang Yantai Univ.This paper intends to investigate tracking control for four Mecanum wheeled mobile platform (FMWMP) under stochastic disturbance. In the deterministic environment, the Lagrange system is obtained using Denavit-Hartenberg (DH) method and analytical dynamics. The stochastic oscillation in the environment is transformed into disturbance to the moment, then the stochastic Lagrangian system is established. A trajectory tracking controller is obtained by using backstepping method, such that the second moment of the tracking error arbitrarily small by adjusting the parameters and all signals of the closed-loop system are guaranteed to be bounded by probability. Simulation results are provided to demonstrate the performance of the designed control.

09:00-09:20 MonA06-4 1642 Stochastic Nonlinear Output Tracking Control with Second-Order Moment Process Ruitao Wang Ludong Univ.Hui Wang Ludong Univ.Wuquan Li Ludong Univ.Output tracking of stochastic high-order nonlinear systems perturbed by

Technical Programmes CCDC 2021 second-order moment process has not been investigated in literature. In this paper, we propose a new design method where piecewise functions are suitably constructed to deal with coupling terms between nonlinear functions and the noise, a state feedback controller is successfully designed, which guarantee that the closed-loop system has a unique solution, all states are bounded in probability, and the tracking error can be arbitrarily small by adjusting the parameters. Finally, a simulation example is given to illustrate the effectiveness of controller designed.

09:20-09:40 MonA06-5 1115 An auxiliary system approach to fault estimation for continuous-time Markovian jump systems Guoliang Wang Liaoning Shihua Univ.Yifan Huang Liaoning Shihua Univ.This paper studies the fault estimation problem for continuous-time Markovian jump systems (MJSs). The stochastic system and deterministic fault make the fault estimation problem more difficult. Using an auxiliary system approach, a deterministic intermediate variable is established. The conditions are given in the form of linear matrix inequalities and a numerical example is given to illustrate the auxiliary system approach method.

09:40-10:00 MonA06-6 1118 Dissipative Control of Uncertain Stochastic Singular Systems with Time-varying Delays Meng WANG Shenyang Jianzhu Univ.Shuangyun XING Shenyang Jianzhu Univ.Wenbo SUN Shenyang Jianzhu Univ.Xishun YUE South China Univ. of Tech.In this paper, the dissipative control for one kind of uncertain stochastic singular systems with time-varying delays is studied. Firstly, for stochastic singular systems with uncertain parameters, an advisable Lyapunov-Krasovskii functional is constructed. Through the use of the auxiliary vector function, Jensen inequality and free weight matrix technique, the sufficient conditions for the stochastic admissibility of the considered systems are derived. Secondly, a state feedback controller is designed to make the closed-loop system satisfy regular, impulse-free, stochastically stable in the mean square, and then the corresponding dissipative controller is designed. At the last, a simulation example is given to verify the validity of the theoretical results.

MonA07 Room07 Complex Networks and Systems (I) 08:00-10:00 Chair: Tao Ren Northeastern Univ.CO-Chair: Zhaochen Zhang The 28th Research Inst. of CETC

08:00-08:20 MonA07-1 702 On Consensus for Urban Traffic Network System Mengyu Yin Northeastern Univ.Tao Ren Northeastern Univ.Yuanwei Jing Northeastern Univ.The signal control problem of urban road traffic network is studied. A CTM-based discrete-time urban road traffic network system model is established. Aiming at the urban traffic network system, the multi-agent consistency idea is applied to the urban traffic network system analysis and control, and the consistency control protocol design method is proposed. Taking the change of the green light duration as the control input, a consistency control protocol is designed using the consistency algorithm of the discrete multi-agent system, the stability of the controlled system is analyzed, and the urban road traffic network is asymptotically consistent stability conditions. The simulation results show that when the stability conditions are met, the method controls the change of the green light time of each link to make the entire system reach asymptotic consistency, that is, the state of all links in the road network is consistent, which verifies the feasibility of the proposed method Sex and effectiveness.

08:20-08:40 MonA07-2 382 A Definable Architecture Design Method of Command and Control Information System Zhaochen Zhang The 28th Research Inst. of CETCSonghua Huang The 28th Research Inst. of CETCXiaobin Mao The 28th Research Inst. of CETCIn the future intelligent war, the command and control information system needs the capability of agile construction. In the paper, the software definition technology is introduced into the command and control information system architecture design, and the open architecture design principle based on resource capability open disaggregation and on-demand aggregation is discussed. The resource "meta elements" and capability measurement standards are defined, and the definable system open architecture design process is proposed to realize the fine-grained flexible combination of system resources. Finally, a typical case is used to verify the feasibility and applicability of the architecture design method for the future flexible and changeable command and control information system construction.

08:40-09:00 MonA07-3

398 State estimation for multi-layer complex dynamical networks with time delays Guo-Ping Jiang Nanjing Univ. of Posts & TelecommunicationsKun Li Nanjing Univ. of Posts & TelecommunicationsXinwei Wang Nanjing Univ. of Posts & TelecommunicationsIn this paper, we investigate the state estimation problem of multi-layer complex dynamical networks with coupling delays. Since the transmission of information will take time, which will affect the process of state estimation for multi-layer complex dynamical networks. First, we consider two kinds of delays in the network model: the intra-layer coupling delay and the inter-layer coupling delay. Then, an observer network with the same node dynamics and topological structure as the original multi-layer complex dynamical network is constructed and the error dynamical network can also be obtained. Lyapunov stability theory is used to solve the observer gain matrix and prove the asymptotic stability of the error dynamical network. Sufficient conditions for the state estimation are derived in the form of linear matrix inequalities. Finally, illustrative simulations are provided to verify the correctness and effectiveness of the proposed scheme.

09:00-09:20 MonA07-4 433 Topology identification for multi-layer complex dynamical networks with coupling time delay Zhiheng Huang Nanjing Univ. of Posts & TelecommunicationsGuo-Ping Jiang Nanjing Univ. of Posts & TelecommunicationsXinwei Wang Nanjing Univ. of Posts & TelecommunicationsThis paper studies the topology identification for a multi-layer complex dynamical network model with the same number of nodes in each layer and arbitrary connections linking nodes from different layers. Since the coupling time delay is common, whose impact cannot be ignored. This paper proposes a new topology identification scheme for the multi-layer complex dynamical network. First, the original network is regarded as a drive network. In order to identify the topology of the drive network, a response network with the same node dynamics is constructed based on the design of the adaptive feedback controller. Then the error dynamical network is obtained. The Lyapunov stability theory is used to prove the asymptotic stability of the error dynamical network. When the response network is synchronized with the drive network, the topology of the original network could be identified by the response network. Finally, a numerical example is shown to demonstrate the effectiveness of the proposed topology identification scheme.

09:20-09:40 MonA07-5 573 The Risk Analysis of International Stock Market Based on Complex Network Approach Yingli Wang Academy of Mathematics and Systems Science

Academy of Military ScienceYan Wang Academy of Mathematics and Systems Science

Univ. of Chinese Academy of SciencesYang Liu E Fund Management Co., LtdJunling Cai Academy of Military ScienceSince the breakout of 2008 global financial crisis, risk management has been a hot and crucial topic in research. In our paper, we construct international stock network of volatility risk and crash risk with the help of the empirical mode decomposition method and Granger causality test. We find that the most influential nations on different time scales are similar despite that the shortest-term components are disturbed. As the time scale increases, the pattern tends to be more clarified that the nations with mature stock market or developed economy, such as, USA, UK and Singapore are the most influential in risk contagion.

09:40-10:00 MonA07-6 314 Observability decomposition of Boolean Control Networks Yifeng Li Nanjing Normal Univ.Jiandong Zhu Nanjing Normal Univ.

This paper investigates the observability decomposition of Boolean control networks (BCNs) under the algebraic frame based on the semi-tensor product of matrices. First, the definition for the observability decomposition of BCNs is proposed, which is consistent with the observability decomposition of traditional linear control systems. Then, by using the vertex partition method, a necessary and sufficient condition for the observability decomposition of BCNs is derived, i.e., the coarsest common concolorous perfect vertex partition (CP-VP) is an equal vertex partition. Finally, an example is provided to illustrate the obtained results.

MonA08 Room08 Theory and Application of Nonlinear Systems (III) 08:00-10:00 Chair: Jin Tao Nankai Univ.CO-Chair: Zixin Huang Wuhan Inst. of Tech.

08:00-08:20 MonA08-1 601 Nonlinear Fixed-Time Bipartite Consensus Algorithm for Multiagent Systems Sifan Yang Dalian Maritime Univ.Pengfei Xing Dalian Maritime Univ.

Technical Programmes CCDC 2021 Guobin Li Dalian Maritime Univ.Jin Tao Nankai Univ.In the paper, we consider the uncertain nonlinear multiagent systems over antagonistic networks and design an algorithm to achieve bipartite consensus in a fixed time. To be specific, the proposed algorithm drives each agent to achieve bipartite consensus, which has the same modules but opposite signs when the antagonistic network is structurally balanced. Compared with the traditional asymptotic consensus and the finite-time consensus, the fixed-time bipartite consensus can reach convergence faster, and the settling time is independent of the initial states of the multiagent system. At last, the simulation results are given to verify the validity of the proposed theoretical results.

08:20-08:40 MonA08-2 587 Comprehensive unified control strategy for planar 2-link underactuated manipulators Zixin Huang Wuhan Inst. of Tech.Zhen Chen Wuhan Textile Univ.Juan Li Wuhan Inst. of Tech.Lejun Wang China Univ. of GeosciencesThis paper presents a unified control strategy based on the trajectory planning and tracking control for the planar 2-link underactuated manipulator. The trajectory of the active link is composed of two parts. The first part trajectories are designed according to the initial and target angles of the active link. The second part trajectories are designed based on the constraints between the underactuated link and the active link. Meanwhile, the parameters of the trajectories are optimized by the differential evolution algorithm (DEA) to ensure all links eventually reach to their target values by tracking the designed trajectories. Then, the sliding mode variable structure controller is designed to make the active link track their trajectories. The effectiveness of such strategy is demonstrated through simulation results.

08:40-09:00 MonA08-3 515 Research on image encryption algorithm based on a class of three-dimensional quadratic polynomial chaotic system Zhongtang Chen Shenyang JianZhu Univ.Yu Zhu Shenyang JianZhu Univ.Shuang Chen Shenyang JianZhu Univ.Yandan Wang Shenyang JianZhu Univ.In this paper, a novel three-dimensional quadratic polynomial chaotic system without equilibrium point is constructed, and a new image encryption algorithm is proposed based on this chaotic system. With the help of the randomness of chaotic system, the algorithm can only complete the encryption process by diffusion processing based on XOR operation. Security analysis is carried out from three aspects: key space, statistical analysis and sensitivity analysis. The results show that the encryption algorithm can effectively resist exhaustive key attacks because of a large key space, which means higher security and certain application value.

09:00-09:20 MonA08-4 626 Dissipative IT2 Fuzzy Control for Nonlinear Systems with Stochastic Occurring Sensor Saturation Xiangdong Hao Shenyang Aircraft Design and Research Inst.Jiuxiang Dong Northeastern Univ.This paper investigates the issue of dissipativitybased fuzzy control for nonlinear systems in the framework of interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy method. The uncertainties that exist in considered systems are depicted by lower and upper membership functions (LUMFs) and interrelated weighting functions. During designing a fuzzy controller, a crucial problem, stochastic occurring sensor saturation, which is built via the Bernoulli random distribution process, is taken into consideration. The controller design is more flexible since the membership functions (MFs) have diverse forms compared with studied systems. Based on Lyapunov stability theory, sufficient conditions are derived to guarantee the stochastic stability and strictly dissipative performance of closed-loop systems. Finally, simulation results are provided to illustrate the usefulness of the control approach proposed in this paper.

09:20-09:40 MonA08-5 472 Event-triggered adaptive neural constraint output control for switched nonlinear system Zhiliang Liu Qingdao Univ.Bing Chen Qingdao Univ.Chong Lin Qingdao Univ.Yun Shang Qingdao Univ.In this paper, an event-triggered adaptive neural control issue is addressed for a class of switched unknown strict-feedback nonlinear system under constraint output. To deal with the unknown nonlinear system, the radial basis function neural networks (RBFNNs) are employed to approximate the unknown nonlinear functions. Under adaptive backstepping technique, associated with barrier Lyapunov function method, an event-triggered controller is designed to ensure that the system’s output signal follows a given reference signal. meanwhile, the system output signal meets the asymmetric constraint requirement.

The proposed control strategy is guaranteed to solve the presented problem. Finally, a simulation example is presented to demonstrate the efficacy of the proposed scheme.

09:40-10:00 MonA08-6 793 Design of Disturbance Rejection Controller for Nonholonomic Systems with Certain Growth Constraints Jiaqian Chen Qufu Normal Univ.Dianguo Cao Qufu Normal Univ.Yuqiang Wu Qufu Normal Univ.This paper studies the global output feedback stabilization problem for a class of nonholonomic systems with non-vanishing external disturbances and uncertain nonlinear dynamics. In order to estimate the system states and external disturbance, an extended state observer (ESO) is developed. The external disturbance term is regarded as a general state, and a new generalized error dynamic system is obtained. By using backstepping and state input scaling technique, a disturbance rejection controller is designed, and the states of the system asymptotically approaches to zero while guaranteeing all signals of the closed-loop system are global boundedness. Simulation example is given to verify the effectiveness of the control algorithm.

MonA09 Room09 Fault Diagnosis and Predictive Maintenance (VI) 08:00-10:00 Chair: Dexin Gao Qingdao Univ. of Science & Tech.CO-Chair: Yan Zhou Zhaoqing Univ.

08:00-08:20 MonA09-1 919 Fault Diagnosis Method of High Power Charging Equipment Based on Neural Network Dexin Gao Qingdao Univ. of Science & Tech.Yiwei Lv Qingdao Univ. of Science & Tech.Kai Wang Qingdao Univ. of Science & Tech.Yi Wang Qingdao Univ. of Science & Tech.Qing Yang Qingdao Univ. of Science & Tech.High power charging equipment is the necessary supporting facilities in the electric vehicle industry, but failure will inevitably occur in the process of equipment use. Through the research on the structure and principle of charging equipment, the influencing factors and causes of equipment failure are analyzed. In this paper, BP neural network algorithm is used to establish the nonlinear relationship between the fault influencing factors and fault causes of the charging equipment with the input and output of the neural network, and the fault diagnosis is carried out for the high-power charging equipment of electric vehicles. Through the simulation, the BP neural network fault diagnosis model has higher fault diagnosis accuracy. The model is applied to the condition monitoring and fault diagnosis system of high-power charging equipment of electric vehicles, and good diagnosis results are obtained, which has practical application value.

08:20-08:40 MonA09-2 1154 L1 Adaptive Output-feedback Fault-tolerant Controller for Multivariable Nonlinear Systems with Actuator Faults Yan Zhou Zhaoqing Univ.

Northwestern Polytechnical Univ.Yongjian Chen Zhaoqing Univ.Jing Li Northwestern Polytechnical Univ.This paper investigates a fault-tolerant control strategy for a class of multi-input multi-output nonlinear systems in the presence of actuator faults, including multiplicative faults and additive faults. The design of an output predictor, adaptive laws and a control law are involved in the controller. Compared with a traditional L1 adaptive controller, piecewise-constant adaptive laws are presented which drive estimation errors to zeros at the sampling interval of integration. A low-pass filter is introduced in the control law to cancel the effects of unmatched uncertainties and guarantee a good tracking performance and robustness. Simulation experiments are given to verify the effectiveness and feasibility of the proposed controller.

08:40-09:00 MonA09-3 931 An Improved Secondary Fault-Tolerant Inverter and its Control Strategy Yan Li Central South Univ.Zhuo Yang Central South Univ.Xin Cheng Central South Univ.The fault-tolerant (FT) inverter in microgrid is an important procedure for the continuity of the performance. When open-circuit (OC) fault occurs, the FT inverter without redundant bridge is generally reconfigured as the three-phase four-switch structure to recovery the circuit. However, it usually repairs the OC fault only once. The paper proposes a secondary FT inverter. It can not only achieve the above function, but also when a new OC fault occurs again, it can be reconfigured to achieve the second fault-tolerance. In order to achieve the FT operation, a FT pulse control algorithm which can reset the SPWM pulse signals is proposed. Furthermore, the paper proposes a corresponding suppression strategy for the deviation of the neutral point voltage on the DC side. The simulation and experimental results validate the FT inverter and its

Technical Programmes CCDC 2021 control strategy.

09:00-09:20 MonA09-4 932 Research on the reconfigurable inverter applied in Microgrid Yan Li Central South Univ.Zhuo Yang Central South Univ.Xin Cheng Central South Univ.The paper proposes a new reconfigurable inverter with secondary fault-tolerant (FT) capability, which can improve the reliability of microgrid. When a primary fault occurs in a power switch of the inverter , the capacitor bridge on the DC side is used to replace the faulty phase; and the inverter is reconfigured as the three-phase four-switch (TPFS) topology to realize the first fault-tolerance. Then if a fault occurs again in another power switch, the inverter is also reconfigured again to realize the second fault-tolerance. To assure the normal FT operation, a FT pulse control algorithm is used to reset the SPWM pulse signals. Additionally, because of the deviation of the DC neutral point voltage, a corresponding control strategy is proposed which can effectively suppress the capacitor voltage deviation. The simulation results verify the correctness of the reconfigurable inverter topology applied in microgrid and the effectiveness of the control strategy.

09:20-09:40 MonA09-5 962 A Modeling Method for Behavior Model of Aging IGBT Tao Peng Central South Univ.Jing Liao Central South Univ.HongWei Tao Central South Univ.The numerous advantages of Insulated Gate Bipolar Transistor (IGBT) power modules and the development for higher voltage and current ratings make them attractive for traction applications. High reliability is required but aging is a common problem. In this paper, the IGBT behavior model under normal conditions is established by using broken line fitting and function description, and the behavior model of aging IGBT was established by combining the aging characteristics and adding aging coefficient. Finally, the model was applied to traction converter. The simulation results show that the model can not only reflect the aging degree and describe the transient behavior of IGBT completely, but also can obtain aging data without aging experiment and reduce the cost of aging research. The simulation results show the effectiveness of the model.

09:40-10:00 MonA09-6 982 Fault Prediction Method of Gear Based on DSAE and GRU Network Liying Jiang Shenyang Aerospace Univ.Liqiang Qu Shenyang Aerospace Univ.Xiao Cui AVIC Aerodynamics Research Inst.Jinglin Wang Aviation Key Laboratory of Science

and Technology on Fault Diagnosisand Health Management

Mingyue Yu Shenyang Aerospace Univ.Xiaochu Tang Shenyang Aerospace Univ.Aiming at the problems of excessive reliance on manual experience and processing of complex signals in traditional gear fault prediction methods, a gear fault prediction method based on DSAE and GRU network is proposed. Firstly, the time domain features of the gear vibration signal are extracted and divided into the training samples and the test samples. Secondly, a DSAE network is trained to adaptively extract the gear health index using the training samples. Then, the GRU network is trained with the health index of the training samples. The optimal fault prediction model is created by constantly adjusting the number of hidden layer neurons. Finally, the health index extracted from the test samples are used to verify the fault prediction model. In order to verify the effectiveness of the prediction model, the ARMA model and the traditional BP network are compared with GRU network in this paper. The prediction results indicate that GRU network is more effective than other prediction methods and has better engineering application value.

MonA10 Room10 Fault Diagnosis and Predictive Maintenance (VII) 08:00-10:00 Chair: Xiaopeng Xi Shandong Univ. of Science and Tech.CO-Chair: Huabei Gou Beihang Univ.

08:00-08:20 MonA10-1 164 A Fractional Degradation Model with Diffusing Diffusivity Xiaopeng Xi Shandong Univ. of Science and Tech.Fractional processes are useful tools to reflect the long-term temporal dependency of degradations inherent in some practical systems. Considering the time-varying characteristic of working conditions, the diffusions may follow non-deterministic variation rates. To combine the uncertain diffusing diffusivity and the memory effect, a generalized Feller process (GFP) based model is constructed with the diffusion term being a state-dependent fractional signal, the remaining useful life (RUL) distributions can also be worked out in a closed form. The proposed prognostic frame is validated by a case study of blast furnace cooling stave.

08:20-08:40 MonA10-2

9 Adaptive Fault-Tolerant Control for a Stratospheric Airship with Input Saturation and State Constraints Huabei Gou Beihang Univ.Ming Zhu Beihang Univ.Zeiwei Zheng Beihang Univ.In this paper, an adaptive fault-tolerant control (AFTC) method based on the backstepping technique is proposed to address the trajectory tracking problem for a stratospheric airship with actuator faults. The state constraints and input saturation are considered as well. The time-varying multiplicative and additive actuator faults are modeled and estimated by the designed adaptive laws. A tan-type Barrier Lyapunov Function (BLF) is introduced to handle the state constraints problem. An auxiliary system combined with a command filter is introduced to deal with the input saturation problem. Stability analysis indicates that the tracking error is about to converge to a neighborhood of zero and the estimation errors of actuator faults are bounded. Meanwhile, the state constraints and the input saturation are never violated. The simulation results confirm that the airship can track the desired trajectory under the actuator faults, state constraints, and input saturation.

08:40-09:00 MonA10-3 1331 Adaptive NN Output-Constrained Fault-Tolerant Control of a Class of Pure-Feedback Systems with Actuator Faults Lei Ma Northeastern Univ.Zhanshan Wang Northeastern Univ.In this paper, an adaptive neural network output-constrained fault-tolerant control approach is investigated for a class of nonlinear pure-feedback systems with sensor failures. By using the mean value theorem and implicit function theorem, the considered nonlinear pure feedback system is transformed into a strict feedback structure. Based on the offset and backstepping technology, an adaptive neural network fault-tolerant control method is proposed. At the same time, a time-varying tangent Barrier Lyapunov function (TBLE) with error variables is used to avoid violation of output constraint. Based on the Lyapunov stability analysis, it can be proved that all signals in the closed-loop system are uniformly ultimately bounded (UUB). The simulation results show the effectiveness of the control method.

09:00-09:20 MonA10-4 1341 A Novel Fault Detection for Three Tank Combined Improved Piecewise Linear Representation with Partial Least Squares Chunming Zhang Shandong Univ. of Science and Tech.Xianghua Wang Shandong Univ. of Science and Tech.Yanheng Ren Shandong Univ. of Science and Tech.In this paper, an improved Piecewise Linear Representation (PLR) algorithm is proposed, compared to conventional PLR algorithm, trend operations including amplitude difference calculation, trend judgement and trend merger are introduced and conducted on all segments, resulting in new segmentation reflecting data characteristics better. Combining the improved PLR algorithm with Partial Least Squares (PLS) algorithm, the PLS multivariate statistical model for the three-tank system is established. Next a fault detection scheme which chooses the SPE and T square statistics as evaluation indexed, is proposed. Finally, simulation experiments are conducted to prove the effectiveness of the proposed method.

09:20-09:40 MonA10-5 1386 Open-Circuit Fault Diagnosis of Voltage Source Inverters for PMSM Drive System using Sine-Wave Injection Method Jiajiang Sun Huazhong Univ. of Science and Techn.Qi Zhu Huazhong Univ. of Science and Techn.Yang Zhou Huazhong Univ. of Science and Techn.Jin Zhao Huazhong Univ. of Science and Techn.This paper proposes a novel high-frequency sine-wave injection-based IGBT open-circuit fault diagnosis method for permanent magnet synchronous machine (PMSM) drive system. The open-circuit fault features are analyzed as the research basis. Unlike the normal current signal-based method, a high-frequency sine-wave voltage is injected into the stationary frame for the open-circuit fault detection. The extraction of high-frequency current is proposed, which is robust against the rotor speed and load variation. As the high-frequency injection voltage is injected positive and negative in one PWM cycle, the interference to the closed-loop current controller is avoided. The fault detection variable based on high-frequency current and the fault localization variable based on current error is proposed. Fault diagnosis thresholds are derived, and the fault table is constructed. The simulation results verify the feasibility and effectiveness of the proposed open-circuit fault diagnosis method.

09:40-10:00 MonA10-6 1470 Remaining Useful Life Prognostics Based on Deep Combined Temporal Bidirectional Convolutional Network Xiaozhi Liu Northeastern Univ.PeiHong Li Northeastern Univ.Yinghua Yang Northeastern Univ.The prognostics and health management (PHM) is the key technology to protect the security and ability of the machine, it has gotten remarkable

Technical Programmes CCDC 2021 theoretical achievement and have broaden application. The remaining useful life (RUL) is the important part of the PHM, which describe the time interval between the current time and fault time. Recent year, RUL based on deep learning has made rapidly development, such as deep convolution neural networks (DCNNs). This paper proposes a new data driven model for prediction using deep combined temporal convolution neural and bidirectional long short-term network. To make an evaluation about this new approach, the C-MAPSS dataset is used for testing the performance of the new model. The comparisons with other approaches also suggest the proposed new deep network offers a new and promising approach.

MonA11 Room11 Intelligent Control, Computation and Optimization (VI) 08:00-10:00 Chair: Juan Chen Beijing Univ. of Chemical Tech.CO-Chair: Xingtang Wu State Key Laboratory of Rail Traffic

Control and SafetyBeihang Univ.

08:00-08:20 MonA11-1 194 An Adaptive Evolutionary Whale Optimization Algorithm Juan Chen Beijing Univ. of Chemical Tech.Hongkun Rong Beijing Univ. of Chemical Tech.Zheng Zhang Beijing Univ. of Chemical Tech.Ruihan Luo Systems Engineering Research Inst.Compared with traditional swarm intelligence algorithm, whale optimization algorithm (WOA) has unique mechanism and simple parameters, with certain advantages, such as high search precision and strong generalization ability. For the shortcomings of whale optimization algorithm: slow convergence speed, and easily getting fallen into local optimum, this paper proposes an adaptive evolutionary optimization algorithm whales (Adaptive elite strategy whale optimization algorithm, AWOA), a method of searching for food and spiral update location introduced adaptive adjusting weighting function, abundant diversity of population while strengthening local optimization ability; Adaptive differential variation disturbance is introduced in the contraction enveloping stage to provide the searching power in the later stage and avoid falling into the local optimum. The simulation results show that the improved adaptive evolutionary whale algorithm enriches the diversity of the search population in the early stage and enhances the search power in the later stage. The proposed algorithm has advantages including: fast convergence speed, high optimization precision and stronger generalization ability.

08:20-08:40 MonA11-2 1301 Forecast of Passenger Flow of Urban Rail Transit Based on WKHDNNC Model Wei Li State Key Laboratory of Rail Traffic Control

and SafetyBeijing Transportation Information Center

Beijing Key Laboratory for ComprehensiveTraffic Operation Monitoring and Service

Min Zhou State Key Laboratory of Rail Traffic Controland Safety

Hairong Dong State Key Laboratory of Rail Traffic Controland Safety

Xingtang Wu State Key Laboratory of Rail Traffic Controland Safety

Beihang Univ.Qi Zhang China Academy of Railway Sciences

Corporation LimitedThe dynamics of the passenger flow for urban rail transit stations are characterized by complexity, nonlinearity, and periodicity. Short-term passenger flow prediction is the basis for rail transit to carry out operational and dispatching management. A single prediction method is hard to fully describe the changing characteristic of the passenger flow, and is not suitable for daily passenger flow prediction of urban rail transit. Aiming to predict the passenger flow for the Beijing rail transit, this paper proposes a Decomposition Neural Network Composition (DNNC) passenger flow prediction model. The simulation results show that the proposed prediction model could effectively improve the accuracy of passenger flow forecasting, and can well describe the changing law of passenger flow.

08:40-09:00 MonA11-3 1501 On-Ramp Dynamic Coordination Based on Grey Wolf Optimization Xiaolin Ye Shandong Univ. of Science and Tech.Changle sun Shandong Univ. of Science and Tech.Hongyan Gao Shandong Univ. of Science and Tech.In order to prevent nonrecurrent traffic congestion on expressway, a method of dynamic coordination control of ramp is proposed. In order to prevent the on-ramp from overflowing, the queue length of the on-ramp is included in the constraint conditions. A ramp dynamic coordination control model consisting of macro traffic flow model, ramp queue length model and congestion queue length model was established. Grey wolf optimization (GWO) algorithm is used to solve the dimensionality disaster problem. Finally, MATLAB simulation software is used to verify the simulation results show that the control system has good dynamic performance, which can effectively reduce the traffic congestion caused

by traffic accidents.

09:00-09:20 MonA11-4 1534 Optimal Security Charging Service for Electric Vehicles by Mobile Charging Stations: Dynamic Contract-based Approach Huwei Chen Tsinghua Univ.

Jiangsu Aerospace Power Machinery & ElectricalCo., Ltd

Yong Zhao Jiangsu Aerospace Power Machinery & ElectricalCo., Ltd

Rongbin Zhang Jiangsu Aerospace Power Machinery & ElectricalCo., Ltd

Zhihui Chen Nanjing Univ. of Aeronautics and AstronauticsShijun Chen Shanghai Univ.Electric vehicles (EVs) are designed to improve the power efficiency as well as it prevents the environment from being polluted, and thus they are widely and reasonably used in the power market. Thanks to mobile charging stations (MCSs), it provides the charging service to EVs more easily with higher payoff and lower consumption, compared with the fixed charging stations (FCSs). In this paper, we propose a dynamic contract-based strategy to apply the secured charging service from MCS in Internet of things (IoTs), considering incomplete information of EV users. Firstly, considering this charging system composed of multiple EV users and MCSs, we design the utility function of each one, where non-contract power are supplied to EV users besides the power in contract items in IoTs. Then, in order to maximize MCS’s profits, an optimization problem is formulated to obtain the optimal contract items satisfying EV users’ power demand. Thirdly, the optimal solution of this problem is efficiently achieved through the presented iterative search algorithm, considering the different types of EV users with different contract items. Finally, simulation results validate the effectiveness of our proposal and each of them in this charging system can be benefited.

09:20-09:40 MonA11-5 548 Control Strategy and Flight Trajectory Optimization Strategy Based on Improved De Casteljau’s Algorithm for Indoor Drone Bowen Jiao Zhejiang Univ.

Zhejiang Univ. Robotics Inst.Zuyi Wang Zhejiang Univ.

Zhejiang Univ. Robotics Inst.Li Xu Zhejiang Univ.

Zhejiang Univ. Robotics Inst.In this paper, we introduced the control strategy of indoor drones and the optimization strategy for UAV flight. For the flight control of the UAV, we use a dual closed-loop control system. Specifically, it is to control the UAV to reach the desired position and achieve the desired attitude by setting the dual closed loop position loop and attitude loop. In order to allow the drone to safely and stably pass the unguided points in the set trajectory, we have improved the traditional De Casteljau’s algorithm so that it can be better applied to the predetermined trajectory of the drone. Specifically, this improved algorithm selects different numbers of control points to construct the Bézier curve in sections according to the actual situation. Finally, a complete optimization strategy is obtained and we have proved through experiments that the improved De Casteljau’s algorithm can indeed reduce drone flight errors and make the flight trajectory smoother and more stable.

09:40-10:00 MonA11-6 849 Substantiations and Numerics of Continuous-Time Linear HTD (Han Tracking Differentiator) and Nonlinear or Unequal-Parameter ZTD (Zhang Time Derivativer) of Order 4 Xiaoyan Zheng Sun Yat-sen Univ.

Guangdong Key Laboratory of ModernControl Tech.

Yunong Zhang Sun Yat-sen Univ.Guangdong Key Laboratory of Modern

Control Tech.Min Yang Sun Yat-sen Univ.

Guangdong Key Laboratory of ModernControl Tech.

Xiao Liu Sun Yat-sen Univ.Guangdong Key Laboratory of Modern

Control Tech.Zhenyu Li Sun Yat-sen Univ.

Guangdong Key Laboratory of ModernControl Tech.

In this paper, continuous-time linear Han tracking differentiator (HTD), Zhang time derivativer (ZTD) introduced by Zhang neural dynamics (ZND) with initially nonlinear activation, and ZTD introduced by ZND with linear activation and unequal parameters are presented. Besides, the HTD and two types of ZTD are adopted and substantiated to obtain time-derivative signals from original signals in numerical experiments, and the corresponding numerical results are displayed and discussed in detail. Through three numerical experiments, the efficacy of HTD and ZTD is substantiated well. Moreover, the effects of different design parameters and activation functions on ZTD are illustrated. The results show that, to some extent, the larger the design parameters are, the better the performance of ZTD is. In addition, the nonlinear activation function does not greatly improve the performance of ZTD for some

Technical Programmes CCDC 2021 specific original signals.

MonAIS Room12 Interactive Session 08:00-10:00

MonAIS-01 496 Research on high frequency pulsating voltage signal injection method of IPMSM based on gradient descent method Yan Li Beijing Inst. of Tech.Zhen Chen Beijing Inst. of Tech.Congzhe Gao Beijing Inst. of Tech.Jing Zhao Beijing Inst. of Tech.In the low-speed sensorless control of interior permanent magnet synchronous motor (IPMSM) drive system, the pulsating high frequency signal injection method is often used to estimate the rotor position by tracking the salient polarity of the motor. Because of the influence of inverter nonlinearity and load disturbance, it is difficult to observe the position information in the current response through the traditional phase-locked loop (PLL). Based on the traditional PLL, an improved observer is proposed in this paper. The gradient descent method is used to update the parameters of PLL parameters online, which not only ensures the accuracy of position extraction, but also has certain robustness to load disturbance. The correctness of the method is verified by simulation analysis.

MonAIS-02 769 Improved Multi-Motor Synchronization Control of Underwater Robot Based on Virtual Shaft Guanjun Yang Jiangsu Univ. of Science and Tech.Jinghao Yan Jiangsu Univ. of Science and Tech.

Nanjing Univ. of Aeronautics and AstronauticsPengfei Zhi Jiangsu Univ. of Science and Tech.Huilin Ge Jiangsu Univ. of Science and Tech.Zhiyu Zhu Jiangsu Univ. of Science and Tech.Underwater robot is the main equipment used to develop underwater resources. This paper solves the problem of speed and load imbalance of multi-motor when the underwater robot works in the random interference environment. A multi-motor synchronization control strategy based on the virtual shaft is proposed. A normalized proportional coefficient distribution method is constructed to achieve all the different speed motor cooperation. Furthermore, it solves the problems of low control accuracy, and slow dynamic response caused by multi-motor cooperation. A backstepping sliding mode controller with dynamic surface is constructed. Combining backstepping control and sliding mode control to eliminate the underwater random nonlinear disturbance. The simulation results demonstrate the multi-motor propulsion under the control of this scheme has good rapidity and robustness when dealing with random disturbance and large instantaneous load disturbance.

MonAIS-03 918 Path Tracking Control of Underwater Vehicle based on Active Disturbance Rejection Control Tao Liu Shanghai Maritime Univ.Wei Zhang Shanghai Dianji Univ.Bing Sun Shanghai Maritime Univ.Daqi Zhu Shanghai Maritime Univ.In order to solve the problem that the underwater vehicle (AUV) is easy to be disturbed by the external environment and the uncertainty of the vehicle when it carries out the underwater path tracking task, through analyzing and establishing the kinematics and dynamics model of the underwater vehicle, the second-order ADRC tracker is established based on the active disturbance rejection control technology, and the horizontal path tracking simulation experiment is carried out. The AUV path tracking controller based on control technology can realize the reference path tracking task, and has fast tracking speed, strong anti-interference ability and good control effect.

MonAIS-04 650 Prediction of ship rolling motion based on NARX neural network Chong Li Dalian Maritime Univ.Wenjun Zhang Dalian Maritime Univ.Tianxin Zhou Dalian Maritime Univ.Shuangfu Ma Dalian Maritime Univ.Cong Wang Dalian Maritime Univ.In order to accurately and efficiently predict the ship's navigation state in waves and ensure the safety of personnel, cargo and ship, this paper proposes a nonlinear autoregressive (NARX) neural network algorithm based on external input. The method takes the measured wind direction and wind speed data as external input, which can effectively improve the prediction accuracy of ship rolling motion The real-time prediction experiment of the rolling situation of the ship "Yukun" is carried out, and the prediction results of NARX model and BP neural network model are compared. The simulation results show that the prediction accuracy of NARX model is better than that of BP neural network.

MonAIS-05 706

Dynamic integral sliding mode control for maximum wind energy tracking of low wind speed wind turbines Yaping Xia Xiangtan Univ.Pei Liu Xiangtan Univ.RuiYu Li China Ship Development and Design CenterCurrently, the higher wind energy extraction efficiency and the lower load are desirable for the maximum power point tracking (MPPT) of wind turbines. However, for the existing control strategies, the higher MPPT efficiency is achieved with the increase of the load. Hence, a dynamic fuzzy integral sliding mode control method is proposed in this paper. This method employs the tracking error of power and its integration as the sliding surface and uses a fuzzy control method to design the parameter of the sliding mode controller, which can improve the MPPT efficiency and reduce the load simultaneously. Then, simulations on FAST (Fatigue, Aerodynamics, Structures, and Turbulence ) code are given to verify the effectiveness of this method.

MonAIS-06 1005 Research on Control Strategy of Novel Synchronous Switched Reluctance Motor Wenfeng Wang Beijing Inst. of Tech.Lei Dong Beijing Inst. of Tech.Maoying Rani Beijing Inst. of Tech.Mengru Li Beijing Inst. of Tech.In order to solve the problems of complex rotor structure and poor mechanical strength of synchronous reluctance motor and the large torque ripple of switched reluctance motor, a synchronous switched reluctance motor is proposed in this paper. The finite element simulation software is used to simulate the motor, and the optimal pole-arc coefficient of the rotor is obtained, realizing the optimization of the motor. A fixed current angle vector control method based on maximum torque per ampere (MTPA) control is proposed. Combined with active disturbance rejection control (ADRC), the motor control strategy is further optimized. While the motor structure and control strategy are simple, the control system can still show good performance. Digital simulation and experimental results show the feasibility and effectiveness of this method.

MonAIS-07 1008 Experimental validation of marine craft heading keeping considering modeling uncertainty and actuator dead-zone Yiming Zhong Shanghai Jiao Tong Univ.Caoyang Yu Shanghai Jiao Tong Univ.Rui Wang Shanghai Jiao Tong Univ.Lian Lian Shanghai Jiao Tong Univ.This paper investigates marine craft heading keeping subject to model uncertainty, actuator dynamics, and dead-zone. First, a simplified heading dynamics model of marine craft is constructed for heading keeping. Second, an adaptive fuzzy sliding mode controller (AFSMC) with a thin boundary layer is developed for heading keeping of marine craft. The adaptive fuzzy logic obtains a better approximation precision of uncertainty; a thin boundary layer reduces the chattering effect with saturation function. Third, the dead-zone effect is compensated by reconstructing the breakpoints. Finally, experimental validation in a water tank illustrates the effectiveness and outperformance of AFSMC compared to the proportional integral differential controller.

MonAIS-08 1025 Hybrid Scheduling Strategy of Networked Motion Control System Yuanzheng Zhang Univ. of JinanFang He Univ. of JinanQiang Wang Univ. of JinanNetworked motion control systems (MNCS) are increasingly used in complex motion control. Appropriate network scheduling strategy is very important for MNCS to ensure the stable operation and possess good system performance. When a single form of network scheduling algorithm is used for complex network motion control systems, serious information conflicts may still occur. Based on the discussion of several network scheduling algorithms for multi-sensor MNCS, this paper proposes a hybrid scheduling algorithm of rate monotonic (RM) scheduling algorithm with early deadline first (EDF) scheduling algorithm, which is established according to the importance level of the tasks of the network nodes. Simulation model of NMCS is set up using True Time simulation toolbox. The proposed hybrid scheduling strategy is compared with several single network scheduling algorithms by simulation. Simulation results show that the proposed hybrid scheduling strategy can make NMCS achieve better overall performance.

MonAIS-09 1050 A Specified Time Obstacle Avoidance Control Strategy for Wheeled Mobile Robots Jinpeng Zhai Peking Univ.Zhiyong Geng Peking Univ.Jianying Yang Peking Univ.In this paper, we study the specified time obstacle avoidance problem of a wheeled mobile robot with non-holonomic constraints. The obstacle avoidance problem is necessary to be considered when the robot is moving in complex environments. After establishing the kinematics model

Technical Programmes CCDC 2021 of a wheeled mobile robot and analyzing the system containing the robot and obstacles, we design appropriate angular velocity of the mobile robot to make the attitude of the robot track a desired direction. Then we use the time-rescaling approach and modify the angular velocity to achieve the obstacle avoidance task in a reasonable time specified in advance. Finally, numerical simulations are carried out and the results verify the effectiveness of the proposed strategy.

MonAIS-10 1169 Region tracking control strategy with the guaranteed boundary on the position error distance for a nonlinear system Xing Liu Ninghai ZJUT Acad Sci & Technol

Harbin Engineering Univ.Mingjun Zhang Harbin Engineering Univ.Yujia Wang Harbin Engineering Univ.Feng Yao Harbin Engineering Univ.In some engineering missions, such as target searching, users focus more on the distance between the location of the system and the corresponding point on the desired trajectory, rather than control precision of each degree of freedom (DOF). For such scenario, a region tracking control strategy with the guaranteed boundary on the position error distance is proposed for a nonlinear system by introducing a special barrier Lyapunov function. From theoretical analysis, it is guaranteed that the distance of the position tracking error is always less than the user-defined boundary and the attitude tracking error on each DOF is always kept within the prescribed boundaries near zero under the proposed control strategy. Finally, simulation results on a nonlinear system with six DOFs verify the effectiveness of the proposed region tracking control strategy.

MonAIS-11 1174 Speed Tracking Control of Permanent Magnet Synchronous Motor Based on Extended State Observer Qiang Chen National Univ. of Defense Tech.Zhen Jia National Univ. of Defense Tech.Peichang Yu National Univ. of Defense Tech.Lianchun Wang National Univ. of Defense Tech.Danfeng Zhou National Univ. of Defense Tech.Jie Li National Univ. of Defense Tech.Ming Gao CRRC Tangshan Co., LtdPermanent magnet synchronous motors have advantages of high speed, high precision, large thrust, low energy consumption, etc., and have been widely used in rail transit, electric vehicles and other fields. The permanent magnet synchronous motor system is a multi-variable, nonlinear and uncertain control object. The precise speed control of the permanent magnet synchronous motor puts forward better requirements for the acquisition of speed signals and the speed tracking control method. In this paper, based on the measured position information, extended state observer is designed to observe the speed signal, so as to obtain more accurate and real-time speed information. At the same time, the observed speed information, other system states and "total disturbances" are used in the speed control system. Combined with the cascade control method of speed loop and current loop, the speed tracking control is realized. Simulation results demonstrate the effectiveness of the proposed method. The effect of observation and speed tracking control is very good.

MonAIS-12 1280 MPC-based Steady-state Drift Control under Extreme Condition Hongyan Guo Jilin Univ.Zhongqiu Tan Jilin Univ.Jun Liu Jilin Univ.Hong Chen Tongji Univ.This paper proposes a steady-state drift control strategy based on model predictive control (MPC). A three degree-of-freedom vehicle model is established to calculate the equilibrium state of steady-state drift. The equation of deviation vehicle dynamics is used to track the equilibrium state. Carsim/Simulink joint simulation is carried out, and the results show that the controller is able to control the steady-state drift of the vehicle accurately. The influence of variation of road adhesion coefficient on vehicle steady-state drift is also analyzed.

MonAIS-13 1643 Non-singular Fast Terminal Sliding Mode Tracking Control of a 2-DOF Robotic Manipulator Using Finite-Time Extended State Observer Yiting Zhu Southeast Univ.Di Wu Southeast Univ.Shihua Li Southeast Univ.This paper studies the position tracking control of rigid robotic manipulators under the circumstance of parameter uncertainties and external time-varying disturbances. For the 2-DOF rigid manipulator, a non-singular fast terminal sliding mode control (NFTSMC) scheme is employed based on a finite-time extended state observer (FTESO) to improve both the dynamic response and the disturbance rejection performance. The NFTSMC method is employed to evade the singularity of conventional terminal sliding mode control method and speed up the

state reaching the sliding surface. The FTESO can observe the lumped disturbances in finite time and perform forward compensation to attenuate the chattering phenomenon of sliding mode control. The closed-loop system stability is demonstrated rigorously based on the Lyapunov theory. Comparative simulation results are given to prove the superiority of the proposed method.

MonAIS-14 1692 Analysis of Swinging Method of Straight Inverted Pendulum Fengzhong Zhang Shenyang Jianzhu Univ.Chenyi Zhu Shenyang Jianzhu Univ.Xianglin Hou Shenyang Jianzhu Univ.The mechanical model of the linear first-stage inverted pendulum is established by using the D'Alembert principle. Discrete control variables are used as design variables, and the end-point constraints are used as the objective function. The control value is calculated by the optimization method. The inverted pendulum is put forward and studied for a short time and fast swing control method. The simulation results of the swing can verify that this optimization method can successfully realize the short time and fast swing control of the linear inverted pendulum, which shows the effectiveness and feasibility of the method.

MonAIS-15 502 Industrial oil drilling operation evaluations using a novel analytic hierarchy process model integrating deep residual network with principal component analysis Yong-jian Wang City Univ. of Hong KongKe Yang Wuhan Univ. of Tech.Yuan Zhao Tianjin Normal Univ.When analytic hierarchy process (AHP) is used to evaluate complex decisions, neural networks are often applied instead of manual selection to optimize assigned weights to avoid subjective errors. However, traditional neural networks often suffer from local extremes and cannot extract deep features in the data. Therefore, this paper proposes a new AHP model based on the deep residual network and principal component analysis algorithm (PIDRN-AHP). First, the principal component analysis (PCA) method is used to extract the operating variables that are most relevant to the target variable as input data. Second, a deep residual network is constructed by using the residual block local deep neural network structural unit. Using deep networks, PIDRN-AHP can implement the best solution for AHP global weights, avoid subjective errors, and the disappearance of gradients in deep networks due to the increase in the number of layers. The proposed method is used to conduct the operational evaluation of industrial petroleum drilling processes. Compared with other methods, the proposed PIDRN-AHP method can achieve the most satisfactory evaluation results.

MonAIS-16 643 Effluent BOD Concentration Prediction in Wastewater Treatment Process Based on MIC and RBFNN Wenqiang Shi Beijing Univ. of Tech.Wenjing Li Beijing Univ. of Tech.Junfei Qiao Beijing Univ. of Tech.In order to deal with the problem that measurement of biochemical oxygen demand (BOD) in wastewater treatment process (WWTP) is difficult to achieve, a soft sensing algorithm for effluent BOD concentration prediction based on maximum information coefficient (MIC) and radial basis function neural network (RBFNN) is proposed. Firstly, the MIC is employed to filter the input variables that have close correlation with the effluent BOD concentration. Secondly, an improved K-means algorithm is used to initialize the center and width of the RBFNN, and the Levenberg-Marquardt (LM) algorithm is used to train the weight of the network. Finally, the benchmark datasets and the real data of the WWTP are used for experiments, the results indicate that the network has a good prediction on the effluent BOD concentration.

MonAIS-17 865 Model-free Adaptive Sliding Mode Control for Cement Particle Size of Vertical Mill Shilei Zhang Univ. of JinanQiang Zhang Univ. of JinanChenguang Wang Univ. of JinanAiming at the large lag and nonlinear characteristics of the cement vertical mill particle size control system, we propose a new adaptive quasi-sliding mode control method. We designed a model-free adaptive controller which is based on the dynamic linearization model of the tight format and combined with discrete sliding mode control. In the dynamic linearization method, the estimation algorithm of the pseudo partial derivative only depends on the I/O measurement data of the controlled system. We have proved the stability of the algorithm through theory, and the simulation results verify the effectiveness of the method. Compared with the traditional model-free control (MFAC) and PID control, the adaptive quasi-sliding mode control method based on the model-free (MFAC-SMC) can realize the control of particle size in cement vertical mill system better.

MonAIS-18

Technical Programmes CCDC 2021 1160 Condition Identification of Copper Flotation Process based on Foam Image and VGG16 Network Zhiqiang Wang Northeastern Univ.Qiang Li Northeastern Univ.Xu Wang Beijing Key Laboratory of Automation of Mining

and Metallurgy ProcessDakuo He Northeastern Univ.Xiang Ma SINTEF IndustryIn the flotation process, the foam in the flotation tank can reflect the characteristic information of the flotation condition category. Based on the characteristic information of the foam, the identification of working conditions and the operational optimization can be carried out. In this paper, the research on image processing and working condition recognition based on deep learning is proposed. Firstly, the flotation foam image is preprocessed through the gray scale, image size processing and filtering methods. Then, the neural network visual geometry group (VGG) is built and trained to realize the recognition of flotation foam image conditions. The VGG16 network is based on the Sequential model in the Keras framework. The main structure of the network consists of 13 convolutional layers, 5 pooling layers and 3 fully connected layers. The VGG16 data set is a collection of 43,800 foam images under six conditions, dividing the data set into a training set, a validation set, and a test set. Finally, the VGG16 network is used for image feature learning to realize six classifications of bubble images and working condition recognition. It is verified that the proposed neural network has high precision and can be used in flotation industrial process.

MonAIS-19 686 Computational Adaptive Optimal Tracking Control of Quadrotor UAV Based on Robust Control with Unknown Dynamics Jiaqig Bai Beijing Inst. of Tech.Yankai Wang Beijing Inst. of Tech.Hao Xing Beijing Inst. of Tech.In this paper, an adaptive optimal tracking control for a quadrotor unmanned areial vehicle (UAV) with unknown dynamics was proposed. A model-free optimal tracking control scheme is designed to solve the problem that quadrotor UAV is difficult to model accurately due to its complex dynamics. Firstly, the model of a four-rotor UAV based on robust control was built. The nominal model of linear and decoupling for each subsystem is given and coupling and nonlinear dynamics, parametric perturbations and external perturbations are considered as uncertainties. Then, a strategy iteration method is proposed to find an online adaptive optimal tracking controller for the nominal model system of quadrotor with completely unknown system dynamics. Finally, simulation results are given to illustrates the effectiveness of the proposed method.

MonAIS-20 343 Adaptive Control Method of UAV Intelligent Rudder Based on Hybrid Genetic Algorithm Qi Sun Shanghai Jiao Tong Univ.Haitao Xu Cleveland State Univ.This paper proposes a control strategy based on fuzzy GA algorithm for Unmanned Aerial Vehicle (UAV) course control. The control process is very complicated because of the strong nonlinearity and randomness of system dynamics. Therefore, this paper applies different control strategies to solve these problems. Different from previous studies, this paper visualizes the effect of UAV course control through track changes. Through the proportional model to achieve the design of the control scheme, the trajectory tracking of UAV is simulated. Then the advantages and disadvantages of different schemes are analyzed. In addition, the feasibility of hybrid genetic control algorithm is verified. This research experiment shows that the combination of intelligent technology and conventional controller is advantageous to deal with a complex system. The traditional control theory system is perfect, but its performance is poor. The limitation of traditional control theory is improved by technology cooperation. GA based on the traditional controller gain scheduling control to make adaptive adjustment.

MonAIS-21 836 Model Free Adaptive Control Algorithm based on GRU network Jinggao Sun East China Univ. Of Science and Tech.Xianfeng Chen East China Univ. Of Science and Tech.Guanghao Su East China Univ. Of Science and Tech.Hongguang Pan Xi’an Univ. of Science and Tech.The application of traditional adaptive control algorithm usually depends on the precise mathematical model of process, but it is difficult to establish a mathematical model for a dynamic process. The existing Model Free Adaptive (MFA) control algorithm structure is based on Back Propagation (BP) Neural Network, which does not fully take into account the continuous characteristic relationship between the input timing error sequences. According to this actual problem, an improved MFA control algorithm based on Gated Recurrent Unit (GRU) network is proposed in this paper, compared with BP network, GRU network has more advantages in processing timing series. It fully considers the information of error sequence, and more appropriate manipulated variable could be generated automatically to satisfy the demand of an open-loop stable and controllable single-variable, multivariable or large delay process system without the need for complicated manual adjustments, quantitative

knowledge of the process or the identifier of the controlled system and learning process. Simulation results demonstrate that the GRU-MFA learning algorithm performs better in terms of stability, response speed and adaptability than the BP-MFA algorithm without human intervention.

MonAIS-22 1026 Design of cascade active disturbance rejection guidance law for miniature strapdown guided munition Shiwei Chen Beijing Information Science & Tech. Univ.Junfang Fan Beijing Information Science & Tech. Univ.Haisen Wang Beijing Information Science & Tech. Univ.Qingdong Mu Beijing Information Science & Tech. Univ.Strapdown seeker is generally used in miniature guided munitions, which leads to the problems of model uncertainty and stability degradation in the guidance system. In this paper, a cascade linear active disturbance rejection control (CLADRC) method is studied. The first-stage active disturbance rejection controller is used to suppress the angular rate of munition-target line of sight. The linear extended state observer (LESO) is used to estimate and dynamically compensate the uncertainty of munition model, and the proportional-differential controller is used to calculate the observed value and the input value to obtain the control quantity. The obtained control command is observed through the acceleration feedback by the munition model in the second-stage ADRC, which can ensure the overload value is less than the maximum available overload of the munition. The simulation results show that the line-of-sight angular rate is well suppressed, the overload at the end of trajectory meets the requirements, and the target can be intercepted accurately.

MonAIS-23 1002 Fixed-time 6-DOF coordinated control of spacecraft formation flying Ruixia Liu Xi’an Univ. of Posts and TelecommunicationsJing Chu Xi’an Univ. of Posts and TelecommunicationsHuanchao Du Xi’an Univ. of Posts and TelecommunicationsThis paper investigates the fixed-time coordinated control problem of six-degrees-of-freedom (6-DOF) dynamic model for multiple spacecraft formation flying (SFF) with external disturbances. A new multi-spacecraft nonsingular fixed-time terminal sliding mode vector is derived. Then, a 6-DOF fixed-time coordinated control strategy with adaptive tuning laws is proposed, such that the practical fixed-time stability of the controlled system is ensured in the presence of upper bounds unknown external disturbances. It theoretically proves that the relative tracking errors of attitude and position can converge into the regions in fixed time. Finally, a numerical example is exploited to show the usefulness of the theoretical results.

MonAIS-24 1042 Application on Neural PID Control of MN-AGC in Continuous Hot Strip Rolling Li bo-qun Univ. of Science and Tech. LiaoningDong Hui Univ. of Science and Tech. LiaoningXia Yi-ming Univ. of Science and Tech. LiaoningZhao Yong Univ. of Science and Tech. LiaoningMa Lei Univ. of Science and Tech. LiaoningRolling technique process needs powerful technical support，but control level of rolling technique has reached a turning point at present, Which means the control effect based on the traditional control theory has reached the limit and Some of the key issues faced have not been completely resolved. Therefore, it is very necessary to realize forward development in control performance by introducing new control theory and method. It is always primary object to improve gauge control accuracy in hot strip mills for product quality. Due to the complexity of the actual rolling process, the non-linearity and the time-variation of the control object, in order to further improve the thickness control accuracy and control quality of strip hot rolling mill. MN-AGC integrate neuron with self-learning and adaptive capabilities and PID control to put forward an improved single neuron adaptive control strategy, which applied to the automatic thickness control system of the finishing mill of Angang 1700 production line to improve automatic gauge control accuracy and quality. The practical application results have tested and verified the controller effectiveness and its response speed and anti-interference ability are obviously better than the traditional control effect.

MonAIS-25 1075 Application of Nonlinear Adaptive PID Control in Temperature of Chinese Solar Greenhouses Yonggang Wang Shenyang Agricultural Univ.Yujin Lu Shenyang Agricultural Univ.Yuhang Liu Shenyang Agricultural Univ.Tan Liu Shenyang Agricultural Univ.Nannan Zhang Shenyang Agricultural Univ.The system of the Chinese solar greenhouses (CSGs) is required to ensure suitable environment for crops growth. However, the greenhouse system is described as complex dynamics characteristics, such as multi-disturbance, parameter uncertainty, and strong nonlinearity. Actually, the conventional PID control method is difficult to deal with above problem. To address above problem, a dynamic model of CSG is

Technical Programmes CCDC 2021 developed based on the energy conservation laws and a nonlinear adaptive control scheme, combining RBF neural network with incremental PID controllers, is applied to the temperature control. In this approach, parameters of PID controller are determined by the generalized minimum variance laws, and the unmodelled dynamics is estimated by RBF neural network. The control strategy is combined with a linear adaptive PID controller, a neural network nonlinear adaptive PID controller and switching mechanism. The simulation results show that the adopted method can achieve excellent control performance, which meets the actual requirements.

MonAIS-26 1166 Adaptive Incremental Kalman Predictor with Unknown Time-varying Parameters Han Zhou Heilongjiang Univ.Guangming Yan Heilongjiang Univ.Xiaojun Sun Heilongjiang Univ.In practical application, adaptive prediction and control problems with unknown time-varying parameters are common, such as industrial manipulator control, hydrological system adaptive prediction, power load adaptive prediction and oil production dynamic prediction. In this paper, for the system under poor observation condition and with unknown time-varying parameters, the incremental equation is introduced to eliminate the unknown measurement error of the system, and then the adaptive incremental Kalman filtering algorithm is used to estimate the unknown time-varying parameters. On this basis, the observation prediction and control are realized. In this paper, taking the water level rise forecast as an example, two kinds of adaptive incremental filtering algorithms are used to estimate the unknown time-varying parameters, and the simulation comparative analysis of the corresponding incremental prediction is given.

MonAIS-27 1196 An Offline Chinese Speech Recognition System for Intelligent Pump Station Jinwei Yao Univ. of Science and Tech. of China.Yong Wang Univ. of Science and Tech. of China.Xiaowen Yin China Water Sunny Data Technology Company

LimitedBo Hu China Water Sunny Data Technology Company

LimitedShaoqing Chen Information Science Laboratory Center of USTCQing Liang Univ. of Science and Tech. of China.This paper proposes a speech recognition model for specific control

vocabulary of pump sta tion, which can meet the requirements of offline single CPU environment in the Intelligent pump station environment. The acoustic model of CNN+BiLSTM+CTC speech recognition based on deep learning is designed, and the training strategy of transfer learning is

used to train the general acoustic model of Chi nese speech recognition on the public Chinese speech dataset, and then the model is fine-tuned on a small number of specific pump station command vocabulary datasets, which improves the generalization ability of the model. Then, a specific vocabulary decoding corpus was established for specific pump station control command words, which improved the traditional HMM language model and corrected the deviation of the acoustic model. Experimental results show that the speech recognition model proposed in this paper greatly improves the recognition effect of specific pump station command vocabulary, and at the same time enhances the scalability of the model.

MonAIS-28 1696 Distributed Adaptive Fault-tolerant Formation Control of Uncertain Nonholonomic Mobile Robots Zhen Han Beihang Univ.Wei Wang Beihang Univ.Huijin Fan Huazhong Univ. of Science and Tech.,In this paper, the distributed adaptive fault-tolerant formation control problem is investigated for a group of nonholonomic mobile robots (NMRs). The communication flow is described by a digraph and it is supposed that only a subset of the NMRs has direct access to the common desired trajectory. Besides, the actuators of each NMR may suffer from intermittent type of actuator faults. To estimate the unknown trajectory information and topology parameters, distributed estimators are proposed based on the neighboring available information. In addition, estimators are also introduced to estimate the lower bounds of the actuator fault coefficients. Based on the estimation values and Young’s inequality, nonlinear damping terms are designed. Then, a novel fault-tolerant control scheme is proposed by applying the adaptive backstepping technique. It is guaranteed that the formation errors will converge to an adjustable compact set and the control inputs are globally uniformly bounded. Simulation results validate the effectiveness of the proposed control scheme.

MonAIS-29 1457 An Improved Control Method of Variable Net Structure with Feature Constraint Parameters Bin Zhang Academy of Military Science

Wei Liu Academy of Military ScienceSiyu Zhu Academy of Military ScienceXiao Wu Academy of Military ScienceZhige Xie Academy of Military ScienceA robust control method is important but also challenging in variable net structure with moving boundary, especially when the deformation is large. In this paper, we present an improved control method with feature constraint parameters to improve the robustness of variable net structure. In details, the size constraint parameters can avoid the collapse of the elements in the net structure, the angle constraint parameters reduce the distortion of poor elements, and the shape constraint parameters correct the elements to obtain a better net structure. Two kinds of net quality parameters are used to evaluate the robustness of variable net structure. Several cases are tested and discussed, including the pitching of a two-dimensional airfoil, and the stretching of a cubic box. The results show that the improved control method can create better-deformed net structure than two classical control methods, even avoids the “negative volume” element when the deformation is large.

MonAIS-30 196 Research on Compound Control Method of Loading Torque of Electric Load Simulator Shuai Wu Northwestern Polytechnical Univ.Yong Zhou Northwestern Polytechnical Univ.Shangjun Ma Northwestern Polytechnical Univ.Yunxiao Lian Northwestern Polytechnical Univ.Jiang Chang Beijing SunWise Space Tech. LtdThis paper takes the Linear Electric Load Simulator (LELS) as the research object. Through analyzing the composition and working principle of the LELS, the paper respectively analysis torque simulation loading system and the measured actuation system, then establishes mathematical model under the motion interference of the tested system. This paper analysis the generation mechanism of the excess force of the simulation system, and discusses the way of inhibit excess force preliminarily based on this mathematical model. These simulation systems are modeled and simulated by the SIMULINK and the feasibility of the control method under constant input and step state is verified. Finally, the LabVIEW is used to perform experiments to verify the correctness of the simulation. The results show that the compound control method has good performance and can meet the engineering requirements.

MonAIS-31 203 Fuzzy Sliding Mode Control under Randomly Occurring Gain Fluctuations: A Component-Based Event-Triggered Approach Yekai Yang Key Laboratory of Smart Manufacturing in Energy

Chemical ProcessBei Chen Shanghai Univ. of Engineering SciencesYugang Niu Key Laboratory of Smart Manufacturing in Energy

Chemical ProcessJing Xu Key Laboratory of Smart Manufacturing in Energy

Chemical ProcessThis paper considers the sliding mode control (SMC) problem for T-S fuzzy system under the component based event-triggering mechanism (ETM). Moreover, the uncertainties and gain fluctuations in a randomly occurring way are taken into account, and these phenomena are described via two independent Bernoulli processes. Under the component-based ETM, the measurable output component is judged and transmitted independently to reduce the transmission frequency. Then, by virtue of the SMC approach, a fuzzy resilient output feedback controller is designed, which only depends on the triggered output signals. Meanwhile, sufficient criteria are derived to ensure the reachability of the specified sliding surface and the mean-square asymptotical stability of the closed-loop fuzzy system. Finally, we provide the simulation results for verifying the proposed control strategy.

MonAIS-32 267 Design of a fractional-order fuzzy PI controller for fractional-order chaotic systems Wei Han Northeast petroleum Univ.Bingkun Gao Northeast petroleum Univ.Haoxuan Guo Northeast petroleum Univ.In this paper, for the stable of fractional order chaotic systems with time delay, based on PID stability theory, a fuzzy PI control scheme is established. In the case of no eliminating the nonlinear part, the controller is designed with fractional order PI controller and fuzzy control. The simulation of the stable linear systems and Chen chaotic systems was conducted by the fuzzy PI controller which effectively shorten stable time and ensure the smooth. The simulation results verify the validity and feasibility of the controller.

MonAIS-33 1126 Reaching Law Sliding Mode Control with Surplus Uncertainties Compensation Liang Tao Anhui Polytechnic Univ.Xiongfeng Deng Anhui Polytechnic Univ.Binzi Xu Anhui Polytechnic Univ.

Technical Programmes CCDC 2021 Bingyou Liu Anhui Polytechnic Univ.Lichao Wang Anhui Polytechnic Univ.This paper proposes a compensatory reaching law sliding mode control method to suppress the influence of surplus uncertainties on the convergence performance of the system. The compensatory reaching law has the function of estimating and compensating the surplus uncertainties by adding a compensation term in the design of the reaching law, so as to avoid the design of disturbance observer or other methods to deal with uncertainties. The designed method can greatly simplify the structure of the controller and improve the control performance of the system. The simulation results show the simplicity and superiority of this method.

MonAIS-34 633 Modeling and Sectional Compensation of Temperature Error of FOG Based on BOA-GBDT Feng Cao Qilu Inst. of Tech.Ting-yang Yan Qilu Inst. of Tech.Min Su Qilu Inst. of Tech.According to the requirements of temperature compensation for real-time and accuracy, a method of using Bayesian algorithm to optimize the gradient boosting tree regression is proposed to establish the temperature error compensation model of fiber optic gyroscope, and it adopts the method of real-time acquisition of temperature change rate with multiple data windows to meet the requirements of online compensation and model input. The fiber optic gyroscope is placed in a temperature box to perform a temperature change test of -40-60°C to obtain measured data. The temperature and temperature change rate are used as input, and Bayesian algorithm optimization gradient lifting tree regression modeling and temperature rising and falling segment modeling are performed respectively. The comparative experiment results show that the proposed model achieves the best compensation effect. Through the compensation comparison test, it is verified that the proposed model has good compensation ability and generalization ability for non-training data.

MonAIS-35 868 Research on Downhole Wireless Power Transfer Technology Based on Magnetically-Coupled Resonance Quanbin Wang Research Inst. of Petroleum Exploration and

DevelopmentXiaohanPei Research Inst. of Petroleum Exploration and

DevelopmentDeli Jia Research Inst. of Petroleum Exploration and

DevelopmentJiqun Zhang Research Inst. of Petroleum Exploration and

DevelopmentZhenkun Zhu Daqing Oilfield Production Engineering

Research inst.In order to achieve the main technological requirements of oilfield development ("separated layer oil production, separated layer water injection, separated layer testing, separated layer research, separated layer management and separated layer transformation; clear separated layer oil recovery, clear separated layer water injection, clear separated layer pressure and clear separated layer water yield”), the battery-powered real-time monitoring and control technology of water injection parameters in downhole intervals has been applied in oilfields. However, a series of problems occur when batteries are used for downhole power supply, such as fast self-discharge in high-temperature environment, and high power consumption during the water injection parameter adjustment of downhole intervals, which results in limited working life of downhole tools. This paper studied the downhole magnetically coupled resonant wireless power transfer (MCR-WPT) technology based on SS (series-series) topology and analyzed the influencing factors of the transmitter power and the receiver power of the magnetically coupled circuit. In addition, it established a magnetically coupled resonant model and experimental device and conducted experiments on the charging effect of the system indoors. The research results show that the designed MCR-WPT system can provide a stable charging voltage and current for the downhole lithium battery, and it provides constant charging current within 80 minutes after starting the charging. When the battery voltage rises to 4.2V, it enters the constant-voltage charging mode. The battery power can reach 100% within 2 hours. The battery charging methods and strategies studied in this paper meet the needs of downhole real-time monitoring and control of the separated layer water injection.

MonAIS-36 975 Calibration of thermal sensors using BP neural network and SVM Wenqing Xie Nanjing Univ. of Science and Tech.Le Yin Southwest Univ.Maojiao Ye Nanjing Univ. of Science and Tech.In this paper, the influences of measuring distance and ambient temperature on the measurement accuracy of thermal sensors are explored through experiment. The data collected during the experiment are analyzed and used to train two machine learning models, i.e., back propagation (BP) neural network and support vector machine (SVM), with different numbers of hidden layer nodes and activation/kernel functions. Then, the models with better performance metrics are selected to

compensate the measuring error of the thermal sensor. The experimental results show that both the BP neural network and the SVM can significantly improve the accuracy of the thermal sensor.

MonAIS-37 1096 Lamb Wave Based Crack Damage Quantitative Imaging Method Shaodong Zhang Nanjing Univ. of Posts and TelecommunicationsJing Xv Nanjing Univ. of Posts and TelecommunicationsWeiwei Hu Nanjing Univ. of Posts and TelecommunicationsQiang Wang Nanjing Univ. of Posts and TelecommunicationsSince that crack damage in metal structures was highly concealed and harmful, quantitative crack detection technology was one of the current research hotspots in structural health monitoring. However, existing guided wave based structural damage monitoring using piezoelectric arrays usually ignore crack direction information and can’t effectively evaluate crack damage. Ring piezoelectric array was introduced and active Lamb wave RAPID imaging to study the crack damage quantitative monitoring. Based on the mechanism that direct wave signal specifically responses to different damage, according to the difference of crack damage to the response signal changes under the orthogonal monitoring path, a cross orthogonal scanning method was proposed to determine the crack direction, and then adjust the signal difference coefficient (SDC) value of damage with parallelly-passing or approximately parallelly-passing path. The proposed method constructs image information of reinforced damage orientation, then realize the image reconstruction and quantitative evaluation of crack damage. Experimental verification was carried out on the aluminum plate, the test results show that the proposed cross orthogonal scanning method and the improved RAPID imaging method can better identify the crack direction and can quantitatively display the crack length.

MonAIS-38 1402 A self-compensation method for resolver error based on characteristic frequency analysis using resister network Yingguang Wang Beijing Inst. of Control EngineeringJiyang Zhang Beijing Inst. of Control EngineeringMing Lu Beijing Inst. of Control EngineeringLimei Tian Beijing Inst. of Control EngineeringYuewei Hu Beijing Inst. of Control EngineeringQiang Zhang Beijing Inst. of Control EngineeringThe resolver error caused by its sine and cosine output of non-orthogonal and non-equal amplitude is second harmonic of the resolver velocity. The resolver error seriously reduces the accuracy of angular velocity of the Control Moment Gyro (CMG). Stability, it reduces the output torque accuracy of CMG. To compensate the resolver angle measurement error, a self-compensation method for resolver error based on characteristic frequency analysis using resister network is proposed. The amplitude corrector and phase corrector are added between the resolver and the Resolver-to-Digital Converter (RDC). Resolver is rotated at a constant speed. The amplitude corrector and phase corrector are adjusted according to the spectrum analysis of RDC velocity. In the end, the resolver error is greatly reduced.

MonAIS-39 1493 Intrinsically Safe Multi-parameter Mine Environment Monitoring Instrument Tiqiang Li Univ. of JinanZhonghua Wang Univ. of JinanMeng Li Univ. of JinanJinguo Sang Shandong GoldSoft Tech. Co., LtdAiming at the monitoring of flammable and explosive gases and other harmful gases in mines, this paper designs and implements a multi-parameter mine environment monitor based on intrinsically safe circuits. The instrument fixes CO2, methane and other gas sensors in the collection box, and is connected directly under the main body of the monitor through a buckle and an electrical connector. After the instrument is initialized, the sensors start to work to obtain the concentration values of the monitored gases. Meanwhile, the instrument also has RS-485, CAN, 4G communication interfaces, which can realize real-time data upload and alarm. After physical object testing, the instrument has high measurement accuracy and can automatically measure underground gas, which meets the expected requirements.

MonAIS-40 1564 Long distance excitation of multi-core cables Lei Gu Nanjing Univ. of Posts and TelecommunicationsCheng Bao Nanjing Univ. of Posts and TelecommunicationsShaodong Zhang

Nanjing Univ. of Posts and Telecommunications

Weiwei Hu Nanjing Univ. of Posts and TelecommunicationsQiang Wang Nanjing Univ. of Posts and TelecommunicationsThe problem of damage detection of high-voltage cables in power systems has not been well resolved. In the current ultrasonic guided wave NDT technology, the excitation method is too simple, the echo signal mode is complex, and the signal-to-noise ratio is low, which leads to energy dispersion, short propagation distance and increased difficulty in signal processing. The multi-point excitation method was introduced to

Technical Programmes CCDC 2021 study the cable damage detection technology. Based on the interaction mechanism of in-phase wave and anti-phase wave, according to the geometry of the cable, a four-point in-phase and anti-phase excitation method was proposed. In the finite element simulation software Abaqus, the four-point excitation method was studied for the cable. The experimental results showed that the four-point in-phase and anti-phase excitation method achieves better mode selection, enhances the desired mode amplitude, suppresses irrelevant modes and other noise. Compared with the traditional single-point excitation method, it greatly improves the signal-to-noise ratio, reduces the difficulty of signal processing, and extends the guided wave propagation distance.

MonAIS-41 1569 Study On Nonlinear Lamb Wave Method For Damage Assessment Of Composite Structures Yao Feng Nanjing Univ. of Posts and TelecommunicationsSiyu Zhu Nanjing Univ. of Posts and TelecommunicationsQiao Bao Nanjing Univ. of Posts and TelecommunicationsQiang Wang Nanjing Univ. of Posts and TelecommunicationsWith the continuous improvement of monitoring technology requirements in engineering practice, the traditional Lamb wave monitoring technology is limited by the principle and has low sensitivity to micro damage, which limits the application and development of Lamb wave structural health monitoring technology. Therefore, the nonlinear relationship between the S0 mode signal of Lamb wave and microcracks is studied in this paper. The experimental results on epoxy plates show that the nonlinear Lamb wave characteristic parameters are very sensitive to the damage such as cracks, which provides a feasible idea for the early damage assessment of composite structures such as cracks.

MonAIS-42 1578 Research on Compensation Method of Random Drift of Vibratory Gyro Based on Phase Space Reconstruction Ning Liu Beijing Information Science and Tech. Univ.Zhong Su Beijing Information Science and Tech. Univ.Vibratory gyroscope has been widely used in many fields such as land, sea and air. Random drift suppression is a major factor affecting this type of gyroscope and has been the focus of research in this field. Taking the MEMS gyroscope as an example, the traditional frequency domain method is usually used for suppression, but this method cannot be adjusted dynamically; while the time series method is used, its data pre-processing is cumbersome and poor in real-time. This paper proposes a method of phase space reconstruction (PSR) based on chaotic factors to suppress random drift. Firstly, a gyro output signal model based on PSR is established, and the relevant parameters are determined using the C-C method; then the traditional Kalman filtering method is used to achieve random drift suppression. The effectiveness of the method is verified by combining simulation analysis with actual verification.

MonAIS-43 744 An Image Encryption Method Combining Wavelet Chaotic Neuron and Cubic Mapping Yaoqun Xu Harbin Univ. of CommerceXinxin Zhen Harbin Univ. of CommerceZhenhua Yang Harbin Univ. of CommerceImage encryption is the conversion of valuable image information through a series of reversible transform operations to protect image information by making it unrecognizable and confusing. Digital image is different from text data, which is a kind of two-dimensional data. It has some inherent characteristics, such as large amount of data, high redundancy and strong correlation between pixels. Therefore, it is difficult to achieve the better encryption effect when traditional encryption methods are applied to image encryption. Chaotic is widely used in image encryption now because of its simple structure, easy to implement and fast iteration speed. In order to simplify the encryption process and reduce the computation complexity while ensuring the effect of encryption, this paper proposes an encryption algorithm that combining cubic mapping and wavelet chaotic neuron. Perform security analysis on the simulation results such as information entropy, grayscale histogram and adjacent correlation of experimental results are analyzed to verify the encryption accuracy of the proposed algorithm.

MonAIS-44 808 An intrusion detection method for the in-vehicle network Anyu Cheng Chongqing Univ. of Posts and TelecommunicationsYibo Peng Chongqing Univ. of Posts and TelecommunicationsHao Yan Chongqing Univ. of Posts and TelecommunicationsXiaona Shen Chongqing Univ. of Posts and TelecommunicationsIn view of the defects of the vehicle controller area network (CAN) bus protocol and the large number of attack interfaces exposed by the vehicle, the vehicle network is vulnerable to network attacks, which leads to vehicle information security and driving safety issues. And current intrusion detection algorithms are difficult to achieve real time online intrusion detection in the in-vehicle network. This paper proposes an in-vehicle CAN network intrusion detection method based on message ID and message cycle. First, this method performs whitelist detection of

message ID, and then performs dual-threshold intrusion detection based on message cycle. The experimental results show that the detection accuracy of this method against in-vehicle CAN network message injection attacks is 94.13%, the precision rate is 92.46%, and the false alarm rate is 4.01%.

MonAIS-45 1084 Leader-follower Consensus-based Privacy-preserving Multi-objective Dispatching for Microgrid Xuelian Shao Suzhou Univ. of Science and Tech.Zhenping Chen Suzhou Univ. of Science and Tech.Baochuan Fu Suzhou Univ. of Science and Tech.Zhengtian Wu Suzhou Univ. of Science and Tech.Considering that the existing distributed multi-objective dispatching methods for microgrids will cause the privacy disclosure of the participant. Literatures on privacy preservation for the leader-follower consensus algorithm are lacking. With the introduction of a fast consensus algorithm, the privacy-preserved multi-objective dispatching problem for microgrids is considered in this paper. First, a multi-objective model is established, and a fast consensus-based leader-follower method is illustrated to achieve the dispatching. The privacy disclosure problems for nodes in the microgrid, especially for the leader nodes, are analyzed. Second, the exponential noises with encryption function and zero-sum are added to the consensus state. Some zero-sum random numbers are added to nodes’power, and then the global power difference is calculated to improve privacy. Third, the (ε, δp)-data privacy is introduced to measure the privacy-preserving degree of the initial value of nodes. The value of (ε, δp)-data privacy for each node is analyzed with the use of the probability statistic method. Last, for microgrids with a modified IEEE14 bus, some simulations are carried out to verify the effectiveness of the proposed method. Simulation results show that the proposed method can not only accelerate the convergence speed, but also ensure the participant’s privacy, the convergence, and the convergence optimality of the final state of microgrid.

MonAIS-46 1382 The Research on DGA domain detecting method based on XGBoost Algorithm Quanbo Pan Shandong Branch of National Computer Network

Emergency Response Technical Team/Coordination Center

Shengbao Li Shandong Branch of National Computer Network Emergency Response Technical Team/Coordinat

ion CenterRui Li Shandong Branch of National Computer Network

Emergency Response Technical Team/Coordination Center

To solve the problem that malicious programs such as Trojan and zombies often use the DGA (domain generation algorithm) domain name method for addressing communication, its dynamic change and random generation characteristics make it difficult to identify malicious domain names. This paper proposes XGB-DGA, a DGA malicious domain detection framework based on XGBoost (extreme gradient boosting). First, it quantifies and extracts domain name multi-dimensional character features (domain name length, information entropy, domain name suffix, n-gram distribution, etc.), and further uses normalization, symbol quantization and other processing methods to form a quantized training matrix that can be used for model training. And then, it finally trained to form a malicious domain detection model. Experiments show that the accuracy, precision and recall rate of the malicious domain detection method using the XGBoost algorithm reached 98.1%, 98.8%, and 98.9%, respectively, which are better than traditional SVM, decision tree and other methods, and are suitable for large-scale traffic on the live network Deployment in scenarios.

MonAIS-47 1670 Research on Privacy Data Protection in Mobile Applications Haihong Chen Chifeng Univ.Yazhen Gu Chifeng Univ.Peng Wang Chifeng Univ.Jie Dong Chifeng Univ.Yanyan Ren Chifeng Univ.The issue of user privacy data protection in mobile applications has become an increasingly challenging issue. Through the investigation of 20 apps, more than 100 settings related to privacy data were found, these settings exist not only in "privacy settings", but also in "message notification", "general" and "about". 80% of apps have personalized recommendation function, and the average path length reaches 5.38. 40% of apps explicitly give the option of closing location in the settings, and 68% of the participants can configure the device permissions correctly. According to the survey of the public's attention to the issue of personal privacy data in smart mobile applications, less than 16% of participants feel that it doesn't matter. This paper analyzes the problem that users can't find out how to protect their own data, and obtains six reasons. Finally, it gives the method of protecting personal privacy data.

MonAIS-48 462

Technical Programmes CCDC 2021 Implementation of a PMSM Sensorless Control Method based on PLL back EMF ZhenShan Li Beijing Research Inst. of Precise Mechatronics

and ControlsXiaoRui Zhao Beijing Research Inst. of Precise Mechatronics

and ControlsYuanYin Wang Beijing Research Inst. of Precise Mechatronics

and ControlsCaiRui Yue Beijing Research Inst. of Precise Mechatronics

and ControlsLiTing Jiang Beijing Research Inst. of Precise Mechatronics

and ControlsThe underwater shaftless propulsion motor is not convenient to install position sensor. In this paper, a rotor angle detection technology based on PLL estimator is proposed. Firstly, the sensorless control principle of PMSM is introduced, and the control strategies of PMSM at zero speed, low speed and high speed are expounded respectively. Then, the code level simulation of PLL estimator based on C-MEX S-function is carried out, especially for the angle and speed estimation accuracy when the motor parameters change. Finally, the simulation code is transplanted to the motor experimental platform for verification. The results show that the algorithm has good adaptability and strong robustness, and has high angle observation accuracy in the range of medium and high speed.

MonAIS-49 469 Path Planning based on Improved Artificial Potential Field Method Hao Zhang Univ. of JinanMeng Li Univ. of JinanZhangang Wu Univ. of JinanThe artificial potential field method is regarded as a widely used method in path planning. However, the application of artificial potential field method in path planning will lead to the problem of unreachable target, easy to fall into traps and low success rate. Aiming at these shortcomings, this paper improves the repulsion field function to solve the problem of unreachable target, and selects a virtual target point when it falls into a trap to make it escape the trap. The effectiveness of the method is verified by simulation.

MonAIS-50 509 Application of improved Dijkstra algorithm in intelligent ship path planning Zhenyu Zhu Dalian Maritime Univ.Lianbo Li Dalian Maritime Univ.Wenhao Wu Dalian Maritime Univ.Yang Jiao Dalian Maritime Univ.In order to ensure the safety of seafarer and comprehensively improve the intelligence of maritime field, the topic of unmanned ship has become more and more popular in recent years, and the ability of collision avoidance path planning of unmanned ship is one of its key technologies, which is an important embodiment of its intelligence. In this paper, aiming at solving the path planning problem of unmanned ship in known environment map, using mathematical modeling software simulation, some algorithm fields of unmanned ship path planning are reproduced, and the running results of related algorithms are compared. Dijkstra algorithm belongs to the single source shortest path algorithm. The original Dijkstra classical algorithm is used to realize the path planning. The experimental results show that there are many unnecessary inflection points after using the Dijkstra algorithm. The pheromone idea of ant colony algorithm is added to the classic Dijkstra algorithm criterion. The experimental results show that the optimized algorithm can greatly reduce the redundant points in the process of path planning and reduce the moving cost of unmanned ship routing.

MonAIS-51 536 Trajectory Tracking of a Quadrotor based on Gaussian Process Model Predictive Control Chuan Peng Wuhan Univ. of Science and Tech.Yanhua Yang Wuhan Univ. of Science and Tech.

Engineering Research Centre for Metallurgical Automation and Measurement Tech. of Ministry

of Education.Due to the nonlinearity and the susceptibility to wind disturbance in actual flight, it is difficult to obtain an accurate model of the quadrotor. In this paper, a trajectory tracking control method for quadrotor based on Gaussian Process (GP) Model Predictive Control (MPC) is proposed. First, a simplified dynamic model of quadrotor is established and the unmodeled dynamic system is learned by GP models. This data-driven modeling method not only makes the modeling more accurate, but also considers the model uncertainty with covariance. Then, according to the nominal and learned GP models, model predictive controllers are separately designed for the translation and rotation subsystem of the quadrotor to track the trajectory. Finally, simulation results demonstrate the effectiveness of the proposed approach.

MonAIS-52 675 A Fast Landing Controller Method for Full-wing Solar-powered UVA by Using Propeller Thrust Reversal

Zhenyun Ma Xi'an Modern Control Technology Research Inst.Gen Wang Xi'an Modern Control Technology Research Inst.Sheng Luo Xi'an Modern Control Technology Research Inst.Qiang Luo Xi'an Modern Control Technology Research Inst.Yangyang Zhao Xi'an Modern Control Technology Research Inst.To solve the problem of full-wing powered UAVS' long landing time and long landing distance, a fast landing control method using propeller thrust reverse is proposed. First, a longitudinal dynamic model of a solar-powered UVA with a full-wing layout is established. Then, based on the theory of active disturbance rejection control (ADRC), the pitch attitude control law is designed as inner loop control law. After that, using propeller thrust reverse to consume system energy, a speed control law which uses the pitch angle command as the control output is designed to have the rapid landing approach of the drone. Finally, a climb speed control law and rollout cotrol strategy are designed for the landing flare,touchdown and rollout process.The simulation results show that the control method can effectively shorten the landing time and distance of full-wing solar-powered UVA and has good wind resistance ability. Due to the low computational complexity of the controller and the use of measureable physical quantities, it provides a reference scheme for further engineering appliacation.

MonAIS-53 766 Multi-UAVs Cooperative Target Tracking Jianao Cao Northeast Univ.Zhicheng Fan Northeast Univ.Yuanwen Wang Northeast Univ.Songchen Sui Northeast Univ.Pei Liang Northeast Univ.Yulong Yuan Northeast Univ.Compared with single UAV, multiple UAVs collaborated in target tracking is much more robust and have better performance in tracking. The key of multiple UAVs collaborated in target tracking is ground target prediction and track planning. As for target prediction, this paper gives a method of rolling forecast based on gray Verhulst model, which could update the history data continuously and predict the value of the forecast gradually, make it to become more precise and cost less time. As for track planning, firstly, this paper let the predicted position as the next target position, then allocate the targets and generate the track. Hungary algorithm allocate the best position to every UAVs, CAPT (Concurrent Assignment and Planning of Trajectories) algorithm could give the safest trail to the target, ensuring the security and efficiency of reforming the UAVs formation. As for flight control, this paper used PX4, for it is reliable and classic. Besides, this paper also builds a simulation environment in Gazebo-ROS, simulating six drones simultaneously, which verified the reliability of the algorithms and could reform the formation of the UAVs with higher timeliness.

MonAIS-54 768 Observer-based Consensus Protocol of Vehicles Platooning with Time Delay Hao Guo Jiangnan Univ.Cheng-Lin Liu Jiangnan Univ.In this paper, we investigate the cooperative control problem of vehicle platooning with time delay and without knowing the velocity of the leading vehicle. To tackle this issue, an observer with time delay is presented to observe the velocity of the leading vehicle, and a distributed control algorithm based on the observed velocity is proposed. With the help of the frequency-domain method, the sufficient conditions for internal stability are obtained. In addition, the string stability is analyzed under the predecessor-leader following (PLF) topology. Numerical experiments are given to verify the effectiveness of the proposed observer and the control algorithm.

MonAIS-55 948 Second Order Fast Terminal Sliding Mode Control for Trajectory Tracking of QUAVs Wei Yang Tiangong Univ.Wuxi Shi Tiangong Univ.Baoquan Li Tiangong Univ.In this paper, a trajectory tracking control scheme is presented for a quadrotor unmanned aerial vehicle (QUAV) which is a class of under-actuated nonliner system. Within this scheme, the control system is employed which is divided into two parts: a fully actuated subsystem and an under-actuated subsystem. For the former, a fast terminal sliding mode controller is proposed, while a second-order fast terminal sliding mode adaptive controller is developed for the under-actuated subsystem. Which ensures that all the signals in the overall system are bounded, and the all state variables can be tracked to the reference trajectories. The simulation results demonstrate the effectiveness of the proposed approach.

MonAIS-56 1163 Telescopic Leg Control Based on the Super -Twisting Observer for Double-Frame Mobile Robot Xingkai Feng Nanjing Univ. of Aeronautics and AstronauticsCongqing Wang Nanjing Univ. of Aeronautics and Astronautics

Technical Programmes CCDC 2021 In this paper, a novel double-frame mobile robot with suction cups for aircraft skin inspection is studied. The double-frame mobile robot’s attitude can be adjusted on curved surface through the control of the robot’s outrigger cylinder. Initially, the dynamic model of the robot’s telescopic leg is established through Newton’s law of kinematics. The change of air pressure in cylinder under different working conditions is analyzed. Then, a super-twisting second-order sliding mode algorithm is modified in order to design a velocity observer for telescopic leg systems. The finite time convergence of the observer is proved. Thus, the observer can be designed independently of the controller. Finally, simulation results are given to verify the effectiveness of the proposed super-twisting second-order sliding mode algorithm.

MonAIS-57 1351 A Fast Finite Time Disturbance Observer Based Sliding Mode Tracking Control of Underactuate Surface Vessels Kanhong Ji Jiangsu Univ.Xin Yu Jiangsu Univ.This paper studies on the problem for the tracking control of underactuate surface vessels (USV). To suppress the effects of disturbance in USV containing wind, wave and current, a fast finite-time disturbance observer (FFTDO) based nonsingular terminal sliding mode controller (NTSMC) is proposed. Compare to existing finite time disturbance observer, the new observer makes its estimation errors converge to zero fastly even if its initial error is far away from the origin. By combining the NTSMC and FFTDO, a composite controller is designed to achieve high performance in trajectory tracking, which avoid large chatting. Finally, the simulations for comparison between the controller we proposed and the existing controller has been given to verify the effectiveness of proposed approach.

MonAIS-58 1370 Speed Tracking Control of Unmanned Patrol Vehicle based on Particle Swarm Optimization Jinlong Zheng Univ. of JinanMeng Li Univ. of JinanZeyun Yang Shandong Longyi Aviation Tech. Co.,Ltd.Unmanned patrol vehicle is a typical mobile robot, which is an important equipment to realize intelligent patrol in outdoor environment such as industrial and mining enterprises, judicial prisons, docks and warehouses. In order to solve the problem of long speed regulation time and poor stability of inspection unmanned vehicle in complex environment, this paper designs a fuzzy PID speed control method based on particle swarm optimization algorithm. On the basis of conventional fuzzy PID, combined with particle swarm optimization algorithm, the membership function of fuzzy controller is optimized to reduce the dependence of fuzzy control on expert experience. Simulation results show that this method can reduce the speed regulation time and improve the stability of speed control.

MonAIS-59 113 Control of ammonia injection in SCR denitration system based on ECDMC algorithm Haowei Chen Shanxi Univ.Xinchun Jia Shanxi Univ.Pengfei Hou Shanxi Univ.Xiaobo Chi Shanxi Univ.Xiaoming Sun Shanxi Univ.After the ultra-low emission retrofit of power plant units, due to frequent changes in boiler load, many power plants tend to use excessive ammonia injection to ensure that the outlet nitrogen oxides (NOX) concentration of the selective catalytic reduction (SCR) denitration system meets the standard. However, this can lead to hazards such as ammonia escaping and blocking downstream equipment. To solve this

problem, this paper proposes an energy conservation dynamic matrix control algorithm (ECDMC) control the SCR denitration system. The manipulated variable is selected as an energy conservation indicator and added to the objective function to control the ammonia injection in the SCR system of a coal-fired power plant. Simulation results show that compared with dynamic matrix control (DMC), the ECDMC designed in this paper has the advantages of small output fluctuation, stable control, little amount of ammonia injection under different operating conditions. The method proposed in this paper provides a reference for energy conservation and environmental protection of coal-fired power plants.

MonAIS-60 326 The Study of Data-driven Automatic Operation Experimental System for Hardware-in-the-loop Simulation of Aerocraft Control System Qian Xu Beijing Aerospace Automatic Control Insti.Gang Hu Beijing Aerospace Automatic Control Insti.Wei Zhang Beijing Aerospace Automatic Control Insti.Ya Ban Chongqing Academy of Metrology and Quality

InspectionKai Yang Chongqing Academy of Metrology and Quality

InspectionGuowei Zhang Chongqing Academy of Metrology and Quality

Inspection

Hongyun Sun Chongqing Academy of Metrology and Quality Inspection

Rui Kang Chongqing Academy of Metrology and Quality Inspection

Qian Wu Chongqing Academy of Metrology and Quality Inspection

The complexity of industrial process brings some problems such as modeling difficulty to traditional monitoring process methods. Aiming at the requirement of automatic operation and safety monitoring of hardware-in-the-loop simulation experiment of aircraft control system, a data-driven automatic operation experiment system is designed and implemented. The data-driven automatic operation experiment system includes temperature measuring safety monitoring module and whole process power supply program control module, using multivariate statistical monitoring method in order to realize the monitoring of process data with high adaptability. Real-time temperature information is collected and uploaded in the complexity of process. According to the experiment flow, each power supply and management computer is controlled to realize the remote automatic control of power supply. The experiment results show that the system can upload and collect temperature data quickly, automatically complete the experiment according to the experiment procedure, and has good real-time performance and high degree of automation.

MonAIS-61 731 Research on Loop Decoupling Control Based on Fuzzy RBF Neural Network Boqun Li Univ. of Science and Tech. LiaoningLin Wang Univ. of Science and Tech. LiaoningShengLin Zhang Univ. of Science and Tech. LiaoningJunjie Wang Univ. of Science and Tech. LiaoningIn hot strip rolling mill, looper system is multivariable, strongly coupled and highly disturbed. These characteristics make the design of multivariable loop system more complex. For this complex nonlinear system, the mathematical model of looper system is established. Due to the better convergence performance of fuzzy control and the self-learning and self-adaptive characteristics of neural network, the conventional PID control is combined with fuzzy control and neural network. A PID control method based on fuzzy radial basis function (RBF) neural network is proposed to decouple the looper system. Through MATLAB simulation, the results show that the decoupling control method has good decoupling effect, fast response speed, good anti-interference performance, and effectively improves the control accuracy of looper system.

MonAIS-62 875 Research on Zinc Layer Thickness Prediction Based on LSTM Neural Network Zhao Lu Wuhan Univ. of Science and Tech.Yimin Liu Wuhan Univ. of Science and Tech.Shi Zhong Wuhan Iron and Steel Co., Ltd. Equipment

Management DepartmentHot-dip galvanizing is a widely used steel anti-rust method. By immersing clean steel products in molten zinc solution, a uniform zinc layer is attached to the surface of steel products, which can slow down the corrosion of steel products to a certain extent. In the products produced by the hot-dip galvanizing process, the thickness, uniformity and firmness of the zinc layer are important technical performance indicators to measure product quality. In this paper, using the advantages of LSTM neural network in processing long sequence information, a zinc layer thickness prediction model based on LSTM neural network is proposed, and the zinc layer thickness prediction model is established based on the data of hot-dip galvanizing production line. The experimental results show that the mean square error of the test set prediction results is 2.790, the average absolute error is 1.359g/m2 and the average absolute percentage error is 1.824%, which achieves effective prediction of zinc layer thickness and proves the feasibility of applying LSTM neural network to zinc layer thickness prediction Sex. Compared with the multiple linear regression model, the MSE, MAE, and MAPE of the LSTM neural network model are better than the multiple linear regression model, and have higher prediction accuracy.

MonAIS-63 1171 Performance assessment and self- healing of PID controller based on model adaption with ZNN Yongliang Zhang Beijing Univ. of Chemical Tech.hong Zhao Beijing Univ. of Chemical Tech.Hao Zhang Beijing Univ. of Chemical Tech.

According to the problem that the performance of PID controllers decline in actual industrial control, a performance evaluation and self-healing method of PID controller based on the ISE-TSV indicators and model adaption with the new neural dynamics (ZNN) is proposed. First, the integral squared error (ISE) and total squared variation (TSV) index are applied to assess the performance of PID controller. Then, a self-healing method for PID controllers based on model adaption with ZNN is proposed to optimize PID controller parameters. Based on the proposed method, the controller performance evaluation and self-healing (CPES) software has been developed. The feasibility and effectiveness of the proposed method and CPES have been verified by simulation and real industrial application.

Technical Programmes CCDC 2021 MonAIS-64 693 Cascade PID Attitude Control Based on Adaptive Feedforward Compensation for Fixed-wing UAV Shou-lei Wang The 41st Inst. of CETCGuang-le Yao The 41st Inst. of CETCFang-zheng Chen The 41st Inst. of CETCThe paper proposed cascade PID attitude control based on adaptive feedforward compensation for fixed-wing UAV. First, take roll channel for example, the significance of adding feedforward compensation based on cascade PID was analyzed form transfer function and physical meaning. Then, using the speed feedback and basic idea of the fuzzy control, feedforward compensation coefficient can be adjusted in real-time online. Finally, the simulation results demonstrate the attitude control system of fixed-wing UAV can have better control performance and stronger robustness by adding adaptive feedforward compensation based on cascade PID.

MonAIS-65 923 Soft Sensing of Feed Pulp Concentration in Thickener Based on Cross Validation LSTM Hyper-parameters Optimization Lianyu Wang Northeastern Univ.Fuli Wang Northeastern Univ.Dakuo He Northeastern Univ.Kang Li Northeastern Univ.Yan Liu Northeastern Univ.In the hydrometallurgical thickening production process, the feed pulp concentration in thickener is difficult to accurately measure online, and thus soft sensor, by constructing an estimation model that takes auxiliary variables as input and dominant variables as output, has been proposed as an effective solution. Deep learning which has excellent learning ability has been introduced in soft sensor to deal with the complex nonlinearity of the process, yet lacking the ability for dynamics. In this study, aiming at the problems of high cost, complex process, and low accuracy of the feed pulp concentration in thickener measurement, a deep neural network structure based on cross validation Long Short-Term Memory (LSTM) hyper-parameters optimization was proposed as a soft measurement method with complex nonlinearity and dynamic characteristics. Then it is applied in a real case of the feed pulp concentration in thickener. The experimental result demonstrates that the model of LSTM network has better effectiveness.

MonAIS-66 1032 Long Short-Term Memory Network Based Tapping Temperature Prediction Model for Electric Arc Furnace Chuang Li Northeastern Univ.Zhizhong Mao Northeastern Univ.Ping Yuan Northeastern Univ.For accurately predicting the tapping temperature of molten steel in electric arc furnace (EAF), a novel prediction model based on the long short-term memory (LSTM) network is proposed in this paper. The smelting process of EAF is firstly analyzed. Then, the prediction model is established based on the energy balance of molten steel in the furnace. Part of the input features of the model are denoted in the form of time series in order to reflect the time-variation characteristic of smelting variables. This characteristic is a critical factor affecting the tapping temperature and is usually ignored in traditional intelligent model. Subsequently, the LSTM network is applied to describe the long time-dependent relationship between the tapping temperature and time-series features. Finally, the model is examined by practical data. The influence of the length of time-series features on the prediction result is also discussed. Comparative experiments with some other intelligent models demonstrate that the proposed model can improve the prediction accuracy, and is usually more reasonable.

MonAIS-67 1485 Control System of High Pressure Nitrogen Generator Based on Embedded Controller Jinche Liu Univ. of JinanZhonghua Wang Univ. of JinanMeng Li Univ. of JinanAccording to the characteristics of PSA nitrogen production technology and the shortcomings of the existing nitrogen generator, such as unable to operate under centralized control, low efficiency and high energy consumption. A high pressure nitrogen generator control system based embedded controller is proposed. The STM32F407ZGT6 microcontroller is selected as the control core, and carried out the software and hardware design. The purpose of reducing energy consumption and improving nitrogen quality is achieved by modifying the adsorption and regeneration time. The high/low pressure nitrogen pressure is monitored in real time through the sensor and the pressurization process is automatically carried out. Through the design of touch screen human-computer interface program, it can complete the functions of authority management, real-time data display, parameter modification and so on. At the same time, the system also has GPRS-based data upload and remote control functions. After testing, the system has simple operation, stable work, strong practicability, and meets the expected requirements.

MonAIS-68 1541 MEMS Gyro Signal Processing based on Improved-Sage-Husa Adaptive Filtering Method Zihan Wang Nanjing Univ. of Science and Tech.Liang Shan Nanjing Univ. of Science and Tech.Zhenxing Wu Nanjing Univ. of Science and Tech.Jianhu Yan Nanjing Univ. of Science and Tech.Jun Li Nanjing Univ. of Science and Tech.Aiming at the problem that the Micro Electro Mechanical Systems gyro (MEMS gyro) noise in the stable platform control system affects the stability and accuracy of the system, this paper proposes an improved-Sage-Husa adaptive gyro signal filtering algorithm. On the basis of the traditional Sage-Husa adaptive filtering method, the selection method of genetic factors and the suppression method of filtering divergence are improved. First, the ARMA model analysis method is used to construct the state space equation of the MEMS gyro. And then the filter is designed based on the gyro state equation. Finally, simulation experiments prove that the improved-Sage-Husa adaptive filter can obviously suppress the MEMS gyro noise and improve the control accuracy of the stable platform.

MonAIS-69 1680 Analysis of Secondary Cooling Solidification Process of Continuous Casting Slab Based on Finite Element Method Zhaofeng Wang Bohai Univ.Yuting Jiang Bohai Univ.The rationality of cooling process in continuous casting process is closely related to the quality of slab, which has important practical significance for the research and optimization of secondary cooling process in continuous casting process. Taking an alloy medium carbon steel as the research object, the transient heat transfer model of secondary cooling solidification of continuous casting slab is established by using the finite element software ANSYS, and the reliability of the model is verified by comparing with the measured values. On this basis, the temperature field and shell growth of continuous casting slab under different casting speeds and spray water volume are simulated and analyzed. The research results provide theoretical support and help for further improving the slab production quality and guiding the production process.

MonAIS-70 389 Application of Adaptive Fuzzy ADRC for Hypersonic Flight Vehicle Tieshan Feng China Academy of Launch VehicleZhiyao Zhang Beijing Aerospace Automatic Control Inst.Feng Gao China Academy of Launch VehicleBaoyu Li China Academy of Launch VehicleHypersonic aircraft has the characteristics of uncertain model parameters, uncertain external disturbances and complicated flight airspace during the reentry flight. Traditional design methods such as PID cannot satisfy reentry flight control. The active disturbance rejection control method can uniformly estimate the various uncertainties inside and outside the model as a total disturbance, and obtain a feedback linearization model through compensation. In addition, in view of the difficulty of parameter setting in the ADRC algorithm, this paper proposes a method based on fuzzy control. Through the formulation of fuzzy rules in fuzzy control, the height and speed of hypersonic aircraft are controlled. It can be seen from the simulation results that this method can better track the altitude command and the speed command, and the system will return to normal immediately after a brief over-tolerance occurs. The above simulation results show that the method meets the requirements of engineering use.

MonAIS-71 528 Station-keeping of the Autonomous Airship Using Adaptive Pursuit Guidance Law Jie Wang Beijing Inst. of Tech.Xiuyun Meng Beijing Inst. of Tech.Cuichun Li Aerospace Information Research Inst.Wenjie Qiu China Academy of Launch Vehicle Tech.The long-endurance station-keeping capability makes the autonomous airship a desirable platform to provide communication and surveillance services. An adaptive pursuit guidance law is proposed to realize the station-keeping of the airship under the influence of the unknown wind. The wind speed is estimated by an adaptive mechanism which is incorporated with the pursuit guidance law to guarantee the convergence to the mission site and realize station-keeping flight with the airspeed equal to the wind speed. The information required by the guidance law is easy to obtain. The results of the simulation show the effectiveness of the adaptive pursuit guidance law.

MonAIS-72 625 Trajectory Planning for the Powered Parafoil at Insufficient Height Erlin Zhu Jiangsu Univ. of Tech.Junjie Zhao Jiangsu Univ. of Tech.Bo Li Jiangsu Univ. of Tech.Haitao Gao Anhui Science and Tech. Univ.According to the flight characteristics of the powered parafoil, the thought

Technical Programmes CCDC 2021 of multi-phase design of the traditional parafoil system is used for the trajectory planning of the powered parafoil. On the basis of the three phases of the traditional parafoil system homing, the phase of task execution is added to the homing trajectory according to the working condition. The problem of trajectory planning is transformed to parameter optimizing on the basis of geometric relationship of each phase trajectory. The situation that the initial height is too low that the powered parafoil cannot fly to the target point is also considered. Applying the Quantum genetic algorithm (QGA) to calculate the objective function, the design parameters of the whole homing trajectory are obtained. The simulation results show that the multi-phase homing is simple and practical, and can meet the requirement of landing point precision.

MonAIS-73 818 Launch Vehicle Guidance Technology and its Development Trend Jianhai Zhang State Key Laboratory of Astronautic Dynamics

Xi’an Satellite Control CenterShufeng Jia State Key Laboratory of Astronautic Dynamics

Xi’an Satellite Control CenterTiening Nie State Key Laboratory of Astronautic Dynamics

Xi’an Satellite Control CenterLei Shi Xi’an Satellite Control CenterJunxiao Bao Xi’an Satellite Control CenterThis paper analyzed the domestic an overseas development of launch vehicle guidance technology, and described the principles of perturbation guidance, interactive guidance and other technologies that are widely used at present, the application conditions and engineering applications of different guidance methods are also described. Based on the theoretical and actual attitude information of a certain type of launch vehicle, the reliability, adaptability, precision and problems in application of interactive guidance are discussed. In the future, with the rapid development of low cost, reuse, artificial intelligence and the increase of requirements such as high reliability, fast response and high fault-tolerant, self-adapting guidance technology will be the development direction of smart launch vehicles.

MonAIS-74 1308 The positioning system use for autonomous navigation of unmanned aerial vehicles Qi Zhang Guilin Univ. of Electronic Tech.Yaoxing Wei Guilin Univ. of Electronic Tech.Xiao Li Guilin Univ. of Electronic Tech.Han Xu Guilin Univ. of Electronic Tech.The autonomous navigation problem for the unmanned aerial vehicle (UAV) is quite difficult in the indoor environment due to the weak GPS signal. Therefore, accurately obtaining the position of UAV in the indoor environment or GPS-denied environment has become the research hotspot in the field of autonomous intelligent UAV. In order to obtain the position of UAV in these environments, the indoor positioning system based on UWB for autonomous navigation of UAV are designed in this paper, which used Kalman filter algorithm to fuse inertial measurement unit (IMU) data. To verify the designed positioning system, the hardware of the positioning system and the software of the ground control station are designed. And the experiment results can demonstrate that the positioning system can realize the stable positioning of UAV and the static positioning error is less than 10 cm, which can be used in autonomous navigation system of UAV.

MonAIS-75 820 An S-type Ascent Trajectory Control Method Based on Scramjet Engine Working Boundary of RBCC Mingang Zhang Science and Tech. on Space Physics LaboratoryJianhui Liu Science and Tech. on Space Physics LaboratoryHui Liu Science and Tech. on Space Physics LaboratoryMing Liu Science and Tech. on Space Physics LaboratoryMing Yang Science and Tech. on Space Physics LaboratoryXing Gao Science and Tech. on Space Physics LaboratoryYinghui Gong Science and Tech. on Space Physics LaboratoryMao Tang Science and Tech. on Space Physics LaboratoryJiajia Hou Science and Tech. on Space Physics LaboratoryIn the development of the Rocket Based Combined-Cycle (RBCC), the scramjet engine is the key problem due to the narrow engine working boundary, which includes limited adjustment capability, and tightly coupled with the RBCC state. It is important to design a reasonable ascent trajectory that exploits the performance of its scramjet engine. Taking account of the working bounds of the RBCC vehicle, an analytical solution method for the ascent trajectory based on the velocity-dynamic pressure standard curve is proposed in this paper, and it uses a preset-height tracking approach to realize the S-type ascent trajectory. This method can be widely applied to design trajectory of RBCC under the performance constraints of scramjet engine, which has strong adaptability and high efficiency of trajectory optimization design.

MonAIS-76 567 A Fault Tolerant Method for Network Distributed Flight Control System based on Task Replication

Yuwei Cui Northwestern Polytechnical Univ.AVIC Xi’an Flight Automatic Control Research Inst.

Based on the application requirements of the vehicle management system in the aspects of functional integration and fault tolerance, a fault tolerant method for network distributed flight control system based on task replication is proposed in the paper. Focusing on flight control system of the typical tasks, the DAG figure task model is established, and the task scheduling method is carried out based on task duplication research and process description. Finally, the simulation of a typical flight control system is completed. From the simulation results, this method can realize the fault-tolerant scheduling of the system task set in the network computer node group to support the improvement of the integrated ability and fault-tolerant ability of the network distributed flight control system.

MonAIS-77 610 A Low-complexity Prescribed Performance Attitude Control of Post-capture Combined Spacecraft Based on Modified Rodrigues Parameters Hantong Mei Northwestern Polytechnical Univ.Hanqiao Huang Northwestern Polytechnical Univ.Wenya Xie Shanghai Electro-Mechanical Engineering Inst.Zihao Wei Beijing Univ. of Aeronautics and AstronauticsThis paper develops a novel low-complexity attitude control method for combined spacecraft with guaranteed prescribed performance based on MRPs (Modified Rodrigues Parameters) in the presence of external disturbance. In this paper, firstly, by utilizing prescribed performance and barrier functions, the attitude angle error converges to a preassigned set of arbitrary small residuals. Then, an effective and robust attitude control method is designed to deal with external disturbance and nonlinearity of the actuator, leading to the attitude tracking error transient and steady state performance can be preset while completely avoiding approximators and adaptive laws, which shows that a low-complexity control is achieved. Hence, accurate attitude stability and tracking are maintained. Moreover, based on a Lyapunov function, the proof of the convergence is completed. Finally, simulation results demonstrate the effectiveness of the proposed method.

MonAIS-78 1278 FTCESO-Based Prescribed Time Control for Satellite Formation Flying Siyuan Li Harbin Inst. of Tech.Zhaowei Sun Harbin Inst. of Tech.Dong Ye Harbin Inst. of Tech.In this paper, a prescribed time control method based on finite-time convergent extended state observer (FTCESO) for satellite formation flying with multiple disturbances and input constraints is investigated. First, the FTCESO is developed to estimate the external disturbance and system nonlinear terms with high precision. Then a backstepping-based prescribed time controller is designed to maintain the specific configuration of follower satellites and the prescribed time convergence is theoretically proved. Finally, numerical simulations are performed to demonstrate the effectiveness of the proposed method.

MonAIS-79 1575 Study on High-Speed-to-Hovering Back-Transition Control of Ducted Fan UAV Feng Chen South China Univ. of Tech.Hailong Pei South China Univ. of Tech.Zihuan Cheng South China Univ. of Tech.The ducted fan unmanned aerial vehicle (UAV) can transit from hovering to high-speed flight by continuously pitching down, reaching a high-speed and high-efficiency status. However, during the back-transition process from high speed to hovering, unstable environmental airflow and improper velocity planning could lead to serious oscillation, or even divergence of the aircraft states. In this paper, we address this control problem of a ducted fan UAV by utilizing an expected velocity planning method and a robust controller. The expected velocity curve is planned to ensure the dynamic effect of the deceleration process and decrease the computational complexity in solving the numerical optimization problem. The robust controller aims to reduce the tracking error of velocity and angular velocity considering modeling uncertainty and disturbance. Furthermore, the unit quaternion is selected to represent the attitude to avoid the singularity problem. Theoretical analysis verifies the stability of the proposed controller based on unit quaternion. Moreover, the simulation and experimental results collectively indicate the effectiveness of our methods.

MonAIS-80 1582 Integrated Attitude and Orbit Control of Spacecraft in Asteroid Soft Landing Zi-jun ZHOU Nanjing Uni. of Aeronautics and AstronauticsZhen HE Nanjing Uni. of Aeronautics and AstronauticsXiao-song YAO Innovation Academy for Microsatellites of CASThis paper mainly studies the integrated attitude and orbit control of spacecraft under external interference when landing asteroid. Firstly, assuming that only one orbital engine is equipped on the spacecraft,

Technical Programmes CCDC 2021 based on the relative orbital dynamic model expressed in the landing point coordinate system and relative attitude dynamics model expressed in the system of proprio-coordinate, an integrated attitude and orbit dynamic model is established. Secondly, based on the filtering Backstepping idea, the nonlinear part of the integrated attitude and orbit model is transformed, the integrated attitude-orbit control law with certain disturbance suppression ability is designed, and the stability analysis of the closed-loop system is given. Finally, the proposed control scheme is simulated by numerical simulation, and compared with the traditional separated attitude and orbit control, the effectiveness and superiority of the integrated attitude and orbit control are verified.

MonB01 Room01 Pattern Recognition and Intelligent Machines (III) 10:20-12:20 Chair: Zhan-Li Sun Anhui Univ.CO-Chair: Jianning Chi Northeastern Univ.

10:20-10:40 MonB01-1 613 A Machine Learning Based Automatic Tomato Classification System Xin Chen Anhui Univ.Zhan-Li Sun Anhui Univ.Xia Chen Anhui Univ.In this paper, a machine learning based automatic tomato classification system is proposed to estimate the ripeness of the tomatoes. In the proposed method, a preprocessing step is first devised to captured a tomato from a given image. Then, Gist feature extraction algorithm is constructed to acquire the multidimensional features of tomato. For each dimensional feature, the corresponding estimation stage is derived from the output of Squeezenet model by minimizing the loss function. Finally, the final stage is determined via the voting results. The effectiveness and feasibility of the proposed system are verified on the some images captured from different environments.

10:40-11:00 MonB01-2 316 Enhanced Class-weighted CNN features for CBIR Dongliang Ma Northeastern Univ.Chendong Wu Northeastern Univ.Jianning Chi Northeastern Univ.Xiaosheng Yu Northeastern Univ.Content-based Image Retrieval in realistic senarios aims to rank in large dynamic datasets, which has great significance in computer vision. Features extracted from convolutional neural network (CNN) are often affected by the background and noise in image when using class activation map method. In order to solve this problem, we propose enhanced class-weighted CNN features based on saliency map and classification results weights. Combing saliency map with largest component area algorithm, ROI of an image can be cropped, which would make the classification more accuracy. We evaluated our proposed method on Oxford5k and Paris6k dataset, and get 0.809 and 0.861 mAP respectively. Experimental results show that our algorithm can effectively improve the mAP of image retrieval.

11:00-11:20 MonB01-3 339 The Chain of Self-Taught Knowledge Distillation Combining Output and Features Yunnan Wang Shanghai Jiao Tong Univ.Hao Chen Beijing Inst. of Tracking and Telecommunication

Tech.Jianxun Li Shanghai Jiao Tong Univ.Knowledge distillation is a model compression technique that uses a cumbersome model to improve the accuracy of a lightweight model. However, if there is no cumbersome teacher model that performs much better than the lightweight student model, the effect of knowledge distillation is restricted. To tackle this dilemma, this paper propose a novel selftaught knowledge distillation framework in which teacher and student models have the same network structure. To extract the knowledge of the model itself, output and feature transfer are studied to align the behavior between the pre-trained teacher and the student trained from scratch. Furthermore, the distilled student model is used as a teacher model to guide the training of a new student model, and this process is repeated as a chain of knowledge transfer to allow the model achieve greater accuracy. The effectiveness of our framework has been demonstrated by extensive experiments on CIFAR100 and STL-10 datasets. Code will be available here: https://github.com/WangYunnan/chain-of-self-KD.

11:20-11:40 MonB01-4 356 Classification of Low-Grade and High-Grade Glioma Using Deep CNN Features with Fine-Tuning Fuquan Sun Northeastern Univ.Zhiqing Cui Northeastern Univ.Peng Zou Northeastern Univ.Chenglong Cong Northeastern Univ.Kun Zhang Northeastern Univ.Glioma is the most common intracranial malignant tumor. The World Health Organization (WHO) classified glioma into low-grade glioma (LGG) and high-grade glioma (HGG) according to their degree of malignancy. Therefore, accurate preoperative classification is crucial for

treatment planning and prognosis prediction. In this paper, we adopted an enhanced deep learning method for LGG and HGG classification based on the BraTs MRI dataset using the pre-trained Inception-V3 network. Our method mainly included two parts: data preprocessing unit and fine-tuning. The preprocessing unit used data augmentation technology to effectively solve the problem of limited medical images and imbalanced categories. We also explored a variety of fine-tuning methods, among which global fine-tuning achieved the highest classification accuracy: 97.4%. We used accuracy, precision, recall, specificity, and F1-score to fully evaluate the model. The research results show that when the availability of medical images are limited, using data augmentation and fine-tuning technology can effectively improve the classification accuracy.

11:40-12:00 MonB01-5 358 WR-IMT: A time series predictive model for Remaining Useful Life Prediction Haibo Xing Shanghai Jiao tong Univ.Fei Xiao Shanghai Jiao tong Univ.Jianxun Li Shanghai Jiao tong Univ.Deep learning has achieved great success in the remaining useful life (RUL) prediction of machines. However, when it comes to the complex time-dependence data in multi-sensor system, most deep learning approaches such as LSTM (Long Short-Term Memory) and TCN (Temporal Convolutional Network) cannot effectively extract the longterm patterns hidden in time series. In this paper, An end-to-end model named WR-IMT (Weight-restricted Improved MGU-TCN) for long-term RUL prediction is put forward. Firstly, an Improved MGU with bidirectional asymmetric residual structure is designed at first to extract the fine-grained temporal patterns. Then, we proposed the Sequence Segmentation and Occlusion Technology (SSOT) by which the subsequences can be input to our Improved MGU with shared weights. TCN is used for further extraction of coarse-grained patterns. To reinforce the network sensitivity to nonterminal time steps, the Coefficient-restricted Regularization is added to the loss function. Experimental results on the NASA-developed Commercial Modular Aero-Propulsion System Simulation(C-MAPSS) dataset confirm the superiority and effectiveness of our model.

12:00-12:20 MonB01-6 898 A Learning-Based Surface Defect Detection Method for Internal Surfaces of Bellows Zizhuo Zhang Univ. of Science and Tech. of ChinaJun Xu Univ. of Science and Tech. of ChinaSong Wang Univ. of Science and Tech. of ChinaFeng Li Univ. of Science and Tech. of ChinaQiang Ling Univ. of Science and Tech. of ChinaRecently, vision-based defect detection methods have been proved effective and found many applications. In this paper, we investigate the detection of defects of internal surfaces of bellows. However, the existing defect detection methods cannot be simply implemented to accomplish this defect detection task, since the defects here scatter in the bellows and may have quite different appearance. To this end, we propose a novel defect detection method with two fundamental vision branches which target at classification and object detection, respectively, and can greatly boost the defect detection performance. The classifier aims to find the defects with obvious appearance while the detector is designed to find small defects that are more difficult to recognize. To improve the accuracy of the detection branch, classification feature maps are integrated into the detection framework to enrich the target information. Our method was verified through some bellow images and achieved an accuracy of more than 75%.

MonB02 Room02 Production Planning and Scheduling 10:20-12:20 Chair: Deming Lei Wuhan Univ. of Tech.CO-Chair: Biao Zhang Liaocheng Univ.

10:20-10:40 MonB02-1 261 A Data-Driven MPC-Based Energy Optimization and Management Framework of an Energy Building Xuan He Huazhong Univ. of Science and Tech.Shichang Cui Huazhong Univ. of Science and Tech.Yan-Wu Wang Huazhong Univ. of Science and Tech.Jiang-Wen Xiao Huazhong Univ. of Science and Tech.A data-driven MPC based energy optimization and management framework is proposed for an energy building. Specifically, the building energy management faces the challenges of multi-time-scale uncertainties such as operations of the energy equipment such as the heating, ventilation, and air conditioning unit and energy storage system. Based on the multi-time-scale energy system modeling, we formulate the two-stage MPC stochastic optimization problem including the large-time-scale scheme and real-time dispatch. The two-stage stochastic problem is then reformulated as a distributionally robust optimization problem, which is less conservative than the conventional robust optimization and doesn’t require accurate uncertain distribution in stochastic programming. A delayed constraint generation algorithm is presented to solve the problem. A real-time dispatch optimization is further formulated for real-time energy management. The simulation

Technical Programmes CCDC 2021 results show the advantages of the proposed energy optimization and management framework in handling the multi-time-scale uncertainties and improving energy efficiency.

10:40-11:00 MonB02-2 364 An imperialist competitive algorithm for energy-efficient flexible job shop scheduling Jiong Guo Wuhan Univ. of Tech.Deming Lei Wuhan Univ. of Tech.In this study, energy-efficient flexible job shop scheduling problem (EFJSP) with sequence-dependent setup times (SDST) is considered, in which total tardiness and makespan are given higher importance than total energy consumption. A two-phase imperialist competitive algorithm (TPICA) is proposed. The importance difference among objectives are implemented by treating all objectives equally in the first phase and making energy consumption not to exceed a diminishing threshold in the second phase. A dynamical differentiating assimilation and a novel imperialist competition with the enforced search are implemented. Extensive experiments are conducted and the computational results show that TPICA is very competitive for EFJSP with SDST.

11:00-11:20 MonB02-3 466 An improved artificial bee colony for distributed assembly flow shop scheduling Zhongyan Zhang Wuhan Univ. of Tech.Deming Lei Wuhan Univ. of Tech.Distributed assembly flow shop scheduling problem (DAFSP) with m dedicated parallel machine and a single assembly machine in each factory is considered. An improved artificial bee colony (IABC) is proposed to minimize makespan. Bi-directional communication is performed between employed bees and onlooker bees based on search efficiency and the differentiated search processes are adopted in these two groups of bees in IABC. Food source of onlooker bee is also updated in a dynamic way. Many experiments are conducted and IABC is compared with other methods from the literature. The computational results show that IABC has promising advantages on solving the considered problem.

11:20-11:40 MonB02-4 655 A Multi-population Adaptive Genetic Algorithm for Test Paper Generation Tangjie Wu Univ. of Science and Tech. of ChinaLei Wang Univ. of Science and Tech. of ChinaHaitao Huang Feng Huo Tai Tech. Ltd.Zefeng Lai Univ. of Science and Tech. of ChinaQiang Ling Univ. of Science and Tech. of ChinaRecently, online examination has well developped. As an essential task of online examination, test paper generation plays a critical role. Genetic algorithm has been used in test paper generation systems. However, traditional genetic algorithm is easy to be stuck at local optimal solutions and can not adapt well to the student’s mastery of knowledge concepts. In this paper, we propose a novel Multi-population Adaptive Genetic Algorithm (MAGA) for test paper generation. Our algorithm introduces the Longest Common Subsequence (LCS) and Hamming distance to judge genetic similarity and comprehensively considers the influence of evolutionary generation, fitness and genetic similarity on parameters of the proposed algorithm. MAGA constructs the initial population which is then fed into the crossover and mutation operators to be optimized and produce the optimal solution. We conducted experiments for the sequential test paper generation on the datasets. Experimental results demonstrate that our algorithm achieves significant improvement over traditional genetic algorithms with faster convergence speed and higher fitness.

11:40-12:00 MonB02-5 1259 A multi-objective evolutionary algorithm for the hybrid flowshop rescheduling problem with lot streaming Biao Zhang Liaocheng Univ.Leilei Meng Liaocheng Univ.Xinli Zhang Liaocheng Univ.With random disturbance events, the actual workshop environment is often dynamic, which makes the original schedule worse or even unfeasible. In this paper, considering the random disturbance of machine breakdown, a multi-objective hybrid flowshop rescheduling problem with lot streaming (MOHFRP_LS) is described. Firstly, a multi-objective optimization model is established with the total flow time and total starting time deviations of sublots. Secondly, based on the the technique for order preference by similarity to an ideal solution (TOPSIS) is introduced to evaluate the solution fitness, a multi-objective migrating birds optimization algorithm (MMBO) is proposed. In the algorithm, a dynamic decoding strategy by considering machine breakdown is proposed, and an improved mechanism is proposed to further improve the solution quality. Population initialization based on Glover operation is carried out to utilize the information contained in the original schedule. In the competition mechanism, population rearrangement technology and fast non-dominant sequencing technology are introduced to adjust the V-shaped population structure, and population reproduction strategy was

used to carry out secondary updating to improve the population quality. In the scout mechanism, a local search approach is proposed based on Pareto property to improve the quality of alternative solutions. Finally, the effectiveness of MMBO is verified by experimental comparison and analysis.

12:00-12:20 MonB02-6 1527 Hybrid flow shop scheduling of new arrival jobs and locked jobs Qing Wang Northeastern Univ.Ying Chen Northeastern Univ.Min Huang Northeastern Univ.Considering an initial set of jobs locked by previous scheduling in a hybrid flow shop environment, as production progresses, another set of jobs that dynamically arrive since then has to be arranged and released for production. In this paper, a rescheduling model is proposed to investigate the problem of hybrid flow shop scheduling with the new arrival jobs. The optimization objective aims to minimize the maximum completion time of the rescheduling. An adaptive genetic algorithm is designed to solve the model. Simulation experimental results testify that the designed model and algorithm are effective and efficient.

MonB03 Room03 Motion Control (II) 10:20-12:20 Chair: Shaobo Lu Chongqing Univ.CO-Chair: Guangrong Chen Beijing Jiaotong Univ.

10:20-12:40 MonB03-01 453 A Large-stroke Motion Controller for Speed-constrained Servo Systems Tong Chen Fuzhou Univ.Wentao Yu Fuzhou Univ.Guoyang Cheng Fuzhou Univ.A modified design of expanded proximate time-optimal servo (PTOS) controller is proposed for typical servo systems characterized by an integrator cascaded with a damping block. The controller resorts to the time-optimal control for initial acceleration, and then switches into a linear control law to achieve a smooth settling. In the presence of speed constraint, a speed regulation stage is inserted in the tracking process. Thus the controller can accomplish large-stroke motion in speed-constrained servo systems. To tackle the unmeasured-speed and unknown disturbance, an extended state observer (ESO) can be included in the controller. The controller is applied to a permanent magnet DC servo system for position control. MATLAB simulation and experimental verification using a TMS320F28335 board are conducted. The results confirm that the proposed controller can track a wide range of target references with desirable performance under speed constraint and load disturbance.

10:40-11:00 MonB03-02 733 Robust adaptive control of UAV Guobing Zhang North Univ. of ChinaTong Guan North Univ. of ChinaPengyun Chen North Univ. of ChinaAiming at the problems of UAV dynamic modeling error and flight interference, robust control algorithm is introduced in this paper. Based on the establishment of UAV motion equation, the state equation of robust control is obtained by linearization, then the weight function is selected and the controller is designed. MATLAB simulation results show that Compared with PI control, robust longitudinal control of UAV has the characteristics of fast response, small overshoot, short adjustment time and insensitive to modeling error, which shows good control performance.

11:00-11:20 MonB03-03 577 A Novel Fractional Order Impedance Control and Its Performance Analysis Guangrong Chen Beijing Jiaotong Univ.Huafeng Lu Beijing Jiaotong Univ.Bowen Hou Beijing Jiaotong Univ.Sheng Guo Beijing Jiaotong Univ.Junzheng Wang Beijing Inst. of Tech.In traditional impedance control model, the contact force can be reduced effectively. However, there exists a tracking error at the stable state due to the existence of stiffness, which is not conducive to tackle tasks based on high performance position control for robots. Therefore, this paper proposes a novel dynamic interaction model: fractional order impedance control, to address this issue. Firstly, an integral item is added into the traditional impedance model to eliminate the tracking error. Secondly, the idea of fractional order is introduced to make the orders of inertia, damping, and stiffness change from integers to fractions to achieve more significant compliant performance. Finally, simulation results validate the advantages of proposed fractional order impedance control and it can be also employed to absorb/increase, hold/keep, and dissipate/decrease system energy to achieve jumping, bouncing and friendly contact, respectively. Besides, stability analysis and three criterions of choosing and tuning 6 classic parameters in the proposed fractional order impedance control are both given out.

Technical Programmes CCDC 2021 11:20-11:40 MonB03-04 1344 Adaptive Coordinated Stability Control of Vehicle Considering Stability Margin Wenjuan Wu Chongqing Univ.Shaobo Lu Chongqing Univ.Caixia Li Chongqing Univ.Aiming at the stability control problem of distributed drive electric vehicles under extreme condition, the divisional adaptive coordination stability control method based on stability margin is proposed. The divisional adaptive coordination is applied for the active front steering (AFS) and direct yaw moment control (DYC) to further improve vehicle lateral stability. In order to achieve adaptive adjustment of the weights between the AFS and DYC, a stability margin is defined based on the phase plane of the sideslip angle and yaw rate (β-ω). And the MPC-based vehicle stability controller is designed to coordinate the AFS and DYC control weight according to the stability margin, and change the wheel longitudinal forces and front wheel steering angle. The co-simulation of CarSim and Simulink shows that the proposed method can effectively ensure the stability of the vehicle under extreme condition, and can reduce the interference to the vehicle speed by 3.32% based on the initial velocity of the vehicle compared with the fixed coordinated weight control.

11:40-12:00 MonB03-05 1499 Research on modeling and motion control of pendulous spherical robot Rui Lin Southwest Univ. of Science and Tech.Manlu Liu Southwest Univ. of Science and Tech.Jianwen Huo Southwest Univ. of Science and Tech.Hua Zhang Southwest Univ. of Science and Tech.Maotao Yang Southwest Univ. of Science and Tech.Mingming Guo Southwest Univ. of Science and Tech.Aiming at the motion control problem of a disturbed spherical mobile robot, a linear and steering motion control method based on nonlinear active disturbance rejection controller(ADRC) technology is proposed. This method uses Euler-Lagrange to study the dynamic characteristics of the spherical shell, frame and pendulum of XK-I spherical robot, and establishes the coupling multi-body dynamic model of the robot. Based on the actual motion control of XK-I spherical robot, the control equations were decoupled and the linear and steering motion controllers were designed in combination with the nonlinear ADRC technology. A comparative test study of PID, variable parameters PID (VAPID) and ADRC was conducted. Simulation results show that the XK-I spherical r obot ADRC controller has better anti interference performance.

12:00-12:20 MonB03-06 732 A terrain matching navigation algorithm for UAV Tong Guan North Univ. of ChinaGuobing Zhang North Univ. of ChinaPengyun Chen North Univ. of ChinaTerrain matching positioning navigation uses the traditional algorithm to linearize the terrain and compares with the datum terrain. It can provide more accurate positioning, but due to the strong nonlinear characteristics of terrain, in the process of terrain linearization, most of the terrain features are ignored. So the matching result error is relatively large. Because the pulse coupled neural network has the characteristics of neuron's characteristic of linear addition and nonlinear multiplication adjustment coupling, it does not need to establish an accurate mathematical model, and it can better deal with nonlinear problems. Therefore, this paper will explore the application of pulse coupled neural network in terrain matching positioning. Firstly, the distance difference matrix between the prior terrain data and the measured matching terrain is normalized, then the nodes on each matching surface of the topographic map will be searched and summed one by one through the coupling relationship between the similar nodes, and the magnitude of ignition times represents the similarity degree. By selecting different regions and different parameters in the topographic map for simulation, the high-precision performance of the algorithm is verified.

MonB04 Room04 Automatic Control of Unmanned Systems (II) 10:20-12:20 Chair: Jang Lou Xi'an Modern Control Tech. Research Insti.CO-Chair: Yaoyao Tan Chongqing Univ.

10:20-10:40 MonB04-01 236 Fault-Tolerant Trajectory Tracking Control of a Quadrotor Suffering a Complete Rotor Failure Pan Tang Beijing Inst. of Tech.Fubiao Zhang Beijing Inst. of Tech.Tianze Zhou Beijing Inst. of Tech.Defu Lin Beijing Inst. of Tech.Yingdong Hu China Aero-Polytechnology EstablishmentThe problem of fault-tolerant position tracking control is proposed for a quadrotor under a complete rotor failure in this paper, which sacrifices the yaw control due to the underactuation. The hierarchical control scheme consists of a model predictive control (MPC) method to track the reference trajectory by taking the actuator saturation into account, together with an incremental nonlinear dynamic inversion (INDI) based

inner controller to stabilize the reduced attitude of the quadrotor and eliminate the effect of external disturbance. A detailed aerodynamic model may be obtained from sensor measurements, which also reduces the model dependency of the INDI approach. To demonstrate the efficiency of the proposed method, numerical simulations are carried out for a quadrotor under external disturbance, model uncertainty and a complete rotor failure.

10:40-11:00 MonB04-02 393 Application of linear active disturbance rejection decoupling control for AUV three-dimensional trajectory tracking control Wei Zhang Harbin Engineering Univ.Wenhua Wu Harbin Engineering Univ.Peng Gong Harbin Engineering Univ.Qiang Wang Harbin Engineering Univ.Saibo Gao Harbin Engineering Univ.To investigate the three-dimensional (3D) trajectory tracking control problem for autonomous underwater vehicles (AUV), a linear active disturbance rejection decoupling control approach is proposed in this paper. Firstly, the AUV five-degree-of-freedom model is given, and the model is decoupled by the introduced active disturbance rejection decoupling technology. Secondly, An AUV trajectory tracking controller is designed, based on linear active disturbance rejection control method. The controller uses tracking differentiator to obtain the differential signal of the reference trajectory, and utilizes linear extended state observer to estimate the total disturbance of the AUV system. Then, in order to reduce the controller parameters, a linear states error feedback control law is designed to compensate for the total disturbance of the system. Finally, simulations verify the effectiveness of the designed algorithm. The simulation results show that the proposed control strategy can make the AUV track the reference trajectory quickly and stably.

11:00-11:20 MonB04-03 423 Active disturbance rejection control with FTCESO applied to tiltrotor transition control Jiang Lou Xi'an Modern Control Tech. Research Inst.Peng Wang Xi'an Modern Control Tech. Research Inst.Xiaobo Gao Xi'an Modern Control Tech. Research Inst.Jing Yang Xi'an Modern Control Tech. Research Inst.In this paper, the active-disturbance rejection control (ADRC) theory is designed for tiltrotor control. The controller including the quadrotor mode, the fixed-wing mode controller and the transition mode control distributor. According to the separation principle, a tracking differentiator, extended state observer, nonlinear state error feedback, and disturbance compensation are designed for each active disturbance rejection controller, and control parameters are given. Nonlinear simulation shows that the ADRC flight control law has good control performance compare with PID controller.

11:20-11:40 MonB04-04 426 Trajectory Tracking Control of Differential Steering Mobile Robot Based on Fuzzy Logic under Time Constraints Yaoyao Tan Chongqing Univ.Baoyu Wen Chongqing Univ.Chunting Jiao Chongqing Univ.Xiaojie Su Chongqing Univ.Fangzheng Xue Chongqing Univ.The differential steering mobile robot realizes steering by controlling the rotation speed of the driving wheels on the left and right sides, that is, enabling the robot to achieve a given speed and angular velocity to complete the trajectory tracking purpose. The goal of this paper is to plan a path in an open environment with known path points, and to pass the trajectory points in an orderly manner within a limited time to achieve the purpose of path tracking. Therefore, this paper proposes a planning combination method, that is, path planning is performed through bezier curves, and local point-to-point trajectory planning is performed through polynomial interpolation, and then fuzzy logic control is used to obtain dynamic speed compensation, hence the trajectory tracking of the entire path is realized in a limited time. Finally, verify the control algorithm and the effectiveness of the controller through carsim simulation platform.

11:40-12:00 MonB04-05 438 Performance Analysis and Improved Algorithm of Gaussian Optimal Filtering for Underwater Target Tracking Chengzhen Zhang Dalian Univ.Yuanming Ding Dalian Univ.Yang Yang Dalian Univ.In order to accurately predict the state of underwater dynamic targets, the performance analysis of Gaussian optimal filtering and its improved algorithm are proposed in this paper. The state estimation performance of Gaussian Hermite Kalman filter (GHKF) and Gauss Hermite Rauch-Tung-Striebel smoother (GHRTS) is analyzed in detail, and the idea combined with particle filter algorithm-Gaussian Hermite Kalman particle filter (GHKF-PF) and Gauss Hermite Rauch-Tung-Striebel smoother particle filter (GHRTS-PF) is proposed. This analysis is done through the variance of Gaussian noise and the change of Gaussian mixture noise. This performance-based research is carried out under the

Technical Programmes CCDC 2021 background of bearing-only tracking (BOT) phenomenon. All the experiments are to find out the RMSE between the real state and the predicted state of the object. The numerical results based on independent Monte Carlo simulation show that under the condition of white Gaussian noise, GHRTS has better performance than GHKF, GHKF-PF is better than GHKF, GHRTS-PF and GHRTS, and GHRTS-PF is better than GHKF-PF. However, under Gaussian mixture noise, the performance of GHRTS is lower than that of GHKF, and the performance of GHKF-PF is better than that of GHKF, GHRTS-PF, which is better than that of GHRTS,GHRTS-PF and lower than that of GHKF-PF.

12:00-12:20 MonB04-06 448 The Path Planning Study of Multi-task Logistics UAVs Under Complex Low Airspace Chentong Xiang Beihang Univ.

Beijing Laboratory for General Aviation Tech.Peng Hao Xihua Univ.Xuejun Zhang Beihang Univ.

Beijing Laboratory for General Aviation Tech.The logistics unmanned aerial vehicles (UAVs) carry out goods to each mission point and return to the starting point, which is a typical TSP problem. However, in real life, logistics UAVs need to complete the TSP problem under the constraints of airspace obstacles. Aiming at this problem, a UAVs path planning algorithm based on the combination of ant colony algorithm and unnecessary point deletion strategy is proposed. First, use ant colony algorithm to complete the path planning without obstacles constraints. Secondly, considering obstacles constraints, obtain the corresponding turning points in the obstacle area, delete unnecessary points and update the turning points and distance. Finally, based on local updates, iterate to obtain global solution. The simulation results show that this method can realize the TSP path planning problem under the condition of multiple obstacles. The planned path is shorter and the convergence speed is faster. In this paper, the TSP problem under obstacle constraints is divided into traditional TSP problems and obstacle avoidance problems, and respectively solved by ant colony algorithm and unnecessary point deletion strategy. In addition, this paper deletes unnecessary points to make the algorithm obtain a shorter path under the premise of ensuring the safety of the path, which overcomes the situation that the ant colony algorithm is easy to fall into the local optimal solution.

MonB05 Room05 Space Vehicle Control (III) 10:20-12:20 Chair: Shilei Cao Harbin Inst. of Tech.CO-Chair: Qi Li Xi’an Modern Control Tech. Research Inst.

10:20-10:40 MonB05-1 773 Robust Control for Unmanned Aerial Vehicles Formation Based on Adaptive Disturbance Observer Bing-qian LI Air Force Engineering Univ.Mu-zhou ZONG Air Force Engineering Univ.Hua CHE Air Force Engineering Univ.Zhi-long XIONG Air Force Engineering Univ.A robust formation control based on adaptive disturbance observer is designed for the parameter perturbation and uncertainty in fixed-wing unmanned aerial vehicle (UAV) formation. To this end, the leader-follower formation model and the UAV model are described. Then, the inner loop robust control is designed to track the command signal produced by the out loop formation controller. In the robust control, the parameter adaptive laws are introduced to estimate the parameter perturbation, the adaptive disturbance observer is designed to compensate uncertainty and the stability analysis is given by the Lyapunov function. Finally, simulation results demonstrate the effectiveness and potential for this robust formation control.

10:40-11:00 MonB05-2 334 Robust Partial-state Feedback Attitude Control for Tracking Tumbling Target Qi Li Xi’an Modern Control Tech. Research Inst.Jinyuan Wei Northwestern Polytechnical Univ.Xueping Wang Xi’an Modern Control Tech. Research Inst.Qiuxiong Gou Xi’an Modern Control Tech. Research Inst.Zhiqi Niu Xi’an Modern Control Tech. Research Inst.This paper investigates the partial-state feedback attitude control problem of tracking an uncontrolled tumbling target in the presence of external disturbances and control input saturation. Firstly, a nonlinear relative attitude motion model is presented, and a dead-zone operator based model is investigated to describe the input saturation phenomenon. Subsequently, a novel model-assisted extended state observer is established to estimate the unmeasured states and the lumped uncertainties. Then, by combining the extended state observer technique with sliding mode methodology, an output-feedback anti-saturation control strategy that removes the need for full-state measurements between the two spacecrafts is proposed. Within the Lyapunov framework, the proposed control strategy is able to guarantee that the relative attitude errors can converge to small regions containing the origin. Finally, numerical simulations are performed to demonstrate the effectiveness of the presented control strategy.

11:00-11:20 MonB05-3 437 Attitude and Angle Rate Determination of Gyroless Spacecraft Based on SVD Kalman Filter Only Using Star Sensor Gaoxiang Ouyang The Inst. of Microelectronics of the Chinese

Academy of SciencesWenliang Lin Capital Aerospace Machinery Co., Ltd.Pingke Deng The Inst. of Microelectronics of the Chinese

Academy of SciencesGuocan Zhao The Inst. of Microelectronics of the Chinese

Academy of SciencesDue to gyro failure, spacecraft attitude control systems (ACS) may face drastic challenges in mission accomplishment. This paper addresses a singular value decomposition (SVD) based extended Kalman filter (EKF) technique for gyroless spacecraft to estimate both the angular rate and attitude vectors of spacecraft only using star sensor measurements. In this work, it is assumed that the angular momentum and control torque of the reaction wheels are measurable and usable for the filter formulation. Because there is no angular rate sensor, a dynamic model of a spacecraft equipped with reaction wheels is required to estimate the angular rate of the spacecraft. For actual implementation, a new SVD based Kalman filter implementation is used in the applications where the accuracy of numerical solution of the associated Riccati equation might be crucially reduced by influence of round off errors. Finally, effectiveness of the SVD-based EKF algorithm proposed in this paper is demonstrated using numerical simulations.

11:20-11:40 MonB05-4 471 An Adaptive Fault Compensation Scheme for Formation Control of Spacecraft Formation Flying Zongxing Zhang Nanjing Univ. of Aeronautics and Astronautics

Jiangsu Key Laboratory of Internet of Things and Control Tech.

Yajie Ma Nanjing Univ. of Aeronautics and AstronauticsJiangsu Key Laboratory of Internet of Things

and Control Tech.Bin Jiang Nanjing Univ. of Aeronautics and Astronautics

Jiangsu Key Laboratory of Internet of Things and Control Tech.

Yuxi Liu Nanjing Univ. of Aeronautics and AstronauticsJiangsu Key Laboratory of Internet of Things

and Control Tech.The relative position model is established to meet the requirement of the leader-follower spacecraft formation. In this model, we consider the follower spacecraft actuator fault which the fault model is combined with loss of effectiveness and constant deviation fault. The adaptive controller is designed to solve the uncertainty caused by the actuator fault. Based on the Lyapunov theory, it is proved that the relative position error and velocity error can converged to zero under the controller. Simulation result proves the effectiveness of this method.

11:40-12:00 MonB05-5 488 Three-Dimensional Interception Guidance Law for Cooperative Aircraft in Finite Time Based on Non-Singular Sliding Mode Control Haoxiang Chen China Aviation Radio Electronics Research Inst.

Nanjing Univ. of Aeronautics and AstronauticsYing Nan Nanjing Univ. of Aeronautics and AstronauticsThis paper proposed a 3D cooperative guidance law for aircrafts in formation based on finite-time convergence and non-singular sliding mode control method. Referring to the distributed "Lead-Follower" structure and set relative adjustment time-to-intercept as distributed signal of communication in formation, the designed guidance law could converge in a limited time and dealing with the situation, which the angles of sight between intercepting aircrafts and target change rapidly and turn to singular. Numerical simulation result proves that the cooperative guidance law designed in this paper is effective and reliable.

12:00-12:20 MonB05-6 590 Boundary control for attitude tracking and vibration suppression of large space truss structure Shilei Cao Harbin Inst. of Tech.Mingying Huo Harbin Inst. of Tech.Naiming Qi Harbin Inst. of Tech.Ce Zhao Harbin Inst. of Tech.This paper designed a boundary control scheme for attitude tracking and vibration suppression of large space truss structure under external disturbance. Continuum modeling method is employed so that the system model can be simplified, which consists of a partial differential equation and an ordinary differential equation. Then a boundary control is designed based on this model. The closed-loop system is proved to be uniformly bounded under external disturbance by using the Lyapunov’s direct method. Finally, a numerical simulation is performed to verify the effectiveness of the designed boundary control scheme. Numerical simulation results show that the designed boundary control effectively suppresses the vibration while completing the attitude maneuver mission.

Technical Programmes CCDC 2021 MonB06 Room06 Space Vehicle Control (IV) 10:20-12:20 Chair: Jing-Wen Yi Wuhan Univ. of Science and Tech.CO-Chair: Xiongfeng Deng Anhui Polytechnic Univ.

10:20-10:40 MonB06-1 1294 Fast Consensus of Multi-Agent Systems on Digraphs by Graph Filtering Kai Li Wuhan Univ. of Science and Tech.Jing-Wen Yi Wuhan Univ. of Science and Tech.Li Chai Wuhan Univ. of Science and Tech.In this paper, we extend the graph filtering method to the consensus problem on digraphs. By introducing the normalized Laplacian matrix, a time-varying control protocol and the corresponding filter are designed. For the case of known digraphs, a graph filter is designed to reach the finite-time consensus. For the case of unknown digraphs, a periodic control strategy is adopted to achieve consensus asymptotically. By optimizing the worst-case periodic convergence rate, the optimal design of the control sequence is presented to reach fast consensus. Numerical simulations are given to illustrate the effectiveness of our results.

10:40-11:00 MonB06-2 955 Adaptive Fuzzy Iterative Learning Consensus Control for Leader-Following Nonlinear Multi-Agent Systems with Uncertain External Disturbances Xiongfeng Deng Anhui Polytechnic Univ.Liang Tao Anhui Polytechnic Univ.Yuan Ge Anhui Polytechnic Univ.Wenzhan Li Anhui Polytechnic Univ.This paper addresses a class of leader-following multi-agent systems, where the model of each following agent consists of unknown nonlinear dynamics and uncertain external disturbances. An adaptive fuzzy iterative learning control protocol is designed to sovle the tracking control problem. It is assumed that the dynamics of leader agent is unknown for each following agent. The nonlinear dynamics and uncertain external disturbances of agents are approximated by introducing the fuzzy logic systems, and a re-designed Lyapunov function is considered to analyze the convergence of the presented control protocol. In addition, the adaptive updating control laws for the introduced parameters are presented. Finally, the effectiveness of theoretical results is verified by one simulation example.

11:00-11:20 MonB06-3 419 Practical Fixed-Time Circle Formation Control for Multi-agent Systems with Network-induced Delay Peng Xu Dalian Maritime Univ.Jin Tao Nankai Univ.

Aalto Univ.Qinglin Sun Nankai Univ.Guangming Xie Peking Univ.Minyi Xu Dalian Maritime Univ.This paper addresses a practical fixed-time circle formation control problem for multi-agent systems subject to network-induced delay. To solve this problem, a novel control protocol, based on a time base generator, is developed to reach the practical fixed-time circle formation control, where the settling time is predetermined without relying on initial states, and the magnitude of initial control input is smaller than other conventional circle formation strategies. Moreover, a Lyapunov functional is constructed, which allows deriving a sufficient stability condition on the fixed-time circle formation. At last, numerical simulations are given to demonstrate the effectiveness of the proposed control law.

11:20-11:40 MonB06-4 559 Distributed consensus of linear multi-agent systems via decentralized output feedback control approach Yangzhou Chen

Beijing Univ. of Tech. Engineering Research Center of Digital Community

Jingyuan Zhan Beijing Univ. of Tech. Engineering Research Center of Digital Community

Xiaolong Huang

Beijing Univ. of Tech. Engineering Research Center of Digital Community

This paper studies the distributed consensus problem of general linear multi-agent systems (MASs) based on a novel decentralized output feedback control approach. By introducing the edge state and a directed-spanning-tree- based linear transformation method, we firstly equivalently transform the consensus problem into the decentralized output feedback stabilization control problem. Then we employ the properties of distributed fixed modes in decentralized output feedback stabilization control problem to derive a necessary and sufficient condition for ensuring the asymptotic state consensus of the MAS under the linear state feedback control. We further present a gradient descent iterative algorithm to design the gain matrix in the linear feedback consensus protocol. Finally, we give a numerical example to demonstrate the effectiveness of the theoretical results.

11:40-12:00 MonB06-5 1195

Finite time cluster consensus of fractional-order multi-agent systems with directed topology Weijin Liu Tongji Univ.Xiwei Liu Tongji Univ.

Key Laboratory of Embedded System and Service Computing

In this paper, the finite time cluster consensus (FnTCC) of fractional-order multi-agent systems (FOMAS) with directed topology is investigated. The fractional-order system is converted into an integer-order system by defining a neighborhood-based error variable, and suitable control rules are designed for the obtained first-order multi-agent system. According to the exponential finite-time stability theorem, suitable Lyapunov functions are designed. Furthermore, the settling time function is given. Numerical simulation results prove the feasibility and validity of our theory.

12:00-12:20 MonB06-6 1153 Model Reference Scheduling and Robust Resilient H-infinity Control Co-design Shunli Zhao Tianjin Univ. of Tech.A representative bandwidth limitation, medium access constraint, has been a research focus in the field of networked control systems until recently. In this paper, an improved model reference scheduling is proposed. Meanwhile, the co-design of the improved model reference scheduling and roust resilient H-infinity control is proposed to cope with the system disturbance and controller perturbation. Finally, the effectivity of the co-design scheme proposed is verified by a numerical example.

MonB07 Room07 Complex Networks and Systems (II) 10:20-12:20 Chair: Zhengxin Wang Nanjing Univ. of Posts and Telecommu

nicationsCO-Chair: Jun Mei South-Central Univ. for Nationalities

10:20-10:40 MonB07-1 1623 Synchronization of Stochastic Multiplex Networks With Impulsive Effects Zhengxin Wang Nanjing Univ. of Posts and TelecommunicationsXin Jin Nanjing Univ. of Posts and TelecommunicationsYanling Lu Nanjing Univ. of Posts and TelecommunicationsYuanzhen Feng Nanjing Univ. of Posts and TelecommunicationsThis paper investigates synchronization of the stochastic multiplex networks with impulsive effects. Firstly, compared with the single-layer networks, multiplex networks can express more information. Meanwhile, stochastic perturbations and impulses are ubiquitous and both can affect synchronization. Therefore, a multiplex network with stochastic perturbations and impulses is proposed. Secondly, in order to solve the effect of impulses, the average impulsive interval method is applied. Based on the Lyapunov function method, It^o′s formula and exponential martingale inequality, the sufficient conditions for the multiplex networks to achieve almost sure exponential synchronization are obtained. Finally, a numerical example of a two-layer multiplex network is given to illustrate the effectiveness of the theoretical results.

10:40-11:00 MonB07-2 960 Finite-Time Synchronization for Complex Networks via Guaranteed Cost Intermittent Pinning Quantized Control Yuanyuan Li South-Central Univ. for NationalitiesZhanying Yang South-Central Univ. for NationalitiesDan Xia Central China Normal Univ.Jun Mei South-Central Univ. for NationalitiesGenerally, complex networks can finite-time synchronization by an appropriate controller with any control gain. In this paper, an optimal guaranteed cost pinning quantized control is designed to realize finite-time synchronization of complex networks. The control schemes presented include both continuous pinning quantized controllers and intermittent pinning quantized controllers. Based on designed Lyapunov function and guaranteed cost finite-time pinning quantized techniques, sufficient conditions for the guaranteed cost intermittent pinning quantized and continuous pinning quantized control laws are formulated in terms of matrix inequalities. Meanwhile, the guaranteed cost is a nonlinear function, which can be addressed nonlinear programming algorithm. The results not only can guarantee adequate level performance but also reduce control cost and save channel resources, and it was rarely considered in the previous literature. Finally, the guaranteed cost periodically intermittent pinning quantized and continuous pinning quantized control strategies are illustrated by simulations.

11:00-11:20 MonB07-3 888 Synchronization of second-order inhomogeneous Kuramoto oscillator systems with time delays and phase shifts Helang Xiong Chongqing Univ. of Tech.Hushuang Hou Chongqing Univ. of Tech.Sisi Xiao Chongqing Univ. of Tech.Hua Zhang Chongqing Univ. of Tech.In this paper, a second-order inhomogeneous Kuramoto oscillator systems(ihKOSs) with communication delays and phase shifts are

Technical Programmes CCDC 2021 investigated. An equivalent form of the second-order ihKOSs is obtained by using singular perturbation analysis. Both of phase synchronization and frequency synchronization are studied under an undirected connected network topology. Some criterion for the phase and frequency synchronization of the second-order ihKOSs are obtained. Finally, the validity and correctness of the results are further verified by some numerical simulations.

11:20-11:40 MonB07-4 937 Turing Instability Analysis of Marine Planktonic Ecosystem Under the Influence of Spatial Heterogeneity Yunxiang Lu Nanjing Univ. of Posts and TelecommunicationsMin Xiao Nanjing Univ. of Posts and TelecommunicationsGong Chen Nanjing Univ. of Posts and TelecommunicationsBinbin Tao Nanjing Univ. of Posts and TelecommunicationsShi Chen Nanjing Univ. of Posts and TelecommunicationsJiaxuan Liu Nanjing Univ. of Posts and TelecommunicationsDue to the inhomogeneity of species distribution in natural world, we consider the Turing instability of phytoplankton-zooplankton marine ecosystems containing diffusion and Neumann boundary conditions in this paper. By means of Routh-Hurwitz criterion, the stability conditions of positive equilibrium of system without reaction-diffusion terms are obtained. The Turing instability conditions are derived and patterns formations of the interior equilibrium are determined through utilizing the theory of partial functional differential equations. Finally, numerical simulation results are provided to validate the theoretical analysis.

11:40-12:00 MonB07-5 951 Robust time-varying Kalman predictor for uncertain singular system with missing measurement Jiayi Zheng Heilongjiang Univ.Chenjian Ran Heilongjiang Univ.For the linear stochastic singular system with missing measurement and uncertain noise variances, the robust Kalman prediction problem is addressed. Applying the singular value decomposition (SVD) method and the fictitious noise approach, the original singular system is transformed to new reduced-order standard system only with uncertainvariance fictitious noises. Applying the minimax robust estimation principle, the minmax robust time-varying Kalman predictor is presented in the sense that its actual prediction error variance is guaranteed to have the corresponding minimal upper bound for all admissible uncertainties. Its robustness is proved by the Lyapunov equation approach. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.

12:00-12:20 MonB07-6 1593 Analysis of transmission factors and diffusion characteristics of respiratory infectious diseases on multilayer social networks Zhaohui Li Wuhan Textile Univ.Laifan Pei Wuhan Textile Univ.Jie Liu Wuhan Textile Univ.Yueying Zhu Wuhan Textile Univ.Firstly, the paper constructs a multilayer network model to reflect the social relationship of primary school students in the typical teaching area in China. The underlying network has a typical hierarchical structure of maintaining the unique social relationship characteristics of both different age stages and different class groups. The connection density within and between layers is then fully considered in the simulation analysis of spreading dynamics. Secondly, an agent-based mathematical model is designed to describe the spreading process of common respiratory infectious diseases on a multilayer network of the above hierarchical characteristics. The model can be used to simulate the real spreading process of class A and class B infectious diseases, and to explain how they reach the peak, by adjusting key parameters. Finally, the simulation analysis of the transmission factors and diffusion characteristics in the teaching area of respiratory infectious diseases is carried out by setting appropriate values of parameters based on the empirical data that have been reported in the published literature. In particular, we also study the influence of wearing face masks on the spreading and prevention of respiratory infectious diseases by comparing the difference of spreading process and infection peak under three conditions including no preventive measures, randomly wearing face masks, and setting teachers to be mobile infected nodes. Based on the underlying results, we put forward some effective prevention and control strategies and suggestions for respiratory infectious diseases among teachers and students in domestic teaching areas during the Covid-19 pandemic.

MonB08 Room08 Theory and Application of Nonlinear Systems (IV) 10:20-12:20 Chair: Wanzhen Quan Rocket Force Univ. of EngineeringCO-Chair: Zhengbao Cao Northeastern Univ.

Key Laboratory of Data Analytics and Optimization for Smart Industry

10:20-10:40 MonB08-1 906 Guaranteed-performance leader-following consensus for one-sided Lipschitz nonlinear multi-agent systems

Wanzhen Quan Rocket Force Univ. of EngineeringXiaogang Yang Rocket Force Univ. of EngineeringXinzhong Han Beijing BlueVision Tech. Limited CompanyJianxiang Xi Rocket Force Univ. of EngineeringThis paper addresses the guaranteed-performance leader-following consensus for one-sided Lipschitz (OSL) nonlinear multi-agent systems. Firstly, a leader-following control protocol is proposed by the joint design between the guaranteed-performance control and the consensus control. Then, based on the quadratic inner bounded condition and the OSL condition, a sufficient condition for the guaranteed-performance leader-following consensus design is proposed by the linear matrix inequality. At last, a numerical example is given to verify the effectiveness of proposed results.

10:40-11:00 MonB08-2 1494 Stability for Switched Nonlinear Systems via Event-triggered Impulsive and Switching Control Zhengbao Cao Northeastern Univ.

Key Laboratory of Data Analytics and Optimization for Smart Industry

Jun Zhao Northeastern Univ.The paper proposes an event-triggered impulsive and switching control mechanism for a class of switched nonlinear systems, where each subsystem of such a switched system is allowed to be unstable, and there does not exist any switching law to stabilize the system. Note that the event-triggered impulsive and switching control can reduce the number of occurrence of impulses obviously. Most importantly, the event-triggered impulsive and switching control can not only guarantee stability of system, but also exclude Zeno behavior. Finally, a numerical example is given to illustrate the effectiveness of the results.

11:00-11:20 MonB08-3 798 Robust Optimal Sliding Mode Controller Design for Uncertain System with Composite Disturbances and Its Application to AUV Qing Yang Qingdao Univ. of Science & Tech.Ji-li Zhou Qingdao Univ. of Science & Tech.De-xin Gao Qingdao Univ. of Science & Tech.In this paper, a robust optimal sliding mode controller (ROSMC) is designed for uncertain system with composite disturbances, which has been applied to the depth problem of the near-surface-sailing underactuated Autonomous Underwater Vehicle (AUV) affected by wave disturbances. Firstly, the exosystem of measurable disturbances is built. Secondly, the feedforward and feedback optimal controller is designed for the nominal system without uncertainties, which makes the quadratic performance index optimal. Then constructing the integral sliding surface, the ROSMC law is designed considering uncertainties and a disturbance observer are constructed in order to solve the physically realizable problem. Finally, the validity and robustness of the presented control approach is demonstrated by a dynamic model of the Remote Environmental Unit (REMUS) AUV.

11:20-11:40 MonB08-4 831 Global stabilization of switched nonlinear time-delay systems with unknown measurement sensitivities Yanan Qi Shandong Univ.Xianfu Zhang Shandong Univ.Yanjie Chang Shandong Univ.Zhiyu Duan Shandong Univ.This paper investigates the global output feedback stabilization problem for switched feedforward nonlinear time-delay systems under asynchronous switching. The key feature of the considered system is that the differentiability of the output function is not guaranteed, since the unknown measurement sensitivities exist in the output. Based on the appropriate state transformation and the observer, a dual-domination method is proposed to design the output feedback controller. Different from the existing works, the sensor uncertainty problem for switched systems is addressed for the first time. By using the average dwell time (ADT) method and the multiple Lyapunov-Krasovskii functionals (MLKFs) theory, the global asymptotic stability of switched nonlinear systems is proved.

11:40-12:00 MonB08-5 894 Exponential State Estimation for Markov jump Neural Networks via Utilizing A New Free-matrix Exponential-type Inequality Xiaoman Liu Yunnan Minzu Univ.Haiyang Zhang Yunnan Minzu Univ.Lianglin Xiong Yunnan Minzu Univ.Tao Wu Southeast Univ.This paper investigates the exponential state estimation problem for a class of neural networks with Time-varying Delay (TVD) and Markov Jump Parameters (MJPs). Firstly, to derive a tighter bound of Exponential-type Integral Quadratic Terms (EIQTs), a new Free-matrix Exponential-type Inequality (FMEI) is provided, which includes some existing ones as its special cases. Secondly, by fully considering the information of attenuation exponent, TVD and MJPs, a novel Lyapunov- Krasovskii Functional (LKF) is constructed. Then, by employing the new FMEI and other analytical techniques, a less conservative criterion

Technical Programmes CCDC 2021 guaranteeing the stochastic stability of error system is obtained in form of Linear Matrix Inequalities (LMIs). In the end, a numerical example is given to show the effectiveness of the obtained result.

12:00-12:20 MonB08-6 882 Spatio-temporal Dynamics Analysis of SIR Epidemic Model with Incidence Rate of Individuals Contact Heterogeneity Rong Qian Nanjing Univ. of Posts and Telecommunicatio

nsMin Xiao Nanjing Univ. of Posts and Telecommunicatio

nsJian Li Nanjing Univ. of Posts and Telecommunicatio

nsYuezhong Zhang Nanjing Univ. of Posts and Telecommunicatio

nsMingyue Zhang Nanjing Univ. of Posts and Telecommunicatio

nsIn this paper, a SIR epidemic model with reaction-diffusion is established. Considering the new incidence rate caused by individuals contact heterogeneity, we study the spatial pattern dynamics of SIR epidemic model, and provide strategic guidance for the prediction and control of epidemic. The existence of endemic equilibrium is discussed in combination with the basic reproduction number. By analyzing the corresponding characteristic equation, the sufficient conditions for the local asymptotic stability and Turing instability of the endemic equilibrium are given respectively. The derived theoretical results are illustrated by some numerical simulations.

MonB09 Room09 Fault Diagnosis and Predictive Maintenance (VIII) 10:20-12:20 Chair: Leilei Chang Hangzhou Dian Zi Univ.CO-Chair: Gengwei Li AECC Shenyang Engine Research Inst.

10:20-10:40 MonB09-1 1055 Investigation of Influence of Identification Precision on Modeling Accuracy in Belief Rule Base Xintao Song Hangzhou Dian Zi Univ.Jiadong Dai Hangzhou Dian Zi Univ.Xiaobin Xu Hangzhou Dian Zi Univ.Leilei Chang Hangzhou Dian Zi Univ.Many current Belief Rule Base (BRB)-related training and learning studies have been focused on BRB parameter, BRB structure learning, or their combination. Comparatively, the number and utilities of scales have not been optimized mainly because they do not have direct influence on the structure of BRB. Nonetheless, it is not clear how the number and utilities of scales would affect the modeling accuracy. Therefore, two hypotheses are tested in this study, namely, whether including the utilities of scales would affect the modeling accuracy, and whether the number of scales would affect the modeling accuracy. With a practical pipeline leak detection case, it is validated that: (1) the utilities of scales do not have direct influence on the modeling accuracy, and (2) the number of scales do not have direct influence on the modeling accuracy. Further t-tests are conducted, which also produces consistent results.

10:40-11:00 MonB09-2 1155 Fault Simulation and Function Verification of the Aero-engine Control System Based on Hardware in Loop Gengwei Li AECC Shenyang Engine Research Inst.Binbin Hao AECC Shenyang Engine Research Inst.Jiuxiang Dong Northeastern Univ.HIL test is an important part in the development of the aero-engine control system. In order to meet the engineering requirements, how to establish the real-time fault simulation models of the control system is an essential problem for HIL. This paper proposes a simulation model with general architecture based on the Simulink platform of the HIL system, which can realize the function of fault simulation. The EEC sends out warning or fault signals according to the detected information, so as to verify the correctness of the EEC control strategy and fault diagnosis function. In this paper, the frequent faults in the engine control system are simulated and verified, which can guide the design and test of the control strategy.

11:00-11:20 MonB09-3 897 A Simple Fault Diagnosis Method for Power Switch System Based on Mean Conditional Entropy Chen Zhao Northeastern Univ.Xiaojian Li Northeastern Univ.This paper presents a simple open-circuit fault diagnosis method based on conditional entropy for two-level threephase voltage source inverter (VSI), which is a typical power switching system. Referring to the concept of entropy in information theory, this paper firstly divides the amplitude of current signal into several intervals, and counts the frequency of phase current data falling into each interval in a current period. According to the frequency, the corresponding probability of each interval under normal and fault modes is estimated to calculate the conditional entropy between two phases in the switching system. Besides, in order to make up for the deficiency of traditional conditional entropy, the concept of "mean conditional entropy" is proposed. According to the difference of mean

conditional entropy of phase currents in normal and fault modes, the threshold value is selected. Then, whether a fault occurs in the inverter can be judged. The proposed method can locate to the fault leg effectively with small amount of calculation. Simulation experiments show its advantages of strong resistance to both load disturbance and speed fluctuations.

11:20-11:40 MonB09-4 1028 Fault Diagnosis Method of Aircraft Anti-skid Brake System Based on GWO-PNN Jianguo Cui Shenyang Aerospace Univ.Ningning Zhang Shenyang Aerospace Univ.Xiao Cui AVIC Aerodynamics Research Inst.Jinglin Wang Aviation Key Laboratory of Science

and Technology on Fault Diagnosisand Health Management

Mingyue Yu Shenyang Aerospace Univ.Dong Liu Shenyang Aerospace Univ.Liying Jiang Shenyang Aerospace Univ.The health of the aircraft’s anti-skid braking system is critical to the flight of the aircraft. The failure of the aircraft’s anti-skid brake will affect the system efficiency and flight safety. Therefore, this paper proposes an aircraft anti-skid brake system fault diagnosis method based on Grey Wolf Optimizer (GWO) and Probabilistic Neural Networks (PNN). First, preprocess the acquired data of five parameters of a certain type of aircraft anti-skid brake system: wheel speed, aircraft speed, braking time, brake pressure, and brake servo valve control current to create a PNN fault diagnosis model. Aiming at the shortcomings of PNN network smoothing factor selection based on experience, the GWO optimization algorithm is proposed to optimize the PNN network to find the optimal smoothing factor. The validity of the optimal GWO-PNN fault diagnosis model created is verified by experiments with the relevant parameter data of the brake system. The results show that the GWO-PNN-based aircraft anti-skid brake system fault diagnosis method proposed in this paper can effectively solve the problem of poor fault diagnosis effect caused by the artificial setting of the smoothing factor of the probabilistic neural network, avoiding the interference and influence of human factors. It has a good fault diagnosis performance. The fault diagnosis accuracy rate of the GWO-PNN model is as high as 95%, which is better than the fault diagnosis of the PNN and Back propagation (BP) diagnostic models.

11:40-12:00 MonB09-5 914 Fault Diagnosis of Wind Turbine Gearbox Based on SSDAE-ELM Jianhua Zhang State Key Laboratory of Alternate

Electrical Power System withRenewable Energy Sources

Haibo Dong North China Electric Power Univ.Rui Shan North China Electric Power Univ.Guolian Hou North China Electric Power Univ.Congzhi Huang North China Electric Power Univ.In recent years, the installed capacity of wind turbine is increasing year by year, and the influence of wind turbine faults on wind farm operation is also increasing. In this paper, a novel fault diagnosis method is proposed to diagnose the fault of wind turbine gearbox. The method is based on stacked sparse denoising auto-encoders (SSDAE) neural network which combines stacked sparse auto-encoders (SSAE) neural network and stacked denoising auto-encoders (SDAE) neural network to extract features from high-dimensional signal data better. In addition, extreme learning machine (ELM) classifier is used to finish the fault diagnosis according to the features extracted from high-dimensional signal data by the SSDAE. Finally, the method is tested based on the simulation experiment, the simulation results demonstrate that the accuracy of this fault diagnosis method is higher than that of other methods when the same parameters are used.

12:00-12:20 MonB09-6 1029 Fault Diagnosis of Bearing Based on Variational Mode Decomposition and Deep Learning Jianguo Cui Shenyang Aerospace Univ.Shan Tang Shenyang Aerospace Univ.Xiao Cui AVIC Aerodynamics Research Inst.Jinglin Wang Aviation Key Laboratory of Science

and Technology on Fault Diagnosisand Health Management

Mingyue Yu Shenyang Aerospace Univ.Wenyou Du Shenyang Aerospace Univ.Liying Jiang Shenyang Aerospace Univ.Aiming at the problem of difficult fault diagnosis caused by serious noise pollution and weak fault characteristic information in the rolling bearing vibration signal, a fault diagnosis method based on the combination of variational mode decomposition (VMD) and deep learning is proposed. First, VMD is performed on the original bearing vibration signal to obtain several Intrinsic Mode Functions (IMF). Then, the envelope spectral entropy of each IMF can be obtained by calculating. The IMF with the smallest envelope spectral entropy is selected as the main analysis IMF. Secondly, a stacked auto encoder (SAE) network initial model is built according to the data characteristics, and the initial values of the model parameters can be obtained by performing unsupervised pre-training on the network model; then the supervised backpropagation algorithm is

Technical Programmes CCDC 2021 used to fine-tune the network parameters to obtain the model of optimal parameter. Finally, the model is use to perform pattern recognition on the test set. Validation of examples and comparative experiments show that this method has higher diagnostic accuracy, better diagnostic effect, and better engineering application value.

MonB10 Room10 Signal Processing and Information Fusion (III) 10:20-12:20 Chair: Xiaoying Song Wuhan Univ. of Science and Tech.CO-Chair: Hongyao Li High-Tech Inst. of Xi’an

10:20-10:40 MonB10-1 631 Brain Network Analysis of Paranoid Schizophrenia Based on Graph Measurements Xijie Yang Wuhan Univ. of Science and Tech.Chaoyu Du Wuhan Univ. of Science and Tech.Xiaoying Song Wuhan Univ. of Science and Tech.Yuxia Sheng Wuhan Univ. of Science and Tech.Schizophrenia is commonly thought of as an abnormality in the diversity and strength of connections between regions of the brain network. In this paper, we analyze the brain functional networks of paranoid schizophrenics based on complex network measurements. We study local measurements for each region of the network and global measurements for the entire brain network. According to the results of 32 subjects (16 normal subjects and 16 patients), we get to the following conclusions: 1) for brain NETs, paranoid schizophrenics have higher global efficiency, local efficiency and clustering coefficient, and smaller betweenness centrality and average path length; 2) paranoid schizophrenics have smaller small-worldness, indicating there is a lower degree of integration of the brain functional network; 3) most NETs of paranoid schizophrenics have more connectivity diversity and stronger connectivity strength than normal subjects.

10:40-11:00 MonB10-2 1015 Cooperative Object Recognition Method of Multi-UAVs Based on Decision Fusion Hongyao Li High-Tech Inst. of Xi’anXueli Xie High-Tech Inst. of Xi’anPengchun Du Unit 96922 of the People’s Liberation

ArmyJianxiang Xi High-Tech Inst. of Xi’anIn this paper, a cooperative object recognition algorithm of multi-UAVs based on decision fusion is proposed. Firstly, the SURF operator is used to get the coordinate transformation matrix and stitch the images. Then, the detection results of each UAV are projected to the coordinate system of the stitched images, and the spatial coordinates of the detection information are unified. Using Jaccard overlap and Hellinger distance to synthesize position and attribute information. Then constructing the probability matrix of the detection box association. The nearest neighbor association rule based on the global optimization is used to operate multi-objective association. Finally, a dynamic switching strategy based on Jousselme distance is adopted, which can adaptively select the DST or DSmT, to fuse the information of association detection bounding boxes according to the degree of conflict. Experiments are performed on the constructed multi-UAVs cooperative object detection datasets, and the performance of several algorithms is compared. The results show that the proposed algorithm can not only increase the patrol range, but also improve the recognition accuracy of the UAV patrol system.

11:00-11:20 MonB10-3 696 A Transfer Learning Method based on VGG-16 Convolutional Neural Network for MI Classification Mingai Li Beijing University of Tech.

Beijing Key Laboratory ofComputational Intelligence and

Intelligent SystemEngineering Research Center of

Digital CommunityDongqin Xu Beijing University of Tech.Brain computer interface (BCI) technology can help the disabled to achieve the recovery of neural function by using the Motor Imagery Electroencephalogram (MI-EEG) based rehebilitation system. However, it is difficult to acquire a large amount of available EEG data, transfer learning technology provides an effective method, and the source domain selection is one of key issues. In this study, we develop a novel parameter transfer learning method based on VGG-16 convolutional neural network (CNN) for MI classification. First, the number of all MI-EEG signals are augmented with the sliding window method, and the short-time Fourier transformation (STFT) is applied to obtain the time-frequency spectrum images (TFSI). Then, the VGG-16 CNN is pre-trained with TFSI of source domain, which is divided into five blocks. The parameters of the pre-trained CNN are transferred to the target network though a new transfer strategy, i.e. utilization of the data of part subjects from target domain to fine-tune the five blocks in turn. Finally, the fine-tuned CNN is used for MI classification of the rest subjects in target domain. This work is evaluated with a public dataset, the best classification accuracy of this study is 96.59%. The results show that the high correlation between the source domain and the target domain is better than using the domains with low correlation, and the proposed

transfer strategy is efficiency.

11:20-11:40 MonB10-4 805 Signal Recognition Method of X-ray Pulsar Based on CNN and Attention Module CBAM Liming Xiang Beijing Inst. of Tech.Zhiqiang Zhou Beijing Inst. of Tech.Lingjuan Miao Beijing Inst. of Tech.Qiang Chen China Academy of Space Tech.Fast and accurate identification of X-ray pulsar signals is an important prerequisite for pulsar navigation. Most of the current identification methods are to extract the cumulative profile features and then compare them with features of the standard profile to complete the identification. However, the profile with high signal-to-noise ratio needs long observation time, which has a great impact on real-time identification. In this paper, the X-ray pulsar signal is converted into time interval sequences, and then the feature extraction and identification are completed by using one-dimensional convolution neural networks. In terms of network architecture design, we introduce convolutional block attention module (CBAM) and propose the CBAM-Inception module to construct the network. This structure combines the advantages of Inception and CBAM, and uses channel and spatial attention mechanisms to enhance the feature extraction capabilities of the Inception network. Experimental shows that the proposed method can greatly shorten the required observation time while ensuring high-accuracy X-ray pulsar signal identification. Moreover, the comparison of convolution blocks shows that the CBAM-Inception block can greatly improve network identification ability.

11:40-12:00 MonB10-5 860 Traffic Image Dehazing Based on HSV Color Space Can Ding Hunan Univ.Zhe Zhang Hunan Univ.Fan Li Hunan Univ.Jing Zhang Hunan Univ.Aiming at the problems of severe degradation, blurredness and low contrast in traffic images in severe weather scenes, this paper proposes a traffic image enhancement algorithm in color space, which converts images into HSV space and then performs S and V components. Constrained adaptive histogram operations and then convert the image to RGB space. Our experiments show that the proposed algorithm can better restore the fogging effect.

12:00-12:20 MonB10-6 602 A Fusion Estimation Algorithm Based on Machine Learning for Nonlinear Systems Zhengxiao Peng Heilongjiang Univ.

Key Laboratory of Information Fusion Estimation and Detection

Gang Hao Heilongjiang Univ.Key Laboratory of Information Fusion

Estimation and Detection.This paper is concerned with distributed estimation problem for multi-sensor nonlinear systems. Based on the approximation performance of the BP network, using local Cubature Kalman Filter (CKF) estimations as inputs and estimations of a distributed fusion CKF weighted by matrices (MW-CKF) algorithm as the target outputs to train the network, a BP net-based MW-CKF algorithm is proposed, which realizes the fusion effect of the MW-CKF algorithm. Compared with the MW-CKF, the proposed method reduces the computational cost of calculating cross-covariance matrices, and the theoretical equivalence of the machine learning method is proved. A target tracking process in a horizontal plane simulation verifies the effectiveness of the proposed fusion algorithm.

MonB11 Room11 Intelligent Control, Computation and Optimization (VII) 10:20-12:20 Chair: Sheng Zhang China Aerodynamics Research and

Development CenterState Key Laboratory of Aerodynamics

CO-Chair: Zhaolei Wang Beijing Aerospace Automatic Control Inst.

10:20-10:40 MonB11-1 549 Reinforcement Learning Control for 6 DOF Flight of Fixed-Wing Aircraft Sheng Zhang China Aerodynamics Research and Development

CenterState Key Laboratory of Aerodynamics

Xin Du China Aerodynamics Research and DevelopmentCenter

Juan Xiao China Aerodynamics Research and DevelopmentCenter

Jiangtao Huang China Aerodynamics Research and DevelopmentCenter

Kaifeng He China Aerodynamics Research and DevelopmentCenter

State Key Laboratory of Aerodynamics

Technical Programmes CCDC 2021 Flight control is a key technique for the autonomous unmanned aircraft. The traditional model-based controller design approaches often aim at concrete plant and are short in universality. Reinforcement learning provides a general controller design paradigm that is adaptive, optimized, model-free and widely applicable, and it is a promising way for the intelligent control. In contrast to the 3 Degree-of-freedom (DOF) flight, the 6 DOF motion better describes the aircraft real flight, while the implementation of the intelligent control is much harder. Based on the multiple continuous states input and multiple continuous action output deep reinforcement learning, the integrated intelligent control for the cruise flight, directly from the vehicle flight states to the aero-surfaces and thrust control, is developed for the full-sized fixed-wing aircraft. It avoids the artificial trajectory and attitude loop separation in the traditional controller design, and is advantageous to the exploitation of the aerodynamics and the nonlinear inertia coupling.

10:40-11:00 MonB11-2 605 Research on Automated Reinforcement Learning: based on Tree-structured Parzen Estimators and Median Pruning Zhaolei Wang Beijing Aerospace Automatic Control Inst.Ludi Wang Beijing Aerospace Automatic Control Inst.Qi Liang Beijing Aerospace Automatic Control Inst.Wuyi Luo Beijing Aerospace Automatic Control Inst.Qinghai Gong Beijing Aerospace Automatic Control Insti.Shanshan Li Beijing Aerospace Automatic Control Inst.Tuning the hyper parameters of machine learning algorithm is regarded as a boring and difficult challenge to the researcher in the artificial intelligence domain. However, with the rapidly development of computer clusters and GPU processors, the automated machine learning algorithm have been proposed to solve this problem. In this paper, an automated reinforcement learning method has been proposed by focusing on the automated hyper parameters tuning of the reinforcement learning, a relatively new branch of machine learning which is more suitable to the motion control. For reducing the amount of calculations and finding the optimization solution more quickly, an open automated optimization framework have been proposed by combing the tree-structured Parzen estimators based Bayesian optimization and median pruning algorithm. A specific simulation has been given based on the deep deterministic policy gradient case to show the effectiveness and practicability of the proposed method.

11:00-11:20 MonB11-3 1555 Intelligent Modeling of Tuyere Raceway of Blast Furnace Bingnan Liu ANSTEEL Iron & Steel Research Inst.Minghan Wu Northeastern Univ.A new soft measurement model of blast furnace tuyere raceway temperature is proposed. Firstly, the image data collected from the tuyere is transformed from RGB space to HSV space, and the features of the image data are extracted after non-uniform quantization. Then, the extracted image features are used as input data, the actual collected temperature data are used as sample labels for KPLS regression prediction. Aiming at the problem that KPLS parameters are not easy to adjust, GWO algorithm is introduced to optimize the parameters, which not only improves the accuracy of the algorithm, but also reduces the difficulty of parameter adjustment, so as to improve the prediction accuracy and training efficiency of the model. Finally, a large number of experiments are carried out with the actual data of a steel plant. The simulation results show that this method can predict the temperature with high accuracy, which shows that this method has good practicability and accuracy.

11:20-11:40 MonB11-4 1098 The Optimal Sweep Angle Design of a Morphing Firebee Drone in a Cruise Mission Weibo Xia Beihang Univ.Weihong Wang Beihang Univ.Wei Zhang Beihang Univ.This paper presents an algorithm to approximate aerodynamic coefficients and computes the optimal sweep angles of a variable-sweep Firebee drone that performs a cruise mission consequently. The aerodynamic coefficients of the morphing aircraft are computed via DATCOM. An approximation algorithm based on the sparse approximation principle is developed to fit the aerodynamic coefficients as concise and accurate functions of Mach, sweep angle, and angle of attack. The derived functions reveal the relations between the sweep angle and the drone’s aerodynamic characteristics, which is used to formulate the optimal sweep angle design problem as an optimal control problem. The optimal sweep angle is derived by numerically solving this optimal control problem. Finally, two scenarios in a 3000 m cruise mission are considered to demonstrate the effectiveness of our proposed methods, while providing some instructions for further research.

11:40-12:00 MonB11-5 978 Optimization of echo state network behavior space based on microbial genetic algorithm Yingqin Zhu Xi’an Univ. of Science and Tech.Zhaozhao Zhang Xi’an Univ. of Science and Tech.

Qiuwan Wang Xi’an Univ. of Science and Tech.Aiming at the difficulty of selecting the parameters of the echo state network reservoir, this paper proposes a method for optimizing ESN parameters based on behavior space. Its essence is to build an ESN behavior space through generalization rank, kernel rank, and memory capacity, and use microbial genetic algorithms to find rules for reservoir parameter selection and the minimum behavior configuration required for learning tasks. This method overcomes the shortcomings of traditional ESN such as multiple parameters and long optimization time, and improves optimization efficiency and network learning performance. The experimental results show that the MGA-ESN method proposed in this paper is basically close to the optimal network structure, the learning performance is better than the GESN network, and the reasons that affect the performance of the ESN network can be explained through the behavior space.

12:00-12:20 MonB11-6 584 Research on the method of path planning for slag-removing on the surface of zinc pot Kebo Li Engineering Research Center for Metallurgical

Automation and Measurement Tech. of Ministryof Education

Wuhan Univ. of Science and Tech.Ling Xiong Engineering Research Center for Metallurgical

Automation and Measurement Tech. of Ministryof Education

Wuhan Univ. of Science and Tech.Zhenzhou Zhang Engineering Research Center for Metallurgical

Automation and Measurement Tech. of Ministryof Education

Wuhan Univ. of Science and Tech.Gang Chen Wuhan Univ. of Science and Tech.During the production process of cold-rolled hot-dip galvanizing, the slag on the surface of the zinc pot will affect the quality of the product. The zinc slag is mostly collected manually or by robots at present. The effect of manual salvage is unstable and the operation is unsafe. The robot cannot identify the location of the zinc slag and can only salvage it according to a fixed rule, resulting in low efficiency. In response to the above problems, a zinc pot slag path planning method based on the greedy algorithm is proposed in this paper. The zinc pot area is divided, the image processing method is used to identify and locate the zinc slag to be picked. And the priority of the zinc pot, the obstacle avoidance problem and the restriction of the weight limit of the slag-removing tool are proposed in combination with the process requirements. Then three salvage strategies based on the greedy algorithm, the shortest distance, the maximum weight, and the minimum weight are proposed respectively. Experimental results show that the greedy algorithm strategy based on the shortest distance performs best, consumes the least energy, and the algorithm runs for a shorter time. It can significantly improve the efficiency of zinc pot slag removal, realize intelligent control of slag removal technology, and can be applied to the actual production of zinc pot slag removal.

MonBIS Room12 Interactive Session 10:20-12:20

MonBIS-01 1620 Time Cooperative Trajectory Planning Method Based on Analytical Solution of Time for Unpowered High-speed Reentry Vehicle Ming LIU Science and Tech. on Space Physics LaboratoryXing GAO Science and Tech. on Space Physics LaboratoryZhen XIAO Science and Tech. on Space Physics LaboratoryMing YANG Science and Tech. on Space Physics LaboratoryDing YANG Science and Tech. on Space Physics LaboratoryYajie GE Science and Tech. on Space Physics LaboratoryXiaoli QIN Science and Tech. on Space Physics LaboratoryMingang ZHANG Science and Tech. on Space Physics LaboratoryAn analytical planning method is proposed to solve the time cooperative trajectory planning problem in this paper. First, a height-speed flight corridor coupled with angle of attack is constructed for the multiple constraints of planning problem. Subsequently, a general expression containing logarithmic function is designed based on the mathematical model of the flight corridor boundary, the analytical solution of flight path length and flight time based on flight profile expressed by logarithmic functions is obtained, we can adjust the flight trajectory and time by adjusting the altitude, angle of attack and other variables. Ultimately, the iterative logic of time cooperative trajectory planning with analytical solution of flight time is proposed. Numerical demonstrations show that the time analytical solution has high accuracy. With Multi-aircraft Time Cooperative Trajectory Planning, terminal time deviation is 0.2 seconds, Terminal position accuracy is less than 500 meters. The method proposed in this paper have high adaptability and engineering application value.

MonBIS-02 1632 Attitude Control of High Area-to-mass Ratio (HAMR) Spacecrafts Considering the Eclipse by the Earth Song Xu Harbin Inst. of Tech.Jun Zhao Harbin Inst. of Tech.

Technical Programmes CCDC 2021 Yongbei Liu Harbin Inst. of Tech.Desong Du Harbin Inst. of Tech.Wenyu Feng Harbin Inst. of Tech.Naiming Qi Harbin Inst. of Tech.High area-to-mass ratio (HAMR) spacecrafts are sensitive to spatial perturbations. For an HAMR spacecraft in geosynchronous orbit (GEO), solar radiation pressure (SRP) torque and gravity gradient torque have a significant effect on the attitude dynamics. During vernal and autumnal equinox, an HAMR spacecraft in GEO will experience the eclipse by the earth and pass umbra and penumbra when it’s in the earth shadow. In penumbra, the sunlight intensity varies with position and SRP-gradient torque is produced. In umbra, the sunlight intensity is zero and SRP torque vanishes. Disturbing torques during the eclipse are different from that in full-light area which needs to be considered in the design of attitude control scheme. In this paper, the SRP torque including SRP-gradient torque and uniform SRP torque during eclipse by the earth is derived. The attitude dynamics of an HAMR spacecraft are modeled according to which the attitude control scheme against small disturbances is designed. Numerical simulations are presented to illustrate the effectiveness of the proposed scheme.

MonBIS-03 1443 Boundedness and Exponential Stability of Neutral-type Stochastic Hybrid Systems Jun Zhou Southwest Forestry Univ.

Donghua Univ.Jian Zhang Southwest Forestry Univ.Wuneng Zhou Donghua Univ.Dongbing Tong Shanghai Univ. of Engineering ScienceNeutral-type stochastic hybrid systems, which not only depend on present and past states but also involve derivatives with delay, have been widely concerned. In recent years, exponential stability and boundedness have become two most popular topics in studies of neutral-type stochastic hybrid systems. In existing results, the Lyapunov function in different modes is bounded by the polynomials with the same order, and the diffusion operator acting on a C2,1-function is bounded by a polynomial with the same order of that C2,1-function. To break those limits, two general criteria which are more effective than existing criteria have been obtained in this paper.

MonBIS-04 803 The pth moment asymptotical stability for stochastic impulsive delayed system with G-brownian motion based on event-trigger Dejun Zhu Southwest Minzu Univ.Jun Yang Southwest Minzu Univ.This paper explores the stability problem of nonlinear stochastic impulsive delayed system driven by Gbrownian motion with regard to event-triggered mechanism in theory. Event-triggered method is designed to guarantee that the system more easily enjoys stable properties. In this method, the impulsive of system is produced by some conditions which combine expectation with impulsive instants. Based on employing L- operator and G-Itˆo formula, this paper proposed consequences supply sufficient conditions for the pth moment asymptotical stability in theory and exhibits the benefits and validity of theoretical analysis. Besides, the non-Zeno behaviour problem is shown in this paper.

MonBIS-05 1417 Optimal Filtering for the 2-D Markov Jump Linear System with Fading Channels Meng Su Univ. of JinanChunyan Han Univ. of JinanThis paper is concerned with the optimal filtering for the two-dimensional (2-D) Markov jump linear system (MJLS) with fading channels, where the two-dimensional system is represented by the Fornasini-Marchesini’s second (FMII) model and the fading channel is described by a stochastic diagonal matrix. The aim of this paper is to design an optimal recursive filter in the 2-D scheme, where the filter gain is subject to Markov jumping property. A probability constraint is required. When the sum of the changing steps in two directions remain the same, the corresponding transition probabilities keep the same. Based on this condition, an optimal recursive Markov jump linear filter under the 2-D framework is designed via the complete mean square method. And the filter gain is deduced based on a recursive coupled difference Riccati equation and a Lyapunov equation. The most challenge exists in this paper is the deduction of the recursive form of the 2-D coupled Riccati equation, where the correlation of the jumping parameter needs to be considered as well as the 2-D circumstances.

MonBIS-06 1419 Finite-Time Stability and Stabilization of Linear Stochastic Parabolic Distributed Parameter Systems Huang Zuo Guangxi Univ. of Science and Tech.Xisheng Dai Guangxi Univ. of Science and Tech.Jianxiang Zhang Guangxi Univ. of Science and Tech.This paper investigates the problem of finite-time stability (FTS) and finite-time stabilization problem for a class of linear Itˆo-type stochastic parabolic distributed parameter systems. First, the concept of FTS and

finite-time stabilization in mean square sense are proposed. Second, several sufficient conditions are given for the concerned systems to be finite-time stable by introducing a Lyapunov function and utilizing linear matrix inequalities (LMIs) technique. Moreover, the results of finite-time stabilization were also given. Finally, a numerical example is given to illustrate the effectiveness of our results.

MonBIS-07 1566 Reflected mean-field backward stochastic differential equations with time delayed generators Hui Min Beijing Univ. of Tech.Chunzhen Wei Beijing Univ. of Tech.In this paper, we focus on a new type of backward stochastic differential equations called reflected meanfield backward stochastic differential equations with time delayed generators (reflected MFBSDEs with time delayed generators, in short). Under some conditions, we establish the existence and uniqueness of the solution.

MonBIS-08 269 Stability analysis for stochastic impulsive system with brownian motion driven by quadratic variation Dejun Zhu Southwest Minzu Univ.Yini Wang Southwest Minzu Univ.Jun Yang Southwest Minzu Univ.In this paper, we aim to explore stochastic stability for nonlinear impulsive system with brownian motion driven by quadratic variation. The mathematical model of impulsive system is made up of differential form and quadratic variation by brownian motion which is independent of the impulsive of the system. Based on employing L-operator, our proposed consequences supply sufficient conditions for γ-moment exponential stability when the impulsive of the system is deterministic. Finally, a practical example is performed to corroborate the benefits and validity of our theoretical analysis.

MonBIS-09 307 Boundary Control of Observation and State Feedback for a Class of 1 D Partial Integro-Differential Equations Aye Aye Than Dagon Univ.Myo Myo Aye Dagon Univ.Naw Sande Ohn Dagon Univ.We consider a problem of boundary stabilization of a class of 1 D linear parabolic partial integrodifferential equations. The state feedback is designed by using the backstepping method. The problem is formulated as a design of an integral operator whose kernel is required to satisfy a hyperbolic P(I)DE. The kernel P(I)DE is then converted into an equivalent integral equation and by applying the method of successive approximations, the equation’s well-posedness and the kernel’s smoothness are established. Then we design the observer to estimate the state variable of the system. Finally, we give simulation results.

MonBIS-10 1131 Necessary and Sufficient Conditions on Stabilization of Unstable Second-Order Delay Systems under PD Control Li Sun Northeastern Univ.Dan Ma Northeastern Univ.Chao Chen AECC Shenyang Engine Research Inst.Unstable delay processess exist in most of the chemical and biological systems. Stabilization of such a delay plant is of great importance. Despite the great achievements of advanced control technologies, it has domenstrated that proportional–integral–derivative (PID) controller plays high impact in practice. This paper investigates the stabilization of second-order systems with two unstable real poles under PID control. By analyzing the stabilizability via the Nyquist stability criterion, we provide necessary and sufficient conditions on stabilization of unstable second-order plant with a constant delay under PD control. Accordingly, an algorithm on how to determine the feasible parameter region of the stabilizing PD controller is also given. Finally, two examples are proposed to illustrate the main results.

MonBIS-11 1572 Output feedback stabilization for distributed parameter systems based on mobile sensor-actuator networks with random measurement delays Jianzhong Zhang Taishan Univ.Guili Lv Taishan Univ.Xiaoqian Li Taishan Univ.Zhengxian Jiang Jiangnan Univ.This paper considers the problem of output feedback stabilization for a class of distributed parameter system. By using the abstract evolution equation of the distributed parameter system, a kind of output feedback control law is designed which is executed by mobile sensor and actuator networks with random measurement delays. Then, sufficient conditions are obtained to stabilize the state of the system by the Lyapunov stability arguments. Meanwhile, control forces to guide the operations of mobile agents are presented which show that the agents’ trajectories are greatly

Technical Programmes CCDC 2021 affected by the probabilities of the random measurement delays. Finally, the MATLAB simulations verify the correctness and effectiveness of the proposed method.

MonBIS-12 989 Fuzzy Active Disturbance Rejection Control for Multi-Motor Networked Control System Jianyu Liu Univ. of JinanFang He Univ. of JinanQiang Wang Univ. of JinanMulti-motor synchronous networked control system (NCS) is widely used in industrial enterprises. In this paper, an improved fuzzy active disturbance rejection control (ADRC) strategy is proposed to reduce the adverse effect of network random delay on multi-motor synchronous control system and improve the synchronous performance of control system. Considering time-varying delay which including long delay and short-time delay, the mathematical model of NCS is established. The improvement of ADRC includes two aspects. First, according to the mathematical model of the control system, the first-order ADRC is adopted to the NCS and tracking differentiator is added to this kind of ADRC to soften the starting process. Secondly, the fuzzy control algorithm is applied to the ADRC to realize real-time tuning of ADRC parameters. One of inputs of fuzzy controller is the network delay. Finally, the simulation model of the three-motor ring coupled synchronous networked control system is set up using Truetime toolbox. The simulation results show that the fuzzy ADRC strategy can obviously improve the tracking performance and synchronization performance of each motor, which proves the feasibility and superiority of the proposed method.

MonBIS-13 1170 Distributed Multi-layer Time-varying Output Formation Tracking Control for Heterogeneous Linear Multiagent Systems Zhe Li Peking Univ.Jiachen Qian Peking Univ.Zhisheng Duan Peking Univ.In this paper, the problem of multi-layer distributed time-varying output formation tracking (TVOFT) control for heterogeneous linear multiagent systems is studied. In the proposed multi-layer structure, multiple formation shapes can be formed in one cluster system. Agents are divided into three layers: virtual leader, subgroup leaders, and followers. The virtual leader provides a reference tracking trajectory for the whole system; The subgroup leader tracks the virtual leader and cooperates with leaders from other subgroups to realize the cooperation among groups; The followers achieve the time-varying output formation tracking of their respective subgroup leaders. Distributed observers with adaptive updating mechanism based on the relative information among the neighbors are established first. Then, a local formation controller is presented by utilizing the estimated state from the observer, the local state, and the desired formation vector. Based on the Lyapunov stability theory, it is demonstrated that the expected multi-layer TVOFT tracking can be accomplished with the control law designed in this paper.

MonBIS-14 1182 Design of Large-Scale Networked Multi-Agent Control Software Framework for Complex Equipments Huai Wu China Academy of Engineering PhysicsBin Yu China Academy of Engineering PhysicsBaoran An China Academy of Engineering PhysicsHuanhuan Fan Graduate School of China Academy of

Engineering PhysicsLarge-scale networked multi-agent control refers to the algorithm or distributed problem solving method in which many simple individuals produce complex intelligent behavior characteristics through mutual cooperation. This paper first introduces the application and development trend of networked multi-agent control technology in military, and analyzes the gaps between domestic and abroad from the perspective of military operations. Finally, the relevant countermeasures are analyzed and a polymorphic large-scale networked multi-agent control software framework is designed to solve the networked multi-agent control problems.

MonBIS-15 1271 Formation Control of Second-Order Multi-Agent Systems By Graph Filtering Yuting Li Wuhan Univ. of Science and Tech.Jing-Wen Yi Wuhan Univ. of Science and Tech.Li Chai Wuhan Univ. of Science and Tech.This paper extends the graph filtering method to the formation control of second-order multi-agent systems. By some model transformation, the formation control problem is convent to the consensus problem of the error system. And the idea of graph filtering is used to design the formation control algorithms. For the case of known graphs, the control sequence of the formation control algorithm is presented to solve the finite-time formation problem. For the case of unknown graphs, a 2-period control algorithm is proposed to achieve the expected formation asymptotically. Finally, numerical simulations are given to verify the

efficiency of theoretical results.

MonBIS-16 21 Distributed Connectivity Maximization for Networked Mobile Robots with Collision Avoidance Yinyan Zhang Jinan Univ.Shuai Li Swansea Univ.Yongdong Wu Jinan Univ.Qingyun Deng Jinan Univ.For networked mobile robots, it is important to have a good initial configuration before executing given tasks, such as cooperative tracking. In this paper, based on our recent work on the distributed estimation of algebraic connectivity for undirected graphs, we present a novel approach to realize autonomous connectivity maximization for networked mobile robots with collision avoidance. The performance of the presented approach is tested via simulations, which substantiate its effectiveness.

MonBIS-17 141 Bearing-based Formation Control of Multi-agents with Dual-leaders Structure Zhixuan Xiao Central South Univ.Xiaoping Fan Central South Univ.

Hunan Univ. of Finance and EconomicsIn this paper, bearing rigidity theory and Routh-Hurwitz stability theory are used to analyze the formation maneuvering of the multi-agent system with dual leaders structure. Different control laws are designed for the second leader and followers respectively and the global formation stability is analyzed. The control law of the followers is distributed because it only needs neighbor information. In the target formation, followers are subjected to bearing constraints while leaders are subjected to bearing and distance constraints. The formation scaling maneuvering can be achieved by adjusting the distance constraint, and the control of the formation’s orientation and rotation can be achieved by adjusting the bearing constraint between two leaders. Finally, some simulation results are provided to verify our analysis.

MonBIS-18 620 Robust H∞ filtering for networked systems with communication delay and data missing Mengyu Gao Hohai Univ.Mingang Hua Hohai Univ.Fan Zhang Hohai Univ.In this paper, we deal with the H∞ filtering problem for a class of discrete-time networked systems with communication delay and data missing. Two kinds of incomplete measurements with random delay and stochastic data missing are considered. The transition probabilities of the Markovian process are uncertain, but they exist in a convex set of known polyhedron types. The main purpose of this paper is to design a robust H∞ filter, which makes the filtering error system stochastic stable and satisfies the given H∞ performance index. A sufficient condition for the existence of the desired filter is established by using linear matrix inequalities. A numerical example shows the effectiveness and applicability of the method.

MonBIS-19 709 Scale-free State Synchronization of Discrete-time Multi-agent Systems in Presence of Nonuniform Communication Delays Zhenwei Liu Northeastern Univ.Donya Nojavanzadeh Washington State Univ.Ali Saberi Washington State Univ.Anton A. Stoorvogel Univ. of TwenteIn this paper we study scale-free state synchronization of discrete-time homogeneous multi-agent systems (MAS) subject to unknown, nonuniform and arbitrarily large communication delays. The scale-free protocol utilizes localized information exchange and is designed solely based on the knowledge of agents’ model and does not require any information about the communication network and the size of the network (i.e. number of agents).

MonBIS-20 500 A Theoretical Analysis on Site Survey in WiFi Fingerprint-based Localization Xiaomeng Li Inner Mongolia Univ.Baoqi Huang Inner Mongolia Univ.The growing interest in indoor location-based services (LBSs) has stimulated many recent developments of indoor localization technologies. Due to the widespread deployment of Wireless Local Area Networks (WLAN), WiFi has become a most promising one. This paper discusses one of the key problems in WiFi fingerprint-based localization, namely how to conduct an efficient site survey. To this end, a probabilistic framework is first established to characterize the ability of distinguishing different fingerprints, and is then extended to multiple fingerprints in one dimensional scenarios. On these grounds, expected localization error is formulated by assuming the well known Lognormal distance path loss (LDPL) model, and moreover, the influence of the key site survey

Technical Programmes CCDC 2021 parameter, i.e. the interval between two neighboring reference points, is investigated. The theoretical analysis reveals that expected localization error increases with the interval between two reference points. Extensive simulations are conducted and confirm the effectiveness of this study.

MonBIS-21 534 Design and Implementation of the automatic jacquard Information Management Platform based on MQTT Mingyu Gao

Hangzhou Dianzi Univ.Zhejiang Province Key Lab of Equipment Electronics

Weiyu Hu Hangzhou Dianzi Univ.Zhejiang Province Key Lab of Equipment Electronics

Huipin Lin Hangzhou Dianzi Univ.Zhejiang Province Key Lab of Equipment Electronics

Yuhan Gao Taiyuan Univ. of Science and Tech.Zhekang Dong

Hangzhou Dianzi Univ.Zhejiang Province Key Lab of Equipment Electronics

In order to facilitate the management of production information for enterprises, this paper designs a production management system of the automatic jacquard based on Internet of Things (IoT) technology. The system consists of hardware, cloud platform and client. The system uses STM32, ESP8266WIFI modules, Message Queuing Telemetry Transport (MQTT) communication protocol and Alibaba Cloud platform to realize the cloud storage of production data of the automatic jacquard. The system has further developed a mobile phone application (app) to realize real-time monitoring and historical query. This system is a typical application of IoT technology in industrial manufacturing. It has the characteristics of stability, reliability and good real-time performance, which meets the needs of daily management of enterprises.

MonBIS-22 595 An aquaculture monitoring system based on NB-IoT LiJun Shu Wuhan Inst. of Tech.XiaoLing Wen Wuhan Inst. of Tech.In the traditional small-scale pond aquaculture field, the vast majority of individual farmers can only carry out the pond aquaculture based on experience and the breeding environment is hard to be accurately monitored, so the lower cost and high profit are unable to be achieved in this paper, an aquaculture monitoring system based on the narrow-band IoT is designed. The system is composed of acquisition and control module, wireless transmission module and user monitoring module. With the STM32L151RCT6 microcontroller as the core, the acquisition and control module are used to collect water quality parameter information and control the actuator to realize the adjustment of water quality parameters. In addition, after the water quality parameter information is transmitted to the STM32F103C8T6 through ZigBee wireless network, it is displayed on the LCD screen in real time and uploaded to the cloud platform. Users can monitor the aquaculture environment through the Web and mobile phone. The experimental results showed that the designed aquaculture monitoring system can realize the collection, monitoring and control of water quality parameters such as dissolved oxygen, PH and temperature.

MonBIS-23 763 Ubiquitous Sensing Intelligent Gateway for Power IoT based on IEC61850 Standard Zhikang Ji Nanjing Univ. of Posts and TelecommunicationsTengfei Zhang Nanjing Univ. of Posts and Telecommunications

Jiangsu Engineering Lab. of Big Data Analysis & Control for Active Distribution Network

Kuihan Chen Nanjing Univ. of Posts and TelecommunicationsJiahong Chen Nanjing Univ. of Posts and TelecommunicationsYi Wang Nanjing Univ. of Posts and TelecommunicationsYirui Zhang Nanjing Univ. of Posts and TelecommunicationsIn view of the fact that existing equipment in the distribution network generally does not support IEC61850, a conversion gateway supporting IEC61850 is designed. This gateway can simultaneously access the traditional protocols used in the field of multiple substations and convert them to the IEC61850 international standard. The gateway system uses a protocol conversion mechanism based on shared memory. The gateway supports a variety of different interfaces and protocols, enabling the equipment of the traditional substation system to be compatible with the IEC61850 standard. It can output standard interfaces to realize the collection of the operating status and parameters of heterogeneous devices and different protocols, and can be used as a core gateway device for constructing the perception layer of a general power Internet of Things system.

MonBIS-24 934 Lightweight RFID Authentication Protocol for Cloud Services using PUF Encryption Qiao YE Jiangnan Univ.ZiWen SUN Jiangnan Univ.

Engineering Research Center of Internet of Things Tech. Applications Ministry of Education

To solve the problem that the existing Radio Frequency Identification (RFID) system authentication protocol is not suitable for large-scale RFID

tag system, a lightweight RFID Security Authentication Protocol Based on cloud server is studied. Using physical unclonable function (PUF), permutation, string matching and other lightweight encryption algorithms to reduce hardware resource consumption. Add timestamp data to authentication information transmitted in wireless channel to ensure the freshness of authentication information. Use double index mechanism for data access in cloud server to prevent the system from asynchronous attack. BAN logic detection is used to prove the untraceable privacy security. Security analysis shows that the proposed protocol can effectively resist multiple attacks. Comparative analysis shows that the resource consumption and security performance of this protocol are better than those of the compared RFID authentication protocols.

MonBIS-25 1079 A system of vibrating wire displacement sensor data acquisition designed base on the NB-IoT Jun Hu Anhui Normal Univ.

Anhui Intelligent Robot Information Fusion and Control Engineering Laboratory

Liguo Qu Anhui Normal Univ.Anhui Intelligent Robot Information Fusion and

Control Engineering LaboratoryZibao Lu Anhui Normal Univ.

Anhui Intelligent Robot Information Fusion and Control Engineering Laboratory

Guohao Chen Anhui Normal Univ.Anhui Intelligent Robot Information Fusion and

Control Engineering LaboratoryVibrating wire sensors are widely used in bridge monitoring, but most of their communication methods are wired or high-power wireless communication methods, which cannot adapt to field bridge monitoring tasks. Therefore, NB-IoT communication technology of the new generation Internet of Things is studied and applied to bridge communication system. As the control core of the system, ARM controller combines Excitation and Pickup Circuits to achieve data acquisition and processing of vibrating wire displacement sensor. Based on the low-voltage progressive frequency sweep mode, the excitation circuit outputs the excitation signal to trigger the forced vibration of the vibrating wire. Vibration frequency signal is collected by vibration picking circuit. Based on frequency and temperature data, quadratic polynomial fitting is used for displacement output fitting. Displacement data is uploaded to the cloud platform by NB-IoT to realize remote real-time monitoring. The test results show that the resolution of measured frequencies is 0.1Hz, and the resolution of displacement is about 0.001mm.the system can meet the design requirements of bridge construction data monitoring with high displacement measurement accuracy, simple communication network and stable data transmission.

MonBIS-26 1220 Research and Design of Greenhouse Environment Monitoring System Based on NB-IoT Jiajian Yang Hefei Univ.Zhenfeng Xu Hefei Univ.Qiang Sun Hefei Univ.The application of Internet of Things technologies in greenhouses is a crucial method to improve agricultural production efficiency and quality. A greenhouse environment monitoring system is designed based on NB-IoT (Narrowband Internet of Things). The system can detect the air temperature, relative humidity, illuminance, and carbon dioxide concentration in greenhouses in real-time and transmit the data to the OneNET cloud platform in a wireless way. Growers can browse the web page through mobile terminals, such as mobile phone, to observe the greenhouse environment information. The experimental results show that the system can detect the greenhouse environment information accurately and run stably and reliably. The automation level of greenhouse environmental monitoring is improved significantly, which lays a good foundation for the au-tomatic control of greenhouse environment.

MonBIS-27 1262 Position estimation of underwater nodes based on relative error Ruomao Yan Wuhan Univ. of Science and Tech.Yajie Ma Wuhan Univ. of Science and Tech.Shaowu Lu Wuhan Univ. of Science and Tech.Yi Hu Wuyi Univ.Fengxing Zhou Wuhan Univ. of Science and Tech.In recent years, the scheme based on Received Signal Strength (RSS) has attracted wide attention in sensor nodes positioning due to its advantage of low cost and lack of synchronization. In this paper, a positioning model is built based on RSS for underwater wireless sensor networks, and the Least Square Relative Error (LSRE) estimation method is adopted to solve the problem of semidefinite programming of prior constraints when the transmission power is unknown. Firstly, the formula of underwater acoustic path loss is approximate to pseudo linear multiplication model by mathematical approximation. Then a nonconvex LSRE problem with the node position and transmission power as variables is established with this model. Finally, a matrix containing compound variables is constructed by using the ascending dimension relaxation technique of semidefinite programming. Based on the external

Technical Programmes CCDC 2021 characteristics of compound variables, prior constraints are added to solve the convex optimization problem. Simulation results show that this algorithm has higher estimation accuracy than that based on absolute error estimation.

MonBIS-28 1373 Design of Safety Supervision System for Regional Hazardous Chemicals Guicheng Wang Shanghai Inst. of Tech.Zian Xia Shanghai Inst. of Tech.Min Zhang Shanghai Inst. of Tech.Chuntao Jia Northwest Engineering&Tech. CorporationJian Ma Shanghai Inst. of Tech.Yuanyuan Han Shanghai Inst. of Tech.By investigating the quo of hazardous chemicals safety management, according to the requirements of hazardous chemicals safety supervision, the solving scheme is proposed. The status information collected in field is uploaded to the cloud server for storage using wireless Internet of Things technology. The supervisory level reads real-time data and relies on the management software analyzes on-site condition online and gives safety supervision decision. The test process of LabVIEW software is given, which proves that system scheme is feasible, and can realize the remote safety supervision of regional hazardous chemicals, and improve the safety management level of government functional departments.

MonBIS-29 1524 The coverage optimization for underwater sensor networks based on SAPSO algorithm Xiaobin Gai Qilu Univ. of Tech.

Shandong Provincial Key Laboratory of Ocean Envir-onmental Monitoring Tech.

National Engineering and Tech. Research Center of Marine Monitoring Equipment

Yifan Hu Qilu Univ. of Tech.Shandong Provincial Key Laboratory of Ocean Envir-

onmental Monitoring Tech.National Engineering and Tech. Research Center of

Marine Monitoring EquipmentPengfei Li Qilu Univ. of Tech.

Shandong Provincial Key Laboratory of Ocean Envir-onmental Monitoring Tech.

National Engineering and Tech. Research Center of Marine Monitoring Equipment

The coverage of underwater sensor network (UWSN) is a basic problem of underwater wireless sensor network, which is related to the integrity and accuracy of underwater sensor network data collection for target monitoring area. In addition, the working environment of underwater sensor nodes is often harsh and the influencing factors are complex, which is not convenient for frequent battery replacement. Therefore, optimizing the coverage of underwater sensor networks can effectively improve the monitoring quality and lifetime of UWSN. Aiming at the shortcomings of previous coverage optimization algorithms, such as easy to fall into the local optimal solution, complex parameters and slow convergence speed, this paper proposes a simulated annealing particle swarm optimization algorithm (SAPSO), which improves the particle swarm optimization algorithm (PSO) by introducing simulated annealing algorithm, so that the algorithm can maintain a faster convergence speed and overcome the shortcomings of PSO algorithm easy to fall into the local optimal solution, so as to improve the coverage of underwater sensor networks, It also makes the distribution of nodes more uniform and reduces the network energy consumption. Simulation results show that SAPSO algorithm can effectively improve the coverage rate of target monitoring area, reduce the redundancy of node coverage and reduce the network energy consumption.

MonBIS-30 522 An EID Load Frequency Control Method for Two-Area Interconnected Power System with Photovoltaic Generation Minghui Yang Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyChunsheng Wang Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyZijian Liu Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyShuhang He Central South Univ.

Hunan Xiangjiang Artificial Intelligence AcademyTwo-area interconnected power system with photovoltaic generation (PVG) has load disturbances and power fluctuations of PVG affected by weather changes. These disturbances can cause system power imbalance and frequency fluctuations. Effective frequency control method is very necessary. This paper proposes an equivalent input disturbance (EID) load frequency control method for two-area interconnected power systems with PVG. The EID method controls the output power of traditional thermal generation subsystem by using the estimated equivalent external disturbance information to form new input signals. In this way, the power fluctuation of PVG and load disturbance are compensated, and the frequency stability is maintained. Compared with the traditional PI control method and the PI control method based on the

firefly algorithm (FA-PI), the results show that the frequency fluctuation range of the proposed EID method is about 90% lower than that of the PI method and FA-PI method. The EID method can more effectively compensate for the effects of external disturbances, suppress frequency fluctuations and stabilize the system.

MonBIS-31 1492 Observer-Based Fault-Tolerant Control for Nonlinear Multi-agent Systems in Finite Frequency Domain Jianliang Chen Wuhan Univ. of Science and Tech.Kang Wang Wuhan Univ. of Science and Tech.Rong Xiang Wuhan Univ. of Science and Tech.In this paper, a fault-tolerant controller is designed for a class of nonlinear multi-agent systems with actuator faults in the finite frequency domain. First, the observer is designed to estimate the value of the state variables. Then, based on the estimated results of the state observer, a robust consensus control protocol is designed. The protocol ensures the external disturbances suppression performance index and the state consensus in the case of fault occurs. The effectiveness of the control protocol is proved in the finite frequency domain. Finally, the simulation results are shown to demonstrate the effectiveness of the consensus control protocol.

MonBIS-32 558 Finite-time H∞ control for stochastic Markov jump systems with uncertainty Long Gao Shandong Univ. of Science and Tech.This article discusses a class of finite-time H∞ control problem for linear stochastic systems with uncertainty and external disturbances. First, the definitions of finite-time stochastic stability and boundedness for linear stochastic systems with uncertainty are given. Then, state feedback controller is designed with the linear matrix inequalities, such that the closed-loop systems satisfy the H ∞ performance conditions, and sufficient conditions for the existence of controllers are presented. Finally, an example is given to show that the state feedback controllers designed in this article are effective.

MonBIS-33 1387 Composite Anti-Disturbance Control of Positive Markov Jump Systems With Disturbances and Time-Varying Delay Yuhan Xu Beihang Univ.Yukai Zhu Beihang Univ.Bo Tian Beihang Univ.Jianzhong Qiao Beihang Univ.Chenliang Wang Beihang Univ.In this paper, the control problem of a positive Markov jump system with multiple disturbances and time-varying delay is considered. First, the analysis of stochastic stability and 1L performance of positive Markov jump systems (PMJSs) with delay is given. Based on disturbance observer-based control (DOBC) and 1L control, a composite anti-disturbance controller for PMJSs is proposed, under which the closed-loop Markov system is stochastically stable and positive and the effect of disturbances can be reduced to satisfy the given performance index. Finally, an illustrative example is presented to show the effectiveness of the proposed scheme.

MonBIS-34 1625 Composite Anti-Disturbance Dynamic Regulation for Systems With Multiple Disturbances: From Stability to Balance Lei Guo Beihang Univ.Yukai Zhu Beihang Univ.Jianzhong Qiao Beihang Univ.Chenliang Wang Beihang Univ.Multiple disturbances widely exist in practical complex control systems, which may destroy traditional system stability with zero equilibrium point (e.g., the asymptotical stability). How to establish a new balance mechanism for control systems in the presence of multiple disturbances needs to be investigated. This paper proposes a kind of composite anti-disturbance dynamic regulation (CADDR) method for the first time, and an example of attitude control system of spacecraft is given to show the application of CADDR. Firstly, the deep-coupling model with multiple disturbances is established, followed by disturbance estimability analysis and disturbance estimation method design. Secondly, a novel CADDR method is proposed to achieve dynamic balance of the system with unknown and time-varying non-zero equilibrium point, where disturbance absorption, compensation and attenuation can be accomplished simultaneously. The proposed CADDR can be regarded as one “green” control technique from the viewpoint of energy consumption. Finally, simulation results are given to verify the effectiveness of the proposed methods.

MonBIS-35 1628 Fuzzy Robust Control of Nonlinear Time-Delay Active Suspension Systems with Limited Frequency Band Yahui Xu South China Univ. of Tech.This paper proposes a finite frequency H ∞ control algorithm for

Technical Programmes CCDC 2021 time-delay active suspension systems based on Takagi-Sugeno (T-S) fuzzy model. First of all, to better investigate suspension dynamics, a typical quarter-car active suspension model with varying masses and actuator time-delay is established for control analysis and design base on a set of T-S fuzzy rules. Then, considering that the human body is much more sensitive to vertical vibrations of 4-8 Hz, a finite frequency performance criterion is presented as the guidance for control design. Meanwhile, other mechanical constraints such as suspension travel and tire dynamic deflection are also considered to guarantee the dynamic stability of suspension systems. Finally, this algorithm is implemented on a quarter suspension test rig. Experimental results show that the proposed approach is more effective to improve suspension body acceleration performances while the robustness and stability of the systems are maintained simultaneously.

MonBIS-36 1675 Projective synchronization of a class of complex-variable chaotic systems by the UDE-based dynamic control method Yaru Zhang Qilu Univ. of Tech.Zuosheng Sun Qilu Univ. of Tech.Rongwei Guo Qilu Univ. of Tech.This paper investigated the projective synchronization of a class of complex-variable chaotic systems with both model uncertainty and external disturbance. First of all, for a given chaotic system with complex variables, the existence of projective synchronization has been proved, and all solutions to this problem have been obtained. Then, a systematic method is developed, through which a complex variable chaotic system is transformed into its corresponding equivalent real-variable chaotic system. Furthermore, a simple and physically achievable controller was designed for the real-variable chaotic system by the uncertainty and disturbance estimation (UDE)-based dynamic control method, thereby obtaining the controller for the original complex variable chaotic system. Finally, illustrative examples based on numerical simulations were used for verifying the correctness and effectiveness of the above theoretical results.

MonBIS-37 47 Non-cascaded Predictive Speed Synchronous Control of Dual Permanent Magnet Motor System without Weight Coefficient Tuning Xiuyun Zhang Tianjin Univ. of Tech. and EducationZhiqiang Wang Tianjin Univ. of Tech. and EducationXiaolin Li Tianjin Univ. of Tech. and EducationJie Bian Yunnan Industrial technician collegeIn order to improve the synchronous tracking accuracy of the dual permanent magnet motor system, this paper proposes a control strategy for dual permanent magnet motor system based on finite control set. Firstly, based on the idea of unified modeling, the motion equation and electrical equation of the driving motor, the mathematical model of the two-level voltage source inverter are combined to establish a unified model. Then, the reference voltage vectors with current and speed information are predicted. In order to solve the problem of weight coefficient tuning in the traditional cost function, and simplify the complexity of the algorithm, the improved cost function is determined according to the ratio of contour error and tracking error, then the candidate voltage vectors are selected and the cost function is optimized. Finally, the simulation research proves that the proposed control strategy can meet the dynamic and steady-state demand and synchronous control accuracy without weight coefficient tuning.

MonBIS-38 122 Research on path planning based on improved RRT-Connect algorithm Hongtao Yang Changchun Univ. of Tech.Huanyu Li Changchun Univ. of Tech.Keping Liu Changchun Univ. of Tech.Weibo Yu Changchun Univ. of Tech.Xiaoyuan Li Changchun Univ. of Tech.Aiming at the low efficiency of path planning of RRT-Connect algorithm and random sampling, an improved algorithm based on RRT-Connect is proposed. The algorithm introduces a target bias strategy on the original RRT-Connect algorithm, guides the sampling point to expand in the direction of the target point, and changes the length of the step during the random tree expansion process, thereby increasing the speed of the random tree exploring the space, and finally adopts the greedy algorithm Prune the random tree to achieve path smoothing. The improved RRT-Connect algorithm is compared with the RRT and RRT-Connect algorithms respectively, and the results show that the algorithm proposed in this paper is significantly better than the compared algorithm in terms of path length and execution time.

MonBIS-39 160 Application of Grey Model Based on Twice Fitting in Short-term Power Load Forecasting Mengmeng Hou Inner Mongolia Univ. of Tech.Linjing Hu Inner Mongolia Univ. of Tech.The accuracy of short-term load prediction is related to the safety

planning and smooth operation of power system, and plays an important role in the safety planning of power system and power supply of power grid. In order to solve the problems of the quadratic fitting grey prediction model, such as weak noise reduction ability and strict requirement on the stability of historical data, this paper proposes an improved method to combine the quadratic fitting grey prediction model with equal dimension and new information technology, and to modify the prediction results by using Fourier series. Problems existed in the work of basic data noise, the historical data preprocessing, considering the number of data pretreatment could lead to input new data equals the number of old data don't delete (called the new interest is unequal), analysis in the case of the new interest is unequal, based on Fourier residual correction of quadratic fitting grey prediction precision of the model. The simulation results show that the accuracy test level of the improved model is level 1, and the prediction model has good stability and the accuracy meets the expected requirements in the case of different dimensions of new information.

MonBIS-40 256 A Forecast of the Quantity of Rural Migrant Workers in Chinese Construction Industry---Based on the Grey System Theory Wenfang SUN Yunnan Univ. of Finance and EconomicsQifa JIANG Yunnan Univ. of Finance and EconomicsBased on the grey system theory, using the number of rural migrant workers in China from 2013 to 2019 as the raw data, a short-term forecast of the quantity of rural migrant workers in construction industry from 2020 to 2025 is made by means of establishing a G,M(1,1) prediction model. The forecast shows that the slightly decreasing trend will result in a less-than-50million workers number by the year of 2021. Then, the test of the G,M(1,1) model indicates that the prediction accuracy will be up to 98%, i.e. the relative error will be less than 2%, for the number of rural migrant workers in construction industry. We can conclude that the prediction level will be Excellent, and the results will reach a high level of reliability. Thus, in order to facilitate the development of the industry, relevant suggestions will heavily focus on the transformation of the industry, such as promoting technical progress, improving the quality of migrant workers and accelerating the process of professionalization, enhancing the state of social security and labor rights for migrant workers to attract more talents, etc.

MonBIS-41 455 The Research on Predictive Function Control of Double-Pendulum Overhead Crane Xuanjun Li Taiyuan Univ. of science and tech.Xuejuan Shao Taiyuan Univ. of science and tech.Zhimei Chen Taiyuan Univ. of science and tech.When the quality of crane hook cannot be ignored, the system presents double-pendulum effect with higher underactuation, and the corresponding complexity and non-linearity increase, which makes the control of the system more difficult. In order to solve this problem, this article fully considers the error between the actual output and the predicted output, and applies the predictive function control (PFC) strategy to the positioning and anti-sway system of the double-pendulum overhead crane. According to the nonlinear mathematical model, the T-S fuzzy model is designed, and the linear quadratic regulator (LQR) is used to obtain the state feedback matrix to stabilize the controlled object, obtain the generalized controlled object, and discretize the obtained generalized controlled object. And using it as a predictive model to design a predictive function controller for a double-pendulum overhead crane. Simulation results show that the proposed method can not only realize rapid positioning, but also effectively suppress the pole swing and residual swing, and it still has good stability and robustness under the condition of adding disturbance or changing parameters.

MonBIS-42 486 A New Anti-interference Fitering Algorithm for Quad Rotor UAV Erdong Shi Henan Univ.Jiakang Xu Henan Univ.Xiao Yang Henan Univ.Chensen Tang Henan Univ.Pengyi Zhang Henan Univ.Dehua Zhang Henan Univ.Chunbin Qin Henan Univ.In recent years, quad rotor Unmanned Aerial Vehicle (UAV) are now used in a broad range of applications. However, due to the interference of White Gaussian Noise(WGN) in the attitude detection data of the aircraft, the stability of the aircraft is poor. These shortcomings are sometimes fatal to UAV. Aiming at the uncertainty of UAV in the course of flight attitude adjustment due to the noise interference of the data obtained by the controller of the quad rotor UAV, a new anti-interference filtering algorithm based on Unscented Kalman Filter(UKF) is designed. The algorithm adopts Kalman linear filtering framework, and uses Unscented Transform(UT) to deal with the nonlinear transfer problems of mean value and covariance. Compared with the traditional Kalman Filter and Extended Kalman Filter(EKF), the simulation results show that the proposed UKF algorithm can effectively reduce the output noise of the control system and have marked improvements in the control performance.

Technical Programmes CCDC 2021 MonBIS-43 556 Research and analysis of the prediction model of wiped film evaporation process based on PSO-SVR Hui Li Changchun Univ. of Tech.Hailiang Xu Changchun Univ. of Tech.Qiliang Zhao Changchun Univ. of Tech.Hao Wang Changchun Univ. of Tech.Due to the relatively high cost of parameter measurement in the wiped film molecular distillation process, the relatively low accuracy obtained and the relatively long measurement time, the soft-sensing model established by traditional support vector machine regression has relatively poor predictive effect. So for the solution of these problems, this paper constructs a combined optimization model based on support vector machine regression and particle swarm algorithm, which uses Partical Swarm Optimization to optimize the penalty factor coefficient C and kernel function coefficient g of support vector machine, and then uses Support Vector Machine regression algorithm (SVR) to realize the prediction of process parameters in the distillation process. At the same time, this paper uses SVR and BP neural network to predict the process parameters. The prediction results are compared with PSO-SVR. The experimental results show that the effect of the soft sensor prediction model of PSO-SVR is much stronger than that of the other two models. PSO-SVR has Higher accuracy, and it can be more economical and practical when faced with more complex measurement systems.

MonBIS-44 561 A Model Predictive controller of a direct expansion air conditioning system for simultaneous temperature and humidity control Na Li Shandong Univ.Xinli Wang Shandong Univ.Youjie Zhan Shandong Univ.Lei Wang Shandong Univ.Lei Jia Shandong Univ.For direct expansion air conditioning systems, simultaneous control of indoor temperature and humidity has always been a tough challenge on account of the complexity of heat and mass transfer. To overcome this issue, in this paper, a controller based on model predictive control (MPC) method is developed for a direct expansion air conditioning system. First, the structure of direct expansion air conditioning systems is briefly introduced and the nonlinear state space model is established by mass and energy balances. By the Runge-Kutta method, the nonlinear model is directly used to build prediction model used in MPC controller. Then, the design of MPC algorithm is described in detail. The cost function is a positive definite quadratic function about indoor temperature and humidity. Finally, the simulation results demonstrate that the MPC controller can regulate indoor temperature and humidity to their respective desired values and enhance the dynamic quality, with shorter settling time, smaller the overshoot, compared to PID control strategy.

MonBIS-45 574 Predictive current control of three-phase three-wire APF with error feedback correction Jialin Huang Dongguan Univ. of Tech.

Shenzhen Univ.Zhi Zhang Dongguan Univ. of Tech.Shaoyong Wang Shezhen Rspower Technology Co., Ltd.Zhaoyun Zhang Dongguan Univ. of Tech.In this paper, a three-phase three-leg shunt active power filter (SAPF) which is based on model predictive control(MPC) is presented. First, the mathematical model of three-leg SAPF is derived, and the predictive current control is adopted for the current loop in order to improve the tracking dynamic response of the compensation current, optimize the effect of compensating harmonic current and improve the adaptive ability of the SAPF. To ensure the reliability of APF system, traditional PI control is introduced into voltage loop to maintain the stability of DC voltage, minimize overshoot and static error. Finally, simulation results show that the proposed MPC method is correct and feasible.

MonBIS-46 600 Dynamic Construction Method of Aircraft Cable Assembly Process Model Based on SLAM Algorithm Kaihai Yang Jiangxi Hongdu Aviation Industry Group

Limited Liability CompanyYaowen Guo Nanchang HangKong Univ.Gang Yuan Nanchang HangKong Univ.Shiming Cheng Jiangxi Hongdu Aviation Industry Group

Limited Liability CompanyFalin Wang Nanchang HangKong Univ.Aiming at problems such as the fact that the aircraft cable assembly process file is not related to actual data and the pose uncertainty of the cable assembly path in actual assembly, this paper proposes a method for constructing a dynamic model of aircraft cable assembly process based on Simultaneous Localization And Mapping (SLAM) algorithm. SLAM of Random Finite Set (RFS) performs position and posture tracking of cables during the laying process to dynamically construct the process model of aircraft cable assembly. In the framework of RFS, this method uses Gaussian Mixture Probability Hypothesis Density

(GM-PHD) to predict and update the feature information of map components according to the data collected by the sensor. Using Square Root Transformed Unscented Kalman Filter (SRTUKF) and a single feature strategy to estimate the pose of the aircraft cable assembly path. Finally, the method proposed in this paper is verified by an example.

MonBIS-47 659 Short-term load forecasting based on two-stage error compensation Linjing Hu Inner Mongolia Univ. of Tech.Zhaoze Guo Inner Mongolia Univ. of Tech.Jingshuai Wang Inner Mongolia Univ. of Tech.Power load forecasting is related to the optimal dispatch and resource allocation of the power grid, but load forecasting is bound to be accompanied by errors. This paper proposes a short-term load forecasting method based on two-stage error compensation. The data after denoising using the EMD method is used for the training of the prediction model and the error prediction model, and the load forecast value and the forecast error value of the day of the forecast are obtained respectively, the forecast error is used for the first correction of the forecast load. Due to the "peak-valley" characteristics of the electric load, the value of each "peak-valley" points is predicted by the trend extrapolation method for the second correction. Finally, a case analysis was carried out based on the power load data of a certain place in Ningxia. The results show that the prediction accuracy of the proposed method is greatly improved, which verifies the feasibility of the method.

MonBIS-48 665 Integral-based Event-triggered Model Predictive Control for Perturbed Nonlinear Systems under Two-channel Transmissions Xiaoda Hu Beihang Univ.Fei Hao Beihang Univ.This paper investigates the event-triggered model predictive control problem for a nonlinear continuous-time plant subject to constraints and bounded disturbances. An absolute triggering condition is employed in the sensor node to reduce the transmission rates and an integral-based one is introduced into the controller node to save the computational resources. In order to ensure that the system states still fulfill the constraint in the presence of disturbances and aperiodic transmissions, a tightened constraint on the predicted state is designed. In addition, a dual-mode control strategy which is combined with the event-triggered mechanisms is utilized. By constructing a feasible control sequence, the sufficient conditions are derived to guarantee the feasibility and stability of the closed-loop system. Finally, a simulation example is provided to show the feasibility and effectiveness of the proposed strategy.

MonBIS-49 670 Neural Network Based Nonlinear Model Predictive Control for Two-stage Turbocharged Diesel Engine Air-path System Chang Ke Beijing Inst. of Tech.Kai Han Beijing Inst. of Tech.Ying Huang Beijing Inst. of Tech.Xu Wang Beijing Inst. of Tech.Sichun Bai China North Engine Research Inst.The air-path system of the two-stage turbocharged diesel engine, the characteristics of which include strong nonlinearity, time delay, coupling and constraints, increases the difficulty in engine control. To solve the control problem of the system, a nonlinear model predictive (NMPC) controller based on nonlinear autoregressive model with exogenous input neural network (NARXNN) is developed. At first, a boost pressure predictive model, of which fuel injection quantity is the first input and bypass valve opening is the second input, and the boost pressure is the output, is established based on NARXNN. Through simulation analysis, the absolute error between the output value of the plant model and the predictive model is smaller than 0.05 bar. Then the predictive accuracy of the predictive model when the predictive horizons are different is analyzed, and the Mean Absolute Percentage Error (MAPE) is less than 2% when the predictive horizon is within 30, indicating that the predictive model has good multi-step predictive performance. At last, the NMPC controller based on the NMPC toolbox in MATLAB is established. And the the step response performance and reference-tracking performance of the controller are verified in the co-simulation platform formed by GT-Power and MATLAB/Simulink. It can be concluded from the results that the step response performance of the NMPC controller is better than that of the PID controller, and the relative error of the reference- tracking simulation is smaller than 15%.

MonBIS-50 710 A parameter compensation FCS-MPC-based temperature balance control method for rectifier Tao Peng Central South Univ.Liangliang Zhang Central South Univ.Chao Yang Central South Univ.Zhiwen Chen Central South Univ.Xinyu Fan Central South Univ.Aiming at the problems of DC voltage unbalance, grid side power factor not close to unity power factor and temperature unbalance between

Technical Programmes CCDC 2021 insulated-gate bipolar transistors (IGBTs), a parameter compensation finite control set model predictive control-based (FCS-MPC) temperature balance control method for single-phase three-level neutral point clamped (3L-NPC) rectifier is proposed in this paper. The parameter compensation FCS-MPC method can achieve parameter online compensation and improve unity power factor under the condition of model parameter mismatch. Meanwhile, the energy loss prediction model of IGBTs is established to realize the temperature balance of rectifier. Compared with the traditional transient current control method, the proposed method can improve the grid side current harmonics and unity power factor, and solve the problem of temperature unbalance. The method proposed in this paper is tested on Simulink, and the simulation results show the effectiveness of the compensation and temperature balance.

MonBIS-51 802 Jamming technology of infrared guided missile based on surface-type infrared decoy method Wei Sun Xijing Univ.Yatin Zhang Xijing Univ.Yu Wang Xijing Univ.In order to study the jamming strategy of infrared guided missile, surface-type infrared decoys are used. In this paper, the influence of carrier maneuver, the decoy release time and method on the jamming efficiency of surfacetype infrared decoy was simulated and analyzed. In the simulation analysis, two most common and effective defense maneuvers are selected, namely barrel roll maneuver and S-type maneuver. Taking hit ratio as evaluation index, the optimal interference strategy under two stages is obtained.

MonBIS-52 913 Path Tracking of Underwater Vehicles Based on Adaptive Model Predictive Control Jinghao Yan Jiangsu Univ. of Science and Tech.Weiran Wang Jiangsu Univ. of Science and Tech.

Nanjing Univ. of Aeronautics and AstronauticsMeng Xu Jinshan Vocational Technical CollegeGuanjun Yang Jiangsu Univ. of Science and Tech.Zhiyu Zhu Jiangsu Univ. of Science and Tech.Aiming at the three-dimensional trajectory tracking problem of unmanned autonomous underwater vehicle (AUV) in maritime work, an adaptive predictive path tracking control method is designed in this paper. According to the motion control model of underwater vehicle space-time system, after the path error in three-dimensional space is linearized, the trajectory tracking problem is transformed into a standard quadratic programming problem, and the actual constraints of system input and state variables are included in the optimization calculation to ensure the accuracy, dynamics and anti-interference of path tracking. Aiming at the shortcomings of fixed model parameters in traditional predictive control. This paper proposes to design adaptive predictive controller by using Kalman filter algorithm to update the parameters in each control cycle. This method effectively improves the dynamic and stability of the system and further improves the control accuracy. The simulation results of Matlab/Simulink show that the adaptive predictive control has better control performance.

MonBIS-53 1091 Robust optimal predictive perimeter control for multiple urban regions based on macroscopic fundamental diagram Wangyan Chen Beijing Jiaotong Univ.Huimin Zhang Beijing Jiaotong Univ.Yin Yuan Beijing Jiaotong Univ.The robust optimal predictive perimeter control for multiple urban regions is investigated in this paper based on a macroscopic fundamental diagram(MFD), which incorporates the uncertainty of the parameters in MFD. The goal of the formulated robust optimal control problem is to adjust the accumulate states to the desired states and to ensure the robustness of the system, so as to reduce the traffic congestion. A real-time algorithm under the robust model predictive control(MPC) scheme is proposed to solve the problem. Then, numerical examples are conducted in the paper to illustrate the practicality and robustness of the model, which can provide a useful and robust way to ensure the desired states in the MFD system.

MonBIS-54 1213 Research on Control Method of Weight Compensation at the End of Concrete Pouring Pass Dong li Shenyang Jianzhu Univ.Chenglong Zhang Shenyang Jianzhu Univ.Ke Zhang Shenyang Jianzhu Univ.Yuhou Wu Shenyang Jianzhu Univ.Wenda Yu Shenyang Jianzhu Univ.Hui Sun Dalian detai Modern Construction Tech.

Co., Ltd.In order to improve the weight accuracy of the concrete pouring of the precast components, the control method for weight compensation at the end of the concrete pouring pass is researched and designed. First,

starting from the production process of concrete pouring of precast components, the reasons for the weight deviation of the concrete pouring at the end of the pass are analyzed. Then, the control idea of weight compensation at the end of the pass is designed. Based on the kinematics theory and control theory, the geometric weight model for component and pouring weight model at the end of the pouring pass are established. Using the equal relationship between the above two models, the control solution of weight compensation at the end of the pass is obtained by mathematical derivation. At last, the execution steps are given. The actual production data of concrete pouring is used to carry out experimental verification. The results show that the control method for weight compensation at the end of the pass can significantly reduce the deviation of the concrete weight at the end of the pass. It can not only improve the accuracy of the pouring weight of precast concrete components, but also reduce the manual intervention and increase productivity.

MonBIS-55 1232 Design of CRAFT cryogenic control system based on EPICS Tingyu Feng Inst. of plasma Physics, and Hefei Inst.

of Physical ScienceUniv. of Science and Tech. of China

Zhiwei Zhou Inst. of plasma Physics, and Hefei Inst. of Physical Science

Qiang Yu Inst. of plasma Physics, and Hefei Inst. of Physical Science

Univ. of Science and Tech. of ChinaThe Comprehensive Research Facility for Fusion Technology(CRAFT) cryogenic control system is composed of a helium refrigerator cryogenic distribution system, cryogenic auxiliary system, and research platform of cryogenic key technology. It aims to design and build a cryogenic control system suitable for the development of future fusion reactor. In this paper, the CRAFT cryogenic control system is briefly introduced. It is concluded that the control system based on the EPICS framework is suitable for the future development direction of fusion reactors by analyzing its functions and requirements, and discussing the EPICS framework and its application in cryogenic control systems. Finally, the preliminary design of the CRAFT cryogenic control system based on EPICS is given.

MonBIS-56 1652 Optimal PI controller tuning for dynamic TITO systems with rate-limiters based on parallel grid search Jingcheng Zhang Shandong Univ. of Science and Tech.Jiandong Wang Shandong Univ. of Science and Tech.Mengyao Wei Shandong Univ. of Science and Tech.Yanan Zheng Shandong Univ. of Science and Tech.Zijiang Yang Shandong Univ. of Science and Tech.As dynamic nonlinearities, rate-limiters make it difficult to obtain globally optimal parameters for proportional-integral (PI) controllers to optimize a given control performance index. This paper proposes a parallel grid-search method to determine optimal PI controller parameters. First, the latent parameter set is formulated subject to feasible ranges and search steps for controller parameters. Second, values of the control performance index are calculated for all members in the latent parameter set. The parameters corresponding to the best control performance index are determined as optimal PI controller parameters. Parallel computations at multi-core CPUs are exploited to ensure all required computations to be completed within a given time span. Numerical and experimental examples are provided to support the proposed method.

MonBIS-57 197 Permanent Magnet Synchronous Motor Control of Electromechanical Actuator Based on Parameter Self-tuning Method Yunxiao Lian Northwestern Polytechnical Univ.Yingnan Guo Northwestern Polytechnical Univ.Jiang Chang Beijing SunWise Space Tech. Ltd.Shuai Wu Northwestern Polytechnical Univ.Yong Zhou Northwestern Polytechnical Univ.With the wide application of electromechanical actuator(EMA) system in aviation industry, permanent magnet synchronous motor, as a key component of EMA, has a good application prospect due to its unique advantages. In this paper, based on the analysis of relevant research at home and abroad, the control system of PMSM with EMA is designed, and the control system model is built with MATLAB and the simulation experiment is carried out. At the same time, parameter self-tuning and fuzzy control methods are used to optimize the performance of the control system. In order to test the performance of the EMA servo control system, this paper designs the EMA system performance test platform, completes the hardware and software design of the test bench, and carries out relevant tests to test the performance of the optimized EMA system. The experimental results show that the parameter self-tuning method used in this paper has a very significant performance improvement for PID control.

MonBIS-58 242

Technical Programmes CCDC 2021 Local Sliding Mode Control Design for T-S Fuzzy Systems with Magnitude and Rate Limited Input Zhina Zhang East China Univ. of Science and Tech.Jiarui Li East China Univ. of Science and Tech.Yugang Niu East China Univ. of Science and Tech.Jun Song East China Univ. of Science and Tech.This work focuses on the sliding mode control (SMC) for a class of T-S fuzzy systems subject to input saturation including bounded magnitude and rate. As the fuzzy model is valid only in the modeling region, it makes more sense to consider the local stabilization of the controlled system. A key issue is how to design an SMC law under the magnitude and rate constraints. To this end, an augmented fuzzy system is firstly developed, in which the input rate can be regarded as an auxiliary control variable. The actual control law is subsequently synthesized as the sum of this auxiliary controller and the actual control at the previous instant. Based on an ellipsoid set, the domain of attraction (DA) is estimated, from which the states originating can remain in the pre-specified modeling region. The magnitude and rate constraints can be guaranteed by converting these constraints into matrix inequalities. Furthermore, sufficient conditions are derived to achieve the local stability of the closed-loop system and the reachability of the prescribed sliding surface. A simulation example illustrates the proposed control method.

MonBIS-59 249 Energy Management Strategy of Full-power Fuel Cell Vehicle Based on Fuzzy Correction Xiaohua Zeng Jilin Univ.Meijie Song Jilin Univ.Dafeng Song Jilin Univ.Renhuan Ji Jilin Univ.Considering the characteristics of the full-power fuel cell vehicle, a power following control strategy based on fuzzy correction is proposed to improve the vehicle economy. At the same time, the battery life issues caused by frequent opening of fuel cell and excessive charging and discharging of battery is taken into account. On the basis of the power following strategy, the fuzzy control is adopted to consider both the battery SOC and the required power. The power correction coefficient is adjusted online in real time to improve the operating point distribution of fuel cells while correcting the SOC. The vehicle simulation model is established by Matlab/Simulink, and the control strategy is simulated in an offline environment. Finally, the simulation results show that the energy management strategy proposed in this paper improves the hydrogen consumption of the vehicle by 1.37% per 100 km and the average fuel cell efficiency by 1.14%.

MonBIS-60 1080 Delay-Dependent Guaranteed Cost Control for Interval Type-2 T-S Fuzzy Descriptor System with Uncertain and Time-Delay Qian Feng Shenyang Jianzhu Univ.Baoyan Zhu Shenyang Jianzhu Univ.Shuangyun Xing Shenyang Jianzhu Univ.This paper studies the issue of delay-dependent guaranteed cost controller design for a kind of uncertain descriptor systems with time-delay described by the interval type-2 (IT2) Takagi-Sugeno (T-S) fuzzy model. By using the Lyapunov stability method and combining with the theory of IT2 fuzzy sets, a sufficient condition on the existence of a delay-dependent guaranteed cost controller is given to guarantee the closed-loop system not only admissible but also an appropriate performance index. The design conditions on the time-delay guaranteed cost controller are expressed as linear matrix inequality (LMI) by introducing slack matrices and also considering the information of uncertainties. Finally, in order to illustrate the effectiveness of the proposed approach a numerical example is provided.

MonBIS-61 1135 Robotic Peg-in-Hole Assembly System Based on Vision and Fuzzy Control Ziman Zhang Ningbo Institute of Materials Tech. and

EngineeringUniv. of Chinese Academy of Sciences

Zaojun Fang Ningbo Institute of Materials Tech. andEngineering

Hongyuan Lian Ningbo Institute of Materials Tech. and Engineering

Chi Zhang Ningbo Institute of Materials Tech. and Engineering

Guilin Yang Ningbo Institute of Materials Tech. and Engineering

A robotic peg-in-hole assembly system based on vision system and force feedback is studied. The whole assembly process consists of three stages. The first step is to locate the position of the hole through the vision system roughly and move the peg to the top of the hole. The next is to search for the center of the hole with the search trajectory of concentric circles after the peg is in contact with the edge of the hole. In the final stage, the insertion process is completed by using the active compliance control method of fuzzy control. Experiments on a six-degree-of-freedom robot arm verify the effectiveness of the assembly strategy.

MonBIS-62 1289 Security Filter Design for T-S Fuzzy System with Adaptive Event-Triggered Mechanism and Multiple Attacks Zeyu Yang Nanjing Univ. of Finance and EconomicsJie Cao Nanjing Univ. of Finance and EconomicsJinliang Liu Nanjing Univ. of Finance and EconomicsThis paper studies the security filter design problem for Takagi-Sugeno (T-S) fuzzy systems with adaptive event- triggered mechanism (AETM) and hybrid cyber-attacks. Firstly, a hybrid cyber-attacks model is established for the T-S fuzzy system, which includes deception attacks, replay attacks and DoS attacks. Secondly, in order to reduce unnecessary waste of network bandwidth resources, an adaptive event-triggered mechanism is introduced, which can adaptively adjust the trigger condition threshold. Then, by considering hybrid cyber-attacks and adaptive event-triggered mechanism, a filter error model is established. Furthermore, based on the Lyapunov-Krasocskii stability theory, sufficient conditions are obtained to ensure the stability of the system, and the parameters of the designed filter are clearly given. Finally, a simulation example is given to illustrate the effectiveness of the method.

MonBIS-63 1312 A Multi-Level Granular Classification Model Based on Granularity Refinement Hao Liu Dalian Univ. of Tech.Degang Wang Dalian Univ. of Tech.Hongxing Li Dalian Univ. of Tech.A multi-level granular classification model (MGCM) based on granularity refinement is proposed in this paper. First, based on the principle of justifiable granularity, a series of information granules are constructed. Then, the information granules are refined with different granularity levels according to the uncertainty of the information granules. Accordingly, the granular classification model is composed of serval sub-classification models with different granularity levels. The complexity and classification accuracy of the model can be taken into account when the data are described by information granules with different granularity levels. In each sub-model, the fuzzy C-means (FCM) method is considered to classify data. And particle swarm optimization algorithm is used to optimize the parameters. Some numerical examples are provided to illustrate the validity of the proposed model.

MonBIS-64 1392 Prescribed Performance Tracking Control of Nonlinear Systems with Unknown Control Directions Xiaolin Wang Air Force Engineering Univ.Jihui Xu Air Force Engineering Univ.Jing Zhang Air Force Engineering Univ.Ning Wang Air Force Engineering Univ.This work addresses the fuzzy adaptive tracking control problem for a larger class of uncertain r-Norm nonlinear systems whose control directions are partial unknown. Different from the existing literatures, both no a priori knowledge on the control direction is needed, and some specified performances (e.g., maximum overshoot and convergence rate) are taken into account simultaneously in this paper. To pursue this, the adding one power integrator method is skillfully incorporated into the design control scheme and the new design involves an extension of conditional inequality appearing in the state of the art which enables multiple Nussbaum gain functions to achieve control robustness while ensuring the closed-loop stability. It is proved that all the signals in the resulting closed-loop system are bounded and that the tracking errors converge to a small residual set with the prescribed performance bounds. The effectiveness of the proposed approach is validated by simulation result.

MonBIS-65 1393 On the Convex Combination of Triangular Subnorms Wenjing Sun Qilu Univ. of Tech.Qi Li Qilu Univ. of Tech.Lizhu Zhang Qilu Univ. of Tech.Qigao Bo Qilu Univ. of Tech.Gang Li Qilu Univ. of Tech.In this paper, the convex combination of triangular subnorms is studied. The weighted quasi-arithmetic mean of the drastic product TD and continuous triangular norms are discussed firstly. Then the convex combination and the weighted quasi-arithmetic mean of triangular subnorms are discussed and it is shown that under some conditions, they are still triangular subnorms.

MonBIS-66 1413 Fuzzy Fractional-order PID Control of The Winding Web Tension in Flexible Electronic roll-to-roll manufacturing process Tao Xiong Wuhan Inst. of Tech.Dangdang Zou Wuhan Inst. of Tech.Qingwei Zhang Wuhan Inst. of Tech.Zicheng Li Wuhan Inst. of Tech.The winding system of the flexible electronic roll-to-roll manufacturing

Technical Programmes CCDC 2021 process is located at the end of the equipment, the web tension accuracy of the winding system directly affects the quality of the final flexible electronic products. Velocity disturbance is the key problem of the web tension control in winding system. In this paper, the web dynamics of the winding system is studied, the tension and velocity models of winding web are established, and the cascade tension control model of winding roller is designed. The numerical realization method and steps of fuzzy fractional PI D l μ (FFPID) controller are presented for winding tension. The structure of the FFPID controller is constructed by using fractional PID unit to replace the PID unit in the traditional fuzzy PID (FPID) controller. The flexibility and freedom of the integer-order PID controller not only increase, but also the adaptive adjustment of controller parameters is realized. Compared with PID and FPID control, the FFPID algorithm proposed in this paper improves the terms of time-domain indexes such as steady-state accuracy, regulation time and overshoot, so as to obtain better control performance. Thus, the quality of roll-to-roll flexible electronic products is guaranteed.

MonBIS-67 1438 An Upper and Lower Approximation Reasoning Method Based on Conditional Intuitionistic Fuzzy Triangle Norm Ye Tian Air Force Engineering Univ.Longyue Li Air Force Engineering Univ.Changan Shang Air Force Engineering Univ.Pengsong Guo Air Force Engineering Univ.Dingding Qi Air Force Engineering Univ.The intuitionistic fuzzy rough set (IFRS) model based on intuitionistic fuzzy triangle norm and an upper and lower approximation reasoning method based on conditional intuitionistic fuzzy reasoning in IFRS were studied. The correlation in the description of the similarity of things between intuitionistic fuzzy logic and intuitionistic fuzzy rough set was discussed. That is, the inference result based on CRI method in intuitionistic fuzzy logic reasoning is the upper approximation of the given evidence as a intuitionistic fuzzy rough set based on intuitionistic fuzzy triangle Norm. An IFRS model was introduced on the basis of rough set and fuzzy rough set, and Conditional intuitionistic fuzzy reasoning was extended into IFRS environment. Then a new upper and lower approximation reasoning algorithm based on conditional intuitionistic fuzzy triangle norm was proposed. Finally, the correctness of the proposed algorithm was validated via a numerical example. The facts have proved that the IFRS model proposed in this paper is reasonable, and the new reasoning algorithm is feasible.

MonBIS-68 1691 Guaranteed-Performance Adaptive Fuzzy Control Scheme For Uncertain Nonlinear SystemsWith Prandtl-Ishlinskii Hysteresis Weijun Huang Guangdong Univ. of Tech.Guanyu Lai Guangdong Univ. of Tech.Kai Huang Guangdong Univ. of Tech.Junwei Wang Guangdong Univ. of Foreign StudiesKairui Chen Guangzhou Univ.When using a smart actuator to drive an uncertain unparametrizable nonlinear system, it is still a challenging problem to design an adaptive controller such that the system output tracking error can converge to a prescribed interval asymptotically, due to the existence of actuator hysteresis nonlinearity and plant nonlinearities. In the paper, we give a solution to the problem, based on an unsaturated Prandtl-Ishlinskii hysteresis model and the well-known lower-triangular form nonlinear system model. The techniques of direct adaptive fuzzy control, nonlinearity decomposition, and backstepping recursive design are used, and with our scheme, it can be shown that all signals of the closed-loop system are bounded, and the plant output can track any given reference signal asymptotically with a user-defined accuracy. The results are also illustrated through simulation studies.

MonBIS-69 124 Equivalent Modeling Based on Long Short-term Memory Neural Network for Virtual Synchronous Generator Ziyi Wen Univ. of Electronic Science and Tech. of ChinaBo Long Univ. of Electronic Science and Tech. of ChinaKeyuan Lin Univ. of Electronic Science and Tech. of ChinaShuyi Wang Univ. of Electronic Science and Tech. of ChinaThe virtual synchronous generator (VSG) is emerging as an effective approach to controlling the gridconnected inverters, which can provide virtual inertia, handling both reactive and active power by mimicking the behavior of the traditional synchronous generator (SG). Traditional VSG modeling methods cannot simulate the dynamic and nonlinear characteristics of VSG effectively, to solve this problem, an equivalent data-driven modeling method of VSG by using long short-term memory (LSTM) network is proposed. Then, a linearized small-signal model of VSG is established in MATLAB/SIMULINK to obtain operational data as the training and testing data. Finally, simulation comparisons between different data-driven models based on various neural networks are performed to verify effectiveness of the proposed model. The results indicate that LSTM-based equivalent model can reflect the dynamic and nonlinear characteristics of the VSG under different operating conditions with high precision and efficiency.

MonBIS-70 142 Ensemble diversity enhancement based on parameters evolution of base learners Shuangye Chen Beijing Univ. of Tech.Rong Zhao Beijing Univ. of Tech.Hanguang Fu Beijing Univ. of Tech.Ensemble learning shows very good performance when dealing with the problem of concept drift. Accuracy and diversity are two important characteristics of ensemble learning, and diversity is the key factor affecting the generalization ability of ensemble learning. In this paper, a modeling method is proposed to enhance diversity among base learners based on parameters evolution of base learners. The method uses online sequential extreme learning machine (OS_ELM) as base learner. The base learners are grouped according to their performance on the sliding window, and perform evolution operations. At the same time, the concept of gene flow is introduced, which increases the diversity among base learners and improves the prediction performance of the ensemble algorithm in dealing with the concept drift data streams. Finally, the rationality and effectiveness of the proposed algorithm are verified by using the synthetic data sets and real-world data sets.

MonBIS-71 161 H ∞ state feedback control for nonlinear systems with input saturation based on policy iteration Yuqing Zheng Henan Univ. of Tech.Guoshan Zhang Tianjin Univ.In this paper, we propose an optimal algorithm based on policy iteration (PI), which is used to learn the H∞ state feedback control solution for nonlinear systems with input saturation. Firstly, The Hamilton-Jacobi-Isaacs (HJI) equation corresponding to the optimal control problem is constructed by a suitable quasi-norm, which can dispose the constraint on the input. Secondly, the PI algorithm is developed to solve the HJI equation without the complete knowledge of internal system dynamics, and an actor-critic-disturbance (ACD) structure neural network (NN) is introduced to implement the PI algorithm. Furthermore, we estimate the ACD-NN weights based on the least-square approach to achieve the optimal solution. Finally, the effectiveness of the PI algorithm is shown on the translational oscillator with rotational actuator (TORA) system and the H∞ controller guarantees the closed-loop TORA system stability.

MonBIS-72 179 Research on Impact of Sea State on Carrier-based Aircraft Landing Performance with Airwake Coupled in Yongtao Yu Shenyang Aerospace Univ.Yuetao Guo Shenyang Aerospace Univ.Linlin Zhu Shenyang Aerospace Univ.Zhiyong Yao Shenyang Aerospace Univ.Research on impact relationship from sea state to air-airwake and carrier-based aircraft landing performance was carried out in this paper. The impact that sea state acts on carrier-based aircraft landing performance was paid more attention by taking air-airwake as middle bridge factor, with this research, the relationship between landing environment and landing performance was investigated. Based on the introduction and analysis of sea state and air-airwake, the impact relationship was researched gradually. Firstly, the impact principle of sea state acts on air-airwake was analyzed and the simulation was performed. Secondly, the carrier-based aircraft landing task was introduced in, aircraft landing in vary sea state was simulated. Qualitative and quantitative analysis of the simulation results validated the impact of sea state on landing performance.

MonBIS-73 537 Decoupling Control for PMSM Based on Data-Driven Control Dong Yu Shenyang Inst. of Computing Tech.Meng Chen Shenyang Inst. of Computing Tech.Ping He Shenyang CASNC Tech Co., Ltd.The performance of the current loop decoupling control of permanent magnet synchronous motor (PMSM) is easily affected by the changes of motor parameters and load during operation. The changes cause decoupling control could not realize complete decoupling, which affects the control precision of the current loop. In order to improve the performance and precision of current loop decoupling control, an online decoupling control strategy of current loop based on data-driven control is studied. In the initial stage of PMSM operation, the PID controller is used to control the PMSM, and the current loop decoupling control module uses forgetting factor recursive least-squares (FFRLS) algorithm to estimate the model parameters according to the input and output data. After current loop decoupling control module parameters estimation is completed, the decoupling control output is carried out in the way of voltage feedforward. The current loop decoupling control model parameters estimation is continuously carried out according to the input and output data to match the changes of motor parameters. The simulation and experimental results show that the current loop decoupling control scheme based on data-driven can improve the precision of current loop when the PMSM parameters change or the load changes

Technical Programmes CCDC 2021 suddenly.

MonBIS-74 787 Design of Integrated Data Simulator for Warship Integrated Platform Management System Based on Interface Adaptation and Data Binding Canzhi Gui System Engineering Research Inst.Cheng Guo System Engineering Research Inst.Long Chen System Engineering Research Inst.Integrated Platform Management System (IPMS) is the physical carrier of the information and control system of the warship platform, including propulsion monitoring, electrical power monitoring, integrated bridge and other subsystems, which involves many objects and complex interfaces. IPMS system couplet testing is to test and verify the interface, function and performance of the system by using the test support environment. In the traditional test, the independent and special simulation model or physical simulation mode of each subsystem is used as data driven. The model development cycle is long and the test cost is high, which cannot meet the needs of multiple types of IPMS system testing. The integrated data simulator designed in this paper encapsulates the application interfaces of heterogeneous simulation models through a unified protocol to realize the interface adaptation between the actual equipment and the model, and the interconnection of test data between subsystems, thus meeting the needs of system-level test. On this basis, through flexible data binding method, the simulation model is extended to different types of IPMS system testing, which greatly broadens the scope of the use of the model, realizes the reuse of the test resources, and effectively improves the test efficiency.

MonBIS-75 830 Data-Driven Controller Synthesis for Parameters Unknown Linear-delay Systems with Input Constraint Jinggao Sun East China Univ. of Science and Tech.Guanghao Su East China Univ. of Science and Tech.Xianfeng Chen East China Univ. of Science and Tech.In practical control engineering, delay and actuator saturation are main challenges which may lead to system performance degradation, or even controlled variables divergence. Combination of Smith predictor and model recovery anti windup method is an effective solution to linear delay system anti-windup synthesis, however model dependent property of the above two methods brings obstacles for the application and promotion. To overcome such a difficulty, a data-driven approach is proposed in this paper, operating data is collected and utilized to simultaneously tune feedback controller parameters and estimate internal model applied in Smith predictor. Subsequently, anti-windup compensator is constructed with the estimated model and a compensator gain optimization structure is proposed based on Lyapunov equation. Finally, simulations have been implemented to three typical processes in chemical engineering, and detailed performance comparison is provided to illustrate the effectiveness of the proposed method.

MonBIS-76 846 Banknote Dirty Degree Identification Method Based on Texture Features of Banknote Images and Multi-layer Support Vector Machines Wei-Zhong Sun Univ. of Chinese Academy of Sciences

Univ. of Science and Tech. LiaoningShenyang Inst. of Computing Tech.

Liaoning Key Laboratory of Domestic Industri-al Control Platform Tech. on Basic Hardware

& SoftwareYue Ma Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Liaoning Key Laboratory of Domestic Industri-al Control Platform Tech. on Basic Hardware

& SoftwareZhen-Yu Yin Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Liaoning Key Laboratory of Domestic Industri-al Control Platform Tech. on Basic Hardware

& SoftwareJie-Sheng Wang Univ. of Science and Tech. LiaoningAi Gu Univ. of Chinese Academy of Sciences

Shenyang Inst. of Computing Tech.Liaoning Key Laboratory of Domestic Industri-al Control Platform Tech. on Basic Hardware

& SoftwareFu-Jun Guo Univ. of Science and Tech. LiaoningThe dirty degree of banknots determines whether them can continue to circulate to some extent. In this paper, a banknote dirty degree identification method based on texture features of banknote images and multi-layer support vector machines (MLSVMs) was proposed. Based on the contact image sensor, the double-sided reflection images of banknotes in red, green, blue and infrared light, as well as images in green and infrared light transmission are collected. By analyzing the images formed by different dirty grades of banknotes under various light sources, the green reflection positive and negative images, blue reflection positive and negative images, green light transmission images and infrared light transmission images are finally selected for image

texture feature extraction. Based on the gray level co-occurrence matrix(GLCM), the texture features such as energy, entropy and inertia were extracted to describe the visual features of banknotes dirty degree. Finally, MLSVMs was used to identify the dirty degree of banknotes, and simulation experiments results show the effectiveness of the proposed method.

MonBIS-77 1036 Cement particle size control based on improved model free adaptive control Zhiqiang Guo Univ. of Jinan

Qiang Zhang Univ. of Jinan

For the cement combined grinding system, this paper briefly introduces the technology, takes the cement particle size as the key index, and describes the mathematical model. Then the particle size is controlled by the improved model free adaptive controller(IMFAC), which linearizes nonlinear system dynamically based on the pseudo-partial- derivative (PPD) concept, and the tracking-differentiator(T-D) is introduced to track the output signal and its derivative and predict the plant future output. The simulation results show that the control performance of improved model free adaptive controller is better than conventional model free adaptive controller, and it has stronger inhibition effect on disturbance.

MonBIS-78 1137 Design of Dredging Process Control System for Cutter Suction Dredger Wei Wang CCCC National Engineering Research Center

of Dredging Tech. and Equipment Co., Ltd.Yanchao Shen CCCC National Engineering Research Center

of Dredging Tech. and Equipment Co., Ltd.Liuyan Wang CCCC National Engineering Research Center

of Dredging Tech. and Equipment Co., Ltd.Dongsheng Wang Dalian Maritime Univ.Yiming Bai Dalian Maritime Univ.The cutter suction dredger is affected by complex working conditions during operation, and it is difficult to ensure a stable output of mud concentration. This paper builds a linear regression model of the dredging process of the dredger based on the actual ship data. Then the model is applied to the design of the predictive controller for subspace identification. In addition, before the predictive controller working, a fuzzy controller is designed to control the step length of the cutter suction dredger. Under the condition that the control quantity is restricted, the simulation results show that under appropriate parameters, the mud concentration can quickly stabilize to the set target concentration. Thus, the stable output of mud concentration is ensured, and the goal of improving the operating efficiency of the dredger is achieved.

MonBIS-79 1336 Injection Molding Process Quality Analysis and Prediction Based on Batch Dynamic Augmentation Luping Zhao Northeastern Univ.Xin Huang Northeastern Univ.Guanghui Yang Northeastern Univ.In order to predict the quality of batch process accurately, this paper takes some work for the typical batch process, injection molding process. Due to the slow time varying nature of the injection molding process, the characteristics of different batches may vary, so it is not easy to predict product quality with a single model. Therefore, in this paper, a batch process quality prediction method based on batch dynamic analysis is proposed for the injection molding process. The sliding window model is constructed, and the region of support is redefined, based on which batch dynamic is included into the building of the PLS regression model. The offline quality analysis and online quality prediction of products in the injection molding process are completed, and compared with the traditional quality prediction method based on PLS, the advantages of the proposed method are presented.

MonBIS-80 1429 Optimization of SAG Control Rule Based on Clustering Learning Algorithm Guicheng Wang Shanghai Inst. of Tech.Xiaojia Sun Shanghai Inst. of Tech.Chuntao Jia Northwest Engineering&Technology CorporationMin Zhang Shanghai Inst. of Tech.Jiale Zhu Shanghai Inst. of Tech.Chuang Feng Shanghai Inst. of Tech.SAG machine is an important part of the SABC process. In view of the characteristics of many variables, nonlinearity, strong coupling, large hysteresis, time-varying, the traditional control methods are difficult to achieve better results, and the introduction of clustering learning algorithm is proposed. The normal operation data of SAG machine is subjected to cluster learning analysis, and compared with the existing operating experience, and self-learning affects the control relationship between the process parameters of its operating conditions, and forming the optimized control rule set, which can be used as the rule base of expert system, instead of the original control method, its operation state is improved, that the cost is reduced, and the energy consumption is saved,

Technical Programmes CCDC 2021 and the economic benefit of concentrator is improved.

MonC01 Room01 Pattern Recognition and Intelligent Machines (IV) 13:30-15:30 Chair: Jun Sun Jiangsu Univ.CO-Chair: Yang Liu Dalian Jiaotong Univ.

13:30-13:50 MonC01-1 74 Identification of living and non-living watermelon seeds based on Hyperspectral Imaging Technology Adria Nirere Jiangsu Univ.Jun Sun Jiangsu Univ.Xin Zhou Jiangsu Univ.Kunshan Yao Jiangsu Univ.Ningqiu Tang Jiangsu Univ.Ahmad Hussain Jiangsu Univ.Seeds storage has been a global challenge for their conservation, and it is crucial to test their germination ability before the operation. Nondestructive detection (NDD) techniques are frequently used in testing and evaluating elements. In this work, the hyperspectral image technology is employed to distinguish the living and nonliving watermelon seeds. First, fifty collected watermelon seeds were kept at 45 C for 72 hours, and another fifty seeds were stored in dry bottle at 20 C. Hyperspectral images of 100 samples were collected. Mean spectral data was calculated with the range of 400~1000nm from region of interest (ROI). Then, Savitzky-Golay (SG) and standard normalized variable (SNV) approaches were utilized to preprocess the spectral data, and principal component analysis (PCA) was used to select intervals to pick the top principal components (PCs). Moreover, a model based on support vector machine optimized by grey wolf algorithm (GWO-SVM) was introduced in this paper. Compared with normal SVM, the proposed scheme is tested and verified using the seed data with a classification rate improved from 87.5% to 97.5%. The overall results showed that nondestructive technic with SVM classification tool could be used in identification of watermelon seeds.

13:50-14:10 MonC01-2 1300 A Pedestrian Detection Algorithm Based on Channel Attention Mechanism Weidong Li Dalian Jiaotong Univ.Shuang Han Dalian Jiaotong Univ.Yang Liu Dalian Jiaotong Univ.The main contribution of this paper is to introduce the channel attention mechanism into the feature extraction network, and propose the channel attention mechanism module CA, which realizes the efficient fusion of multi-scale features. The deformable convolution is used to replace the traditional convolution operation, and a new detection head is designed, which can predict the position of pedestrians more accurately than the original detection head. CSP is a pedestrian detection algorithm with high accuracy and fast speed, and its structure is very simple. However, there is still great potential for improvement in multi-scale feature fusion and detection head design. This paper proposes a pedestrian detection algorithm based on channel attention mechanism, which is called CA-CSP. On the basis of the original CSP algorithm, the channel attention mechanism module CA is added, and the original detection head is replaced with a detection head based on deformable convolution. The new annotation is used to evaluate the proposed pedestrian detection algorithm CA-CSP on Caltech pedestrian dataset. On the reasonable setting, using a single Nvidia 1660 GPU, CA-CSP has obtained 3.97% of MR 2, and the original algorithm CSP has reached 4.59% of MR-2. Compared with CSP, CA-CSP has lower MR 2. Therefore, CA-CSP has better performance than the original CSP algorithm.

14:10-14:30 MonC01-3 1521 Face super resolution with texture prior Yuan Wu Southeast Univ.Zhangxing Bian Univ. of MichiganHong Pan Southeast Univ.Siyu Xia Southeast Univ.Recently, Face super-resolution (SR) based on deep learning and face shape priors have achieved great success. For the prior information provided by face images, people pay more attention to the shape prior represented by the heat maps and parsing maps of face landmarks. However, the texture prior is also crucial for this task. In this paper, we propose a novel SR method with texture prior. In particular, we construct a prior network to extract the texture prior information of face images. At the same time, a fine SR network is used to recover a high-resolution image from low-resolution input. After that, the obtained texture priors are concatenated with the preliminary SR results and sent to the subsequent fusion network to obtain the final output images. The results of quantitative and qualitative experiments show that our method is superior to state-of-the-art face SR methods.

14:30-14:50 MonC01-4 30 Visual SLAM Based on Dynamic Object Detection Bocheng Chen Huazhong Univ. of Science and Tech.

Key Laboratory of Image Processing and Intelligent Control

Gang Peng Huazhong Univ. of Science and Tech.Key Laboratory of Image Processing and

Intelligent ControlDingxin He Huazhong Univ. of Science and Tech.

Key Laboratory of Image Processing and Intelligent Control

Cheng Zhou Key Laboratory of Image Processing and Intelligent Control

Huazhong Univ. of Science and Tech.Bin Hu Shantui Construction Machinery Co., Ltd.On the one hand, traditional visual SLAM does not consider dynamic objects in the scene, on the other hand, deep learning technology has been widely used in computer vision. This paper combines the two organically, and proposes an algorithm that uses dynamic object detection to improve the robustness of visual SLAM in a dynamic environment. Firstly, we use the object detection network integrated into the attention mechanism to detect the dynamic target in the key frame. Then, we follow the optical flow detection to further determine the dynamic feature points in the scene and eliminate them. Finally, we use the static feature points for camera tracking to achieve highly robust monocular visual SLAM. The method described in this paper can not only eliminate dynamic feature points, but also retain as many static feature points as possible. The method described in this paper is compared with the original ORB-SLAM2 algorithm and DS-SLAM algorithm, and tested with public data sets. The results show that the method described in this paper can effectively eliminate the influence of dynamic objects on the visual SLAM algorithm.

14:50-15:10 MonC01-5 145 Semi-supervised Learning Framework in Segmentation of Retinal Blood Vessel Based on U-Net Yaning Li Northeastern Univ.Zijun Pei Northeastern Univ.Jiaguang Li Northeastern Univ.Dali Chen Northeastern Univ.Medical image analysis of retinal blood vessels is of great value for the disease warning of diabetic retinopathy and the diagnosis of cardiovascular and cerebrovascular diseases. An accurate segmentation of the vascular tree in fundus images is essential for medical analysis of retinal images. In this paper, a semi-supervised framework for segmentation of blood vessels based on U-Net is proposed, aiming to abate the workload of data annotation. The framework includes three steps. Firstly, U-Net is trained with the enhanced ground truth labels; secondly, use the trained network to predict unlabeled data, and take the filtered prediction results as pseudo-labels; thirdly, combine data amplification and dropout strategies to update the training set. Repeating these steps until the predetermined iteration times is reached. Then we use the trained model to segment the retinal vessel images, and report the segmentation performance on the test set. Through comparison experiments with fully-supervised learning method, we find that the proposed framework has better performance than fully-supervised learning under the same amount of labeled data, which can improve the effect of blood vessel segmentation and reduce the workload of data labeling.

15:10-15:30 MonC01-6 717 KNN-Attention-CNN Model for Text Emotion Classification Jia-yi Han China Univ. of Petroleum, Beijing CampusJian-wei Liu China Univ. of Petroleum, Beijing CampusXiong-lin Luo China Univ. of Petroleum, Beijing CampusConvolutional Neural Network (CNN) shows superior performance in the field of emotion classification, but existing approaches input single text matrix to CNN, which are the lack of considering similar texts in training dataset, and effect on the overall classification performance. In this paper, we put forward a kind of combining KNN algorithm which is used to extract the weighted text matrices, self-attention mechanism to obtain the fusion attention text matrices, and CNN text emotion classification model, and we dub it KNN-Attention-CNN. First, KNN algorithm is used in the pre-processing part to extract the similar features of training text dataset and obtain the weighted text matrix. Then the self-attention mechanism is introduced to extract the emotional features with the remote dependence relationship, and finally the classification of the emotional features is implemented through the convolutional neural network. The experimental results are conducted on three real data sets show that the proposed KNN-Attention-CNN model are superior to ordinary CNN, and the accuracy is 3.12% higher than CNN. The comparison between KNN-Attention-CNN and five other baseline models verifies the promising performance of KNN-Attention-CNN. We change the length of the input sentences and K value in KNN algorithm respectively, affirm that the addition of the self-attention mechanism has a more obvious effect on the emotional classification on long sentences, and the appropriate K value can optimize the classification performance.

MonC02 Room02 Decision-making Theory and Method (I) 13:30-15:30 Chair: Yingying Sun Northwest Univ.CO-Chair: Hang-Yu Qin Fuzhou Univ.

Technical Programmes CCDC 2021 13:30-13:50 MonC02-1 344 An Incomplete Probabilistic Linguistic Multi-criteria Group Decision-making Method Based on Statistical Variance Zhi-Qiu Han Fuzhou Univ.Hang-Yu Qin Fuzhou Univ.An incomplete probability linguistic preference relation is introduced for establishing a multi-criteria group decision-making method. In this method, a minimized consistency bias model is used to inference the unknown information. Using such statistical variance based method, the alternatives ranks by each decision-maker can be gotten. Then, these ranks are combined to get the ideal choice of the group. Finally, some numerical examples are also proposed.

13:50-14:10 MonC02-2 415 Research on the Investment Decision of Backdoor-listed Companies Based on the Intrinsic Value of Stocks Yingying Sun Northwest Univ.Jinmian Han Northwest Univ.Companies met with red tapes and qualification problems when applying for IPO in Chinese stock markets often resort to M&A with an already listed company. Such practices are dubbed “backdoor listing”. Backdoor listings in Chinese stock market surged to a new highest level in 2015. During that year, 43 companies applied for backdoor listings, among which 30 companies successfully landed in the capital markets. This article endeavors to look into the 30 companies by comparing their stock performance with the rise and fall of the Shanghai (securities) composite index. It also tries to explain their stock price fluctuations by conducting a stepwise regression study of their asset-liability ratio, current ratio, inventory turnover rate, turnover rate, basic earnings per share. This study found most of the 30 companies performed poorly. Their stock prices were prone to change together with turnover rates and asset-liability ratios. And relevant factors affecting their stock prices were more likely to turn negative when the target companies are already inflicted with poor performance. A handful of companies performed exceptionally well, traditional companies listed in a backdoor manner were object to higher risk. To catch up to the market, investors are not recommended to buy the stock prices that have been fully reflected. Many stocks have started to fall for a long time after the backdoor wave recedes, so keep rational while the market is crazy.

14:10-14:30 MonC02-3 461 A Discrete Beetle Swarm Optimization Algorithm-based method for Multi-Targets Assignment for Multi-missiles Coordinated Attack Haojun Sun Huazhong Univ. of Science and Tech.Wei Wang Beijing Aerospace Automatic Control Inst.Gaoxiang Peng Huazhong Univ. of Science and Tech.Zhongtao Cheng Huazhong Univ. of Science and Tech.Bo Wang Huazhong Univ. of Science and Tech.Lei Liu Huazhong Univ. of Science and Tech.Due to the nonlinearity and difficulty in the process of the cooperative target assignment, a discrete beetle swarm optimization algorithm is proposed. Firstly, the target assignment model is established based on the damage probability and target value of missile attacking target. Secondly, according to the constraint characteristics of the model, a particle integer coding method is proposed to reduce the complexity of the problem. Then, the discrete beetle swarm optimization algorithm is designed and used to solve the problem, which is an improved particle swarm optimization algorithm by introducing beetle antennae search algorithm. The simulation results show that the algorithm combines the advantages of particle swarm optimization algorithm and beetle antennae search algorithm, and has fast convergence speed and is not easy to fall into local optimum. Compared with other algorithms, the effectiveness of the algorithm is verified.

14:30-14:50 MonC02-4 503 A Novel Evaluation Method of Air Target Threat Based on D Number Theory of Interval Numbers Shuanglin Li Xi’an Research Inst. of Navigation Tech.Lin Li Xi’an Research Inst. of Navigation Tech.Yebi Cui Northwestern Polytechnical Univ.Target threat level assessment is an important part of air defense command operations, and it plays an increasingly important role in modern air defense wars. As an act of reasoning and decision-making, threat assessment always involves many factors. Based on the consideration of multiple threat indicators, combined with D number theory and interval number, an air defense target threat assessment method is proposed in this paper. Through real-time quantification of all indicators data to generate interval numbers, the evidence under each indicator is constructed and the D number theory is applied to fuse the evidence to achieve accurate assessment of threat targets. The simulation of a set of experimental data shows the effectiveness of the proposed method.

14:50-15:10 MonC02-5 524 A new random forest method based on belief decision trees and its application in intention estimation

Xinyu Li Northwestern Polytechnical Univ.Mingda Li Northwestern Polytechnical Univ.Yu Zhang Northwestern Polytechnical Univ.Xinyang Deng Northwestern Polytechnical Univ.Random forest algorithm is a classification and prediction model, which is used in many fields. Random forest is composed of multiple decision trees. In the face of more and more complex uncertain environments, ordinary decision trees can no longer meet the requirements, so belief trees based on belief functions appear. This paper proposes a new random forest method based on belief trees. Compared with ordinary random forest in which voting or average method is used to combine the result of each decision tree, the proposed method fully considers the influence of the weight of each tree, and combine the result of each belief tree through a weighted averaging combination of belief structures. In order to demonstrate the effectiveness of the proposed method, it is used in intention estimation. The results show that the accuracy of intention recognition is improved by using the proposed method compared with original random forest algorithm.

15:10-15:30 MonC02-6 764 Passenger Volume Interval Prediction based on MTIGM (1, 1) and BP Neural Network Shaomei Lv Guilin Univ. of Electronic Tech.Xiangyan Zeng Guilin Univ. of Electronic Tech.Long Huang Guilin Univ. of Electronic Tech.Lan Wu Guilin Univ. of Electronic Tech.Wei Jiang Guilin Univ. of Electronic Tech.The ternary interval number contains more comprehensive information than the exact number, and the prediction of the ternary interval number is more conducive to intelligent decision-making. In order to reduce the overfitting problem of the neural network model, a combination prediction method of the BP neural network and the matrix GM (1, 1) model for the ternary interval number sequence is proposed in the paper, and based on the proposed method to predict the passenger volume. The matrix grey model for the ternary interval number sequence (MTIGM (1, 1)) can stably predict the overall development trend of a time series. Considering the integrity of interval numbers, the BP neural network model is established by combining the lower, middle and upper boundary points of the ternary interval numbers. The combined weights of MTIGM (1, 1) and the BP neural network are determined based on the grey relational degree. The combined method is used to predict the total passenger volume and railway passenger volume of China, and the prediction effect is better than MTIGM (1, 1) and BP neural network.

MonC03 Room03 Supply Chain and Logistics Management (I) 13:30-15:30 Chair: Hongmei Guo Sichuan Univ.CO-Chair: Songpo Yang Beijing Univ. of Tech.

13:30-13:50 MonC03-1 79 Research on backward integration strategy of dominant retailer in the face of encroachment Shi Xu Nanjing Univ. of Science & Tech.Yi-fan Lv Nanjing Univ. of Science & Tech.Qian Xu Nanjing Univ. of Science & Tech.A two-level supply chain is studied which includes an incumbent manufacturer, an entrant manufacturer and a dominant retailer. Supply chain models under different integration strategies are established to explore the backward integration strategy of the dominant retailer when facing the entrant’s encroachment, and analysis the impact of encroachment on the incumbent. The results show that the encroachment of entrant can weaken the double marginal effect in the supply chain. Encroachment increases retailer’s profit, and in most cases will harm the interests of incumbent manufacturer. Retailer’ non-integration strategy is a strictly dominated strategy. When the retailer integrates with one manufacturer, it is unfavorable to another manufacturer that is not vertically integrated. Backwardintegration can further alleviate the double marginal effect of the supply chain. The dominant retailer’s optimal backward integration strategy mainly depends on the role of service efficiency, quality difference and production cost. When the retailer chooses the optimal partial integration strategy, it can increase the total profit of the supply chain system and consumer surplus.

13:30-13:50 MonC03-2 239 Bullwhip Effect Analysis for Supply Chains using a Fuzzy Forecast Approach Songpo Yang Beijing Univ. of Tech.Liang Chen Beijing Univ. of Tech.In this paper, we propose a centralized adaptive inventory control model for a multi-level multi-cycle supply chain consisting of one supplier and one retailer with non-stationary random demand. In our approach, the fuzzy exponential smoothing method adopted to forecast the future demand, and the EOQ (Economic Order Quantity) model determines the ordered quantity. Besides, a reinforcement learning algorithm is developed to evaluate the effects of safety factor. Our objective is to satisfy a given target service level predefined for the retailer. Two types of demand process patterns, known and unknown demand distribution, are considered. Moreover, the bullwhip effect generated while processing of demand information is provided. The results show that the proposed

Technical Programmes CCDC 2021 control method can improve the service level and reduce the bullwhip effect to some extent.

13:30-13:50 MonC03-3 505 Incentive Contract with Loss-averse Manufacturer Hongmei Guo Sichuan Univ.Shuiliang Gu Chengdu Univ. of Tech.In this paper, some supply chain incentive models are established to determine the optimal payments in consideration of private information and loss-averse manufacturer. The separating contract and the pooling contract are obtained. In separating contract, manufacturers pass on private information by reducing retailers risks. Both manufacturers and retailers receive reservation earnings. Loss aversion weakens the influence of type on contract. In pooling contract, manufacturers do not strictly distinguish their own types. The types are divided into two intervals, corresponding to two different payments-base payment and premium payment. Both manufacturers and retailers get more revenue. Loss aversion enlarges the range of premium payment.

13:30-13:50 MonC03-4 873 Truthful double auction for intra-city freight transportation with carpooling mode Hao Yu Northeastern Univ.Min Huang Northeastern Univ.Nian Wu Northeastern Univ.Carpooling is an effective way to achieve lower transportation cost, higher vehicle utilization and alleviate air pollution and traffic congestion by allowing the joint coverage of overlapped distance. It has been extensively studied for passenger transportation while attracts little academic attention for freight transportation. In this paper we investigate intra-city freight transportation service transaction problem (IFTSP) with carpooling mode and propose truthful carpooling double auction (TCDA) mechanism for solving it. We prove theoretically that TCDA mechanism is incentive compatible (IC), budget balanced (BB), individual rational (IR) and asymptotically efficient (AsE). Numerical experiments show that TCDA can significantly improve the social welfare and vehicle utilization compared with conventional McAfee mechanism. It can also greatly enhance customers’ utility and balance the market power in sellers’ market.

13:30-13:50 MonC03-5 881 Social responsibility inspiration in the fourth party logistics service supply chain: A game-theoretic approach Nian Wu Northeastern Univ.Hanbin Kuang Northeastern Univ.Min Huang Northeastern Univ.Hao Yu Northeastern Univ.Corporate Social Responsibility (CSR) is considered in a fourth-party logistics service supply chain, consisting of a Fourth Party Logistics supplier (4PL) and a Third Party Logistics provider (3PL). In this research, a transportation price contract is designed to effectively improve the CSR performance, which is mainly determined by the 3PL's output of carbon dioxide (CO2) emission. The associated cost for improving CSR performance level of the fourth-party logistics service supply chain is measured by a variable, which is only bring by the 3PL with an expectation of being shared with the 4PL via the transportation price contract. As such, the key issue is how the 4PL can identify the contract parameters to effectively transfer the associated cost of the 3PL in the 4PL service supply chain dominated by 4PL, and to encourage the 3PL to improve the performance level of CSR. A game-theoretical analysis is carried out on two games, bringing about different interaction plans between the 4PL and the 3PL, to derive their corresponding equilibria. Through the numerical analysis on the equilibria, we find that the 4PL service supply chain can achieve a win-win situation for both parties when the parameter values can be properly identified by the 4PL in designing the transportation price contract. Moreover, a well-designed contract can effectively motivate the 3PL to improve the performance level of CSR.

13:30-13:50 MonC03-6 885 Fourth Party Logistics Network Design Considering Demand Surging and Quantity Discount Songchen Jiang Northeastern Univ.Jin Chen Xiamen City Univ.Min Huang Northeastern Univ.Yuxin Zhang Northeastern Univ.Mingqiang Yin Northeastern Univ.Under the complex and changeable business environment, demand uncertainty brings great challenges to the operation of supply chain and how to design effective supply chain is an important issue in supply chain management. The fourth party logistics (4PL) is a fast developing and efficient supply chain operation mode, and in the 4PL system, 3PLs often launch discount promotion to attract more customers, in order to explore how to better design 4PL network, a novel 4PL network design problem considering quantity discount is proposed. Considering the constraint of service level, a chance constraint programming model is established, the service level constraint and quantity discount lead to the nonlinear

characteristic of model, therefore a MIP reformulation is given to ensure the model can be sovled straightly. The model is solved using CPLEX and numerical experiment with different scales are conducted, results show the effectiveness of proposed model. Furthermore, the impact of demand level, quantity discount and discount coefficient on 4PL network designing are revealed.

MonC04 Room04 Automatic Control of Unmanned Systems (III) 13:30-15:30 Chair: Feng Pan Beijing Inst. of Tech.CO-Chair: Huaijian Li Beijing Inst. of Tech.

13:30-13:50 MonC04-01 508 AUV Trajectory Tracking Based on Nonlinear Model Predictive Control Zheping Yan Harbin Engineering Univ.Peng Gong Harbin Engineering Univ.Wei Zhang Harbin Engineering Univ.Wenhua Wu Harbin Engineering Univ.This paper focuses on the trajectory tracking of the autonomous underwater vehicle (AUV). To find a feasible method to reduce the computational burden, a model predictive control algorithm is designed. Firstly, the coupled nonlinear AUV models is convert into second-order affine forms using state feedback linearization method. Then, the original optimization problem is transformed into several subproblems. The amount of computation can be significantly reduced by solving the subproblems. In addition, the stability analysis based on Lyapunov method proves the nominal stability of the controllers. Finally, the effectiveness of the controller is verified by simulation experiments.

13:50-14:10 MonC04-02 922 Experimental Image-based Recognition of Thickener Mud Layer using Deep Learning Framework Chenyu Fang Northeastern Univ.Fuli Wang Northeastern Univ.Kang Li Northeastern Univ.Yan Liu Northeastern Univ.Thickener mud layer height is an important production control index of thickener underflow concentration. A soft sensor model of thickener mud layer height based on convolutional neural network and image processing is proposed due to real-time online detection is difficult to implement. The method uses Convolutional Neural Network (CNN) to extract the dynamic features of the image samples, then the dynamic features that were extracted are trained, finally, the predicted values of mud layer height of thickener are obtained. This paper uses the bottom mud layer image samples to carry on the model establishment and the training. The results show that the soft sensor model is feasible. These model parameters can be directly used for the acquisition of actual thickener mud layer height values by using transfer learning.

14:10-14:30 MonC04-03 746 Image-based Visual Servoing of Unmanned Aerial Vehicles for Variable Angle Target Xuefei Wang Northeastern Univ.Yizhen Yin Northeastern Univ.Hongjun Ma Northeastern Univ.Hua Bai Northeastern Univ.This paper presents a double virtual plane method for a quadrotor unmanned aerial vehicle(UAV) using a monocular camera and an inertial measurement unit sensor(IMU). Using the above method and an image-based visual servoing controller, the UAV can achieve the tracking of the target both on the horizontal plane and on the inclined plane. Utilizing the image moments, the image features are defined and and adopted by the IBVS controller. The inclination angle of the target plane can be obtained through the image distortion in the horizontal virtual image plane. The closed-loop system is proved globally asymptotic stable by means of Lyapunov analysis. And the simulation of the method is completed, which can be used to prove the correctness and stability of the theory.

14:30-14:50 MonC04-04 762 Multi-UAV Formation Distributed Fault-tolerant Control Liqing Chen Northeast Univ.Yijing Zhang Northeast Univ.Zichun Liu Northeast Univ.ZIyang Nian Northeast Univ.Yulong Yuan Northeast Univ.Aiming at the fault tolerant control problem of quadrotor UAV, a distributed fault tolerant control method of multi-UAV is proposed.In distributed fault-tolerant control system, each UAV system contains a layered controller divided into two layers. The upper layer is a virtual system, which deals with the issues of track planning, formation control and cooperative communication among multiple UAVs. The lower layer is a real system, which deals with the track tracking and fault tolerance of a single UAV system. The complex multi-UAV group collaboration problem is decoupled into the fault tolerance problem of a single UAV. In the virtual system, the flight path planning and formation control algorithm

Technical Programmes CCDC 2021 plan the formation path and formation of UAV, and give the ideal state of each UAV. The real system of the lower layer tracks the ideal state of the UAV through the track tracking algorithm. When a UAV in the formation fails to track the ideal state, the uav will quit the formation. Since the real UAV system only receives the status information from the virtual system and does not transmit the information to the virtual system, the fault information is avoided to spread in the formation.

14:50-15:10 MonC04-05 767 Design and implementation of a GNSS navigation terminal automation test system Huaijian Li Beijing Inst. of Tech.Ziye Hu Beijing Inst. of Tech.Rongjing Xu Beijing Inst. of Tech.Xinbo Wu Beijing Oriental Inst. of Measurement & TestIn order to solve the problem that it is impossible to carry out multi-scene and multi-station tests for satellite navigation terminal in the market, an automatic test system is developed. By integrating multiple test scenes and multiple test sources, multiple navigation terminals can be tested simultaneously. The test system is classified and constructed according to hardware and software, and the data flow and control command flow among subsystems are introduced. Based on the concept of functional modular design, the test system uses Qt to develop the test software. Finally, a commercial navigation terminal is taken as an example to verify the test system. The satellite navigation terminal test system constructed by multi-source and multi-scene test method has high test efficiency and the test results prove that the test system is reliable.

15:10-15:30 MonC04-06 801 Research on An Autonomous Tunnel Inspection UAV based on Visual Feature Extraction and Multi-sensor Fusion Indoor Navigation System Shengyang Ge Beijing Inst. of Tech.Feng Pan Beijing Inst. of Tech.Dadong Wang Beijing Inst. of Tech.Ning Pu Yunnan Remote Sensing CenterUAVs have been widely used in line erection traction and inspection of outdoor high-voltage power grids due to its flexibility and low cost. Most of the inspection systems inside underground cable tunnels now adopt track-type and trolley-type robots, which are stable and safe, but also have disadvantages of high laying cost, low utilization efficiency, sensitive to terrain and so on. A UAV system for autonomous tunnel inspection based on visual feature extraction and multi-sensor fusion in GPS-denied environments is proposed in this paper. Kalman filter and GCC1 fusion method is thealgorithm utilized for UAV indoor positioning using the optical flow sensor and the UAV inertial sensor. And then the flight stability was further enhanced by attitude decoupling. For visual feature extraction, this paper used the LAB color gamut threshold segmentation method to extract the foreground, the Hough transform matching straight line to extract the features of the horizontal deviation and forward direction, and designed a method to separate straight lines, curves and corners, thereby improved the UAV's perception of the tunnel environment. A series of inspection processes such as steering, height change, hovering and return voyage mission with vision feedforward PID controller were designed as tunnel inspection tasks. The on-site flight verification was conducted in the power transmission underground tunnel.Experimental results show that this system has the advantages of light weight, low cost, reliability, highly efficiency, low operation and maintenance costs. It can be deployed quickly and efficiently, and has certain engineering application prospect in the autonomous tunnel inspection.

MonC05 Room05 Stochastic Systems 13:30-15:30 Chair: Chuang Liu Northwestern Polytechnical Univ.

CO-Chair: Jiafan He Nanjing Research Inst. of Electronics Eng-ineering

13:30-13:50 MonC05-1 1311 Hybrid Non-fragile Attitude Stabilization for Post-capture Spacecraft Chuang Liu Northwestern Polytechnical Univ.Ziyu Yang Northwestern Polytechnical Univ.Xiaokui Yue Northwestern Polytechnical Univ.Xuechuan Wang Northwestern Polytechnical Univ.This paper investigates the problem of hybrid non-fragile attitude stabilization for post-capture spacecraft in the presence of external disturbances, unknown and uncertain inertia and coexisting controller’s gain perturbations. The aim of this work is to design a hybrid non-fragile dynamic output feedback controller such that the closed-loop attitude control system is stabilized, while the actual control input is confined into a certain range. Based on the Lyapunov stability theory, the existence condition of such non-fragile controller is derived in terms of linear matrix inequalities. It is worth mentioning that the controller’s additive and multiplicative perturbations are accounted for simultaneously. Finally, an illustrative example is given to demonstrate the effectiveness of the proposed hybrid nonfragile controller.

13:50-14:10 MonC05-2 1483 Modified Rodriguez Parameters Based Event-Triggered Attitude Control of Flexible UCAV with Uncertainties Jiafan He Nanjing Research Inst. of Electronics EngineeringQingwei Li Nanjing Research Inst. of Electronics EngineeringChao Liu Nanjing Research Inst. of Electronics EngineeringMan Wang Nanjing Research Inst. of Electronics EngineeringFeng Fang Nanjing Research Inst. of Electronics Engineering

Hangzhou Dianzi Univ.Aiguo Fei Nanjing Research Inst. of Electronics Engineering

Beijing Univ. of Posts and TelecommunicationsThis study investigates robust event-based attitude control of an uncertain flexible UCAV in terms of modified Rodriguez parameters. In this manuscript, we develop an input-to-state stabilizing controller for the uncertain UCAV attitude control system from external error to translated states, to quantify the effect of sampling error due to the event-triggering mechanism. Such an external stability leads to embed an event-triggering mechanism, implementing an efficient event-triggered controller for robust attitude maneuver with any specified precision. A numerical simulation is shown to illustrate the proposed results.

14:10-14:30 MonC05-3 616 Robust Adaptive Compound Control for the High-speed Aircraft with Actuator Dynamics Yuan Zhang Beijing Aerospace Automatic control Inst.

National Key Laboratory of Science and Tech. on Aerospace Intelligent Control

Wanwei Huang Beijing Aerospace Automatic control Inst.Jiaming Zhang Beijing Aerospace Automatic control Inst.

National Key Laboratory of Science and Tech. on Aerospace Intelligent Control

During the reentry phase of the high-speed aircraft, the atmospheric characteristics are unusually complex, and the density of the atmosphere varies strongly, it is significant for us to design a strong robust adaptive compound controller. Fistly, cinsidering the actual reentry progress, the whole reentry progress is divided into three parts, including the reaction control period, the cmpound control period and the aerodynamics control period; Secondly, the full channel attitude robust adaptive controller is researched based on the nonlinear dynamic inverse theory and nonlinear extended state observer; Thirdly, to achive a better control performance, a smooth distribution coefficient is introduced in allocation system, and the expected control moment is divited into reaction control subsystem and aerodynamic control subsystem; Fourthly, the actuator dynamics were also taken into consideration; Lastly, the numercial simulaion results show that the proposed approach has a excellent ability to solve the compound control problem for the high-speed aircraft.

14:30-14:50 MonC05-4 1101 A dimension reduction guidance method for orbital pursuit-evasion games Chengming Zhang National Univ. of Defense Tech.Yanwei Zhu National Univ. of Defense Tech.Leping Yang National Univ. of Defense Tech.Weiwei Cai National Univ. of Defense Tech.Improving on-orbit service techniques with non-cooperative spacecraft is attracting more and more scholars’ attention. The free time orbital pursuit-evasion problem is a typical problem in this field. Generally, solving the orbital pursuit-evasion game results in solving a 24-dimension two points boundary value problem which is really complicated with such high dimension and the numerical methods used to obtain the solution is sensitive to the initial estimate of the costates. To overcome the weaknesses, this paper presents a dimension reduction guidance method to transform the free-time orbital pursuit-evasion game into a 3-dimension parameters optimization problem based on the costate normalization technique where each of the parameters needs to be optimized has a strict boundary. Comparing to the previous methods, the decrease of the dimension and the boundary of the parameters reduce the searching space together. The simulation results and the statistical analysis demonstrate that this method is efficient and it improves the computation consumption 67.7% comparing with the classical genetic algorithm.

14:50-15:10 MonC05-5 824 Local extrema refinement strategy for quadrotor’s nonlinear attitude-systems with tensor product model transformation Fei Chang Inner Mongolia Univ.Bao Shi Inner Mongolia Univ.Xiaogang Liu Inner Mongolia Univ.Guoliang Zhao Inner Mongolia Univ.The classical sampling method that is equi-interval sampling is often used in tensor product (TP) model transformation, however, the information of local extrema for the model can easily be omitted by this method. In this paper, a local extrema refinement strategy based TP model transformation method for quadrotor nonlinear attitude subsystem is proposed. Linear matrix inequalities based Lyapunovs theorems are applied to parallel distributed compensation (PDC) controller design, the

Technical Programmes CCDC 2021 stabilization and external disturbance results of the PDC controller designed under the classical sampling method and the local extreme refinement sampling method are compared to fully demonstrate the effectiveness of our proposed method.

15:10-15:30 MonC05-6 973 ADP-Based Prescribed Performance Attitude Tracking Control for Rigid Spacecraft Haoyang Yang Beihang Univ.Yizhong Fang The State Key Laboratory of Experiment Physics

& Computational MathematicBeijing Inst. of Space Long March Vehicle

Ming Guo Key lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Bo Zhang Key lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Yonghe Zhang Key lab of MicrosatelliteInnovation Academy for Microsatellites of CAS

Qinglei Hu Beihang Univ.This paper proposes an adaptive dynamic programming (ADP)-based prescribed performance controller for rigid spacecraft attitude tracking control subject to cost optimizing. As a stepping stone, the modified Rodrigues Parameters (MRPs) are employed to characterize the relative attitude dynamics. A state transformation method is used to transform the constrained dynamics to an unconstrained dynamics by the log-type function. Then, an ADP algorithm is developed to deal with the Hamilton–Jacobi–Bellman equation of optimal control problem. The convergence and stability are analyzed by the Lyapunov-based method. Finally, the effectiveness of the proposed scheme is demonstrated by numerical simulation.

MonC06 Room06 Network Cluster and Networked Control 13:30-15:30 Chair: Chunhui Zhao Northwestern Polytechnical Univ.CO-Chair: Xiaojun Zhu Economic Research Inst. State Grid

Jiangsu Electric Power Co. Ltd.

13:30-13:50 MonC06-1 597 Fixed-Time Extended Disturbance Observer-Based Robust Control for Quadrotor Vehicle Mati Ullah Northwestern Polytechnical Univ.Chunhui Zhao Northwestern Polytechnical Univ.Hamid Maqsood Government Polytechnic Inst.This paper proposes a novel output feedback robust tracking control of a quadrotor unmanned aerial vehicle (UAV). Since quadrotor UAVs encounters enormous and different types of exogenous disturbances such as wind during flight operation, the impact of such disturbances can degrades the system stability. To attenuate the influence of these disturbances fed into the system, a fixed-time extended disturbance observer (FTEDO) is developed that can accurately estimates the lumped disturbances based on which a sliding manifold is constructed and updated. The proposed control scheme also assure the tracking error converge to zero in fixed-time. A comparison of the proposed FTEDO is carried out with the conventional nonlinear disturbance observer (NDO) to affirm its enhanced disturbance estimation performance. The simulation results of the quadrotor with exogenous disturbances prove the robustness, efficacy and fast convergence rate of the proposed control scheme.

13:50-14:10 MonC06-2 1322 A robust optimization method for IES with adjustable degree of conservation Hongwei Zhou Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Sheng Zou Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Xuanjun Zong Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Qun Zhang Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Min Zhang Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Xiaojun Zhu Economic Research Inst. State Grid Jiangsu

Electric Power Co. Ltd.Integrated energy system (IES) will become the main bearing forms of energy. Owing to the massive coupling, the energy structure and energy utilization efficiency of the IES are improved. In the meantime, IES’s optimal scheduling is more complex than the traditional energy structure. The uncertainty of renewable energy, which consists of wind and PV, is a significant consideration affecting the safe operation of IES. For the sake of solving this problem, firstly, a robust constraint model of renewable energy with adjustable degree of conservation is established. Secondly, establishing the pricebased demand response model to have the power load curve optimized. On this basis, the robust optimization model of industrial system with economy as the objective function is established. Finally, verifying the effectiveness of this method by giving a simulation example.

14:10-14:30 MonC06-3 192 Robust Controller Design for Dynamic Positioning System Based on μ Synthesis Zhenyuan Shan Harbin Engineering Univ.Mingyu Fu Harbin Engineering Univ.Bo Zhao Harbin Engineering Univ.This paper describes the design of a robust controller for ship dynamic positioning using the μ synthesis method. In the process of design, the parameters uncertainty of the control object and the disturbance of marine environment are considered. In order to avoid the wear and tear caused by frequent start and stop of propulsion system, the controller is insensitive to the first-order wave force by selecting appropriate weighting function. The order reduction method based on Hankel norm model is used to reduce the order of the controller to 5 order, which ensures that the controller has good real-time performance in engineering application. Finally, the simulation experiment of S175 ship is carried out. The simulation results show that the controller has good control performance and is insensitive to the first-order wave force in a certain peak frequency range.

14:30-14:50 MonC06-4 311 Robust anti-pitching control for high-speed multihull under stochastic wave disturbance Zhilin Liu Harbin Engineering Univ.Guosheng Li Harbin Engineering Univ.Linhe Zheng Harbin Engineering Univ.Yong Li Harbin Engineering Univ.During the navigation of high-speed multihull under the stochastic wave disturbance, large motion amplitudes of heave and pitch can be caused resulting to the adverse effects, such as the seasickness of crew members, worse control performance and even the system instability, thus a robust H∞ control method has been proposed for the vertical stabilization of high-speed multihull in this paper. Firstly, with the consideration of the uncertainty of system parameters, the vertical control model with applied appendages, i.e., T-foil and flap, has been established. Then, based on the control and theory developed for the stochastic system, the control law can be obtained by utilizing the technique of linear matrix inequality (LMI) which guarantees the defined H∞ performance index, such that the desired control performance and system robustness can be both guaranteed. Lastly, the effectiveness of the proposed method has been verified by numerical simulations, and the motions of heave and pitch have been reduced significantly.

14:50-15:10 MonC06-5 420 Robust Adaptive Control for a class of uncertain nonlinear systems with an optimized smooth input Xiao Chen Nanjing Univ. of Science and Tech.Jianyong Yao Nanjing Univ. of Science and Tech.Modeling uncertainties and disturbances in physical systems seriously deteriorate the tracking performance. In this paper, a modified robust adaptive control strategy is proposed for a class of uncertain nonlinear systems, in which all kinds of uncertainties (i.e., parametric uncertainties, unconsidered dynamics and external disturbance) can be handled simultaneously. The robust control law is developed by an improved form, which can ensure the input continuous thoroughly, to attenuate the lumped uncertainties including unconsidered dynamics and external disturbances. In addition, a smooth projection mapping is used at each step of the adaptive backstepping design procedure to ensure the boundedness of parameter estimation. The developed controller does not require the prior knowledge of the bound of any uncertainty. The stability of the whole closed-loop system is obtained, and the global asymptotic output performance can also be guaranteed. Comparative simulation results are provided to illustrate the effectiveness of the proposed control scheme.

15:10-15:30 MonC06-6 1012 Intelligent Control for Switched Systems with Time Delay via Deep Reinforcement Learning Ruijia Song Northwestern Polytechnical Univ.Bolan Wang Shanghai Electro-Mechanical Engineering Inst.Haoyu Cheng Northwestern Polytechnical Univ.Hanqiao Huang Northwestern Polytechnical Univ.Jie Yan Northwestern Polytechnical Univ.The problem of intelligent control for switched systems with time delay is investigated in this paper. Firstly, the state feedback controller is proposed for switched systems with time delay by robust control theory and non-fragile control. Multiple Lyapunov-Krasovskii functional method and average dwell time (ADT) approach are combined to analyze the stability of closed-loop system. Sufficient conditions to guarantee the stability and prescribed performance are given in the form of linear matrix inequalities (LMIs). In order to achieve better performance, the deep deterministic policy gradient (DDPG) approach is proposed to optimize the controller parameters in the neighborhood of parameters obtained by robust control. Numerical example is given in the end to illustrate the effectiveness and superiority of proposed method.

Technical Programmes CCDC 2021 MonC07 Room07 Identification and Estimation (I) 13:30-15:30 Chair: Jian Guo Harbin Engineering Univ.CO-Chair: Xiaoyong Liu Zunyi Normal Univ.

13:30-13:50 MonC07-1 957 Modeling and Simulation of Power Grid Voltage Harmonic Detection Method Based on Adaptive Kalman Filter Jian Guo Harbin Engineering Univ.Chao Jiang Harbin Engineering Univ.Chenxi Guan Harbin Engineering Univ.Tao Zhang Harbin Engineering Univ.Jiangkun Pu Harbin Engineering Univ.Aiming at the problems of harmonic distortion in power systems, Kalman filter is used to detect the power harmonics. This method cannot reflect the systematic real situation because its systematic procedure noise variance is constant value and it is quite different from reality. In order to solve these problems, the adaptive Kalman filter is presented. According to the system state, the noise variance of the process is defined and the simulation model is established. The simulation results of the detection of the steady harmonic and the transient harmonic in power grid are given respectively. And the contrastive analysis is also given based on these simulation results. This detection method of the adaptive Kalman filter has higher rapidity and precision than the detection method of Kalman filter. It showed that this method can detect not only the steady harmonic but also the transient harmonic.

13:50-14:10 MonC07-2 622 A Novel Method of Identifying Optimal Interval Regression Model Using Structural Risk Minimization and Approximation Error Minimization Xiaoyong Liu Zunyi Normal Univ.Jing Liu Zunyi Normal Univ.Xiaoyu Chen Zunyi Normal Univ.Uncertain measurements derived from many practical applications tend to be constructed as interval regression model (IRM), consisted of upper regression model (URM) and lower regression model (LRM). Motivated by interval regression analysis, a novel method of identifying IRM is proposed in this paper by combining the principle of structural risk minimization with approximation error minimization. Taken the superiorities of model sparse representation and computational efficiency of linear programming support vector regression (LP-SVR) and some ideas from ℓ 1-norm minimization on approximation error into consideration, the proposed method not only possesses the characteristics of adjusting a flexible interval spread, but also independently constructs URM and LRM, instead of adopting the traditional estimated center model and estimated radius of IRM which is the incapability of dealing with asymmetrical interval. More importantly, model complexity for IRM is under control by our approach. First, ℓ1-norms minimization on approximation error for URM and LRM are constructed, and the both optimization problems subject to respective constraints are integrated into LP-SVR to form new upper and lower optimization problems, respectively. Following that, optimization problems corresponding to URM and LRM are solved by linear programming and IRM is thus constructed. Finally, several simulations are provided to show the validity and applicability of the proposed method.

14:10-14:30 MonC07-3 468 Data–Driven Control Based on Function Approximation with Binomial Coefficients Guoliang Zhu Beihang Univ.Lei Chen Beihang Univ.

Beijing Inst. of Tech.Kexin Liu Beihang Univ.Jinhu Lu Beihang Univ.This paper investigates data–driven control methods based on function approximation with binomial coefficients and sampling techniques. We propose two approximate models and corresponding controllers formed from sampled data. It shows that the information truncated in the first approximate model is saved in the latter with parameter estimation method. Simulation results validate the effectiveness of proposed approaches.

14:30-14:50 MonC07-4 493 A dynamic model of supercritical boiler-turbine unit based on immune genetic algorithm parameter identification Guolian Hou North China Electric Power Univ.Zhiyan Tang North China Electric Power Univ.Linjuan Gong North China Electric Power Univ.Huilin Su North China Electric Power Univ.Bo Hu State Grid Liaoning Electric Power Supply Co. Ltd.Yuanzhu Zhao State Grid Liaoning Electric Power Supply Co. Ltd.In recent years, with the development of coal-fired units towards large capacity and high parameters, the position of supercritical once-through boiler unit has become more prominent. In this paper, a nonlinear mathematical model of the supercritical boiler unit is established through

a series of assumptions and formula derivation, and then the parameters are identified through regression analysis and immune genetic algorithm (IGA) combined with the operation data of a 1000MW supercritical unit. The inputs of the model are fuel command, feed water and turbine governor valve position while the outputs are turbine power, main steam pressure and steam enthalpy at separator outlet. The simulation results show that the model can characterize the dynamic characteristics of the unit and has acceptable accuracy.

14:50-15:10 MonC07-5 1035 Variational Bayesian Filter for Nonlinear System with Gaussian-Skew T Mixture Noise Ruxuan He Beijing Inst. of Tech.Xiaoxue Feng Beijing Inst. of Tech.Shuhui Li Beijing Inst. of Tech.Feng Pan Beijing Inst. of Tech.

Kunming-BIT Industry Tech. Research Inst. INCNing Pu Yunnan Remote Sensing CenterIn the actual application scenario of target tracking and positioning, the target is affected by maneuvering interference, measurement outliers, and abnormal values, and system noise and measurement noise may obey non-Gaussian heavy-tailed and skew distribution. In this case, the traditional Kalman filter based on Gaussian noise modeling fails to obtain the satisfying estimation performance. Aiming at non-Gaussian thick-tailed noise, this paper proposes a hierarchical multivariate Gaussian-Skew T mixture model. Using the variational Bayesian theory, the estimation of the state probability density function is converted into two probability density functions of the unknown noise and the nonlinear state. Using Bayesian inference, an iterative algorithm for joint estimation of state and unknown noise is proposed. And the effectiveness of the algorithm is verified in the target tracking simulation experiment and UWB positioning experiment.

15:10-15:30 MonC07-6 1586 Particle Network Ensemble Kalman Filter Xinjia Li Fudan Univ.Wenlian Lu Fudan Univ.Longbin Zeng Fudan Univ.As a typical method of data assimilation, the Ensemble Kalman filter (EnKF) generalizes the linear Kalman filter by introducing a Monte Carlo approximation to predict the means and the covariances. Towards distributed sampling, conventional EnKF usually needs a centralized server to integrate the prediction of all particles, which may cause insufficient computing power or traffic jams on the central node in the case of computing a large-scale model. In this paper, we propose a novel distributed scheme of EnKF designed for network setting of sampling, named by the Particle Network EnKF. This method does not contain any central node and every sampling particle communicates with its neighbors over a locally connected network. Different from the well-known distributed Kalman filter(DKF), this method focuses on the distribution of sampling particles rather than sensors as done by the DKF. The numerical experiments on the Lorenz-63 system show the comparable performance of the proposed Particle Network EnKF to the standard EnKF with proper communication rounds, even on a sparse particle network.

MonC08 Room08 Theory and Application of Nonlinear Systems (V) 13:30-15:30 Chair: Yanfang Li Yulin Univ.

Northwestern Polytechnical Univ.CO-Chair: Yaoli Zhang Northeastern Univ.

Key Laboratory of Data Analytics and Optimization for Smart Industry

13:30-13:50 MonC08-1 953 Command Filter Based on Back-stepping Control for Deployment of an Electrodynmaic Tether System Yanfang Li Yulin Univ.

Northwestern Polytechnical Univ.Aijun Li Northwestern Polytechnical Univ.Changqing Wang Northwestern Polytechnical Univ.This paper studies back-stepping control for deployment of an electrodynamic tether system with the consideration of known upper bound of external disturbances. Command filter design is developed to achieve the controller, which can remove the inherent problem of “explosion of complexity” in back-stepping. The hybrid control of current and tension methods based on the back-stepping method is adopted to stabilize the motions of electrodynamic tether system during its deployment by regulating the tension and electric current in the tether, independent of propulsion to deploy the tether to the specified length. Furthermore, the Lyapunov analysis is used to gain a deep understanding of the characteristics of the designed hybrid control laws. Finally, the simulation results demonstrate that the proposed control laws can realize the tether fast and stable deployment.

13:50-14:10 MonC08-2 1346 Unilateral tracking control for switched linear systems

Technical Programmes CCDC 2021 Yaoli Zhang Northeastern Univ.

Key Laboratory of Data Analytics and Optimization for Smart Industry

Jun Zhao Northeastern Univ.In this paper, we design the controllers to asymptotically track a constant signal without overshoot for switched linear time-invariant systems. The component of error is shaped by one mode to realize no overshoot. Under arbitrary switching signal, the closed-loop switched system is stable based on common Lyapunov function. We design a state-dependent switching signal to achieve the nonovershooting tracking, when the stability or the monotonally increasing error for some one subsystem is not satisfied. A numerical example is given to illustrate the effectiveness of the proposed method.

14:10-14:30 MonC08-3 1140 Asynchronous control for Markov jump systems subject to actuator saturation Chao Wang Huazhong Univ. of Science and Tech.Lei Liu Huazhong Univ. of Science and Tech.Wei Wang Beihang Univ.Xiaobing Luo Wuhan PolytechnicHuijin Fan Huazhong Univ. of Science and Tech.In this paper, the asynchronous controller problem is considered for Markov jump systems (MJSs) subject to actuator saturation. The asynchronous phenomenon between the controller and the plant is addressed by introducing a hidden Markov model and the closed-loop hidden MJSs are obtained. Based on the closed-loop hidden MJSs and using convex hull technique and linear matrix inequality technique, a controller design algorithm is derived to ensure that the closed-loop hidden MJSs satisfy the stochastic stability. Finally, a numerical example is provided to demonstrate the effectiveness of the proposed asynchronous controller design algorithm.

14:30-14:50 MonC08-4 1328 Fully Distributed State Feedback Controller Design for Bipartite Consensus Tracking of Lipschitz-like Nonlinear Systems over Directed Topology Xiaoya Nan Peking Univ.Yuezu Lv Southeast Univ.Zhisheng Duan Peking Univ.This paper studies the bipartite consensus problem for general nonlinear multi-agent systems over direct- ed cooperative-competitive network. By introducing a Lipschitz-like assumption on the nonlinearity, a state feedback controller with PI gains is designed for bipartite consensus achievement. The designed controller dosen’t required any global information, which is in a fully distributed way. A numerical example is presented to support the main result of this paper.

14:50-15:10 MonC08-5 1345 Dissipative Control for a Class of Uncertain Polynomial Nonlinear Systems Chaoqun Li China Univ. of PetroleumLi Sheng China Univ. of PetroleumIn this paper, the problem of dissipative control is investigated for continuous-time polynomial nonlinear systems with uncertainty. The purpose of the addressed problem is to design a state feedback controller such that the closed-loop system is asymptotically stable and the prescribed dissipativity performance is satisfied. By resorting to sum of squares method, sufficient conditions are derived for the existence of the anticipant controller taking advantage of the solution to the polynomial matrix inequality, thus the complex Hamilton-Jacobi inequalities and iterative programmings are avoided. Finally, two numerical examples are applied to confirm the effectiveness of the proposed method.

15:10-15:30 MonC08-6 920 Finite-time synchronization of fractional-order PMSM with unknown parameters Keyong Shao Northeast Petroleum Univ.Xinyu Huang Northeast Petroleum Univ.This paper takes the PMSM system as the object, firstly, the PMSM model in the rotor-field-oriented coordinate system is transformed into a simple dimensionless mathematical model through coordinate transformation, and the chaotic dynamic behavior of fractional-order PMSM model is analyzed through numerical simulation. Then, a finite-time controller is designed by using Lyapunov stability theory, and the parameter identification rules are introduced into the finite-time synchronization controller to realize the synchronization of PMSM system with unknown parameters. Finally, the simulation results are given by numerical simulation, which proves the effectiveness of the scheme for fractional-order PMSM chaotic system when the system parameters are unknown.

MonC09 Room09 Fault Diagnosis and Predictive Maintenance (IX) 13:30-15:30 Chair: Youqing Wang Shandong Univ. of Science and Tech.CO-Chair: Rui Yang Xi’an Jiaotong-Liverpool Univ.

13:30-13:50 MonC09-1 128 Multi-Step Canonical Correlation Analysis for Dynamic Process Monitoring Qing Chen Beijing Univ. of Chemical Tech.Xin Ma Beijing Univ. of Chemical Tech.Zhanzhan Liu Beijing Univ. of Chemical Tech.Youqing Wang Shandong Univ. of Science and Tech.In this article, a novel approach named multi-step canonical correlation analysis (MS-CCA) is developed for fault detection and monitoring of dynamic processes. The canonical correlation analysis (CCA) has been applied to static processes and achieved a good result. However, dynamic characteristics prevail in real industrial processes, then dynamic CCA (DCCA) is proposed to address this issue. The main idea of DCCA is performing CCA on augmented original process and quality data matrices, but it does not consider the noise information in the original process data and then this will result in some false alarms. For this motivation, MS-CCA that considers noises change sufficiently in process data is proposed. Finally, a numerical example and continuous stirred-tank reactor both indicate the outstanding detection performance of MS-CCA compared with other relevant methods.

13:50-14:10 MonC09-2 1557 Incipient Fault Detection Based on Just-in-time-learning and Wavelet Transform for DAB DC-DC Converter Yu Zhang Shandong Univ. of Science and Tech.Xianghua Wang Shandong Univ. of Science and Tech.Rui Yang Xi’an Jiaotong-Liverpool Univ.In this paper, a novel fault detection method for incipient fault occurring in Dual-active-bridge (DAB) DC- DC converter is proposed. The incipient fault has the characteristics of small amplitude, weak influence on system performances and easily being covered by noise, hence it is difficult to detect incipient fault for DAB DC-DC converter. This work proposes to use wavelet transform to process data, which can amplify the fault feature and simultaneously weak the noise. And then Just-in-time-learning (JITL) algorithm is utilized to model the system dynamic process online, followed by singular-value-decomposition (SVD) and covariance calculation. The threshold is obtained when applying the proposed method to the normal data, and then to the test data in order to get its covariance, which when is larger than the threshold, an alarm will be triggered to imply the fault occurrence. Finally, establish a simulation model in the Matlab environment, and analyze the experimental results to verify the effectiveness of the proposed method.

14:10-14:30 MonC09-3 1225 A Multi-level Classification Method for Fault Diagnosis of Grid-connected Inverter Guangfeng Jin Shanghai Maritime Univ.Junchao Geng Shanghai Maritime Univ.Yue Yu Shanghai Maritime Univ.Tianzhen Wang Shanghai Maritime Univ.This article proposes a multi-level classification fault diagnosis method for the open-circuit fault of power semiconductor; signal-stuck, gain-variation, and zero-offset of the current sensor. First, the average value, range value of current, and the mean value of absolute current signal are used to divide all faults into five major types of faults; then for power semiconductor open-circuit fault, fast Fourier transform (FFT) is used to extract the fault characteristics; The range and average value of current are selected as the fault characteristics for gain-variation and zero-offset fault, respectively. Three ELM classifiers are constructed to classify the faults for the above three types of faults. The proposed method is verified on the fault data collected in experimental platform, and achieves extremely high diagnostic accuracy. Moreover, this method can still perform well even when the gain coefficient and zero-offset of sensor change, showing its high robustness characteristics.

14:30-14:50 MonC09-4 1231 A Fault-tolerant Inverter with Auxiliary Switch and Its Control Method Ziyi Zhao Shanghai Maritime Univ.Junchao Geng Shanghai Maritime Univ.Tianzhen Wang Shanghai Maritime Univ.As multi-level inverters are widely used in high-power applications, the fault-tolerant control of multi-level inverters is also paid more and more attention, because it is very important to ensure the stability and reliability of the system. In this paper, a fault-tolerant inverter with auxiliary switch (FIAS) and its control method are put forward. In order to reduce the damage of Insulated Gate Bipolar Transistor (IGBT) failure of cascaded H-bridge (CHB) inverter, added fault isolation switches and relays on five-level CHB. Using the above switches can complete fault isolation and health bridge reconfiguration after the system receives the fault signal, so that the CHB will keep fault tolerant operation with voltage peak. The effectiveness of proposed method is verified by simulation and experiment of five-level CHB.

14:50-15:10 MonC09-5 1248

Technical Programmes CCDC 2021 Eddy Current Signal Defect Detection Algorithm Based on Improved SSD Network Shaoxuan Zhang Northeastern Univ.Jian Feng Northeastern Univ.Xinbo Zhang Northeastern Univ.Yiyun Mao Northeastern Univ.The main function of eddy current testing is to compare the results of magnetic flux leakage nondestructive testing. By analyzing the defects, it is possible to analyze whether the position of the defect is inside or outside the pipeline. The noise interference is so severe that the conventional threshold method cannot be adapted to the eddy current signal. This paper proposes a method to convert eddy current signals into pseudo-color images. The pseudo-color image obtained by eddy current signal conversion has high resolution and clear defect features, which is suitable for labeling and network training. This paper also proposes an improved single shot detection (SSD) algorithm to better solve the problem of misclassification of external defects. The method proposed in this paper is more applicable to low-noise pipes. The results show that the method proposed in this paper can improve the positioning accuracy of eddy current data.

15:10-15:30 MonC09-6 1267 Actuator Fault Diagnosis Research of Control System Based on EWT-SOM Method Haohan Li North China Electric Power Univ.Shenghui Wang Huaneng Taiyuan Dongshan Gas

Power Company LimitedWenguang Zhang North China Electric Power Univ.Yuguang Niu North China Electric Power Univ.As the execution terminal component of the control system, the actuator fault often leaded to a series of serious consequences, and the harsh working environment further leaded to a higher fault rate of the actuator. In this paper, a fault diagnosis method of control system actuator based on the combination of empirical wavelet transform and self-organizing map neural network was proposed. The method used the empirical wavelet transform to mine fault signal features in depth at first, and then used the self-organizing map neural network to carry on fast fault classifications. Finally, the method proposed in this paper was verified by simulation experiments on the actuator fault simulation semi-physical test platform,and we compared the proposed method with the wavelet neural network fault diagnosis method. The results showed that the proposed fault diagnosis method can efficiently complete the task of actuator fault diagnosis, and the diagnosis accuracy was better than that of wavelet neural network.

MonC10 Room10 Signal Processing and Information Fusion (IV) 13:30-15:30 Chair: Fang Yang Wuhan Univ. of Science and Tech.CO-Chair: Yin Gao Quanzhou Inst. of Equipment Manufacturing

13:30-13:50 MonC10-1 583 Siamese Differential AE-Net: Noisy Patch Comparison and Its Application in NLM Shichao Wang Wuhan Univ. of Science and Tech.Jun Yang Wuhan Univ. of Science and Tech.Fang Yang Wuhan Univ. of Science and Tech.The structural similarity of image patches is widely used in image processing because of its simple calculation and effectiveness. However, the calculation of image patch similarity is often affected by noise, which makes it difficult to adjust the parameters for image processing tasks, such as the image denoising, classification etc. In this paper, we propose a Siamese differential Auto-Encoder network (AE-net), which applies the deep features of the noise patches to the noise patches comparison. The self-learning characteristic of the AE-net helps to adjust the parameters and extract the deep features of similar patches according to the cost function. The proposed cost function can effectively reduce the difference between the deep features of the noisy and noise-free images. We apply our scheme to the Non Local Means (NLM) denoising method. The experimental results show that our method outperforms the traditional NLM in terms of the PSNR and SSIM values and can better preserve the texture detail of the image while denoising.

13:50-14:10 MonC10-2 1069 Semi-selective image dehazing Yin Gao Quanzhou Inst. of Equipment

ManufacturingHuiqin Xu Quanzhou Inst. of Equipment

ManufacturingLanzhou Jiaotong Univ.

Feng Xie Inst. of Automation and CommunicationJun Li Quanzhou Inst. of Equipment

ManufacturingIn this paper, we propose a new image dehazing method via the semi-selective method. Firstly, we obtain the effect range of the global atmospheric light according to the statistical prior method. Secondly, a relative total variation with adaptive boundary constraint is developed to optimize the transmission. Finally, a semi-selective method is used to select the image with the best visual sense. Experimental results show that our method outperforms state-of-the-art haze removal methods in

dehazing visual effects.

14:10-14:30 MonC10-3 1016 Segmental Compensation of FOG Temperature Error Based on ELM Prediction Model Baidong Zheng Naval Aviation Univ.Wei Liu Naval Aviation Univ.Ming Lv Naval Aviation Univ.Rui Wang Naval Aviation Univ.Hongde Dai Naval Aviation Univ.Aiming at the complex nonlinear relationship between temperature and the zero bias of fiber optic gyro (FOG), combining the prediction model of extreme learning machine (ELM) with the idea of piecewise modeling. A segmented compensation method based on ELM prediction model is proposed, improving the temperature performance of FOG. Analyzing the influence of temperature on the optical fiber gyroscope zero bias. Studying the effect of the ELM model’s parameters on the prediction precision, giving the ELM neural network method for determining the number of hidden layer neurons. The simulation analysis results to the collected measured data of FOG show that compared with linear regression model and single ELM neural network model, the segmented compensation method based on ELM prediction model has more significant effects. And has good temperature applicability. After compensation, the RMSE of gyro offset data is reduced by more than 90%.

14:30-14:50 MonC10-4 1057 Sequential Fast Covariance Intersection Fusion Kalman Filter for Multi-Sensor Systems with Random One-step Measurement Delays and Missing Measurements Ke Wu Heilongjiang Univ.Ke Xu Heilongjiang Univ.Yuan Gao Heilongjiang Univ.

Key Laboratory of Information Fusion Estimation and Detection

Yinlong Huo Harbin Ecological EnvironmentalMonitoring Center of Heilongjiang

ProvinceIn order to handle the fusion estimation problem for the multi-sensor systems with random one-step measurement delays and missing measurements, a Sequential Fast Covariance Intersection (SFCI) fusion structure is presented by the augmented state technology with the fictitious noises, which can avoid large computational burden about the unknown cross-covariance matrices and is not sensitive to the sequential fusion orders. The accuracy of the presented SFCI fusion Kalman filter is higher than each of local estimators, less but close to that of the information fusion Kalman estimator weighted by matrices. The simulation example shows the effectiveness and the estimation accuracy of the proposed algorithm.

14:50-15:10 MonC10-5 1403 Vital Signs Detection Based on Differential Evolution and Variational Mode Decomposition Algorithm Yuhao Chen Southeast Univ.Haiyang Hu Southeast Univ.Zhenxing Sun Southeast Univ.Shihua Li Southeast Univ.Aiming at the problem that human breathing and heartbeat signals are difficult to detect in a low signal-to-clutter environment, this paper proposes a signal processing method that combines differential evolution algorithm and variational modal decomposition algorithm. In order to achieve non-contact detection of vital signs, this paper uses ultra-wideband radar to collect the signal reflected by the human body, and suppresses the interference of the background signal by removing the background of the signal, and then extracts the signal reflected by the human chest in the fast time domain. After that, according to the frequency range of breathing and heartbeat signals, low-pass filter and band-pass filter are used to filter useless signals. Aiming at the problem that the variational modal decomposition algorithm needs to manually adjust parameters in advance, this paper takes the maximum effective frequency band energy ratio as the objective function, and uses differential evolution algorithm to automatically search for the best parameter combination of variational modal decomposition, which reduces the workload of parameter selection. Finally, variational modal decomposition by the best parameter combination is used again to decompose the signal, and the breathing and heartbeat signals are reconstructed from the decomposed modal components. Experiments show that the method proposed in this paper can measure the human body's respiratory rate and heartbeat rate with high accuracy.

15:10-15:30 MonC10-6 1061 Research and Application of Complex Event Processing Method Based on RDF Stream Bihui Yu Chinese Academy of SciencesHe Wang Univ. of Chinese Academy of Sciences

Chinese Academy of SciencesQi Wang Chinese Academy of Sciences

Technical Programmes CCDC 2021 The development of Semantic Web technology has produced massive resource description framework (RDF) data, most of which are collected from the Internet of Things in the form of streams and analyzed in real time. How to reason and query RDF streams, and to mine valuable information from the streams for decision-making has become a hot research topic. Complex event processing (CEP) can perform real-time analysis of event streams, but the processed data usually lacks semantic information, is not suitable for semantic interoperability between multi-source heterogeneous data, and cannot combine data streams with domain ontology. In order to solve the above problems, CEPR, a complex event processing method based on RDF flow, is proposed. CEPR is based on answer set programming ASP, using LARS that extends ASP, combining Datalog and relational algebra, to implement RDF flow inference and query on the Flink platform. Use the PHM 2010 tool wear data set to evaluate the performance of CEPR, and compare CEPR with C-SPARQL and Strider. Experiments show that CEPR has a greater advantage in query latency.

MonC11 Room11 Intelligent Control, Computation and Optimization (VIII) 13:30-15:30 Chair: Yuanyuan Liu Wuxi Vocational College of Science and

Tech.Jiangnan Univ.

CO-Chair: Hongzhi Sun Liaoning Earthquake Agency

13:30-13:50 MonC11-1 667 A CLCC Impedance Matching Method Under Maximum Efficiency Tracking in Wireless Power Transfer System Yuanyuan Liu Wuxi Vocational College of Science and Tech.

Jiangnan Univ.Fei Liu Jiangnan Univ.Guoxin Zhang Wuxi Vocational College of Science and Tech.Qinghui Yin Wuxi Autowell Tech. Company Limited.The key problem of the wireless power transfer (WPT) system is transfer efficiency, transfer efficiency will be affected by the variation of transfer distance or load resistance. The CLCC matching network is designed to eliminates the influence of nonlinear characteristics of the rectifier circuit, the programmable capacitor array (PCA) is introduced to realize the wide capacitance adjustable range, which ensures the system can always track the optimal resistance. The experimental results agree well with the theoretical analysis, the suggested impedance matching network can achieve load variation up to 600Ω under maximum efficiency tracking control (METC).

13:50-14:10 MonC11-2 1425 Development and Application of a Smart Monitoring and Transmission System for Seismic Stations Hongzhi Sun Liaoning Earthquake AgencyYimei Sun Liaoning Earthquake AgencyFei Luo Liaoning Earthquake AgencyLei Zhao Liaoning Earthquake AgencyFei Gong Tianyuan Times Automation Instrumentation

CO.LtdBingchen Zhao Tianyuan Times Automation Instrumentation

CO.LtdHow to restore seismic observation signals as soon as possible when there is a failure in a seismic station’s instrument or transmission channel has become a challenge that needs to be solved urgently in our seismic observation work. By taking enhanced single-chip microcomputer (SCM) as the core of hardware, and the third/fourth-generation (3G /4G) of industrial-level mobile communication modules as a communication bridge for information exchange, this study proposed an instrument that allows us to carry out real-time monitoring for a seismic station’s equipment running status via its network interface and serial interface, thus realizing a remote control over the powering-up and restart of the equipment, and switching between the main equipment and standby equipment, meanwhile, when there is a failure in the transmission channel, the 3G/4G wireless data transmission function can be invoked so that seismic observation data can be transmitted timely to the seismic station network center, thus an uninterrupted transmission of seismic observation signals is ensured.

14:10-14:30 MonC11-3 1099 Weapon-Target Assignment Based on Improved PSO Algorithm Haoran Zhai Beihang Univ.Weihong Wang Beihang Univ.Qingze Li Beihang Univ.Wei Zhang Beihang Univ.In the research of modern combat command, the weapon-target assignment (WTA) problem contains many variables, which is a typical NP (Non-deterministic Polynomial) problem. The WTA optimal allocation model of multi-target cooperative attack is established by synthesizing various factors affecting operational effectiveness evaluation. Aiming at the shortcoming that the basic PSO algorithm is easy to fall into the local optimal solution, this paper proposes an improved PSO algorithm to improve the calculation speed and accuracy. The improved PSO algorithm uses the inertia weight of nonlinear decrement, improves the acceleration constant and introduces the adaptive mutation operation, which improves the convergence speed and accuracy of the algorithm,

and avoids falling into the local optimal solution. Aiming at the WTA problem, the decimal coding strategy is adopted for the particle, and the position and velocity of the particle are discretized and limited by the boundary. Finally, the improved PSO algorithm is obtained. For large-scale WTA problems, the improved PSO algorithm is compared with the basic PSO algorithm. The simulation results show that the improved PSO algorithm speeds up the convergence speed of the algorithm, has better solution accuracy, and can effectively solve the WTA problem.

14:30-14:50 MonC11-4 1007 An method for Image Edge Recognition Based on Multi-Operator Dynamic Weight Detection Yuedong Wu Hohai Univ.

Geotechnical Engineering Research Center ofJiangsu Province

Engineering Research Center of DredgingTech. of Ministry of Education

Yongyang Zhu Hohai Univ.Geotechnical Engineering Research Center of

Jiangsu ProvinceJian Liu Hohai Univ.

Geotechnical Engineering Research Center ofJiangsu Province

Bin Chen Off-Season Breeding Management Station ofJiangsu Province

Wei Xu Hohai Univ.Geotechnical Engineering Research Center of

Jiangsu ProvinceImage edge detection in virtual reality scenes is one of the most important technologies in the field of image processing and computer vision, which can be used in automatic measurement in civil engineering. It occupies an important position in the image processing system and is a key factor that affects the performance of the entire system. The quality of the algorithm directly affects the performance of the computer's visual system. This paper briefly describes the basic theoretical methods of image edge detection, studies the adaptive multi-scale edge detection method based on Canny algorithm, and through theoretical analysis, compares the advantages and disadvantages of various algorithms in image edge detection. In the process of acquiring image edges, the local maximum value of the modulus is calculated along the gradient direction and the threshold is adaptively selected based on the image blocking principle. Comparing with the traditional modulus maximum edge detection method, this algorithm overcomes the contradiction that can suppress the interference of noise and obtain better edge detection effect to a certain extent. Experiments show that the method in this paper not only suppresses the interference of impulsive noise to the image edge detection, but also greatly reduces the possibility of false edges, and obtains a satisfactory accurate binary edge image.

14:50-15:10 MonC11-5 1011 Mixed Logic Dynamic modeling used for Cloud Services Optimization in Smart City Jian Liu Hohai Univ.

Geotechnical Engineering Research Center ofJiangsu Province

Huiguo Wu Northeastern Univ.Yuedong Wu Hohai Univ.

Geotechnical Engineering Research Center ofJiangsu Province

Bin Chen Off-Season Breeding Management Station ofJiangsu Province

Wei Xu Hohai Univ.Geotechnical Engineering Research Center of

Jiangsu ProvinceThe Internet of Things (IoT) technology for cloud services has made some progress. However, there are still many limitations in largescale information flow perception modeling and performance optimization. In this paper, the Mixed Logic Dynamic modeling method of IoT information flow perception is studied. The IoT sensor nodes, controlled nodes and coordination nodes are used to describe the system application scenarios, whereas the automaton is used to carry out internal information transmission, This lays an important foundation for the coordination and optimization of the system. Then, an open queuing method based on large-scale information flow perception modeling and network delay analysis method is proposed. By analyzing the end-to-end delay of the node path and the average delay analysis of the whole queuing network, the open queuing is obtained, So as to maximize the performance of the perceived network in the IoT. Aiming at the characteristics of distributed data of information flow perception in the IoT, a priority-based queuing network is proposed to model and analyze the aggregation nodes based on embedded multi-core System on Chip (SoC), which greatly improves the performance of embedded multicore SoC.

15:10-15:30 MonC11-6 1168 Neural Network-Based ADP Cotrol for Nonliear Systems with Prescribed Performance Constriant Can Ding Hunan Univ.

Technical Programmes CCDC 2021 Jing Zhang Hunan Univ.Yingjie Zhang Hunan UnivZhe Zhang Hunan Univ.Xiaoyao Li Hunan Univ.In this paper, the trajectory tracking control problem of nonlinear system with prescribed performance constraint was discussed. adaptive dynamic programming (ADP) is investigated to solve the problem. By introducing the constraint transformation, which is used to convert the constrained system into unconstrained one, and prescribed performance function (PPF), the steady and transient performance of closed-loop system are guaranteed. After obtained the unconstrained system, a critic network is proposed to approximate the solution of Hamilton-Jacobi-Bellman (HJB) equation. Then an optimal control was developed. Throughout the Lyapunov theory, the update laws of critic network was obtained and the stability of closed loop control system was proved. Finally, a simulation experiment was carried out to validate the effectiveness of the proposed method.

MonCIS Room12 Interactive Session 13:30-15:30

MonCIS-01 1552 Deep forest regression prediction model for dioxin emission concentration by using new representation strategy Jian Tang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Heng Xia Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemWen Xu Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

The dioxin (DXN) is a key environmental indicator for municipal solid waste incineration (MSWI) process. However, the concentration of DXN’s emission prediction model always relies on long-term and expensive offline experimental analysis. To address this issue, some researchers employed deep neural networks (DNN) to construct a soft measurement model, but it has poor training efficiency, inflexible model size, and weak interpretability. Recently, the deep forest regression (DFR) algorithm has achieved success in the field of modeling domain. Therefore, it is used for DXN emission prediction in this paper. To encourage the diversity of features between layers, this paper endeavors to implement DFR for DXN emission concentration soft measurement by a new representation strategy. Firstly, the dioxin emissions and data characteristics of the MSWI process are described. Then, the structure of DFR is analyzed and the problems of representation strategy are stated briefly. Finally, we explore the representation learning inside the DFR structure based on stacked generalization. The effectiveness of the proposed method is verified by DXN data of the MSWI process.

MonCIS-02 1587 A multimode soft sensor method combining common feature transfer and special feature correction Pengdong Han Taiyuan Univ. of Tech.Yiming Hou Shanxi Gemengzhongmei Clean Energy Research

and Development Co., Ltd.Zefu Ye Shanxi Gemengzhongmei Clean Energy Research

and Development Co., Ltd.Guoyong Li Taiyuan Univ. of Tech.Gaowei Yan Taiyuan Univ. of Tech.Aiming at the problem of multiple conditions for complex industrial processes, in this paper, a multimode soft sensor method combining common feature transfer and special feature correction is introduced. First, a novel public information extraction method is used to extract common features and special features of multi-condition data. After using the geodesic flow kernel (GFK) to achieve distribution alignment, the initial condition is selected as the modeling condition, respectively, the partial least squares regression (PLSR) models with other historical conditions and target conditions are established. Then, the label biases of historical conditions can be obtained by comparing the real label with the pseudo label. The relationship between label biases and special features in historical conditions can be represented by the label correction model. Finally, label correction value of target condition can be obtained by its special feature, and soft sensor value of the dominant variable is obtained. The simulation experiment in the Tennessee Eastman (TE) process proves the validity and practicability of the model.

MonCIS-03 167 A sequential outlier ensemble for process industries Biao Wang Shenyang Aerospace Univ。Wenjing Wang Liaoning Vocational College of Ecolo

gical EngineeringMotivated by the development of data-driven techniques, the problem of outlier detection has drawn much attention in process industry during recent decades. However, performances of traditional single detectors are usually not stable, which has promoted the development of outlier ensembles. Existing parallel outlier ensembles only take into account the variance reduction, and the performance is not sufficient in some

applications. To this end, this paper proposes a sequential outlier ensemble. In specific, a parallel ensemble is used to provide soft labels for all instances. Then a iterated procedure is used to remove outliers successively. Finally, outlier scores of all data on all iterations are combined to derive the final outlier scores. Both bias and variance are considered in our method. Three datasets stemming from three real-life process industries are used to validate the effectiveness of our method. Experimental results have approved the superiority.

MonCIS-04 283 Recognition of Combustion Conditions in MSWI Process Using Convolutional Neural Network Hao Zhang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Xi Meng Beijing Univ. of Tech.Beijing Key Laboratory of Computationa

l Intelligence and Intelligent SystemJian Tang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Zixuan Wang Beijing Univ. of Tech.Beijing Key Laboratory of Computationa

l Intelligence and Intelligent SystemHaoshan Duan Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent System

Junfei Qiao Beijing Univ. of Tech.Beijing Key Laboratory of Computationa

l Intelligence and Intelligent SystemThe non-linear, time-varying, and uncertain characteristics of municipal solid-waste incineration (MSWI) process cause the difficulty of recognizing the combustion condition of MSWI. Currently, engineers judge the operating conditions in MSWI plants using a video of the combustion flame inside the incinerator, but this method does not help maintain stable operating conditions. Thus, this paper proposes a convolutional neural network-based recognition method for controlling combustion conditions in MSWI processes. First, the flame image is pre-processed by resizing, and its features are extracted using the convolutional neural network. Thereafter, the classification of combustion conditions is realized from the Softmax layer. Moreover, the proposed method is validated with experimental results based on flame image data of an MSWI plant in Beijing.

MonCIS-05 473 Wear Detection of Metro Catenary Based on Binocular Vision Qingfeng Tang Beijing Jiaotong Univ.Xiukun Wei Beijing Jiaotong Univ.Siyang Jiang Beijing Jiaotong Univ.Compared with the traditional catenary wear detection method, the new non-contact image detection method has the characteristics of high efficiency and high precision. In this paper, binocular vision 3D reconstruction is used to detect the abrasion of metro catenary, and in the process of self-calibration and stereo correction, topological structure constraints are proposed to improve the accuracy of the SURF feature point matching. The experimental results show that the improved feature point matching has high accuracy and good real-time performance.

MonCIS-06 487 Model-Based Active Fault-Tolerant Control for Power Electronic Converter in a Hybrid AC/DC Microgrid Saeed Jadidi Concordia Univ.Hamed Badihi Nanjing Univ. of Aeronautics and AstronauticsYoumin Zhang Concordia Univ.A novel fault-tolerant control (FTC) approach for an AC/DC pulse-width modulation (PWM) power electronic converter at microgrid level is proposed in this paper. A cluster of loads and renewable energy resources including wind turbines (WT) and solar photovoltaic (PV) array are considered as a hybrid AC/DC microgrid. In order to tolerate the effects of power-loss faults in the PV system and prevent their propagation through the entire microgrid, an active FTC approach based on fuzzy logic for the AC/DC PWM converter is presented. The effectiveness of the proposed approach is demonstrated in MATLAB/Simulink environment using a well-designed microgrid benchmark model.

MonCIS-07 979 Adaptive Switching Control of Wind Power System with Actuator Failures and Uncertainties Hongwei Wang Xinjiang Univ.

Dalian Univ. of Tech.Zhang Qian Xinjiang Univ.For the wind power generation system with actuator failures and uncertainty, the switching control method is used to study it. First of all, the working modes and states of this system are discussed. The failure and recovery of the actuators are regarded as two events, and the wind power system can be regarded as the wind power system with two event

Technical Programmes CCDC 2021 switching modes, including temporary failure event mode and health recovery event mode; Secondly, the switching control method is adopted, that is, the controller is stopped during the actuator failures, and the controller is applied after the actuators recover well. Then, based on Lyapunov stability theory, the switching controller of the wind power system with actuator failures is given. Finally, the double-fed wind power system is studied to prove the effectiveness of the proposed control method.

MonCIS-08 1018 A SVPWM-based fault-tolerance control method for three-level inverter under different working conditions Tao Peng Central South Univ.Yansong Xu Central South Univ.Hongwei Tao Central South Univ.Zhiwen Chen Central South Univ.Xinyu Fan Central South Univ.Aiming at the open-circuit fault of three-level NPC inverter used in traction system, the difference of current path at inverter legs before and after the fault is analyzed, and the change of space voltage vectors distribution is discussed. Then, based on the principle of Space Vector Pulse Width Modulation(SVPWM) and through the reconstruction of the basic voltage vectors, two fault-tolerant methods for different operating conditions of the train are proposed, and the idea of weak magnetism is innovatively proposed to realize fault-tolerant control at the condition on high speed. Simulation results show the effectiveness of the proposed methods.

MonCIS-09 1039 Type recognition of partial discharge source based on PCA and GWO-SVM Li Li Wuhan Univ. of Tech.Yuepeng Chen Wuhan Univ. of Tech.Guang Yang Hubei Electric Power Survey and De

sign Inst. Co. LTDCuimin Mao Hubei Electric Power Survey and De

sign Inst. Co. LTDHuajun Zhang Wuhan Univ. of Tech.As an important indication and manifestation of insulation failures in high-voltage equipment, the type identification of partial discharge (PD) is important for the assessment of the insulation state of electrical equipment. In order to identify the type of partial discharge source accurately, this paper presents a method for partial discharge source type identification based on principal component analysis (PCA) and support vector machine (SVM) with grey wolf optimization algorithm (GWO). The PCA method is used to select the three features out of 10 that best represent the original data, namely the phase, E/Ipeak and Ipeak/Q. On this basis, the SVM kernel function g and the non-negative penalty factor c are optimized by GWO to establish a support vector machine classification model based on the grey wolf optimization algorithm (GWO-SVM). The results show that PCA is able to extract the main features well and that GWO-SVM can identify partial discharge source more accurately than the genetic algorithm-based support vector machine (GA-SVM), multilayer perceptron (MLP) and k-nearest neighbor (kNN) classifier.

MonCIS-10 430 Voltage Balance of DC Microgrid Based on Switching Strategy Li Gong Anhui Normal Univ.

Anhui provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot

Hao Ding Anhui Normal Univ.Kaijian Tian Anhui Normal Univ.Fangyun Sun Anhui Normal Univ.Yuan Zhao Tianjin Normal Univ.Zibao Lu Anhui Normal Univ.

Anhui provincial Engineering Laboratory on Information Fusion and Control of Intelligent Robot

This paper mainly focuses on the research of the bus voltage balance in DC microgrid. According to the relationship of power balance of the system, three voltage regulation modes in the microgrid are defined , such as the mode for renewing resources, the mode for energy storage unit charging, and the mode for energy storage unit discharging. Based on three oprerating modes proposed, the microgrid is modeled as a linear switching system. Then the design method of the switching system controller is given and the stability of the switching system is analyzed. Finally, simulation results verify the effectiveness of the switching strategy.

MonCIS-11 211 Fixed time quadrotor trajectory tracking neural network backstepping control Mingyu Wang Qingdao Univ.Xu Yuan Qingdao Univ.Bing Chen Qingdao Univ.Chong Lin Qingdao Univ.Yun Shang Qingdao Univ.This paper aims at the trajectory tracking of a quadrotor. A novel

fixed-time backstepping control design scheme is proposed for the quadrotor based on adaptive neural control approach. The suggested adaptive continuous controller ensures that the quadrotor well tracks the desired trajectory in fixed time in spite of appearance of model uncertainties. Finally, simulation results are given to verify the effectiveness of the proposed control strategy.

MonCIS-12 212 Adaptive Fuzzy Backstepping Design for Nonlinear Systems with Asymmetric Full-State Constraints Xu Yuan Qingdao Univ.Mingyu Wang Qingdao Univ.Bing Chen Qingdao Univ.Chong Lin Qingdao Univ.Yun Shang Qingdao Univ.

Qingdao Univ. of Science and Tech.When backsteppinng is used to construct controllers for systems with full-state constraints, an open and challenging problem is how to ensure the designed virtual control signals remain in the corresponding state constraints all the time during the operation. This research deals with this problem and presents a novel adaptive fuzzy control method to solve this problem. The designed adaptive fuzzy controller can make sure that all the closed-loop signals keep bounded and the tracking error tends to a small neighborhood of origin, meanwhile, the asymmetric constraints on each system state are not violated during the operation. Finally, the proposed control scheme is further tested by a simulation example.

MonCIS-13 394 Research of Local Similarity Index Based on OWA Integration Operator in Terrorist Network Link Prediction Method Tingting Li National Univ. of Defence Tech.Chengyi Zeng National Univ. of Defence Tech.Yuan Feng National Univ. of Defence Tech.Yu Zhang National Univ. of Defence Tech.Kaiqiang Wang National Univ. of Defence Tech.Based on the known terrorist network structure, mining hidden terrorist networks is a matter of great concern to the security agencies of all countries in the world. With the emergence and development of machine learning methods, the use of various attributes of nodes can be accurately and efficiently predict potential terrorist network links. However, due to the inaccuracy of terrorist network node information, powerful machine learning methods cannot work. Based only on the structure of the network, link prediction methods to find probable links have become a hot issue of concern to experts in related fields in various countries. The link prediction performance of the OWA ensemble operator was compared with the link prediction algorithm of a single similarity index. Experimental results show that the OWA algorithm can effectively reduce the variance of the local information link prediction algorithm and improve the chain stability of link prediction.

MonCIS-14 806 Observer-based lag group consensus of nonlinear heterogeneous multi-agent systems Weixun Li Tianjin Univ. of Tech. and EducationDan Wang Tianjin Univ. of Tech. and EducationLiqiong Zhang Tianjin Univ. of Tech. and EducationLimin Zhang Zhongyuan Univ. of Tech.This paper studies the leader-following heterogeneous multi-agent system composed of the full time delay of first-order follower agent with a nonlinear term and second-order follower agent with lag delay respectively. Firstly, based on the relevant information of neighbor agents, this paper proposes a distributed control protocol, which makes all the agents in the system comply with the control protocol to achieve group consensus. Considering the speed of the leader is unknown, an observer is designed for each group to observe the speed information of the leader in different groups. Then, based on graph theory and Lyapunov stability theory, sufficient conditions for the system to achieve group consensus are obtained. Finally, the results are simulated by Matlab to make the theoretical results more intuitive.

MonCIS-15 1421 A rumor propagation model with penalty factors in the weighted network Dan Wang Shenyang Univ.Qi Fan Shenyang Univ.Qianqian Huang Shenyang Univ.By analyzing the influence of penalty factors on rumor propagation, a rumor propagation model with penalty factors is established in weighted network, an average field equation with penalty factors is obtained. The experimental results showed that the greater the weight, the greater the impact on rumor spread. Moreover, with the increase of penalty factor, the influence of rumor propagator in the whole system becomes smaller and smaller. The results also show that the penalty factors have the greatest impact on the susceptible. After the rumor broke out, increasing the punishment to the susceptible can effectively reduce the density of rumor infectors in the system.

Technical Programmes CCDC 2021 MonCIS-16 1633 Research on Operation Loop Recommendation Method Based on DQN Gang Chen National Univ. of Defense Tech.Boyuan Xia National Univ. of Defense Tech.Zhiwei Yang National Univ. of Defense Tech.Kewei Yang National Univ. of Defense Tech.Shuai Hou National Univ. of Defense Tech.Conglin Yang National Univ. of Defense Tech.The recommendation of operation loop (OL) needs to be completed quickly in the changeable battlefield environment, and traditional static-based evaluation and optimization methods cannot adapt to such dynamic, uncertain, and fast situations. In this paper, an OL recommendation model based on Deep Q Network (DQN) is proposed, which can respond quickly according to the equipment positions of both the enemy and ourselves within a certain range. Firstly, based on the OL theory, the weapon combat models are established from three levels: node, edge and ring, and the corresponding effectiveness evaluation models are established for each edge of the OL. Secondly, the network structure of DQN is described, and then the state space, action space and reward function of the OL recommendation problem are designed in the face of the uncertainty of equipment position, and the algorithm steps are designed based on the DQN. Finally, the feasibility and effectiveness of the proposed method are verified by an example containing 10 equipment of our side and one target of the enemy. The proposed method has shown a set of new characteristics, e.g., strong generalization ability, fast solving speed and small error in comparison with Dueling DQN and enumeration method. The experimental results show that the model of the paper can generate a near-optimal OL recommendation solution for changing equipment positions, which provides some theoretical guidance for decision makers involved in the combat.

MonCIS-17 1221 Robust time-varying Kalman predictor for descriptor system with uncertain-variance noise and packet dropout Wentao Liu Heilongjiang Univ.Chenjian Ran Heilongjiang Univ.For the linear stochastic descriptor system with packet dropout and uncertain-variances noise, the robust prediction problem is addressed. Applying the singular value decomposition (SVD) method, augmented state approach and the fictitious noise approach, the original descriptor system is transformed to new standard system only with uncertainvariance fictitious noises. Based on Kalman filtering and minimax robust estimation principle, the robust time-varying Kalman predictor is presented by replacing the unrealization conservative measurement by the actual measurement in the conservative Kalman predictor. Further, the robustness is proved by the Lyapunov equation approach, i.e., the actual estimation error variance are guaranteed to have minimal upper bounds for all admissible uncertainties. A simulation example about circuits system verifies the correctness and effectiveness of the proposed results.

MonCIS-18 1223 A weight initialization method for fuzzy neural network based on rule partition Xuefeng Wang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent Systems

Engineering Research Center of Intelligent Perception and Autonomous Control

Ministry of EducationWenjing Li Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent Systems

Engineering Research Center of Intelligent Perception and Autonomous Control

Ministry of EducationJunfei Qiao Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligence and Intelligent Systems

Engineering Research Center of Intelligent Perception and Autonomous Control

Ministry of EducationThe fuzzy neural network has a strong ability in solving pattern recognition, function approximation and control problems, so it plays an increasingly important role in the field of artificial intelligence. Scholars have confirmed that the initial weights have a great impact on the subsequent learning of the fuzzy neural network. However, due to the complexity and uncertainty of the black-box model, the initialization of the model is always a problem. This paper proposes a weight initialization method based on rule partition for the fuzzy neural network to solve this problem. The connection weights between the membership function layer and the rule layer are initialized by classifying weight intervals according to different rules, which could reduce the similarity of fuzzy rules. Finally, the proposed method is verified by some experiments. The results show that the fuzzy neural network initialized by rule partition achieves better generalization performance, which proves the superiority of this method.

MonCIS-19 1313 Turing Instability of A Reaction-Diffusion Predator-Prey Model with Holling Type-II Function Jian Li Nanjing Univ. of Posts and Telecom

municationsMin Xiao Nanjing Univ. of Posts and Telecom

municationsMingyue Zhang Nanjing Univ. of Posts and Telecom

municationsLu Wang Nanjing Univ. of Posts and Telecom

municationsRong Qian Nanjing Univ. of Posts and Telecom

municationsBinbin Tao Nanjing Univ. of Posts and Telecom

municationsThe diffusion of population exists inevitably in predator-prey model due to the heterogeneity of space. The Turing instability of a predator-prey model with diffusion and Neumann boundary conditions is considered in this paper. Sufficient and necessary conditions are obtained for the diffusion-independent stability. We also perform a detailed analysis on the distribution of the characteristic roots for the system under diffusive effects. In addition, the conditions for the Turing instability are investigated. Numerical simulations are presented to illustrate the influence of spatial diffusion on the spatiotemporal dynamics.

MonCIS-20 739 Multi-group Consensus for Delayed Heterogeneous Multi-agent Systems Based on Cooperation and Competition Interaction and their Strength LiangHao Ji Chongqing Univ. of Posts and TelecommunicationsShuo Tong Chongqing Univ. of Posts and TelecommunicationsYong Wang Guilin Univ. of Electronic Tech.Fengmin Yu Chongqing Univ. of Posts and TelecommunicationsIn this paper, a novel multi-group consensus protocol is designed for the delayed heterogeneous multiagent systems, in which not only the coexisted cooperative and competition interaction relationship but also the different interaction strength among the agents are considered. Based on system stability theory and matrix theory, some sufficient conditions for the realization of multi-group consensus are established. The obtained results show that the decreasing strength of cooperative or competitive among the agents will reduce the convergence rate of the system. Finally, some numerical simulations are given to verify the effectiveness of our theoretical results.

MonCIS-21 1321 Development Planning Method of Weapon Equipment System Based on Dynamic Game Qihong Chen National Univ. of Defense Tech.Qingsong Zhao National Univ. of Defense Tech.Wei Qiu 21units of 96901 TroopsAiming at the feature of weapon equipment systematic confrontation in modern war, this study analyzed the problems of weapon equipment system planning and development from the perspective of rivalry game, provided its analysis elements, established the dynamic game framework of weapon equipment system’s development planning, set up the dynamic game model of incomplete information weapon equipment’s development planning, and gave an example analysis.

MonCIS-22 85 An Improved Soft Sensor Modeling Method Based on LSTM Shiwei Gao Northwest Normal Univ.In industrial production, in order to effectively monitor production status and achieve stable and reliable control, the timely detection of key quality parameters is very important. With the wide application of various information systems represented by Distributed Control System in the production process, the data information of the process is greatly enriched, which makes the soft sensor technology based on data-driven become an important means to realize on-line detection of key variables in industrial process. In recent years, the rapid development of artificial intelligence technology represented by deep learning provides a new theoretical basis for data-driven soft sensor technology. In the actual industrial production process, due to the limitations of production environment and technical, there are only a small amount of labeled data, and most of the samples are unlabeled samples only containing auxiliary variables. Most of the soft sensor modeling methods only use this small part of labeled samples to model, and discard a large number of unlabeled samples. In this way, it is not only unfavorable to establish an accurate soft sensor model, but also wastes useful information contained in unlabeled samples. In this paper, a semi-supervised soft sensor modeling method based on Long Short-Term Memory unit is proposed to solve the problem of limited labeled samples, which can not only make full use of unlabeled samples, but also solve the problem of poor modeling effect when the sequence length becomes longer.

MonCIS-23 147

Technical Programmes CCDC 2021 Predicting tapping temperature in electric arc furnace based on an ensemble pruning framework Wenjing Wang Liaoning Vocational College of Ecolo

gical EngineeringNa Wang Liaoning Vocational College of Ecolo

gical EngineeringBiao Wang Shenyang Aerospace Univ.Zhizhong Mao Northeastern Univ.The accurate prediction of tapping temperature is significant for high quality products in electric arc furnace. Although existing data models have achieved better performance than mechanism models, their accuracy still needs improvement. To this end, this paper proposes an ensemble pruning framework dedicated to the prediction of tapping temperature. In contrast to traditional ensemble models that fuse results of all base models, our framework can prune some redundant base models via the clustering technique. Though such a pruning strategy, both the diversity and the accuracy have been considered, which are two key factors of ensemble learning. A dataset from a real-world electric arc furnace is used to validate our framework, effective of which has been approved by the experimental results.

MonCIS-24 777 Gradient-based and multi-innovation gradient-based iterative identification methods for dual-diode photovoltaic cell models Xiangxiang Meng Qingdao Univ. of Science and Tech.Yan Ji Qingdao Univ. of Science and Tech.This paper considers the parameter identification problems of dual-diode photovoltaic cell models. A gradient-based iterative algorithm is proposed for the photovoltaic cell model by using the negative gradient search method. In order to improve the parameters accuracy and data utilization, a multi-innovation gradient-based iterative algorithm is proposed to estimate the model parameters based on the multi-innovation identification theory. The simulation test results indicate that the proposed algorithms are effective.

MonCIS-25 954 Modelling and Prediction of Ocean Wave Parameters With MEA-SVR Algorithm Yao Xiao Wuhan Univ. of Science and Tech.Yongli Yang Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech

nology of Ministry of EducationHuikang Liu Wuhan Univ. of Science and Tech.

Engineering Research Center for Metallurgical Automation and Measurement Tech

nology of Ministry of EducationAiming to overcoming the shortcomings of strong dependence on data volume and low prediction accuracy in ocean wave amplitude prediction, the mind evolutionary algorithm was combine with into the support vector regression algorithm (abbreviated as MEA-SVR algorithm) to modelling and prediction the ocean wave amplitude in this paper. In the paper we selected 87600 observation data points from China Oceanic Administration observations in 10 locations of Chinese Ocean, conducted modelling and simulation experiments with MEA-SVR algorithm, and selected GA-BP, MEA-BP, PSO-SVR algorithms for comparative analysis purpose. Simulation results show that the prediction results of our MEA-SVR algorithm are better than other models in parameters of average absolute error, relative error error, correlation coefficient, root mean square error, and consistency index. so has a broad application prospect in ocean energy harvisting.

MonCIS-26 1325 Separable joint gradient based iterative algorithm for multi-frequency sine signals Ling Xu Wuxi Vocational Inst. of CommerceThis paper considers a problem of signal modeling for multi-frequency signals. Because many characteristic parameters are contained in the multi-frequency signals, it is difficult to obtain these estimates of these characteristic parameters. According to the different relations between the signal output and the amplitude parameters and frequency parameters of the multi-frequency sine signal, the signal parameters are divided into two parameter sets. In terms of these two different parameter sets, two identification sub-models are constructed for deriving two identification sub-algorithms by means of the gradient search and iterative technique. By uniting two identification algorithms, a joint gradient-based iterative signal modeling method is presented for estimating the total parameters of the multi-frequency sine signal. The performance of the proposed signal modeling method is validated through the computer simulations.

MonCIS-27 65 Mach number prediction based on a hybrid ensemble model Bin Huang Liaoning Vocational College of Ecological EngineeringYue Wang Liaoning Vocational College of Ecological EngineeringIt is very important to predict Mach number in wind tunnel systems. In order to improve the accuracy of Mach number prediction, this paper has

proposed a hybrid ensemble model. In this ensemble, information concerning both variance and bias have been taken into account. Motivated by the idea of meta learning, the bias has been reduced in an iterated way. Moreover, the variance has been reduced by technique Bagging. We have used several real-world datasets to verify our predictive model, and compare it with several competitors. Experimental results with respect to two evaluation indexes have approved the effectiveness of our model.

MonCIS-28 84 Co-Estimation and Validation of Driving States of a 3-DOFs Vehicle Model Based on UKF Approach Peng Wang Xi'an Univ. of Tech.Hui Pang Xi'an Univ. of Tech.Zijun Xu Xi'an Univ. of Tech.Jiamin Jin Xi'an Univ. of Tech.Accurate acquisition of driving states is the premise of realizing active safety control (ASC) of a vehicle. This paper proposes an effective co-estimation method of vehicle driving states including the sideslip angle, yaw rate and longitudinal speed of the electric vehicle based on the Unscented Kalman Filter (UKF) algorithm. First, a three degree-of-freedoms (DOFs) nonlinear vehicle dynamics model is established as the nominal control plant. Then, based on CarSim software, the simulation results of front steer angle, longitudinal and lateral acceleration are obtained under a variety of working conditions, which are regarded as the pseudo-measured values. Finally, the joint simulation of vehicle state estimation on the sideslip angle, yaw rate and longitudinal speed is realized in the MATLAB/Simulink environment by using the pseudo-measured values and the UKF algorithm, together. The results of co-estimation show that the proposed UKF-based vehicle driving state estimation method is accurate in different working scenarios.

MonCIS-29 1640 Relative navigation method based on auxiliary circular orbit for medium and long range Qiyang Hu Sun Yat-sen Univ.Zhigang Wu Sun Yat-sen Univ.Ping Liu Sun Yat-sen Univ.Relative navigation for non-cooperative spacecraft is the critical technique for the on-orbit servicing mission, the research on which has theoretical value and engineering significance. A method of orbit determination based on the auxiliary circular orbit is proposed in this paper to address the observability problem of the medium- and long-range navigation under the assumption of the orbit model based on CW equation. A measurement pseudo-linearization method together with the least-squares method is proposed for the initial orbit determination algorithm. Then, the sufficient and necessary condition of the method is then proposed and analyzed. The validity of the method is proved by numerical simulation.

MonCIS-30 742 Loss minimization vector-controlled SPMSM based on online parameter identification Guanghua Huang Changchun Univ. of Tech.Niaona Zhang Changchun Univ. of Tech.Aiming at the problem that the model-based loss minimization control strategy (MLMCS) depends on the motor parameters, an improved model reference adaptive system(MRAS) is proposed to identify the core loss resistance, rotor magnetic-flux linkage, and the inductance step by step, which avoids the under-ranking problem when multi-parameters are identified online. The identified parameters are used in the online calculation of the optimal stator current, which can reduce the total electrical loss to achieve the purpose of energy saving. Finally, the validity of the parameter identification method designed in this paper and the energy-saving effect of the motor are verified by simulation experiments under different speed and torque conditions.

MonCIS-31 1209 STM32 based Semi-physical Simulation Platform of Heating Furnace Caixia Zhang Weihai Vocational CollegeZhiyong Jing Univ. of Chinese Academy of SciencesLi Xin Dalian Huarui Heavy Industry Group Co.,LTDThe simulation system is always used in system design. However, a pure software simulation system is not suitable for the system debug, including the software and hardware. Therefore, the semi-physical simulation platform has advantage for the system design. Through the analysis and derivation of the physical principle of heating furnace, the semi-physical heating furnace simulation platform based on single-chip is built. The transfer function and difference equation of heating furnace are established. At the same time, the simulation model is transplanted into the STM32 MCU, with the power input and temperature output based on frequency and analog signal in order to interface with hardware. The simulation platform not only can simulate the furnace, but also can communicate with the host computer. After experimental verification, the semi-physical heating furnace simulation platform based on STM32 can simulate the heating furnace. It has great reference significance for system debugging and controller constructing.

Technical Programmes CCDC 2021 MonCIS-32 1277 The Identification Strategy of Bouc-Wen Model Based on Improved Particle Swarm Optimization Algorithm Zicheng Li Wuhan Inst. of Tech.Ruirui Xu Wuhan Inst. of Tech.Hou-Neng Wang Wuhan Inst. of Tech.Tao Xiong Wuhan Inst. of Tech.The piezoelectric micro-positioning system consists of a piezoelectric actuator that operates a positioning system. Hysteresis nonlinearity is one of the significant variables limiting the positioning precision of these stages. This paper proposes an asymmetrical Bouc-Wen (B-W) model to describe the hysteresis of piezoelectric actuator. In order to solve the problem of low parameter identification accuracy, an improved particle swarm optimization (IPSO) algorithm is presented. The inertial weight and learning factor nonlinear change strategy is proposed to quickly find the optimal model parameters. The results show that the algorithm has better overall searching ability in the early stage and faster convergence speed compared with the standard particle swarm optimization (PSO). The proposed algorithm can improve the solution accuracy and accelerate the global convergence.

MonCIS-33 18 Finite-time Output Consensus for a Class of Heterogeneous Linear Multi-agent Systems via an Output Regulation Approach Ting Yu Beijing Inst. of Tech.Qinghe Wu Beijing Inst. of Tech.Zhuoyue Song Beijing Inst. of Tech.This paper investigates the finite-time output consensus problem of heterogeneous multiagent systems in both leaderless and leader-following cases. Different from the traditional asymptotic convergence consensus algorithms, a finite-time dynamic compensator is proposed to generate the reference output trajectory in finite time. Meanwhile, we extend the previous finite-time tracking algorithm for single agent to multi-agent systems in the output regulation controllers. The above two parts are combined to achieve the finite-time output consensus of heterogeneous multi-agent systems. Finally, the effectiveness of the proposed results is demonstrated by two numerical examples.

MonCIS-34 716 Reliable constrained control of DC-DC Buck-Boost converter based on signal compensation and inner-outer convex combination optimal control Changzhi Chen Univ. of JinanMengdi Chen Univ. of JinanShi Li Univ. of JinanKezhen Han Univ. of Jinanhis paper considers the constrained reliable tracking control problem for a class of Buck-Boost converters. The proposed control strategy is consisted of four parts. First, a signal compensation policy is designed to eliminate the influences from fault/attack and unknown disturbances. Second, an inner robust tracking control policy is designed to basically guarantee the closed-loop stability of tracking error systems when the constraints are not activated. Third, an outer feasible control is constructed based on invariant set theory and it is used to guarantee the feasibility of whole control law. Fourth, the convex combination optimization method is applied to optimize the constrained control performance. It should be noted that this method only involves a linear programming, which has less online computation burden than the conventional predictive control method. Finally, the effectiveness of such reliable control strategy is validated by a simple numerical example.

MonCIS-35 822 Oscillation criterion for a class of advanced 2D discrete system Chunhua Yuan Univ. of JinanShaoli Jin Univ. of JinanThis paper is devoted to oscillations of a class of advanced 2D discrete system. By converting finding the regions of non-positive roots of characte ristic equation to finding the regions where the tangent plane of an envelope does not pass through based on the envelope theory, an explicit necessary and sufficient condition to determine oscillations of all solutions of an advanced 2D discrete system is obtained. An example is given to illustrate the result of this paper.

MonCIS-36 869 Stability Analysis of Linear System with Time-Varying Delays via new Negative Conditions for a Quadratic Function Bin Yang Dalian Univ. of Tech.Zefei Yan Dalian Univ. of Tech.Xuejun Pan Dalian Univ. of Tech.Xudong Zhao Dalian Univ. of Tech.This paper is concerned with the stability analysis of linear system with time-varying delays. First, new conditions that ensure the quadratic function is negative in finite interval are proposed by dividing the time interval into N parts and taking full advantage of the convex and concave properties of the quadratic function in each part. Second, a new zero

equality including the square terms of time-varying delays is proposed, which is helpful to obtain a less conservative stability criterion. Third, a less conservative stability criterion is proposed based on the new negative conditions and the new zero equality. Finally, a typical example is used to verify the advantages and effectiveness of our newly proposed methods in the stability analysis of linear system with time-varying delays.

MonCIS-37 1354 H-infinity Group Consensus Control for Directed Nonlinear Multi-agent Systems Yahui Qi Naval Aviation Univ.Shuailei Wang Naval Aviation Univ.Shi Yan Naval Aviation Univ.Zhicai Xiao Naval Aviation Univ.H-infinity consensus control for nonlinear multi-agent systems is investigated. Lipschitz nonlinear items and external disturbance are considered for high order multi-agent systems. Based on in-degree balance, both the Laplacian matrix and group consensus matrix of the directed system can be decomposed as a product of transforming matrix and another special matrix. The transforming matrix can turn the group consensus problem into a stability problem. Designing group consensus variables and a H performance index, and utilizing Lyapunov method as well as the linear matrix inequality, the correctness of the designed control inputs is theoretically proved. Simulation result presents the effectiveness of the algorithm.

MonCIS-38 714 Extrapolated PSS Iterative Method for Sub-positive Definite Quaternion Equations Shanshan Zhang Guangxi Univ. for NationalitiesJingpin Huang Guangxi Univ. for NationalitiesHao Xiong Guangxi Univ. for NationalitiesYun Wang Guangxi Univ. for NationalitiesIn this paper, we introduce a new splitting, called positive-definite and skew-selfconjugate splitting (PSS), and then establish a class of PSS methods similar to the Hermitian and skew-Hermitian splitting (HSS) method for iteratively solving the sub-positive definite matrix equation, and equivalently transform it iterative format on the basis of the PSS iteration method, heuristically establishes the extrapolated PSS iteration method. Theoretical analyses show that the PSS iteration method unconditionally converges to the exact solution of the equation. Using equivalent relationship between both PSS and extrapolated PSS iterations, the extrapolated PSS iteration method converges under certain conditions. Moreover, we minimize the upper bound of the spectral radius of iteration matrix. Finally, Numerical examples illustrating the effectiveness of both PSS and extrapolated PSS iterations are presented.

MonCIS-39 1148 Metro Traffic Regulation Using Multi-Step State Feedback Control Jiate Luo Southwest Jiaotong Univ.Yin Tong Southwest Jiaotong Univ.Due to the fast development of urban metro traffic and the increase of travel needs of passengers, it is important to develop efficient traffic regulation methods. This paper investigates the design of a metro traffic regulation controller subjected to the limitation of the range of control inputs. To describe the evolution of metro traffic flow, a state-space model is presented and the relationship of time deviations between successive trains is discussed. This paper modifies the control algorithm such that the saturated control actions are compressed into a controllable area. To improve the performance of the designed controller, a multi-step performance index is presented for the controller to recover the original timetable from disturbances as soon as possible if necessary. Finally, numerical results are provided to demonstrate the effectiveness of the designed multi-step controller.

MonCIS-40 1302 Positive Filter Design for Positive Markov Jump Linear Systems with Time-Delay, Unknown Disturbance Input and Measurement Noise Jiyang Xie Shandong Univ.Shuqian Zhu Shandong Univ.Dawei Zhang Shandong Univ.This paper proposes some positive filter design results for a class of positive discrete-time delayed Markov jump linear systems against unknown disturbance input and measurement noise in the l1-gain performance sense. Based on the measurement output with noise, a positive delay-dependent and mode-dependent filter is presented. Resorting to the existing necessary and sufficient conditions of stochastic l1-gain performance, some sufficient conditions of positive filter design and a design optimization method are established by the forms of linear programming. Compared with the existing filter design results, the main contributions of the proposed results lie at two aspects: the effect of time-delay on filtering performance is considered and the positivity of the augmented filtering error system can be ensured for the cases that disturbance input and measurement noise are different, and particularly there is no measurement noise. Finally, a numerical example is given to show the design validity.

Technical Programmes CCDC 2021 MonCIS-41 323 Event-Triggered Control Based on Principle of Self-Support Tian Feng Shaanxi Normal Univ.Baowei Wu Shaanxi Normal Univ.Yangquan Chen Univ. of CaliforniaDue to the superiority that the event-triggered mechanism can significantly reduce the number of samplings in dealing with control issues, and promise better resource utilization, a specific event-triggered tracking control problem is investigated in this paper. Firstly, based on the principle of self-support (PSS), which emphasizes the importance of control input, a novel event-triggered mechanism is proposed for the first time to design an error feedback controller, such that tracking error can be driven into a prescribed bounded feasible region in a finite time. Secondly, as a software realization of the event-triggered control, a self-triggered scheme is presented accordingly. Finally, some comparisons are given in numerical simulations to verify the effectiveness of the designed triggered control schemes.

MonCIS-42 752 Optimized event-triggered control for linear systems with state observer Haoyun Li Anhui Univ.Lizhen Li Anhui Univ.Rong Cheng Anhui Univ.Yuan Fan Anhui Univ.In this paper, an optimal event-triggered control method based on observer for linear systems is proposed. In order to reduce resource consumption and computing cost, we propose an event-triggered strategy, a linear state observer based on event-triggered mechanism is designed. Using the event-based strategy, the signal transmitting and controller updating occur in an aperiodic manner. It is proved that the linear system using the event-triggered optimal control strategy is asymptotically stable and the event-triggered method can avoid the existence of Zeno behavior. Simulation results have proved the effectiveness of the proposed method.

MonCIS-43 1658 An Adaptive Weighted Envelope Based Piecewise Fitting Method for Molding Process Data Yaojie Pan Univ. of Electronic Science and Tech. of ChinaQiu-an Huang China South Industries Group Automation Rese

arch Inst. Co. LtdFeng Gao China South Industries Group Automation Rese

arch Inst. Co. LtdYong Chen Univ. of Electronic Science and Tech. of ChinaYue-zhi Liu Univ. of Electronic Science and Tech. of ChinaData preprocessing and fitting are important and challenging problems, which are used in system control and modeling in industrial processes nowadays. In the paper, a piecewise fitting method for molding process data based on the adaptive weighted envelope is proposed. Due to the characteristics of the multi-period molding process, the data is first segmented by different application conditions in the molding process. Next, the upper and lower envelopes of different data segments are calculated, the adaptive weights of which are designed to synthesize smooth curves by adding a sliding window. Then, two ways of polynomial fitting and Gaussian fitting are adopted to fit the data according to the characteristics of different subsection data. Finally, compared with the moving average filtering algorithm, the proposed method has a smaller absolute percentage error (APE) and lower root mean square error (RMSE) value of the fitting curve, which demonstrates the superiority of the proposed method.

MonCIS-44 669 Projective Lag Synchronization of Delayed Chaotic Systems via Intermittent Control with Two Sub-periods Bin Zheng Shanghai Univ.Jianping Cai Minnan Normal Univ.Jin Zhou Shanghai Univ.Intermittent control strategy is used for achieving projective lag synchronization between two delayed chaotic systems. Different from the commonly used single-periodic control, the control period adopted in this paper is two-periodic, where a complete control period consists of two sub-periods. Some synchronization criteria in the form of algebraic inequalities are derived by way of Lyapunov stability theory. Finally, a time-delay Chua's circuit system is taken as the simulation example, and the numerical results demonstrate the effectiveness of the proposed method.

MonCIS-45 711 TCP/AQM network congestion prescribed performance event-triggered control with input saturation Jiqing Chen Northeastern Univ.Yuanwei Jing Northeastern Univ.Xuelei Qi Northeastern Univ.In this paper, the overall window model is used to describe the network operation for networks under TCP protocol, taking into account the

interference of UDP streams and http streams to the network operation process. Based on this model, taking into account the input range of the network packet loss rate, processing the impact of input saturation on the control effect and introducing a prescribed performance method, designing an event triggering network congestion controller. The designed controller can track the queue to the preset value and has certain transient and steady-state performance. Finally, the effectiveness of the controller is verified by simulation.

MonCIS-46 756 A Fault Diagnosability Evaluation Method for A Class of Affine Nonlinear Systems Considering Sensor Faults Yufeng Qin Naval Aviation Univ.Xianjun Shi Naval Aviation Univ.Yufeng Long Naval Aviation Univ.Jiapeng Lv Naval Aviation Univ.Zhilong Zhang Naval Aviation Univ.Li Zhao Naval Aviation Univ.In the present study of fault diagnosability of nonlinear systems, the fault diagnosability of sensor fault is not considered. The paper considered the sensor fault and the fault diagnosability evaluation method for a class of affine nonlinear systems is proposed by using the theory of differential geometry. The diagnosability of faults is regarded as an inherent property of the system and does not change with the change of diagnostic methods. Based on the controllability and observability decomposition theorems, the controllability distribution and observability distribution of faults are defined, and then the detectability and isolability criteria between process faults and sensor faults are given based on the containment relationship between each distribution. The analysis results show that the proposed method is suitable for the system which exists sensor faults.

MonCIS-47 1447 Adaptive Weighted Control for A Class of Nonlinear Systems Wanxin Chen Shenyang Inst. of Automation

Institutes for Robotics and Intelligent Manufacturing Univ. of Chinese Academy of Sciences

Bi Zhang Shenyang Inst. of Automation Institutes for Robotics and Intelligent Manufacturing

Univ. of Chinese Academy of SciencesJin Sui Shenyang Inst. of Automation

Institutes for Robotics and Intelligent Manufacturing Univ. of Chinese Academy of Sciences

Xingang Zhao Shenyang Inst. of Automation Institutes for Robotics and Intelligent Manufacturing

Univ. of Chinese Academy of SciencesIn this paper, an adaptive weighted control method for Hammerstein-Wiener nonlinear systems has been derived by considering the tracking of the references while simultaneously minimizing the control effort. Based on the parameterization model of Hammerstein-Wiener systems, parameter adaptation is implemented throughout the use of internal variable estimations. Based on these estimates, the control law is designed by analyzing a new criterion function which takes the whole nonlinear systems into account and penalizes the excessive control action according to the nonlinear gains. By simplifying the above complex control law, we obtain a simple adaptive weighted control algorithm, which reduces overshoot and oscillation effectively. The closed-loop stability and output tracking properties are analyzed to demonstrate the proposed novel concepts. Illustrative simulation results are presented to verify the effectiveness of the proposed algorithms.

MonCIS-48 1656 Projective synchronization problem of a new Lorenz-Stenflo system by single input feedback controller Zuosheng Sun Qilu Univ. of Tech.Rongwei Guo Qilu Univ. of Tech.This paper investigates the projective synchronization problem of a new Lorenz-Stenflo system. Firstly, the existence of the projective synchronization for the Lorenz-Stenflo system is proved. Then, two single input controllers are proposed, by which the projective synchronization problem of such system are realized. Finally, numerical simulations are used to verify the validity and effectiveness of the theoretical results.

MonCIS-49 87 Partial self-time-delay projection synchronization of complex chaotic systems Chunrui Ma Qilu Univ. of Tech.Junmei Guo Qilu Univ. of Tech.Fangfang Zhang Qilu Univ. of Tech.

Shandong Computer Science CenterShandong Artificial Intelligence Inst.

This paper mainly studies the self-time-delay projection synchronization (STDPS) of the complex Chen and time-delay Lü systems. Firstly, the definition and mathematical analysis of STDPS are given, Secondly, based on the Lyapunov stability theory is used to design the STDPS controller, and according to the linear feedback method, it is found that only partial STDPS can be realized. Finally, the effectiveness of the

Technical Programmes CCDC 2021 controller and the effect of partial STDPS are verified by numerical simulation.

MonCIS-50 452 Multi-Agent Based Adaptive Tracking and Coordination Control for High Speed Trains Rui Yang Beijing Jiaotong Univ.Feng Liu Beijing Jiaotong Univ.Peng Li Beijing Jiaotong Univ.For high-speed trains, effective tracking and coordination control is of great significance to ensure stable and reliable operation especially in case of uncertainties. This paper presents a non-linear dynamic model for high speed trains based on multi-agent, with considering the actual system operation. We analyze the non-linear dynamics, the unpredictable coupling force between the carriages, as well as the inherent uncertainties of the operation system. Then, a robust adaptive controller is proposed for tracking and coordination control of the position and velocity for each carriage, in conditions of nonlinear model and unknown coupling force between carriages. Comparative studies are performed on the dynamic characteristics of controllers under the same conditions. The results show that the proposed adaptive controller does not rely on the parameters of the train operation system. Moreover, it compensates for the influence of the nonlinear model and the coupling forces between carriages, and thus ensures the safe and effective operation of the train.

MonCIS-51 458 Observer-based adaptive neural network control for time-delay systems with non-strict feedback Yuanyuan Xu Qingdao Univ.Bing Chen Qingdao Univ.Lili Zhang Qingdao Univ.Yun Shang Qingdao Univ.In this paper, a class of nonlinear non-strict feedback systems with input delays are controlled by adaptive neural network and output feedback. First, a state observer is constructed for the nonlinear non-strict feedback system to estimate unmeasurable state variables. The structural characteristics of the radial basis function are used to overcome the difficulties caused by the non-strict feedback. Secondly, through the combination of integral transformation and adaptive neural network control, an adaptive neural network control scheme based on backstepping design is given. The result shows that the proposed controller guarantees the semi-global bounds of all signals in the closed-loop system. Finally, a simulation example verifies the effectiveness of this method.

MonCIS-52 1509 Solvability for a class of a system of nonlinear fractional differential equations Yige Zhao Univ. of JinanYibing Sun Univ. of JinanThe solvability for a class of a system of nonlinear fractional differential equations is considered in this paper. A sufficient condition of solutions for a system of nonlinear fractional differential equations is obtained based on Schauder fixed point theorem. Finally, we present an example to demonstrate our results.

MonCIS-53 31 TCP/AWM Network Congestion Control Based on Minimax Theory Yun Bai Northeastern Univ.Jindong Shen Northeastern Univ.Yuanwei Jing Northeastern Univ.To solve the problem of TCP/AWM network congestion control with external disturbance, a nonlinear TCP/AWM network model considering external disturbance is established. Based on this model, the controller is designed under the circumstance that the external disturbance has the greatest influence on the system by combining the backstepping method and minimax theory. It ensures that the TCP/AWM network system is asymptotically stable under the worst disturbance. Simulation results show that the proposed method has good convergence and robustness and it is superior to the previous methods.

MonCIS-54 61 Analysis on blow-up time of parabolic equation system with nonlinear boundary conditions Xiaoyue Zhang Taiyuan Univ. of Tech.Lingling Zhang Taiyuan Univ. of Tech.In this paper, a class of parabolic system with nonlinear boundary conditions is studied. By making some necessary assumptions, the upper and lower bounds of parabolic equation system are derived with the approach of constructing auxiliary functions and a series of differential inequalities. Moreover, we offer an example to support the main results at the end of the paper.

MonCIS-55 760

Signal Coordination Control of Intersection Adjacent to Main Road Based on Hybrid Automata Minan Tang Lanzhou Jiaotong Univ.Qianqian Wang Lanzhou Univ. of Tech.Kaiyue Zhang Lanzhou Jiaotong Univ.Jiandong Qiu Lanzhou Jiaotong Univ.Yajiang Du Lanzhou Jiaotong Univ.Aimin An Lanzhou Univ. of Tech.Chenyu Wang Lanzhou Jiaotong Univ.In order to alleviate the congestion of urban road network, the dynamic characteristics of the hybrid automaton model were analyzed in depth. According to the dynamic characteristics of phase difference of green wave between adjacent intersections, an improved hybrid system model of traffic signal optimization and coordination control was proposed through taking the minimum queue length of vehicles in each phase of an intersection within one signal period as performance index, so as to find the optimal phase conversion order and optimal green signal ratio for the coordinated control of traffic signals at two adjacent intersections under dynamic traffic conditions. Taking the intersection of Qingyang Road and Yongchang Road in Lanzhou City to the intersection of Qingyang Road and Jiuquan Road as the research object, the simulation results verify the effectiveness of the proposed method.

MonCIS-56 821 An envelope surface method for determining oscillation of an advanced 2D discrete convection system Chunhua Yuan Univ. of JinanShaoli Jin Univ. of JinanThis article is concerned with an advanced two-dimensional (2D) discrete convection system. By means of the theory of envelopes, we consider the regions of non-positive roots of its characteristic equation. A sufficient and necessary condition for all solutions of this system to be oscillatory is obtained. A numerical example is provided to demonstrate the main result of this article.

MonCIS-57 857 Quadratic L2 Performance Analysis of Switched Uncertain Linear Systems Yufang Chang Hubei Univ. of Tech.Guisheng Zhai Shibaura Inst. of Tech.Lianglin Xiong Yunnan Minzu Univ.Bo Fu Hubei Univ. of Tech.We analyze quadratic L2 performance (quadratic stability and certain L2 gain) for switched uncertain linear systems (SULS) with norm-bounded uncertainties. Assuming that no single subsystem achieves quadratic L2 performance γ but a convex combination of the subsystems can make it, we propose a state-dependent switching law such that the SULS achieves the same performance.

MonCIS-58 1187 Event-Triggered Control for Switched Systems With Recurrent Neural Networks Subject to Stochastic Cyber-Attacks Honglin Geng Shenyang Aerospace Univ.

Beijing Power Machinery Inst.Yiwen Qi Shenyang Aerospace Univ.

Beijing Power Machinery Inst.Weiyu Jiang Beijing Power Machinery Inst.Wenke Yu Shenyang Aerospace Univ.

Beijing Power Machinery Inst.Xiujuan Zhao Shenyang Aerospace Univ.

Beijing Power Machinery Inst.Ning Xing Shenyang Aerospace Univ.

Beijing Power Machinery Inst.Simeng Zhang Shenyang Aerospace Univ.

Beijing Power Machinery Inst.This paper concentrates on event-triggered control for switched systems under stochastic cyber-attacks. The neural network method is adopted to approximate unknown items that exist in system dynamics. Then, the augmented closed-loop switched system model is established, which can characterize the multi time-delays and the impacts of stochastic cyber-attacks. For the switched systems with neural networks, a co-design method is proposed for observer and event-triggered controller. By utilizing Lyapunov functional technique, sufficient conditions are given to guarantee the mean-square exponential stability of closed-loop switched systems. Subsequently, the parameters of controller, observer and event-triggering scheme are obtained by solving linear matrix inequalities (LMIs). At last, a numerical simulation is given to demonstrate the effectiveness of the proposed method.

MonCIS-59 1530 Event-triggered control for switched descriptor systems based on sampled-data implementation Jiasheng Shi Northeastern Univ.

Key Laboratory of Data Analytics and Optimization for Smart Industry

Jun Zhao Northeastern Univ.This paper studies event-triggered control problem for a class of switched

Technical Programmes CCDC 2021 descriptor systems based on sampled-data implementation. The switched descriptor systems are first to be decomposed into a equivalent form, and then a reduced-order switched system is obtained. Then, a sufficient condition ensures that the closed-loop system is exponentially stable under average dwell time switching signals. Moreover, the Zeno behavior is ruled out. In the end, a numerical examples is demonstrated the merit and effectiveness of the proposed methods.

MonCIS-60 1648 Connected PHEV Energy Management based on Global Driving Cycle Construction Biao Liang Beijing Inst. of Tech.Chao Sun Beijing Inst. of Tech.Bo Liu Beijing Inst. of Tech.In this paper, a global vehicle driving cycle construction method is newly proposed, to enhance the energy management performance of a connected plug-in hybrid electric vehicle (Connected PHEV). We propose a three-step novel real-time future driving cycle construction method. First, historical driving cycles are collected and each of them is divided into a number of speed segments to form a database. Artificial neural network (ANN) is employed to learn the nonlinear correlation between the key features of adjacent speed segments along the entire trip. Finally, this trained ANN model is deployed in real-time to predict the next most possible speed segment based on current driving condition of the vehicle. By sequential operating, the global driving cycle can be constructed. The method is validated in a fixed-route city bus driving scenario using real-world data. Model predictive control (MPC) are adopted to solve the energy management problem. Simulation results illustrate that the driving cycle construction method is able to improve the fuel economy of PHEV by over 29% compared with traditional energy management method.

MonCIS-61 276 Power Line Recognition and Foreign Objects Detection Based on Image Processing Zhenlin Song Nanjing University of Science and Tech.Shaou Xin Nanjing University of Science and Tech.Xinying Gui Nanjing University of Science and Tech.Guoqing Qi Nanjing University of Science and Tech.In order to improve the efficiency of power line inspection, this paper uses the power line image collected, through the method of image processing, to identify power lines and detect foreign bodies. First, the Canny operator is improved by using Otsu adaptive acquisition of double thresholds for edge extraction of power line images. Then an improved Hough transform is used to detect straight lines in edge images for improving the accuracy of power line detection. Furthermore, a local contour detection method without considering the relationship between power lines and foreign objects is proposed to extract foreign objects on power lines, which can realize decoupling of power line recognition and fault detection. Through the simulation test of the collected power lines, the accuracy of the proposed method for power line extraction and foreign objects detection in the image is verified.

MonCIS-62 335 A Satellite Fault Diagnosis and Analysis Method based on Extreme Gradient Boosting Xiaopeng Liu China Academy of Space Tech.Yuechuan Wang Beijing Inst. of Tech.Senchun Chai Beijing Inst. of Tech.Zhaoyang Li China Academy of Space Tech.In recent years, the applications of the satellite have been more and more widespread. Fault diagnosis as the main research field in the satellite system has attracted much attention from the industrial and academic areas. The performance of the traditional fault diagnosis method is degraded significantly when the satellite system becomes more complex. Data driven based diagnosis method, which depends on the machine learning algorithm, has high flexibility in the complex and changeable system. In this paper, we propose a fault diagnosis and analysis method based on the nearest neighbor state and xgboost. In order to illustrate the performance of the proposed method, four experiments have been carried out. First, we construct a data set which can be used for model input based on the nearest neighbor state alignment method. The second experiment is based on the principal component analysis of the data that mines the data characteristics without the labels. Then the fault diagnosis model based on xgboost is implemented. The classification results show that the model can effectively shrink the error while the training process is still fast. In the final experiment, we mine the parameters that can better describe or cause the fault from the historical telemetry data of the satellite, which is of great significance for the operation and maintenance of the satellite in orbit.

MonCIS-63 341 A Bearing Fault Diagnosis Method with Unsupervised Deep Adaptive Network Qing Yang Shenyang Ligong Univ.Baocai Cui Shenyang Ligong Univ.Hui Xue Shenyang Ligong Univ.Dongsheng Wu Shenyang Ligong Univ.

To improve the ability of the predictive model to generalize unlabeled data in fault diagnosis, an improved bearing fault diagnosis method with unsupervised deep adaptive network (UDAN) based on second-order statistics is presented. First, the motor vibration signal is transformed into a two-dimensional gray image to improve the extraction of transfer features. Then, the second-order statistics alignment of source domain and target domain is used to minimize the difference in domain distribution in the deep residual network. Finally, the combined loss function is constructed to realize the end to end adaptive fault diagnosis. Compared to other methods of unsupervised learning, experimental results show that UDAN fault diagnosis method has better generalization ability.

MonCIS-64 345 Remaining Useful Life Prediction for Aero-Engine Based on LSTM and CNN Diwang Ruan Tech. Univ. of BerlinYuheng Wu Tech. Univ. of BerlinJianping Yan Tsinghua Univ.Data-driven Remaining Useful Life (RUL) prediction for aero-engine has evolved rapidly in recent years. Especially, deep learning-based methods like Convolutional Neural Network (CNN) and Long Short Term Memory (LSTM) have achieved excellent results. However, there is still limited study to identify the effect on network performance from the number of convolutional layers, LSTM layers and their combination structure. Therefore, the optimal number of convolutional layers and LSTM layers was first determined for CNN and LSTM respectively in this paper. A combined network CNN-LSTM was then constructed. Three kinds of deep networks (CNN, LSTM and CNN-LSTM) were compared on aero-engine RUL prediction. Experimental results on the C-MAPSS dataset indicated that LSTM with 2 dense layers achieved the highest prediction accuracy.

MonCIS-65 529 Foreign Matter Recognition Technology of Robot in GIS Based on Ultraviolet/VisibleLight Jiaxin Liu Electric Power Research Inst. of State

Grid Liaoning Electric Power Co., Ltd.Xuchen Lu Electric Power Research Inst. of State

Grid Liaoning Electric Power Co., Ltd.Yuwei Bao State Grid Liaoyang Electric Power

Corporation Supply CompanyLi Yu Northeast Branch of State Grid

Corporation of ChinaJianwei Bai Northeast Branch of State Grid

Corporation of ChinaDan Song Northeast Branch of State Grid

Corporation of ChinaThe proportion of internal foreign matter is the largest in the insulation fault of GIS (Gas Insulation Switchgear) equipment, and when the foreign objects inside the long-tube structure GIS equipment are detected, the traditional vacuum cleaner cleaning and manual wiping methods are difficult to play an effective role. Based on the intelligent inspection and cleaning robot platform of the GIS equipment, this paper designs the dual-light source light filling technology which relies on the combination of ultraviolet light source and visible light source in order to clean the foreign objects in the GIS pipeline more effectively, and studies the foreign matter recognition algorithm to process images taken by robot using dual light source compensation, which can be used as a completely new scheme to check and verify the foreign matter defects of GIS equipment and to clean up foreign matter. Under the two kinds of light sources, the images taken inside the pipeline are processed to make foreign matter recognition judgment, which provides guidance for the robot to clean foreign matter. In this paper, while realizing the visualization of the foreign matter inside the GIS cavity, the problem that the single light source of the intelligent inspection and cleaning robot inside the GIS equipment is difficult to find some foreign particles is solved. It also avoids the problem that the robot needs manual experience to discriminate the foreign object, and improves the efficiency of the foreign object inspection inside the GIS equipment.

MonCIS-66 552 Abnormal Battery Location Recognition and State Estimation in Lithium Battery Pack Ping Wu Huazhong Univ. of Science and Tech.Hao Xu Huazhong Univ. of Science and Tech.Jianhua Jiang Huazhong Univ. of Science and Tech.Xi Li Huazhong Univ. of Science and Tech.There are many problems in the abnormal diagnosis of the lithium battery pack, such as incomplete research structure, insufficient positioning accuracy of abnormal batteries, and inadequate combination of diagnosis and treatment. To deal with these problems, this paper systematically achieves the goal of precise positioning, state estimation, and decision-making processing of abnormal batteries in a complete series-parallel battery pack. It also provides effective basic methods and exploration ideas for lithium battery energy storage systems to achieve intelligent diagnosis, intelligent prediction and precise positioning. Firstly, the attenuation model of the single battery is built from the perspective of the three coupling characteristics of heat, electricity and attenuation, and

Technical Programmes CCDC 2021 then build a 3*3 small battery pack model. Then study the impact of different degrees of attenuation of batteries in different positions in the battery pack on the overall thermal and electrical characteristics of the battery pack, and compare it with the normal battery pack to analyze and extract the characteristic variables that characterize the attenuation battery. Next, based on the LS_SVM and BP neural network algorithm, the feature classifier and regression feature model are established respectively to locate and estimate the attenuation rate of abnormally attenuated batteries, and conduct simulation tests. The simulation results prove that all the accuracy and effectiveness of establishing the classifier and regression model are above 95%. Finally, relevant solutions are proposed for abnormally attenuated batteries.

MonCIS-67 653 Event-triggered State and Fault estimation of Flight Control System for Aquadrotor UAV Xiujuan Zheng Wuhan Univ. of Science and Tech.Huaiyu Wu Wuhan Univ. of Science and Tech.Shiqian Zhang Wuhan Univ. of Science and Tech.In this paper, the state and fault estimation problem is considered for a flight control system of unmanned aerial vehicle (UAV) under event-triggered transmission framework. Firstly, considering the minimum variance estimation theory, an event-triggered state and fault estimator are designed. Then the upper bounds of the estimation error covariances are obtained in the form of Rickett-type difference equations. And the estimator gains are obtained in realtime by minimizing the upper bounds through a recursive algorithm. Finally, the effectiveness and practicability of the method are verified by the flight control system of a quadrotor UAV.

MonCIS-68 743 Efficient Fault Diagnosis Method of Electric Power Information System based on Rough Set Theory Knowledge Representation System Jing Guo Aostar Information Technologies Co., Ltd.Di Liu State Grid Information and communication

industry groupDesheng Yang Aostar Information Technologies Co., Ltd.Yanbin Jiao State Grid Information and communication

industry groupThe Electric Power Information System is an extremely important support for the smooth operation of the power grid. Accurately assessing the operating status of the information system and performing fault diagnosis in a timely manner has always been an important issue for the industry. In this paper, we propose an efficient diagnosis method for Electric Power Information System faults. First, establish knowledge representation systems and analytic hierarchy model for the evaluation indicators of the information system, calculate the relative importance of the evaluation indicators and arrange them in descending order, and then construct the attribute indicators set according to the importance of the attribute indicators. Reduce the evaluation index number by rough set theory, get the smallest index subset that can accurately judge whether the information system is faulty, so as to improve the efficiency and accuracy of information system fault diagnosis. Finally, according to the minimum index subset and the index weight, calculate the operation status of the information system quantitatively and make the diagnosis finally. The paper describes in detail the implementation steps of an efficient fault diagnosis method. The method is simulated and verified with an information system as a test object. The results indicated that the proposed of method measure the operating state of the system accurately, perform fault diagnosis quickly, improve the efficiency of troubleshooting and has better practical value.

MonCIS-69 807 Servo Motor Fault Diagnosis Based on Data Fusion Jing Huang Jiangsu Univ. of Science and Tech.Liang Qi Jiangsu Univ. of Science and Tech.Jiaye Gu Jiangsu Univ. of Science and Tech.Zhu Lu Jiangsu Univ. of Science and Tech.Jie Sun Jiangsu Univ. of Science and Tech.Chaochun Yu Jiangsu Univ. of Science and Tech.Servo motor is the core part of automatic production line. Its performance and reliability have great influence on the normal operation of the whole automatic production line. In this paper, a servo motor fault diagnosis method based on data fusion is proposed aiming at the problem that single sensor's diagnosis result is uncertain, the fault degree diagnosis and multi-fault diagnosis are difficult. Adaptive weighted fusion algorithm is used to fuse the vibration signals of two channels at the data layer to enhance the signals. The weighted fusion of fault data with different fault degree is carried out to generate new data with various faults. EWT is improved to denoise the new data generated by fusion and the envelope spectrum diagram of the de-noised signal is obtained. The envelope spectrum diagram is input into the YOLOV3 network as a training set for training, so as to realize fault diagnosis. The results show that the proposed method does not rely on human experience and can be accurately identified in multi-fault state. The experimental results show that the proposed method has high recognition rate and stability.

MonCIS-70

871 Fault Text Classification of Rotating Machine Based BERT Ling Chen Wuhan Univ. of Science and Tech.Yimin Liu Wuhan Univ. of Science and Tech.Lianlian Ji Wuhan Univ. of Science and Tech.Accurate classification of equipment fault is one of the effective ways to improve the efficiency of fault analysis and maintenance. How to establish an effective equipment fault classification model has become a hot research topic. Therefore, a fault text classification method for rotating machine based on BERT (Bidirectional encoder representation from transformers) is proposed, which uses fine-tune on the BERT pre-training language model to make it suitable for downstream tasks and takes bidirectional transformers for feature extraction to get understanding of text semantics. The experimental results show that the F1 of the model on the test set is up to 97.4%, which is 18.4% higher than the F1 of the THUCTC (THU Chinese Text Classification) classification tool on average; the model based on BERT not only has a higher accuracy rate, but also has a certain reference significance for similar downstream tasks of natural language processing.

MonCIS-71 907 Real Time Prediction of Ship Pitch Motion Based on NARX Neural Network Wenhao Wu Dalian Maritime Univ.Lianbo Li Dalian Maritime Univ.Zhenyu Zhu Dalian Maritime Univ.Yang Jiao Dalian Maritime Univ.In order to improve the accuracy of ship pitch prediction, a Nonlinear AutoRegressive with eXogenous inputs (NARX) neural network prediction model is proposed, through the introduction of non-linear meteorological data to train the neural network, so that it can obtain the optimal training effect and improve the prediction accuracy. Finally, the model is simulated and tested by using the real ship pitch data of Dalian Maritime University Teaching Experimental ship "YUKUN", and the results are compared with the Back Propagation (BP) neural network. The experimental results show that the model selected in this paper has high prediction accuracy.

MonCIS-72 1172 Research on Mechanical Fault Detection Method of 252kV GIS Internal Disconnector Li Yu Northeast Branch of State Grid

Corporation of ChinaDan Song Northeast Branch of State Grid

Corporation of ChinaXiufeng Han State Network Jilin Province Electric

Power Co., Ltd. Inspection CompanyShixun Sun Northeast Branch of State Grid

Corporation of ChinaJian Li The Chinese people's liberation army

31434 troopsIn order to solve the problems of pin falling off, mechanical jamming, opening and closing not in place, etc. of 252kV GIS internal disconnector in actual operation, this paper proposes a mechanical fault detection method based on the combination of drive motor winding current and external crank arm rotation angle displacement. The DTW (dynamic time warping) algorithm is used to analyze the data of the opening and closing angular displacement, and the current curve is used to evaluate the state. The results show that the analysis method of driving motor current and angular displacement can accurately judge whether there is mechanical jamming, pin falling off and opening / closing not in place during the operation of disconnector in GIS, which effectively improves the accuracy of fault detection, and has a good application prospect.

MonCIS-73 1229 Fault prediction of elevator operation system based on LSTM Jian Liu Shenyang Jianzhu Univ.Chao Zhang Shenyang Jianzhu Univ.Na Li Shenyang Jianzhu Univ.In this paper, a fault prediction method based on the time series data of elevator operation fault is proposed, and the grid search method LSTM neural network is used to select the parameters, so as to minimize the root mean square error of the prediction results. The experiment is carried out with log data of centralized warning system of rail transit communication and compared with ARIMA model. Experimental results show that LSTM algorithm is superior to ARIMA algorithm in fault prediction, it has strong practical significance.

MonCIS-74 1512 Research on Fault Tolerant Algorithm of SINS/BDS/GPS Integrated Navigation System Lu Wang Inner Mongolia Univ.Xin Zhao Inner Mongolia Univ.Quanhu Li Inner Mongolia Univ.Weide Kang Inner Mongolia Univ.The fault-tolerant technology of integrated navigation is an important research area.In order to improve the reliability of the system, the fault

Technical Programmes CCDC 2021 detection module algorithm needs to be more perfect. Past researches focus on a single navigation system;however, a single system has many important problems for many challenging applications. This contribution focuses on the federated SINS/BDS/GPS integrated navigation system and the fault-tolerant algorithms applicable to the system.Based on the federated filter structure, this paper designs a combined navigation system for SINS/BDS/GPS fault detection and isolation. Combining fuzzy adaptive residual detection algorithm with improved SPRT detection method, a joint detection algorithm is proposed. The simulation experiments has been performed that, compared with other basic fault detection algorithms, this scheme can detect the fault source and isolate the fault part even when the sub-filter fails, effectively ensuring the positioning accuracy of the system and improving the reliability.

MonCIS-75 1522 SAE-CCA-based Fault Detection of Tandem Cold Rolling Zhi Wang Northeastern Univ.Jie Sun Northeastern Univ.Xing Lu Northeastern Univ.Yunjian Hu Shenyang Jianzhu Univ.Dianhua Zhang Northeastern Univ.With the expansion of production scale of the tandem cold rolling and the increase of complexity, quality monitoring and fault detection are particularly important. In this paper, the traditional multivariate statistical analysis method and deep learning method are combined to build a fault detection model of tandem cold rolling. Firstly, the deep learning method of stacked autoencoder (SAE) is used to learn and reconstruct the input samples to complete the feature extraction of the data. Secondly, the multivariate statistical method of canonical correlation analysis (CCA) is used to establish the monitoring model of characteristic variables and quality variables. 2 T statistic and Q statistic and their control limits are calculated based on the above model. The proposed method was used to analyze the fault data of the roller eccentricity in the tandem cold rolling process. The result shows that the combined fault detection method can detect the specific moment of faults occurrence accurately, and its effect is significantly better than that of traditional canonical correlation analysis method (CCA).

MonCIS-76 1570 Observer-Based Fault Tolerant Control for a Class of Nonlinear Multi-Agent Systems with Sensor Fault Jianliang Chen Wuhan Univ. of Science and Tech.Rong Xiang Wuhan Univ. of Science and Tech.Wanzhao Luo Wuhan Univ. of Science and Tech.This paper investigates fault tolerant control problem for a class of nonlinear multi-agent systems. First, an adaptive fault estimation observer is designed to estimate the immeasurable state values and sensor fault values. Second, a dynamic output feedback controller based on observer is used to eliminate the influences of disturbances and sensor faults on system. To obtain the robust H1 conditions of observer and controller, the linear matrix inequality technique is applied to solve the nonconvexity problem. At last, the effectiveness of observer and controller is verified by one example.

MonCIS-77 1543 Bearing Fault Diagnosis Method Based on EMD-VMD Adjacent Reconstruction and Secondary Decomposition Zhenyang Wu China Univ. of Mining and Tech.Fan Jiang China Univ. of Mining and Tech.

Jiangsu Provincial Key Laboratory ofMine Mechanical and Electrical

EquipmentXi Shen China Univ. of Mining and Tech.Feng Jiang China Univ. of Mining and Tech.Traditional empirical mode decomposition (EMD) algorithm has drawback of mode mixing when dealing with complicative vibration signals, and this will reduce the accuracy of bearing fault diagnosis. By considering the superior performance of variational mode decomposition (VMD) in signal processing, this paper proposes a bearing fault diagnosis method based on EMD-VMD adjacent reconstruction and secondary decomposition algorithm. Firstly, the vibration signal is decomposed by EMD. Secondly, a mixed index normalized from kurtosis and root mean square is proposed to locate the sensitive IMF component. Then, an adjacent reconstruction method is designed by combining the obtained sensitive component and its neighbor IMFs. Finally, the modal component parameter of VMD is set by these IMFs used for new reconstructed signal, and bearing fault diagnosis is realized with Hilbert analysis. Simulation analysis experimental analysis results show that the proposed method has advantages in baring fault diagnosis compared with some traditional methods.

MonCIS-78 119 Proportional Multiple Integral Observer-based Fault Estimation for Takagi-Sugeno Fuzzy Systems with Interval Time-varying Delay Fuqiang You Northeastern Univ.Chao Wang Northeastern Univ.Xiaoxiao Wang Northeastern Univ.

Fu Sun Northeastern Univ.This article studies the fault estimation problem of T-S fuzzy systems with interval time-varying delay, actuator fault, sensor fault and external disturbance. The designed Proportional Multiple Integral (PMI) observer can estimate system states, sensor and actuator fault simultaneously. A new Lyapunov-Krasovskii function that fully considers the upper and lower bounds of the time delay is constructed to prove the proposed method. In order to get the gain matrices of the designed PMI observer, one sufficient condition for the existence of the fault estimation observer is given in form of Linear Matrix Inequality (LMI). By introducing H1 performance index, the influence of external disturbance on fault estimation is suppressed. A numerical example is proposed to verify the effectiveness of the proposed method.

MonCIS-79 1458 An Accurate and Effective Life Predictionife Method Based on LSTM and Self-attention Yixuan Zhang Shandong Inst. of Space Electronic

Tech.Chao Tan Shandong Inst. of Space Electronic

Tech.Ning Yang Shandong Inst. of Space Electronic

Tech.Panfeng Wu Shandong Inst. of Space Electronic

Tech.Xiaoyu Wang Shandong Inst. of Space Electronic

Tech.Timely and accurate prediction of the remaining life for the equipment is of great significance to the safe and stable operation. In order to make full use of the effective information of multiple sensor data to further improve the accuracy of the equipment life prediction, a life prediction method based on long and short-term memory network (LSTM) and self-attention mechanism is proposed in this paper, which directly uses raw data as the input for the life prediction without any feature extraction. In the experiment, NASA’s CMAPSS data set is used to verify the proposed method, which is more accurate and effective compared with the existed machine learning models of life prediction. The experiment results show that the method proposed in this paper can fully use multiple sensor data to predict the remaining life of the equipment.

MonCIS-80 1307 Ground Test Verification Method for Fault Diagnosis and Disposal of Satellite Attitude and Orbit Control Subsystem Kai Yan China Academy of Space Tech.Qiang Li China Academy of Space Tech.Ran An China Academy of Space Tech.Yajie Chang China Academy of Space Tech.Xinyu Zhang China Academy of Space Tech.Jiaxin Luo China Academy of Space Tech.Autonomous function is becoming more and more important in satellite attitude and orbit control subsystem. In order to ensure the robustness of on-orbit independent operation and the safety of the whole satellite, fault diagnosis and disposal function have gradually become an important part in the design of attitude and orbit control subsystem. The ground test verification method for the fault diagnosis and disposal function of the attitude and orbit control subsystem also needs to be further studied. Based on the composition of satellite attitude and orbit control subsystem, fault diagnosis and disposal logic, the ground test verification method is studied in this paper. It mainly includes fault simulation method and test verification method. The fault simulation method introduces three methods: environment simulator, device simulator and bus data injection. This paper introduces the test verification method for the result of fault logic design and the test verification method for the requirement of fault logic design. The application characteristics and requirements of each method are described in detail, and the actual test cases are finally presented. The method can be used to guide the design of test and verification scheme of fault diagnosis and disposal logic of satellite attitude and orbit control subsystem and provide reference for other subsystems or spacecraft to carry out similar work. epidemic. The existence of endemic equilibrium is discussed in combination with the basic reproduction number. By analyzing the corresponding characteristic equation, the sufficient conditions for the local asymptotic stability and Turing instability of the endemic equilibrium are given respectively. The derived theoretical results are illustrated by some numerical simulations.

MonD01 Room01 Pattern Recognition and Intelligent Machines (V) 15:50-17:50 Chair: Yuequan Yang Yangzhou Univ.CO-Chair: Hailong Xu Air Force Engineering Univ.

15:50-16:10 MonD01-1 294 Pose Estimation and Robot Grasping Research Based on Deep Convolutional Network Learning: Status and Challenges Yuequan Yang Yangzhou Univ.Song Yu Yangzhou Univ.Fudong Li Yangzhou Univ.Zhiqiang Cao Chinese Academy of SciencesDing Jiang Yangzhou Univ.

Technical Programmes CCDC 2021 Tianping Zhang Yangzhou Univ.With the development of industrial robots, service robots and machine vision technology, the recognition and pose estimation of 3D objects become particularly important. The premise of object 6D pose estimation is object detection and location of image. Firstly, this paper analyzes the research of target detection and pose estimation based on deep learning from 2D image input data, 3D image and point cloud with input data format point of view combining Perspective-n-Point method, template (base) based method and active mixed method, respectively. Combined with the problem of occlusion, the strategies and applications of direct voting and indirect voting are reviewed. According to the role and position of deep convolutional network in robot grasping task, the research progress of intelligent grasping is reviewed from the aspects of integrated learning grasping method, multi-stage learning method and single-stage learning method, respectively. Finally, some problems and challenges are proposed such as the generalization and transfer capability of convolutional neural network learning and grasping of objects, perception and understanding of complex scenes, establishing and enrichment of robot practical data sets and so on.

16:10-16:30 MonD01-2 1497 Uncertainty SVM Active Learning Algorithm Based on Convex Hull and Sample Distance Hailong Xu Air Force Engineering Univ.Longyue Li Air Force Engineering Univ.Pengsong Guo Air Force Engineering Univ.Changan Shang Air Force Engineering Univ.In the process of traditional supervised learning, it is difficult to obtain large numbers of labeled samples and challenging to reduce the cost of data labeling. In response to this issue, and combining the convex-hull vector and the mechanism of support vector machine (SVM), an SVM active learning algorithm based on convex-hull vector and sample distance was proposed. Through the sample distance and convex-hull vector, the algorithm could actively select the samples most valuable to the current SVM classifier, i.e., the samples most likely to be support vectors. The experimental results demonstrated that with no negative impact on the classification accuracy, the proposed algorithm demanded significantly fewer labeled samples compared to random sampling, which reduced the sample labeling cost in learning, enhanced the SVM generalization performance, and increased the training speed.

16:30-16:50 MonD01-3 223 Dehazing algorithms based on credibility fusion Ludi Wang Beijing Aerospace Automatic Control Inst.Zhaolei Wang Beijing Aerospace Automatic Control Inst.Qinghai Gong Beijing Aerospace Automatic Control Inst.The mainstream of existing dehazing algorithms can be arranged into two categories: algorithms based on restoration and algorithms based on image enhancement. But the advantages and disadvantages of the two categories are converse. Combining and compensating their characteristics, two fusion-based dehazing methods are designed in this paper, namely Weber Contrast Weighting Method and Credibility-Fusion Adversarial Network Method, with which the dehazing visual effect, details and recognizabilities of dehazed images can all be enhanced. At the same time, the thoroughness of dehazing is also maintained. Compared with the rigid combination in existing papers, this paper fuses softly at each pixel. Experiments suggest that both algorithms proposed in this paper achieve good results and outperform the original algorithms.

16:50-17:10 MonD01-4 273 PE-Net: A Plane Extraction Network Generating Plane Constraints for Pose Estimation Suwei Liu Beijing Inst. of Tech.Xiaopeng Chen Beijing Inst. of Tech.Yan Zhao China Astronaut Research and Training CenterPeiyuan Zhao Beijing Inst. of Tech.Qihang Wang Beijing Inst. of Tech.Plane, as a common indoor geometric feature, is widely used in robot positioning or navigation. At present, traditional algorithms used to extract planes are limited by sensors and other aspects, while methods such as deep learning methods to extract planes from RGB images are limited to empirical information, and there are some other problems such as uncertain scales. On the other hand, the visual positioning systems based on feature points or the direct method also have the problem of low accuracy when there are few textures. This paper designs a deep convolutional neural network that takes RGB-D (RGB-depth) images as input and performs instance segmentation on planes, and a visual positioning algorithm based on Elastic Fusion and plane constraints to improve camera positioning. The experimental results show that the algorithm that fuses RGB-D information has better effect on plane classification and instance segmentation than related algorithms that simply use color or depth information. The visual positioning algorithm using the extracted plane can improve the positioning accuracy of the camera by the 3D reconstruction algorithm.

17:10-17:30 MonD01-5 171

Optic Disc and Fovea Localization based on Anatomical Constraints and Heatmaps Regression Ling Luo Northeastern Univ.Feng Pan Northeastern Univ.Dingyu Xue Northeastern Univ.Xinglong Feng Northeastern Univ.Jiwei Nie Northeastern Univ.In this paper, we deal with anatomical landmark localization as a heatmap regression problem. Based on this, we introduce a lightweight architecture to simultaneously localize fovea and optic disc (OD). Additionally, considering that directly attaching argmax to the output layer can lead to confidence map offsets errors, we propose a centroid clustering algorithm to address this issue. Extensive experiments are constructed on the IDRiD dataset, confirming the superiority of the proposed method. In particular, the Euclidean errors on fovea and OD are 45.034 and 21.101 (in pixels), respectively, which exceeds the other competitors of IDRiD Challenge 2018 by a large margin. Furthermore, at a resolution of 420356, the 90ms inference speed of a single image is conducive to large-scale clinical diagnosis.

17:30-17:50 MonD01-6 304 Multi-Branch Supervised Learning on Semantic Segmentation Wenxin Chen Beijing Inst. of Tech.Ting Zhang Beijing Inst. of Tech.Xing Zhao Beijing Inst. of Tech.Autonomous driving is a research and development focus of various countries in recent years, and its popularity cannot be separated from the support of semantic segmentation. Aiming at the problem that the shallow layers of the semantic segmentation model cannot obtain effective and immediate supervision, this paper proposes a multiple branch supervised method, which can simultaneously supervise the shallow layers of model during the training process. In order to increase the information interaction among multiple network layers, a module named "Layer Attention Mechanism" is proposed, which can increase the network's attention to more effective information when the network layers are merged. Based on UNet, experiments in the autonomous driving datasets show that the structure proposed in this paper is better than the UNet3+ network, which improves the accuracy of semantic segmentation and optimizes the effect of semantic segmentation.

MonD02 Room02 Decision-making Theory and Method (II) 15:50-17:50 Chair: Guoqiang Li CCCC First Highway Consultants

Co., Ltd.CO-Chair: Qiuni Li Airforce Engineering Univ.

15:50-16:10 MonD02-1 1113 A robust target intention recognition method based on dynamic bayesian network Qunli Xiao Northwestern Polytechnical Univ.Yuanna Liu Northwestern Polytechnical Univ.Xinyang Deng Northwestern Polytechnical Univ.Wen Jiang Northwestern Polytechnical Univ.The enemy’s tactical intention is one of the important basis for the commander’s decision. The accuracy and timeliness of the judgment of the enemy’s tactical intention will directly affect the correctness and effectiveness of our combat command decisions. In this paper, a robust target intention recognition method based on dynamic bayesian network is proposed. Self-organizing feature maps is introduced to preprocess the track information to estimate the stable heading of the target and various characteristic factors related to the air target combat intention are integrated to construct a dynamic bayesian network model for the recognition of the enemy’s target intention. In the simulated air combat scene, the proposed method can effectively realize combat intention recognition.

16:10-16:30 MonD02-2 1202 Evaluation of Water Resources Utilization of Liaoning Province Based on Improved DEA Model Jing-ming Li Liaoning Technical Univ.Lei-fu Gao Liaoning Technical Univ.Jun Tu Liaoning Technical Univ.Liaoning Province has a shortage of water resources and uneven distribution. The rational and effective utilization of water resources has a profound impact on the economic and social development of Liaoning Province. It is of great significance to grasp the current situation of water resources utilization in Liaoning Province and conduct effective evaluation. This article evaluates the water utilization efficiency of cities in Liaoning Province and reveals the key factors affecting water utilization. Considering the advantages and disadvantages of the data envelopment analysis method, the water resources utilization efficiency evaluation is realized based on the traditional DEA model and the improved DEA model respectively. Based on the Tobit regression model, the maximum likelihood method is used to obtain the estimation of the regression model, revealing the key factors affecting the efficiency of water utilization. Research shows that by considering the weight preference of decision makers, the improved DEA model can achieve the ranking of cities with effective water resources utilization. Significant factors affecting water utilization efficiency include the per capita GDP accounted

Technical Programmes CCDC 2021 for regional GDP, industrial added value accounted for regional GDP and the water storage project accounted for the total water supply.

16:30-16:50 MonD02-3 1255 Control System Actuator Risk Assessment Based on ITOPSIS Method Yangzhi Shen North China Electric Power Univ.Shenghui Wang Huaneng Taiyuan Dongshan Gas Power Com

pany LimitedWenguang Zhang North China Electric Power Univ.Yuguang Niu North China Electric Power Univ.In order to improve the reliability of actuators and ensure the stable operation of control system, a hierarchical structure model of actuator faults of control system was established using AHP firstly; Then, the improved fuzzy analytic hierarchy process (IFAHP) was used to obtain the composite weight of evaluation criteria by the dynamic combination of subjective and objective weighting methods; Afterwards, the Improved Technique for Order Preference by Similarity to an Ideal Solution (ITOPSIS) was obtained by introducing grey correlation degree, and the final sequence of each failure mode was calculated. Finally, the numerical analysis of the pneumatic actuator in a gas turbine power plant was carried out. The results show that the evaluation results of the proposed method are basically consistent with the actual situation, and compared with the traditional TOPSIS method, it can deal with the uncertain factors in the evaluation process, reduce the subjective sensitivity, ensure the comprehensiveness of decision rules.

16:50-17:10 MonD02-4 1326 Research on the Model of Road Crossing Based on Pedestrian Psychology Guoqiang Li CCCC First Highway Consultants Co., Ltd.The main objective is to conduct analysis of how to make pedestrians safer and more comfortable when they cross the road. A model of road crossing is presented according to subjective consciousness characteristics and psychology of pedestrians. The track of pedestrians crossing the road is the function of such dependent variables as crossing time, average vehicle speed, road width, average density of pedestrians and vehicles, characteristic factor of the road. In addition, the interaction of crossing track, crossing speed, road width and average vehicle speed could be quantitatively evaluated. By this evaluation an integrated index is put forward. This index serves to decide permitted maximum vehicle speed, crossing control scheme and road reconstruction optimization scheme. Ultimately the safety and comfort degree in road crossing are enhanced. In the paper, the reliability of this model is proved by a sample road from Xi'an to Xian yang. The result shows that both road crossing safety and comfort degree change oppositely with overlap of average speed and road width.

17:10-17:30 MonD02-5 1355 Fast Construction Algorithm of Cooperative Relay Network for Unmanned Aerial Vehicle Guoqiang Wang Hefei Univ. of Tech.

Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education

Engineering Research Center for Intelligent Decision-making & Information Systems Tech., Mini

stry of EducationPengfei Cheng Hefei Univ. of Tech.

Key Laboratory of Process Optimization and Intelligent Decision-Making, Ministry of Education

He Luo Hefei Univ. of Tech.Key Laboratory of Process Optimization and Int

elligent Decision-Making, Ministry of EducationKey Laboratory of Urban ITS Tech. Optimization and Integration, Ministry of Public Security of

ChinaIn view of the situation that the relay network needs to be constructed between the combat nodes with communication interruption in battlefield environment, a fast construction algorithm of Unmanned Aerial Vehicle (UAV) cooperative relay network is proposed, which is based on the theory of multiple shortest paths and strategy of giving priority to the relay placement node that is the most difficult to reach for UAV. Compared with the existing algorithms, this algorithm can further shorten the construction time of UAV cooperative relay network on the premise of minimizing the number of UAVs used for relay. Finally, the simulation experiment results demonstrate the effectiveness of the proposed algorithm.

17:30-17:50 MonD02-6 1655 An algorithm of air combat maneuver strategy based on two layer game decision-making and distributed MCTS method with double game trees under uncertain interval information conditions Qiuni Li Airforce Engineering Univ.Yong Chen Airforce Engineering Univ.Zhenyu Huang Airforce Engineering Univ.Yao Sun Airforce Engineering Univ.Chao Feng Unit 66133 of People Liberation ArmyIn this paper, the model of target assignment decision and maneuver

decision is established based on the position situation information such as angle and distance, the performance of the fighter, the threat of combat intention, and the multi-fighter collaboration effect, a two layer game decision algorithm based on double game trees distributed Monte Carlo strategy search is proposed, and the operational rules of interval numbers and the possibility degree comparison rules are adopted to solve the designed method. The experiment results show that the model and algorithm designed in this paper are correct and effective and the opponent’s combat intentions, trajectories and battlefield situation can accurately be predicted by the fighters in air combat. The two layer game decision-making method can precut the huge game tree strategy space, which improves the efficiency of strategy search. The distributed Monte Carlo strategy search method with double game trees can quickly search out the optimal air combat game decision scheme by adapting the opponent's strategy in real time.

MonD03 Room03 Supply Chain and Logistics Management (II) 15:50-17:50 Chair: Chao Qi Huazhong Univ. of Science and Tech.CO-Chair: Yiken Chen

Nanjing Univ. of Science and Tech.

15:50-16:10 MonD03-1 940 Dynamic Pricing of New Experience Products in the Presence of Online Reviews Huaming Song Nanjing Univ. of Science and Tech.Yiken Chen Nanjing Univ. of Science and Tech.In this paper, we assume that monopolistic online retailer releases new experience products and obtains the signal of test marketing sales before the sales period. Based on this situation, we established a two-period dynamic pricing model. We studied the impact of online reviews on the dynamic pricing strategies of firms, and analyzed the equilibrium prices, demand, and profits. Research shows that for most firms with high-quality products, they prefer vague, low-information reviews, while for most firms with low-quality products, they prefer accurate, highly-informed reviews. When firms observe different test sales signals, the level of informativeness will affect the transfer of demand in the sales cycle. Finally, we also find that low-cost firms should adopt a skimming pricing strategy, while high-cost companies should adopt a penetration pricing strategy.

16:10-16:30 MonD03-2 943 Product Green Degree Decisions of Dual-Channel Green Supply chain Considering E-tailer’s Green Invest Qian Xu Nanjing Univ. of Science and Tech.Yingying Sun Nanjing Univ. of Science and Tech.Huaming Song Nanjing Univ. of Science and Tech.This article discusses the product green degree decision and the downstream green investment decision of a two-stage green supply chain consisting of a manufacturer and an e-tailer. Based on whether the e-tailer makes green investment or not, this study discusses the equilibrium green degree and equilibrium price of green product under centralized decision and decentralized decision, and analyzes the conditions for the e-tailer to make green investment. The results show that centralized system is better than decentralized system from the perspective of total profit of supply chain members. When the unit cost of the e-tailer's green investment is high, the e-tailer will not make green investment. Consumers' preference for green products, unit cost of green investment and "green value-added service effect" brought by the e-tailer will also affect the strategic choice of e-tailer.

16:30-16:50 MonD03-3 956 The freight undertaking strategy for fresh product e-commerce supply chain with different power structures Ruoyan Guo Shandong Univ. of Finance and EconomicsGuohua Sun Shandong Univ. of Finance and EconomicsHuizhong Cheng Shandong Freshwater Fisheries Research Inst.This paper investigates a fresh product e-commerce supply chain (FPESC) consisting of one supplier, one e-retailer and one third-party logistics (3pl) provider. The impact of different power structures and freight undertaking strategies on the equilibrium decisions of the FPESC are explored. According to different power structures and the freight undertaking strategy, six Stackelberg models are constructed. It is found that no matter who acts as the Stackelberg leader and who undertakes the freight, the total profits of the FPESC are not affected.

16:50-17:10 MonD03-4 1234 A study on Double Traveling Salesman Problem with three-dimensional container loading for Bulky-item Delivery Chao Qi Huazhong Univ. of Science and Tech.Chunyue Shen Huazhong Univ. of Science and TechIn recent years, with the rapid development of e-commerce, ordering bulky items (such as refrigerators or wash machines) online has gradually emerged, and the development of bulky-item logistics has great potential. This paper is based on the background of bulky-item logistics, abstracts it into a Double Traveling Salesman Problem with three-dimensional container loading constraints (3L-DTSP), builds a mathematical model of

Technical Programmes CCDC 2021 3L-DTSP considering constraints such as path constraints, spatial geometrical relationship of goods, stability and blocking relationship. A heuristic algorithm is designed to solve the problem effectively. The algorithm is divided into three modules based on a backward thinking of Delivery-Loading-Pickup. The first module obtains the delivery route by Discrete Symbiotic Organisms Search algorithm based on simulated annealing (SA-DSOS). The second module constructs a heuristic loading algorithm for three-dimensional heterogeneous container loading. The third module is to obtain the pick-up route. Finally, the efficiency of the proposed algorithms is verified in a numerical study.

17:10-17:30 MonD03-5 1299 Research on Service Effort Coordination of E-tailer Supply Chain Based on Differential Game Zijiao Sun Liaoning Technical Univ.Jun Tu Liaoning Technical Univ.Chang Su Liaoning Technical Univ.In a supply chain consisting of an e-tailer and a third-party logistics (TPL) supplier, both logistics service effort and the level of brand goodwill can affect the demand of commodity. This paper studies the coordination of supply chain in the context of logistics service effort. Using differential game theory, the optimal results of logistics service effort and brand goodwill are obtained both in centralized and decentralized decision-making scenarios, and the market demand and total profit of the supply chain system are compared through numerical experiments. The results show that the logistics service effort, market demand and total profit of the supply chain are the largest under the centralized decision-making scenario. It can be seen that the centralized decision-making can achieve the largest efficiency of the supply chain system. The contract provided by the e-tailer can incentives the TPL to work hard, but the total profit of the supply chain is reduced under the decentralized decision-making scenario. This paper can provide a theoretical basis for the scientific decision-making of supply chain members in service level improvement behavior, incentive mechanism design, and choice of game structure.

17:30-17:50 MonD03-6 1329 A Stable Matching based Crowd Logistics for The Parcel Delivery in Cities Hechuan Wei Jianmai Shi

National Univ. of Defense Tech.National Univ. of Defense Tech.

Jiankang Zhong Guilin Univ. Of Aerospace Tech.With the fast development of information technologies, online shopping has become a particularly common consuming phenomenon, leading to a surge requirement in parcel delivery. How to decrease the delivery cost and increase delivery efficiency are the main concerns of express companies, and attracting a lot of researches. The paper proposes a novel stable matching based crowd logistics method for using crowd delivery capability to realize the sharing idea in parcel delivery. First, the framework is introduced to describe the primary design idea of crowd logistics. Next, the preference calculation model between the delivery demander (DD) and the delivery provider (DP) is defined as the input of the stable matching model. Then, the stable matching based crowd logistics model is constructed. Next, a small scale case is studied to illustrate the feasibility and effectiveness of the proposed method, and large scale cases are tests to prove the potential for solving large scale problems.

MonD04 Room04 Automatic Control of Unmanned Systems (IV) 15:50-17:50 Chair: Jingliang Sun Beijing Inst. of Tech.CO-Chair: Huan Zhou Air Force Engineering Univ.

15:50-16:10 MonD04-01 1106 Nonsingular Fast Terminal Sliding Mode Attitude Control for a Quadrotor Based on Fuzzy Extended State Observer Zhe Lin Huaqiao Univ.Ping Li Huaqiao Univ.During the flight, the quadrotor system is always subjected to unknown external disturbances. This paper investigates the design of a robust control scheme based on the nonsingular fast terminal sliding mode control (NFTSMC) technique and fuzzy extended state observer (FESO). First, a novel fuzzy extended state observer is designed to cope with unknown lumped disturbances, and the gain of the observer is adjusted by the fuzzy system to improve the estimation performance in an intelligent way. By using the estimation information of FESO, a new controller is proposed with NFTSMC algorithm to ensure that the quadrotor system can quickly track the desired attitudes, and tracking error converges to zero in a finite time. Finally, a comparative simulation experiment shows that the proposed control strategy can improve control performance and increase the robustness against disturbances effectively.

16:10-16:30 MonD04-02 1183 Path planning of an AGV based on artificial potential field and model predictive control Weiyi Shen Dalian Univ. of Tech.

Di Wu Dalian Univ. of Tech.In order to solve the obstacle avoidance problem when the external environment changes during AGV movement in the intelligent factory, this paper proposes an AGV obstacle avoidance path planning method based on artificial potential field(APF) and model predictive control(MPC). First, the environmental potential field is constructed to determine the value of potential field received by AGV at each point in the process of movement. Secondly, the vehicle kinematics model is constructed, and the vehicle kinematics model is linearized and discretized. Then, the potential field value obtained by APF method is taken as input, and the MPC method is adopted to obtain the control sequence with the minimum target performance function index by using the error between the predicted trajectory and the expected trajectory. Finally, the effectiveness of the proposed method is verified by Simulink/Casim co-simulation. The results show that this method can carry out path planning autonomously in the intelligent factory area.

16:30-16:50 MonD04-03 1448 Distributed Optimal Cooperative Formation Control Method for Multi-UAVs Systems Junzhi Li Beijing Inst. of Tech.Jingliang Sun Beijing Inst. of Tech.Teng Long Beijing Inst. of Tech.Yangjie Wang Beijing Inst. of Tech.Guangtong Xu Beijing Inst. of Tech.Considering the rotation of formation, the distributed cooperative

formation control problem for multi UAVs is investigated. The linearized path following error dynamics and the double-integrator formation dynamics are established, respectively. To decrease the formation error, the rotation acceleration compensation scheme is introduced. By utilizing the LQR technique, a path following algorithm is given for the leader. Furthermore, to maintain a given formation, an optimal consensus control law is presented for the followers. The dynamic adjustment strategy of the leader and communication topologies is given to increase the robustness of the proposed distributed formation control algorithm. Subsequently, theoretical analysis reveals that the proposed method is able to guarantee the leader follows a given path accurately as well as the formation maintain. The numerical simulation is presented to demonstrate the effectiveness and robustness of proposed method.

16:50-17:10 MonD04-04 1449 Information-Compensation-based Receding Path Planning for Multi-UAVs under Communication Delays Yangjie Wang Beijing Inst. of Tech.Jingliang Sun Beijing Inst. of Tech.Teng Long Beijing Inst. of Tech.Junzhi Li Beijing Inst. of Tech.Guangtong Xu Beijing Inst. of Tech.This paper presents a novel receding path planning method for multi-UAVs to increase the success rate of cooperative path planning under communication delays. Firstly, path planning problem for multi-UAVs is decomposed into a series of short horizon sub-problems via receding planning framework to reduce the computational burden. An analytical solution to the collision avoidance algorithm is deduced, which can ensure the safety among discretized path points. To enhance the success rate of path planning for multi-UAVs under communication delays, Kalman filtering method is utilized for compensating delay information, which the current position of UAV is estimated by its previous position and delay time. Finally, the numerical simulation demonstrates the effectiveness of the proposed method.

17:10-17:30 MonD04-05 1604 Path Planning and Following Control of a Quadrotor Helicopter in Three-Dimensional Space With Limited Information Dehai Zhu Northwestern Polytechnical Univ.Ban Wang Northwestern Polytechnical UniversityThis paper presents a real-time path planning and following control strategy based on rapidly exploring random tree (RRT) and Line of Sight (LOS) for achieving autonomous flight of a quadrotor helicopter in threedimensional complex space with limited available obstacle information. Regarding the path planning problem, a new three-dimensional path planning algorithm is proposed to generate efficient and executable paths based on RRT and cubic B´ezier curve. With respect to path following control, LOS algorithm is employed and extended to three-dimensional space to enable the quadrotor helicopter to follow the preplanned path. Finally, a series of simulation tests are carried out to validate the effectiveness of the proposed strategy. The demonstrated results show that with the proposed path planning and tracking control strategy, the studied quadrotor helicopter can successfully accomplish the deployed mission in both static and dynamic obstacle environments.

17:30-17:50 MonD04-06 1659 Lateral flight control method of UAV based on small disturbance and root locus theory Huan Zhou Air Force Engineering Univ.Senyu Zhang Air Force Engineering Univ.

Technical Programmes CCDC 2021 Changjian Ru PLAThe flight control is the key technology for the unmanned aerial vehicle(UAV), which can ensure the flight security of UAV. Compared with the longitudinal motion, the lateral motion mode and frequency domain characteristics of the UAV are much more complex, which makes the design of lateral flight controller more challenging. Therefore, the lateral flight control method of UAV is designed based on small disturbance and root locus theory. Based on the coupled nonlinear motion model of UAV, the longitudinal and lateral small disturbance motion equations are obtained by using decoupling grouping and small disturbance linearization strategy. The lateral motion mode and lateral frequency domain characteristics of UAV are analyzed, and the lateral flight control law based on small disturbance and root locus theory is designed by using root locus and PID control theory of classical control theory. Finally, the UAV flight control simulation experiment is carried out to verify the effectiveness and superiority of the proposed method.

MonD05 Room05 Robust Control 15:50-17:50 Chair: Qiufan Yuan Shanghai Aerospace Systems Engine-

ering Research Inst.Shanghai Engineering Research Center of

Space RoboticsCO-Chair: Ran An Inst. of Telecommunication and Navigation

Satellites

15:50-16:10 MonD05-1 993 Integrated maneuvering and vibration control for spacecraft with single high-flexible structure based on global modal dynamic model Qiufan Yuan Shanghai Aerospace Systems Engineering

Research Inst.Shanghai Engineering Research Center of

Space RoboticsPengfei Zheng Shanghai Aerospace Systems Engineering

Research Inst.Bin Song Shanghai Aerospace Systems Engineering

Research Inst.Shanghai Engineering Research Center of

Space RoboticsYuzhi Xiao Shanghai Academy of Spaceflight Tech.In order to improve maneuvering control performance for the spacecraft with single large flexible structure, an integrated maneuvering and vibration controller is proposed based on the global dynamic model. the global dynamic model is constructed by combing rigid modes and flexible modes. Piezoelectric patches are modeled into the model for active vibration suppression. Compared with the maneuvering controller without active vibration suppression, vibration magnitudes of rigid body’s angular velocity and linear velocity are largely reduced by the integrated controller. Higher accuracy and stability are realized for the attitude control of the rigid body.

16:10-16:30 MonD05-2 1317 Design of the Station Keeping Scheme of the Lunar Relay Satellite Based on Electric Propulsion Ran An Inst. of Telecommunication and Navigation SatellitesMin Wang Inst. of Telecommunication and Navigation SatellitesYiming Liu Inst. of Telecommunication and Navigation SatellitesKai Yan Inst. of Telecommunication and Navigation SatellitesKezhen Song Inst. of Telecommunication and Navigation SatellitesChen Zhao Inst. of Telecommunication and Navigation SatellitesIn the design of the orbit transfer of the lunar relay satellite at the L2 point of the Earth-Moon using the electric thruster, the satellite station keeping design should not be limited to the Halo orbit under the restricted three-body model. The design of the station keeping scheme under the high-precision ephemeris model affected by the sun's gravity, the moon, and the earth's perturbation force is very important. For the above problems, using the quadratic optimal control method and the hybrid method of the optimal control algorithm, a station keeping scheme under the circular restricted three-body model will be designed. Then use it as the initial value of the quadratic optimal control, and obtain the design result of the station keeping scheme under the high-precision ephemeris model. This scheme has strong engineering practical value.

16:30-16:50 MonD05-3 1086 Research on the Rapid Large-angle Attitude Maneuver of the LEO Communication Satellite Xiaojie Yao CASIC Space Engineering Development Co. Ltd.Xing Xin CASIC Space Engineering Development Co. Ltd.Chao Bei CASIC Space Engineering Development Co. Ltd.A specific type of the large-angle attitude maneuver problem is concerned for the low earth orbit communication satellites, characterized by the closely connected maneuver stages, and with both high-precision and finite-time stabilize emphasized. To address such issues, the control scheme is proposed by combining a trajectory optimization and a closed-loop attitude controller. As a practice, the seven-segment cosine angular acceleration planning and the real-coded genetic algorithm are applied to provide the maneuver trajectory optimization, generally considering the practical performance of the sensors and actuators.

Meanwhile, the controller is designed by an improved global fast terminal sliding mode method which brings a better convergence. Numerical simulation shows that the high accuracy is guaranteed during the fast attitude tracking even considering the actuator limitations.

16:50-17:10 MonD05-4 1101 Adaptive-Robust Cooperative Guidance Strategy for Simultaneous attack with Impact Angle Constraints Jinhao Liu Peking Univ.Jianying Yang Peking Univ.The paper proposes a cooperative guidance stategy for missiles’ simultaneous attack. Multiple missiles can hit a stationary target concurrently to implemente saturation attack. In addition, by using optimal control strategy, it can make missiles attack the target with desired impact angle, which can improve the fire strike capability of missiles. It can also enable missiles to tackle external disturbances. The guidance law needn’t know the upper bound of the disturbances by tuning the adaptive gains. Consensus of time is proved using Lyapunov theory. Numerical simulation is performed to verify the effectiveness of the guidance law.

17:10-17:30 MonD05-5 1164 Online Trajectory Planning for Docking to Tumbling Spacecraft with Active Maneuver Haolong Wang Beijing Inst. of Tech.Haoping She Beijing Inst. of Tech.In the field of On-Orbit Services, docking to tumbling target plays a key role. The paper proposes an online trajectory planning algorithm for docking to a tumbling spacecraft. Different from conventional trajectory planning, our algorithm involves sudden change of the target motion raised by its active maneuver or debris impact during final approaching. With energy-optimal trajectory generated by gauss pseudo-spectral method and improved online replanning, the algorithm can guide the service spacecraft to the specified region. Cuboid rather than spherical non-fly zone surrounding the target is presented as path constraints in planning to save more energy. Some docking scenarios are simulated and results show less energy consuming and calculating time of this algorithm.

17:30-17:50 MonD05-6 1239 Cooperative Covering Guidance Strategy Design: A Virtual Targets Approach Ziwei Guo Peking Univ.Tao Xu Peking Univ.Zhao Zhang Peking Univ.Zhisheng Duan Peking Univ.A novel cooperative guidance law is designed in this paper to intercept a maneuvering target, where the concept of virtual targets is proposed and utilized. Taking the bounds of the target’s maneuverability as a prior knowledge, each missile executes the improved proportional guidance law and covering strategy to intercept the real target. In addition, when a complex maneuvering target is considered, the probability of successful interception is calculated based on the reachability analysis. Numerical simulations are performed to verify the developed strategy.

MonD06 Room06 Theory and Application of Linear System 15:50-17:50 Chair: Junyan Yu Univ. of Electronic Science and Tech. o

f ChinaCO-Chair: Yanfeng Wang Huzhou Univ.

15:50-16:10 MonD06-1 1368 Average group consensus of third-order discrete-time multi-agent systems with finite subnetworks Junyan Yu Univ. of Electronic Science and Tech. of ChinaHaoxing Ma Univ. of Electronic Science and Tech. of ChinaMengtao Cao Univ. of Electronic Science and Tech. of ChinaThis paper studies average group consensus of third-order discrete-time multi-agent systems in which agents in the multi-agent network form finite subgroups due to environmental differences or task allocation. Assuming that the communication topology is undirected, a novel distributed protocol is designed. By using graph theory and matrix theory as the main analysis tools, necessary and sufficient conditions and precise final states are obtained for the agents achieving the average group consensus. Finally, an example is presented to demonstrate effectiveness of the theoretical results.

16:10-16:30 MonD06-2 1435 Controller design for networked Markov jump system with data packet dropout in both S/C link and C/A link Xiaoyue Sun Huzhou Univ.Yanfeng Wang Huzhou Univ.Peiliang Wang Huzhou Univ.Xianrong Jiang Huzhou Univ.Haipeng Tang Huzhou Univ.Lidi Quan Huzhou Univ.

Technical Programmes CCDC 2021 The controller design for a class of networked Markov jump system with data packet dropout in both S/C link and C/A link is investigated in this paper. By using Lyapunov stability theorem and linear matrix inequality (LMI), the sufficient conditions on the stochastic stability of the closedloop system are obtained, and the design method of the controller is also given. Numerical simulation results verify the effectiveness of the proposed method.

16:30-16:50 MonD06-3 482 Derivative and Integral Mixed Equalities and Inequalities of Zhang Equivalency in Addition to Pure Derivative Ones Yunong Zhang Sun Yat-sen Univ.

Guangdong Key Laboratory of Modern Control Tech.

Wuyi Yang Sun Yat-sen Univ.Guangdong Key Laboratory of Modern Co

ntrol Tech.Liangjie Ming Sun Yat-sen Univ.

Guangdong Key Laboratory of Modern Control Tech.

Min Yang Sun Yat-sen Univ.Guangdong Key Laboratory of Modern Co

ntrol Tech.Jinjin Guo Sun Yat-sen Univ.

Guangdong Key Laboratory of Modern Control Tech.

In this paper, we employ Zhang equivalency (ZE) to discuss time-varying problems from the perspective of the overall error. Firstly, the concept and the general formulas of ZE are reviewed, including equality type and inequality type. Then, applying ZE general formulas, we propose derivative and integral mixed ZE (DIMZE) general formulas to discuss ZE based on the overall error. Besides, the order-(-1), order-0, order-1, and order-2 DIMZE general formulas of equality type and inequality type are organized, respectively. Furthermore, we discuss the inequalities about time-varying problems in two situations, i.e., the matrix situation and the state-control situation. Therefore, to handle these two different situations of time-varying problems, the corresponding order-(-1), order-0, order-1, and order-2 DIMZE formulas of inequality type are proposed.

16:50-17:10 MonD06-4 623 Modeling of aero-engine starting process based on rising rate of rotating speed Binbin Hao AECC Shenyang Engine Research Inst.Tao Qu AECC Shenyang Engine Research Inst.Yuanwei Jing Northeastern Univ.In this paper, an identification method based on rising rate of rotating speed is proposed. To carry out the dynamic simulation of aero-engine starting process, the proposed modeling method is used to establish the model of aero-engine starting process using the test data collected from starting process and cold running process. The feasibility and efficiency of the proposed modelling method are validated by application to a semi-physical simulation tester. The output accuracy of established model can meet the function and performance requirements for control system in semi-physical simulation tester. The presented modeling method lays an important foundation for design of actual control system.

17:10-17:30 MonD06-5 895 Novel Exponential Synchronization criterion of Markov jump Neural Networks with Additive Time-varying Delays Haiyang Zhang Yunnan Minzu Univ.Xiaoman Liu Yunnan Minzu Univ.Lianglin Xiong Yunnan Minzu Univ.Tao Wu Southeast Univ.This paper addresses the Exponential Synchronization Problem (ESP) for a class of Markov Jump Neural Networks (MJNNs) with Additive Time-varying Delays (ATVDs). Firstly, a new Free-matrix Exponential-type Inequality (FMEI), which can derive a tighter bound of Exponential-type Integral Quadratic Terms (EIQTs), is proposed. Secondly, to capture more information about the attenuation exponent, ATVDs and Markov jump parameters, a novel Lyapunov–Krasovskii functional is constructed. Then, by using the new FMEI and other analytical techniques, a less conservative criterion guaranteeing the stochastic synchronization of master-slave system is described in form of Linear Matrix Inequalities (LMIs). Finally, a numerical example is given to illustrate the effectiveness of the derived result.

17:30-17:50 MonD06-6 861 An Improved Capacitor Voltage Balanced Method of Modular Multi-level Converter Xiaoli Chai Xi’an Jiaotong Univ.Jingjing Huang Xi’an Jiaotong Univ.Geng Li Xi’an Jiaotong Univ.Aimin Zhang Xi’an Jiaotong Univ.Wei Zhang Xi’an Jiaotong Univ.Lei Zhang Xi’an Polytechnic Univ.Modular Multi-level Converter (MMC) has been widely used in the field of flexible DC transmission due to its modularity, low switching loss, low

harmonic distortion rate, etc. The balance of the sub-module capacitor voltage in MMC control system is critical and it is hardly guaranteed by the traditional Carrier Phase Shift Modulation (CPSM) scheme. Therefore, an improved capacitor voltage balanced scheme is proposed in this paper. By analyzing the composition of the sub-module (SM) DC capacitor voltage variation, it is found that the bridge arm current has a great influence on the voltage balance of SMs. Thus, the bridge arm current is employed in the CPSM scheme. This improved voltage balanced method can effectively increase the transient settling speed of the SM voltage balance without introducing extra switching loss and large calculation cost. The simulation results show that the proposed capacitor voltage balanced method can reduce the transient settling time by more than 75% when comparing with the traditional scheme, which verifies the effectiveness and superiority of proposed method.

MonD07 Room07 Identification and Estimation (II) 15:50-17:50 Chair: Man Yang CASIC Space Engineering Developme

nt Co.LtdCO-Chair: Yunpeng Zhang Nankai Univ.

15:50-16:10 MonD07-1 899 An efficient micro-satellite attitude estimation algorithm based on QUEST and UD-MEKF Man Yang CASIC Space Engineering Development Co.LtdYang Chen CASIC Space Engineering Development Co.LtdXing Xin CASIC Space Engineering Development Co.LtdThis paper designed an efficient algorithm for attitude estimation using the information from the sun sensors/magnetometers/ Micro-Electro -Mechanical System (MEMS) gyroscopes. The proposed algorithm uses the Quaternion Estimator (QUEST) and multiplicative extended Kalman filter (MEKF) with Upper triangular matrix-Diagonal matrix (UD) factorization to reduce computational complexity, thereby achieving a real-time attitude estimation. Firstly, the QUEST uses the measurement information from sun sensors and magnetometers to generate a coarse attitude. Then, these estimated values are used as quaternion measurements in the MEKF algorithm. UD factorization is used in the filter to reduce the computational burden. Additionally, the estimated quaternion is fed into the QUEST to determine the initial value of the iteration, thus improving the convergence speed. The simulation results show that the designed algorithm is effective and the attitude estimation accuracy meets the requirements for microsatellites.

16:10-16:30 MonD07-2 1362 High-Bandwidth Hysteresis Modeling of Piezoelectric Actuators Based on Modified NARX Neural Network Yunpeng Zhang Nankai Univ.Heng Duan Nankai Univ.Yanding Qin Nankai Univ.Jianda Han Nankai Univ.Piezoelectric actuators (PEAs), widely used in high-precision positioning applications, exhibit rate-dependent hysteretic nonlinearity, which seriously deteriorates the tracking performance. An accurate model is essential in the study of the PEA’s hysteresis. In this paper, a modified nonlinear autoregressive with external input (NARX) neural network is proposed to predict the output of PEAs. Different from the traditional NARX neural network, the input rate is integrated into the neural network as an additional input. This modification helps to account for the rate-dependence of the PEA’s hysteresis. Experiments are carried out on a commercial PEA, where a 1-100 Hz swept sinusoidal signal is utilized in the parameter identifications. Further, a sinusoidal signal with descending amplitude and a 1-300 Hz swept sinusoidal signal are used to verify the effectiveness of the proposed model in modeling the PEA’s hysteresis. Experimental results show that both the RMS and maximum errors of modified NARX neural network are smaller than traditional NARX network, especially for high-frequency signals and amplitude-varying signals. The proposed modified NARX neural network achieves higher flexibility and accuracy in the bandwidth of 300 Hz, and thus is a promising hysteresis model for PEAs.

16:30-16:50 MonD07-3 1511 An Identification Method for Axial Unbalance of GyroWheel Rotor Based on Multi-position Calibration Shuo Chen Harbin Inst. of Tech.Xin Huo Harbin Inst. of Tech.Hui Zhao Harbin Inst. of Tech.Yu Yao Harbin Inst. of Tech.Aiming at the GyroWheel rotor with special support structure, this paper proposes a new method for identifying the rotor axial unbalance. Different from the traditional rotor which only has radial unbalance, the axial unbalance of GW rotor will affect the measurement accuracy and is difficult to identify. Based on the axial unbalance disturbance torque model, combined with the GW drift error model, the idea of identifying through the error coefficient is adopted. Using the multi-position calibration test, the coefficients Dx

x and Dyy containing the axial

unbalance information are estimated. Then, according to the influence coefficient method, the test weight is added to calculate the original unbalance. The simulation results verify the effectiveness of the proposed method, and the error between the estimated value and then

Technical Programmes CCDC 2021 actual value is 0.22% and 0.24%, respectively.

16:50-17:10 MonD07-4 1551 State Estimation for Two-Dimensional Systems with Time-Correlated Multiplicative Measurement Noises Shiwei Gao Shandong Univ.Wei Wang Shandong Univ.Linkai Geng Shandong Univ.This paper addresses the state estimation problem for a class of two-dimensional discrete-time linear systems with additive noises and time-correlated multiplicative measurement noises. The time-correlated multiplicative noises are described by a linear system model with white noises. The main objective of this paper is to construct a recursive estimator that achieves the minimum error variance of the state estimation at each step. By introducing the method of the line of cross-cut and geometric to deal with second-order variables, a recursive algorithm is proposed for the system under consideration in the sense of linear minimum mean-square error.

17:10-17:30 MonD07-5 360 Calibration method of accelerometer without turntable based on NI-PSO Yang Zhao Naval Aviation Univ.Shaowu Dai Naval Aviation Univ.Hongde Dai Naval Aviation Univ.Haijun Li Naval Aviation Univ.Xiaoyu Zhang Naval Aviation Univ.In order to quickly complete the calibration of the micro-accelerometer, improve the calibration accuracy and reduce calculation amount, a Newton iterative-particle swarm optimization algorithm (NI-PSO) combining the Newton iteration and the PSO algorithm in the intelligent algorithm is proposed. The algorithm completes the modulus observation calibration of the micro-accelerometer without turntable. Based on the theory of modulus observation, this scheme transforms the calibration problem of the three-axis micro accelerometer into a non-linear function extreme optimization problem. Then, apply the PSO algorithm to solve the nonlinear extreme optimization problem. After reaching certain accuracy, switch to classic Newton iteration algorithm, and use the optimal result as the initial value of the Newton iteration. The proposed method solves the problem of the sensitivity of Newton iterative algorithm to the initial value and the problem that the convergence speed of the PSO algorithm decreases after a certain number of iterations. The simulation results show that the method proposed in this paper avoids the stringent requirements of conventional accelerometer calibration for experimental equipment, and the calibration accuracy is improved by 2 orders of magnitude relative to the Newton iteration, and 1 order of magnitude higher than that of the PSO algorithm The new NI-PSO based calibration method of micro-acceleration provides a valuable reference for engineering application.

17:30-17:50 MonD07-6 607 A Binomial-base Hammerstein modeling for nonlinear cooling power control system of MGT-CCP Xingjian Liu Northeastern Univ.Xinge Zhao Northeastern Univ.Sizhan Wang Northeastern Univ.Hammerstein model has its distinct advantages in nonlinear system simulation and control design. A major problem in designing nonlinear controllers using Hammerstein models is that the inverse functions of their static nonlinear functions are difficult to obtain analytically, thus a novel Binomial-base Hammerstein model is proposed to solve it and the parameter estimation approach is discussed in this paper. A micro gas turbine-LiBr refrigerating machine combined cooling and power system (MGT-CCP) is modeled by the approach and the accuracy is well verified. Finally, the Binomial-base Hammerstein model is used to compensate the nonlinearity of the MGT-CCP in cooling power control system design.

MonD08 Room08 Theory and Application of Nonlinear Systems (VI) 15:50-17:50 Chair: Wenbi Zhao Northwestern Polytechinical Univ.CO-Chair: Zhihua Chen Beijing Inst. of Control Engineering

15:50-16:10 MonD08-1 930 Adaptive Fast Terminal Sliding Mode Control for Automatic Load Alleviation Problem of Refueling Boom Against Disturbances Wenbi Zhao Northwestern Polytechinical Univ.Yaohong Qu Northwestern Polytechinical Univ.The refueling boom is widely adopted in recent years because of its advantages in autonomous aerial refueling. During the refueling docking process, the contact between the refueling boom and the refueling receptacle of the receiver UAV may generate an excessive radial force which will cause the refueling boom bent or even damaged. To solve this problem, an automatic unloading system of the refueling boom is designed in this paper. Firstly, the attitude kinematic model of the refueling boom and the loading dynamic model are established, meanwhile the disturbance observer is designed based on sliding mode theory to estimate the exogenous disturbance. Then, the adaptive fast

terminal sliding mode control method is adopted to control the attitude of the refueling boom, and the traditional PID control method is further used to achieve the automatic unloading. Finally, numerical simulation results validate the efficacy of the proposed control system.

16:10-16:30 MonD08-2 1649 Stability Analysis of A Class of Second-Order Phase Plane Control Systems: A Simulation Study Zhihua Chen Beijing Inst. of Control EngineeringYongchun Xie Beijing Inst. of Control EngineeringYong Guo Northwestern Polytechnical Univ.Kai Zhang Southwest Jiaotong Univ.Jinhua Guo China Academy of Launch Vehicle Tech.Wangkui Liu Beijing Aerospace Tech. Inst.Yong Li Beijing Inst. of Control EngineeringZhibin Zhu Beijing Inst. of Control EngineeringThis paper investigates the stability analysis problem for a class of second-order phase plane control (PPC) systems via computer simulation. Due to the PPC method’s nonlinearities arising from state-dependent switching and periodic sampling, the stability analysis problem of the closed-loop PPC system is very difficult even for the secondorder linear system. In this paper, we aim at studying the stability of a class of second-order PPC systems by proposing a so-called globally uniformly ultimate boundedness (GUUB) conjecture based on the computer simulation results. In particular, to a certain extent, the future proof of this conjecture will further promote the development of the stability analysis of general sampled-data switched control systems.

16:30-16:50 MonD08-3 1378 Backstepping Control of a Multimegawatt Variable-speed Wind Turbine for Maximum Power Point Tracking Jingjing Li Central South Univ.Lihui Cen Central South Univ.Yuqian Guo Central South Univ.Yongfang Xie Central South Univ.Fang Liu Central South Univ.Since the dynamic nonlinear characteristics and the strong inertia of the multimegawatt variable-speed wind turbine(MVWT), the conventional method on maximum power point tracking (MPPT) causes a large wind speed tracking deviation and significant power losses. Due to the ability of fast response, a backstepping control applied to MVWT is proposed. A new nonlinear dynamic model in a lower triangular form is constructed by transforming the complex system into multiple subsystems. The virtual control of each subsystem is selected from top to down of the lower triangular model. And the virtual control laws are obtained based on Lyapunov stability criterion. Until the augmentations recover the last subsystem, the control law of the last subsystem also is the actual control law. Simulation is implemented by using a FAST(Fatigue, Aerodynamic, Structure, and Turbulence) simulator based on the NREL(Nation Renewable Energy Laboratory) offshore 5-MW baseline wind turbine. The results demonstrate that the backstepping control in this paper can improve the tracking performance of rotor speed and increase the power efficiency. The asymptotic stability of the wind turbine system is also guaranteed under circumstance of uncertainties.

16:50-17:10 MonD08-4 1506 Reduced-order state estimation of delayed memristive neural networks Mei Zou Yunnan Minzu Univ.Lianglin Xiong Yunnan Minzu Univ.Li Cai Yunnan Minzu Univ.The issue of reduced-order state estimation of delayed memristive neural networks is investigated in this paper for the first time. A new observer contains three gain matrices and a sign function is proposed. Then, by constructing an apposite Lyapunov-Krasovskii functional(LKF) and employing integral inequalities and linear matrix inequality technique, a timevarying delay-dependent reduced-order state estimation criterion is obtained. Finally, a numerical example is utilized to give a demonstration of the effectiveness and performance of the proposed estimation.

17:10-17:30 MonD08-5 1577 The Influence of Observation Interference on System Evolution Prediction Jun Meng Zhejiang Univ.Huize Yu Zhejiang Univ.Aiming at the contradiction between the uncertainty principle and the law of causality, this paper proves that the evolution of all systems is a causal model rather than a probability model. The introduction of observers is equivalent to increasing the dimensionality of the system, so the observation results of the high-dimensional coupled system cannot be used to predict the original low-dimensional system. Even if some systems in the quantum world cannot make long-term predictions, they can still achieve short-term predictions through the Lyapunov index.In addition, this article explains the wave-particle duality, thinking that the particles themselves are actually in the form of energy storage gyroscopes. The high-speed spin of the particle itself produces fluctuations in the energy of the neighboring universe, and at this time the

Technical Programmes CCDC 2021 observer’s observation intervention affects the observed particle and its environmental energy fluctuations. Furthermore, this article believes that the description of the uncertainty principle is a subset of the initial sensitivity of the manifold in the chaotic state. The definition of the uncertainty principle itself does not exist, and is not rigorous.

17:30-17:50 MonD08-6 1495 Design and analysis of Gaussian sum high-order CKF for nonlinear/non-Gaussian dynamic state estimation Lei Wang Anhui Science and Tech. Univ.Wei-xia Gao Anhui Science and Tech. Univ.Le Wang Anhui Science and Tech. Univ.Fu-zhi Hu Anhui Science and Tech. Univ.In order to solve the problem of state estimation for nonlinear, non-Gaussian system, a novel Gaussian sum high order cubature Kalman filter (GS-HCKF) is proposed. In the GS-HCKF algorithm, a new version of high-order cubature Kalman filter (HCKF) is combined with Gaussian sum filter (GSF). The key idea of the proposed algorithm is that the posterior probability density of state can be approximated by a set of Gaussian distribution and each Gaussian distribution is estimated by high-order cubature Kalman filter. Numerical simulation results show that the proposed GS-HCKF is compatible with the advantages of traditional GSF and Gaussian sum unscented Kalman filter (GS-UKF), that is, the proposed GS-HCKF has low computational complexity and high estimation accuracy.

MonD09 Room09 Fault Diagnosis and Predictive Maintenance (X) 15:50-17:50 Chair: Sai Li Wuhan Inst. of Tech.CO-Chair: Jinheng Han Tsinghua Univ.

15:50-16:10 MonD09-1 1548 Bearing Fault Diagnosis Using Support Vector Classifier Based on Sine Cosine Algorithm Sai Li Wuhan Inst. of Tech.Rui Jiao Wuhan Inst. of Tech.Zhixia Ding Wuhan Inst. of Tech.Liheng Wang Wuhan Inst. of Tech.Xuan Ye 722 Research Inst.In order to ensure the normal operation of rotating machinery, it is necessary and important to carry out fault diagnosis of rolling bearings. This paper proposes a fault diagnosis algorithm for rolling bearings, which is based on singular value decomposition with traditional empirical mode decomposition, and support vector classifier with sine cosine algorithm. First, empirical mode decomposition is utilized to characterize the complexity of vibration signals. Furthermore, singular value decomposition is applied to extract the fault feature. Subsequently, support vector classifier with sine cosine algorithm is proposed for fault recognition under various conditions. The performance of the proposed method has been verified by its successful application in rolling bearings experiments. Compared with the existing methods, this approach can detect bearings faults effectively, and improve the classification efficiency.

16:10-16:30 MonD09-2 45 Adaptive Fault Estimation for the Motorized Cylinder of the Electro-Hydraulic Braking System with Disturbances Jinheng Han Tsinghua Univ.Chao Li Tsinghua Univ.Chengkun He Tsinghua Univ.Junzhi Zhang Tsinghua Univ.This paper develops a novel robust finite time adaptive parameter estimator for motorized cylinder of the Electro-Hydraulic Braking system to estimate the faults extent. For the purpose of designing the fault estimator, the fault dynamics model of the motorized cylinder combined with disturbances and uncertainties is constructed. Then, a novel robust finite time adaptive parameter estimator based on the parameter estimation error is proposed via an exponential power form to achieve finite time convergence. Moreover, the finite time convergence property with disturbances is proved by input-to-state stability (ISS) theory in this paper. Finally, the simulation results of the fault estimation are demonstrated to validate the effectiveness of our estimator algorithm.

16:30-16:50 MonD09-3 1332 Crack Detection and Classification of a Simulated Beam Using Deep Neural Network Linming Zou South China Univ. of Tech.Yonggui Liu South China Univ. of Tech.Jiachen Huang South China Univ. of Tech.Crack is an important damaged feature of structures in civil infrastructure which needs to be detected before disastrous accident. Simply, damaged severity of structures may vary from different crack lengths which results in changes of vibration characteristics. In structural health monitoring system (SHM), the vibration-based method to detect damage in structures attracts considerable attention. This paper aims at introducing a novel framework by using deep neural network to do some crack recognition and classification tasks. The crack damage was simulated with a finite element model of a simply supported beam monitored by a

group of acceleration sensors. Mulitiple vibration data was fed into designed framework for training and testing. The results are compared with fully convolutional networks (FCN), residual network (ResNet), long short-term memory network (LSTM) and bidirectional LSTM (Bi-LSTM) that are well-performed in tackling time series problems. The experiments show that crack damage is correctly detected and classificated using the monitoring data with 3% accuracy improving at least compared with those four methods. Finally, we apply the proposed framework to an SHM and visualize the results on PC via the Internet cloud techniques.

16:50-17:10 MonD09-4 1287 Maintenance Decision-making Model Based on Partially Observable Markov for Railway Traction Substation Equipment Pengfei Guo China Academy of Railway Sciences

Corporation LimitedZhihua Wang China Academy of Railway Sciences

Corporation LimitedJunyao Zhang Beijing Jingwei Information Tech. CoTraditional planned maintenance mode for railway traction substation equipment may lead to excessive maintenance or inadequate maintenance. To solve this problem, this paper combined the actual railway inspection operations and presented a novel condition-based maintenance decision-making model under partial observation. In consideration of the random failure and deterioration failure, partially observable Markov process (POMDP) was used to describe the state transition process of the device. Furthermore, with considering the uncertainty of equipment repair, the instantaneous availability of equipment is solved. And then, the quantitative expression of the failure risk and maintenance risk were provided, in which the inspection interval and maintenance time was the parameters. Finally, the maintenance decision-making model based on POMDP was built up to minimize the sum of equipment risk and system operation risk. Genetic algorithm was used to solve this model. The example analysis shows that the model is feasible and effective, and has a certain practical application value.

17:10-17:30 MonD09-5 1304 Bearing Remaining Useful Life Prediction by combining CNN with PSO−LSSVM Yuxia Gao Shandong Univ. of Science and Tech.Xianghua Wang Shandong Univ. of Science and Tech.Liping Yan Beijing Inst. of Tech.The remaining useful life (RUL) for bearings is of crucial importance to ensure system availability and re- duce maintenance costs. In this article, a novel approach combining Convolution Neural Nets (CNN) with Particle Swarm Optimization LeastSquares Support Vector Machine (PSOLSSVM) is adopted to predict the RUL of bearings. To be specific, firstly, the Relative Root Mean Square (RRMSnorm) not affected by individual differences is calculated as train- ing label to depress noise in raw vibration signals. Then, the CNN is trained by the raw data and its training label, which makes it possible to extract a new degradation feature. From the new degradation features, the prediction model based on the PSOLSSVM is constructed to predict the RUL of the bearings. Note that Particle Swarm Optimization (PSO) is introduced to automatically optimize the important parameters of LeastSquares Support Vector Machine (LSSVM), which is a contribution of the proposed methodology. Finally, the performance of the proposed method is verified by actual vibration data from the experiment platform.

17:30-17:50 MonD09-6 1584 A Fault Diagnosis Method for Planetary Gearboxes Based on Bi-LSTM and Feature Screening by Two-sample Z Test Ke Zhang Chongqing Univ.Shenying Cao Chongqing Univ.Jiuwen Yang Taiyuan Satellite Launch CenterGan Zhou The 6th Research Institute of China

Electronics CorporationZhifeng Yin The 6th Research Institute of China

Electronics CorporationLu Wang Chongqing Univ.For the fault diagnosis of planetary gearboxes, a method combined Bi-LSTM and two-sample z test is proposed. First, the two-sample z test method is used to calculate z-values between different faults, of each characteristic index under the same working condition. Then, the minimum z-values and the mean z-values of each characteristic index, are selected to form two characteristic screening matrices. Next, the first three maximum z-values of each row in the two matrices are selected respectively. And the characteristic indexes corresponding to the selected z-values, are selected as more discriminative characteristic indexes for various faults, to form a new characteristic set. The Bi-LSTM model is used for richer characteristic information, to perform classification of fault characteristic sequences. This method can decrease the degree of characteristic redundancy. Therefore, the computer memory consumption is greatly reduced, and the efficiency of model training gets higher. In addition, the experiments show that, the influence of local fault characteristic sequences aliasing has been effectively lessened. And the robustness and accuracy of fault diagnosis results have been improved.

MonD10 Room10 Signal Processing and Information Fusion (V) 15:50-17:50

Technical Programmes CCDC 2021 Chair: Yuqing Shi Northwest Minzu Univ.CO-Chair: Yichun Niu China Univ. of Petroleum (East China)

15:50-16:10 MonD10-1 1297 Robust Low-Rank and Sparse Tensor Decomposition for Low-Rank Tensor Completion Yuqing Shi Northwest Minzu Univ.Shiqiang Du Northwest Minzu Univ.Weilan Wang Northwest Minzu Univ.Low-rank tensor completion (LRTC) is a hot research direction in computer vision and machine learning because it can effectively recover the missing entries of tensor. However, most of the existing LRTC methods not only need to repeatedly calculate the time-consuming SVD decomposition, but also only consider a noise distribution in the model. To overcome the above shortcomings, based on the tensor-tensor product (t-product), we propose a new LRTC method-the robust low-rank and sparse tensor decomposition model (RLRST) for tensor completion. Firstly, in order to estimate the unknown entries in tensor data more accurately, two kinds of noise: sparse noise and Gaussian noise are considered simultaneously in RLRST. Secondly, the low-rank recovery tensor is equivalently decomposed into two smaller tensor t-products, which effectively saves the running time of the algorithm. Then, based on the alternate direction method of multipliers (ADMM), an efficient iterative updated algorithm is presented for our RLRST optimization. Finally, numerical experiments on image inpainting tasks demonstrate the effectiveness of our method over other related state-of-the-art tensor completion methods.

16:10-16:30 MonD10-2 1347 Particle Filtering for Nonlinear Systems with Round-Robin Protocol and Uniform Quantization Miaomiao Shi China Univ. of Petroleum (East China)Yichun Niu China Univ. of Petroleum (East China)Li Sheng China Univ. of Petroleum (East China)Ming Gao China Univ. of Petroleum (East China)In this paper, the particle filter problem is investigated for a class of nonlinear systems with communication constraints. The communication between sensor and remote filter is subject to uniform quantization effects. Moreover, in order to solve the problem of limited network capacity, the Round-Robin protocol is introduced in this paper, which can effectively avoid communication conflicts and reduce packet loss. Through mathematical hypothesis and stochastic analysis, an explicit expression of likelihood function combined with the probability information of networked phe- nomenon is established under the recursive Bayesian estimation framework. A novel particle filter algorithm is proposed for nonlinear state estimation in the simultaneous presence of uniform quantization and Round-Robin protocol. Finally, a simulation example is given to illustrate the effectiveness of the proposed particle filtering method.

16:30-16:50 MonD10-3 1371 GM-PHD Filter based Multi-target Tracking Method for Radar and Monocular Camera Dong Zhou Chongqing Univ. of Posts and

TelecommunicationsMing Cen Chongqing Univ. of Posts and

TelecommunicationsYi Zhang Chongqing Univ. of Posts and

TelecommunicationsYinguo Li Chongqing Univ. of Posts and

TelecommunicationsTarget tracking is one of the most important technologies in intelligent vehicle environment perception. Because of complicated environment and the unknown interference, there are many problems in practical application, such as, the single sensor tracking method cannot effectively meet the application requirements, so the millimeter wave radar and camera fusion method is an effective method. But considering that large targets may correspond to multiple radar echoes, existing data association methods can’t effectively address this situation. An improvement Gaussian mixture probability hypothesis density (GM-PHD) multi-target tracking algorithm based on monocular camera and radar is proposed. The camera target is converted to polar coordinates and associated based on the angle. Associating the target of radar and camera in polar coordinate by angle range of camera target, and then divide the measurement set into several levels, and set the confidence for each level. Combining the radial range of the millimeter-wave radar with the azimuth of the camera to estimate the position of the target more accurately, then the fusion measurement data is introduced into the improved GM-PHD multi-target tracking algorithm to update the multi-target state. The real target data are used to verify the tracking algorithm. The experimental results show that the proposed tracking algorithm can effectively improve the tracking accuracy and robustness.

16:50-17:10 MonD10-4 1422 Calibration of Smartphone’s Integrated Magnetic and Inertial Measurement Units Hongyu Zhao Dalian Univ. of Tech.

Yanhui Wang Dalian Univ. of Tech.Ruichen Liu Dalian Univ. of Tech.Fang Lin Dalian Univ. of Tech.Fengshan Gao Dalian Univ. of Tech.Sen Qiu Dalian Univ. of Tech.Zhelong Wang Dalian Univ. of Tech.Smartphones are built with a wealth of sensors, which are characterized by small size, lightweight and low cost, etc. With the popularity of smartphones, developments and applications using smartphone’s built-in sensors have attracted increasingly research interest in recent years. However, low-cost sensors have inherent disadvantages, such as low measurement accuracy and poor measurement stability. Therefore, in practical applications, the errors of smartphone’s built-in sensors cannot be simply ignored. To estimate the sensor errors, this paper analyzes the error characteristics of low-cost accelerometer, gyroscope and magnetometer respectively, and calibrates each sensor by using improved six-position method, Allan variance method and ellipsoid fitting method. Experimental results of sensor error estimation demonstrate the effectiveness of the calibration methods on the built-in micro-electro-mechanical system (MEMS) sensors of smartphones.

17:10-17:30 MonD10-5 1529 Study on Shipboard Navigation Method Based on MEMS/GNSS Integration Yi Jiang Dalian Maritime Univ.Junsen Wang Dalian Maritime Univ.The excellent performance of the combined structure of Strapdown Inertial Navigation System (SINS) and Global Navigation Satellite System (GNSS) has been proven in maritime applications. However, as a classical inertial device, the SINS is not standard shipboard equipment specified by IMO, and its application is limited in the maritime field owing to the high cost. This paper studies the integration of Micro Electro Mechanical System (MEMS) and GNSS to prove the applicability of low-cost MEMS to ship navigation systems. Considering the navigation degradation problem caused by the drift of MEMS, a tightly coupled structure with an error feed-forward loop is proposed. In order to meet the requirement of precision and stability, a nonlinear error model is established and a kind of Square Root Unscented Kalman filter (SRUKF) is designed. Compared with EKF, the proposed algorithm not only improves the positioning accuracy greatly but also avoids the computational burden of matrices. The efficacy and superiority of the proposed SRUKF have been verified through simulations and comparison analysis. The results demonstrate that bridging the maritime short-term GNSS interruption can be implemented in this way.

17:30-17:50 MonD10-6 1261 A Novel Gas Meter Verification Method via Adaptive Template Matching and Pulse Activation Decision Qingqing Ye Hangzhou Dianzi Univ.

Zhejiang Province Key Lab ofEquipment Electronics

Chenjie Du Hangzhou Dianzi Univ.Zhejiang Province Key Lab of

Equipment ElectronicsHuipin Lin Hangzhou Dianzi Univ.

Zhejiang Province Key Lab ofEquipment Electronics

Mingyu Gao Hangzhou Dianzi Univ.Zhejiang Province Key Lab of

Equipment ElectronicsZhiwei He Hangzhou Dianzi Univ.

Zhejiang Province Key Lab ofEquipment Electronics

In the industrial field, many researchers utilize the machine vision algorithm to verify the gas meter. This paper put forwards an efficient gas meter verification approach, which is consists of the core gear location and gear overlap judgment. First, due to the uncertainty of size, position, and shape of a gear, an adaptive template matching method is proposed to solve the problems of the size variation and location uncertainty. Meantime, because the gear owns the centrosymmetric characteristic, a maximum connected domain strategy is utilized to confirm the initial rotation location of the gear. And a pulse activation decision strategy to measure the gear overlap. Then, we employ the error analyzer of the gas meter to assess the performance of our algorithm. For verification purposes, the proposed algorithm has conducted a series of experiments in the cooperating factories to demonstrate the effectiveness and accuracy of the entire scheme.

MonD11 Room11 Intelligent Control, Computation and Optimization (IX) 15:50-17:50 Chair: Lijie Zhang Ningxia Inst. of Science and Tech.CO-Chair: Haoyu Cheng Northwestern Polytechnical Univ.

15:50-16:10 MonD11-1 835 Evacuation navigation strategy in complex building fires Lijie Zhang Ningxia Inst. of Science and Tech.Jianchang Liu Northeastern Univ.Shubin Tan Northeastern Univ.Aiming at the problem of resource optimization of fire evacuation route in

Technical Programmes CCDC 2021 complex buildings, based on the idea of minimum total evacuation time, network flow path planning is combined with PSO algorithman to construct the evacuation model. The model realize the goal of route saturation evacuation and parallel evacuation.The simulation results show that routes saturation evacuation improves utilization of available resources, and reflects the importance of leaders in the critical path. The model gives an evacuation plan with flexible evacuation mode, providing a method to guide evacuation based on public smart building management.

16:10-16:30 MonD11-2 1281 Intelligent H∞ Control for UAVs via Fuzzy Deep Reinforcement Learning Haoyu Cheng Northwestern Polytechnical Univ.Meng Wang Shanghai Electro-Mechanical Engineering Inst.Yifeng Ma Northwestern Polytechnical Univ.Jiayue Jiao Northwestern Polytechnical Univ.Ruijia Song Northwestern Polytechnical Univ.The problem of intelligent H∞ control for unmanned aerial vehicles (UAVs) is investigated in this paper. The linear model of UAV can be obtained by the aid of Jacobian linearization. Considering the time delay and packet losses in the network, the robust H∞ controller is proposed to overcome the undesirable response caused by time delay and packet losses. The deep reinforcement learning is introduced to improve the performance. Moreover, to improve the learning efficiency, we utilize a fuzzy reward system for the control process. The non-fragile control theory and Lyapunov functional method are combined to ensure the stability of closed-loop system. Simulation results in the end demonstrate the effectiveness and superiority of proposed method.

16:30-16:50 MonD11-3 1356 Shift Modeling Based on Long Short-Term Memory for Manual Transmission System and Shift Process Improvement by Using Bionic Optimization Mingming Lin Zhejiang Univ.Xin He Zhejiang Univ.Li Xu Zhejiang Univ.Shift modeling has increasingly attracted wide attention since it plays a vital role in the intelligent control system for manual transmission (MT) vehicles. In this paper, a modeling scheme based on distal control and real-world vehicle test data (VTD) is proposed to make it possible to imitate the drivers’ shift operation. The shift model adapts to be the inverse model of running vehicle, with vehicle speed and engine speed acting as inputs while operation signals as outputs. To tackle the problem of mapping signals from sequence to sequence, the long short-term memory (LSTM) algorithm is adopted, imitating human operations on gas or clutch pedals during shift process. Modeling results reveal that LSTM algorithm obtains satisfying fitting performance. Finally, in terms of fuel economy and driveability, particle swarm optimization (PSO) algorithm is utilized to improve the shifting process. Simulations are conducted to verify the effectiveness of the proposed scheme.

16:50-17:10 MonD11-4 1288 Path planning model for UAV collaborative search task Based on NGA Hong Huang National Univ. of Defense Tech.

Hunan Key Laboratory of Multi-energy SystemIntelligent Interconnection Tech.

Shengjun Huang National Univ. of Defense Tech.Hunan Key Laboratory of Multi-energy System

Intelligent Interconnection Tech.Weijian Qin National Univ. of Defense Tech.

Hunan Key Laboratory of Multi-energy SystemIntelligent Interconnection Tech.

Huihui He National Univ. of Defense Tech.Hunan Key Laboratory of Multi-energy System

Intelligent Interconnection Tech.Tao Zhang National Univ. of Defense Tech.

Hunan Key Laboratory of Multi-energy SystemIntelligent Interconnection Tech.

To solve the problem of large search demand and insufficient search ability of UAVs, this article has proposed a task planning method of UAV group based on nested learning algorithm. According to the characteristics of the target path, the method combines task allocation strategy and path planning algorithm to make decision. This method can give reasonable suggestions on the number of UAVs, and effectively improve the efficiency of UAV group search task. The algorithm uses the elite individual selection strategy based on tournament selection method to improve the optimization efficiency. And the algorithm uses neighborhood method to avoid local optimum. Finally, the algorithm is verified by the data of a search task. The experimental results show that the planning method used in this paper is suitable for the UAVs’ cooperative search problem. Compared with other planning methods, it has faster solution speed and is conducive to emergency decision-making. This method also has reference value for the cooperative planning of search tasks.

17:10-17:30 MonD11-5

1305 Position Sensorless Control Method of Two-three Conduction Brushless DC Motor Based on Line Back EMF Zicheng Li Wuhan Inst. of Tech.Hongrui Li Wuhan Inst. of Tech.Hou-Neng Wang Wuhan Inst. of Tech.Tao Xiong Wuhan Inst. of Tech.According to the mathematical model and the conduction mode of the brushless DC motor (BLDCM), a method to estimate the rotor position is presented, which is based on the conduction state of the corresponding power tube obtained by the rate of change of the non-commutative phase current combined with the zero-crossing of line back EMF. It is also applied to the sensorless control of the two-three conduction BLDCM. The brushless DC motor can run in no-load or load, low speed or high speed with this method, and the system uses the current inner loop and the speed outer loop to form a double closed-loop control, so that the motor runs more smoothly. The rotor position information of BLDCM under different conduction states is located. The simulation results are consistent with the theoretical analysis, which verifies the correctness and feasibility of the method.

17:30-17:50 MonD11-6 1654 Research and analysis of heating efficiency and pollution emission based on heat dissipation of building walls Kuo Meng Shenyang Jianzhu Univ.Zhengxing He Shenyang Jianzhu Univ.Haiyi Sun Shenyang Jianzhu Univ.In order to study the relationship between the indoor radiator water supply flow and indoor temperature, this paper considered the building structure, building area, indoor and outdoor temperature etc. of the Shenyang Jianzhu University student dormitory building. Calculate the actual heat consumption on the wall, and analyze the heating performance of the radiator through the empirical calculation. At the same time, based on the simplified physical model of radiator, the carbon dioxide emissions caused by heating are estimated by calculating and comparing the water consumption of fixed flow and variable flow in one day. On this basis, the fuzzy adaptive PID control model is established to control the water supply flow in real time, which provides a reference for energy saving, emission reduction and improvement of heating efficiency.

MonDIS Room12 Interactive Session 15:50-17:50

MonDIS-01 72 A New Kernel Trick Embedded Discriminant Model for Fault Detection and Diagnosis Chuyue Lou Wuhan Univ. of Tech.M. Amine Atoui Wuhan Univ. of Tech.Xiangshun Li Wuhan Univ. of Tech.In this paper, a new kernel trick embedded discriminant model is proposed for industrial process fault detection and diagnosis. The proposed method uses kernel trick to improve the discriminant model for fault detection and diagnosis of processes with nonlinear characteristics. The performance of the proposed method is evaluated at a numerical case and Intelligent Process Control-Test Facility. The results show that the proposed method improves the accuracy of fault diagnosis.

MonDIS-02 68 Time-matching Recursive Extended Target Probability Hypothesis Density Filter for High Resolution Phased Array Radar Ya Zhang The 28th Research institute of China

electronics tech. groupcorporation

Ming Liu The 28th Research institute of China electronics tech. group

corporationKai Zhao The 28th Research institute of China

electronics tech. groupcorporation

Yiyue Gao Hohai Univ.Tao Zhang 91001 Unit of People's Liberation

Army of ChinaExtended target probability hypothesis density (ET-PHD) filter is a random finite set-based multi-target tracking (MTT) method. It implements the multi-target Bayesian filter approximately by propagating the first-order moment of target posterior, and achieves good real-time performance, attracting wide interest in the field of radar applications. This paper is devoted to proposing a time-matching recursive filtering method to improve the MTT ability of ET-PHD filter in high-resolution phased array radar (PAR) scenarios. Based on a pre-partitioning strategy and time matching framework, the proposed method calculates PHD for each sector independently, which can meet the requirements of PAR for flexibility. Furthermore, it also uses the recursive filtering method of multi-sensor MTT to update single-sector PHD by sequentially applying multi-scan measurements of the sector, and improves the accuracy of target state estimation. The simulation is implemented by using Gamma Gaussian inverse Wishart model, and validates the effectiveness of the proposed method.

Technical Programmes CCDC 2021 MonDIS-03 69 Feature Aided Extended Target Tracking For High Resolution Radar Ming Liu The 28th Research inst. of China

electronics technology groupcorporation

Kai Zhao The 28th Research inst. of Chinaelectronics technology group

corporationYa Zhang The 28th Research inst. of China

electronics technology groupcorporation

Yiyue Gao Hohai Univ.Tao Zhang 91001 Unit of People’s Liberation

Army of ChinaIn high-resolution radar applications, the scatter distribution of target usually evolves non-linearly with the target movement, and thus it is difficult for a Bayesian filter to achieve an accurate estimation of the target extension state on its own. To solve this problem, we propose to use measurement features to help to propagate the posterior of the target extension in this paper, resulting in a feature-aided extended target probability hypothesis density (FA-ET-PHD) filter, where the features are applied to calculate the partition weights. Since the feature-based weights do not abide by the Bayesian inferring framework, the FA-ET-PHD filter can effectively avoid the deterioration of the performance in multi-target tracking caused by the nonlinear change of the distribution of scatters. Simulation results show that the proposed method can improve the accuracy of the multi-target state estimation as well as the robustness.

MonDIS-04 237 On cumulative belief entropy Huizi Cui Northwest A&F Univ.Bingyi Kang Northwest A&F Univ.Cumulative residual entropy and cumulative entropy are new methods based on the cumulative distribution of random variables and could be used to correctly measure the uncertainty of information in probability theory. However, how to determine the uncertain degree is also an open issue in Dempster-Shafer evidence theory (D-S evidence theory), even though there are different kinds of methods now, but they have not considered about the cumulative effect of the belief probability assignment (BPA), so we refer to the cumulative idea and consider about the relationship between the probability theory and evidence theory so that the structure could be transformed to belief intervals of the single subsets, then comprehensively think about the belief function Bel and the plausibility function Pl which as the limitations. As a result, a new method to ascertain the uncertain degree of the information is presented in the background of D-S evidence theory, some numerical analysis and comparative analysis are performed in order to demonstrate the new cumulative belief entropy could provide a optimistic way of measuring the uncertain degree and the results received are in line with expectations well.

MonDIS-05 262 Determine the Weight between Attributes Based on Shapley Value Lingge Zhou Northwest A&F Univ.Bingyi Kang Northwest A&F Univ.Jianfeng Zhang Northwest A&F Univ.In the classifier design based on Dempster-Shafer evidence theory (DST), in order to improve the accuracy of classification, a new method to determine evidence fusion among attributes based on the generation of basic probability assignment (BPA) is proposed. The degree of contribution of evidence between different attributes to the fusion results has different weights (reliability), which is calculated based on the Shapley value is proposed in this work. The training data are used to find the best weight for each attribute of the data. Meanwhile, the inter-attribute evidence is discounting according to the best weight and then results are combined using Dempster’s combination rule (DCR). Experiments provide the results of classification of Iris data set show that the overall recognition rate is 98.33%. Compared with the previous work, encouraging results are obtained in terms of classification accuracy, which without a large amount of training data and indicated the effectiveness of the proposed method.

MonDIS-06 390 Application of Enhanced Feature Fusion Applied to YOLOv5 for Ship Detection Shuaiyu Jin Tianjin Univ. of Tech.Lei Sun Tianjin Univ. of Tech.Ship detection is very important in improving navigation efficiency. In recent years, YOLO series object detection algorithm has achieved remarkable achievements. As the representative work of the YOLO series, version 5 (YOLOv5) is widely used in target recognition tasks for its high recognition accuracy and lightweight model. However, the sea scene background is complex and variable, which is greatly affected by extreme weather such as light, rain and fog. For this reason, an improved YOLOv5s algorithm with attention mechanism is proposed to enhance the feature fusion module. Firstly, the feature map extracted from different layers was aligned on the number of channels. Then, different weight

coefficients were added to the corresponding feature map. Finally, the experimental results demonstrate that the improved algorithm obtain 3.1% higher mAP_0.5 than YOLOv5s on MS-COCO datasets. In the meanwhile, the performance on BOAT data is also competitive in terms of quality.

MonDIS-07 416 An Algorithm for Waist Circumference Measurement Based on Kalman Filter Shixiong Zhai Southeast Univ.Chunyun Li Southeast Univ.Zuding Tang Southeast Univ.Waist-circumference measurement is an important function of wearable intelligent devices, but the performance of traditional measurement of waist circumference in the case of equal space sampling is not good, and the accuracy is generally not high. This paper mainly introduces a waist circumference measurement algorithm based on Kalman filter for wearable devices. The data of equal-displacement sampling is converted into motion data at any time by data processing, and then waist circumference data is calculated and predicted by Kalman filter. From the perspective of effect, compared with the traditional method, the waist circumference measurement algorithm proposed in this paper effectively improves the measurement accuracy and resolution of the waist measurement device, generally increasing the measurement accuracy by more than 30%.

MonDIS-08 687 Acoustic Signal Target Recognition Using Improved Clustering Autoencoder Jiaxiang Meng Harbin Engineering Univ.Xingmei Wang Harbin Engineering Univ.Anhua Liu Harbin Engineering Univ.Yuezhu Xu Harbin Engineering Univ.According to the small-sample data discretization problem, this paper proposes an acoustic signal target recognition model using improved clustering autoencoder (ICAE) to complete acoustic signal target recognition. Specifically, the clustering loss function of the proposed ICAE is developed to encode and cluster the identity authentication (I-vector), which can solve the large gap between a small amount of target-related data and the poor recognition effect. Moreover, the linear discrimination analysis (LDA) is adopted to project the dataset on the feature subspace that can differentiate the different targets with dimensionality reduction. The experimental results show that the recognition model using the proposed ICAE can achieve better recognition performance and strong adaptability. Compared with other methods, the proposed ICAE in this paper has an obvious clustering effect on a small amount of data.

MonDIS-09 797 Research on Object Measurement Based on 3D Stereo Vision Xinghua Xia Shenyang Jianzhu Univ.Shilong Dai Shenyang Jianzhu Univ.Hongfeng Qi Shenyang Jianzhu Univ.Zilong Xu Shenyang Jianzhu Univ.Shuang Wang Shenyang Jianzhu Univ.Mingxu Zhang Shenyang Jianzhu Univ.In traditional measurement methods, the measurement tools used by people have strong specificity, poor versatility, and time-consuming. They are often only measured for two-dimensional plane geometric quantities. It is impossible to accurately measure some complex three-dimensional surfaces. Therefore, the stereo measurement technology based on 3D reconstruction has increasingly become a hot issue of research. This paper improves the fusion method of the binocular stereo vision measurement system and the TOF measurement system. In order to make the results accurate, before starting the measurement, the binocular and TOF are calibrated, and then the data obtained by the TOF camera is filtered and denoised. On the basis of ensuring accuracy, fast dense reconstruction can be completed. This article will use binocular and TOF camera to detect foam board, bucket and fire extinguisher in order to describe the above process in detail.

MonDIS-10 1238 Anti-Disturbance Multi-Objective H2/H∞　Fusion Scheme for End-EffectorPosition Estimation of Space Manipulator Weilong Ding Beihang Univ.Wenshuo Li Beihang Univ.Jianzhong Qiao Beihang Univ.This paper is concerned with the end-effector position estimation problem of space manipulators in the presence of disturbances in joint dynamics and non-Gaussian noises with unknown statistic characteristics. A multi-objective 2 H2/H∞ fusion scheme based on disturbance 　observers is put forward to achieve simultaneous processing of vibration disturbance and non-Gaussian measurement noises. Firstly, the filtering model is established based on kinematics and multi-sensor measurement, which takes vibration disturbance in joint dynamics and non-Gaussian noise into account. Then, two sub-filters based on disturbance observers are designed to estimate the disturbance and deal with Gaussian noise, which takes full advantage of measurement data of

Technical Programmes CCDC 2021 redundant sensors. Subsequently, a main filter is designed to fuse estimates of two sub-filters in a weighted form, where the 2 H2/H∞　 performance indicators are set to constrain the fusion error caused by norm-bounded non-Gaussian noise. The framework composed of the above filters can improve the accuracy and reliability of end-effector position estimation in the space environment. Finally, numerical simulation is conducted to verify the effectiveness of the proposed methods.

MonDIS-11 1507 Research on Image Clustering Algorithm Based on Multi-features Extraction Peng Huang Jianghan Univ.Xueliang Pan Jianghan Univ.Jun Tao Jianghan Univ.Image clustering is one of the classical problems in the field of machine learning and image processing. The extraction of image features is the most important aspect of image clustering. In view of the poor performance of traditional image feature extraction, the characteristics of various image feature extraction algorithms including SIFT, ORB, and color histogram are discussed. A proposed method is of preprocessing the image first, then performing multi-features extraction and fusion, finally proceeding clustering. At the same time, multiple groups of comparative experiments are carried out. It can be seen from the experimental results that both clustering accuracy and clustering speed are taken into account by the image clustering method. Among them, the clustering accuracy can reach 99%, which shows that this method has more advantages in image clustering tasks.

MonDIS-12 1583 Study on Unsupervised Community Detection Algorithm Based on GCN Model Gui Wu Jianghan Univ.At present feature information on non-Euclidean distance data can be effectively extracted by the graph convolutional neural network. This paper provides an unsupervised community detection algorithm based on graph convolutional network model for application. The algorithm first selects some nodes in the graph to add artificial flags to simulate the input signal, which can satisfy the requirements of the propagation rule of the graph convolutional network. Then the algorithm would pass the flags to the neighboring nodes through the modified graph convolutional network propagation rule. While comparing the different flags on the same node and getting the attribution result of the nodes, the community partitioning results can be obtained by optimizing the result. Finally, the real-world data is used to test this algorithm and to evaluate by comparing with some classic community detection algorithms. The experimental results show that the algorithm provided by the paper can achieve a better result completely and correctly. in different types of graph data sets.

MonDIS-13 1592 An Airborne LiDAR Data Registration Method Based on Combining Multiple Information Linyi Pan Dalian Univ. of Tech.Dan Song Northeast National Grid Co., LtdWeiwei Yin State Grid Jilin Maintenance CompanyRei Li State Grid Jilin Maintenance CompanyKe Han State Grid Jilin Maintenance CompanyHongyu Wang Dalian Univ. of Tech.Aiming at the problems of uneven resolution, sparse irregularity, low overlap rate and a large number of redundant ground points in airborne LiDAR point cloud data, this paper proposes an intensity-constrained registration algorithm with an initial estimation algorithm based on ground segmentation combining motion information. First, the rotation transformation relationship of adjacent frames can be calculated by the normal vector of ground point clouds segmented by an improved RANSAC (Random Sample Consensus) algorithm. Combining the rotation parameters with the translation information obtained by UAV platform provides good initial value estimation for registration, on the basis of which an intensity-constrained Generalized-ICP registration algorithm is proposed. This method makes full use of the intensity information of LiDAR to constrain the corresponding point set participating in the iteration to achieve the precise registration. The experimental results show that the proposed algorithm is able to obtain the accurate transformation relationship between laser point cloud data and has higher registration accuracy and efficiency.

MonDIS-14 1309 An Improved ORB Feature Extraction and Matching Algorithm Guangyun Wu Jiangnan Univ.Zhiping Zhou Jiangnan Univ.

Engineering Research Center ofInternet of Things Engineering Tech.

ApplicationThe fixed threshold selection of traditional ORB algorithm results in many false extractions and mismatches, which cannot solve the problem of sensitivity to changes in light. Aiming at this problem, an improved ORB

feature point extraction and matching method based on quadtree was proposed. Firstly, set the local adaptive threshold, and propose the adaptive threshold selection criteria to achieve the accurate extraction of ORB feature points; then on the basis of the improved ORB feature points, the improved quadtree is used to screen the feature points; Finally, the LMedS method is used to complete the matching according to the selected feature points. Experimental results show that the improved method has strong adaptability to brightness changes, and the calculation speed and extraction accuracy have been improved. The total matching time is reduced, the number of mismatched points is less, the correct matching rate is higher, and it has good accuracy and real-time performance.

MonDIS-15 1133 Noise Detection from Single Image Based on Harris Operator Wenying Lu Nanjing Univ. of Science and Tech.Yaobin Mao Nanjing Univ. of Science and Tech.Yi Zhuo Zhejiang Huayun Information

Technology Co. LTDNoise detection has been a long-established research topic in modern digital image processing technology, since noise can reveal the quality degradation information of images. The fundamental premise of most existing algorithms is that the statistical distribution of noise is provided, while in practice it is unlikely to get the type of noise in advance. To solve this problem, an efficient noise detection method derived from the general Harris corner operator is proposed. The real corner and noise points are simultaneously extracted in the first stage. Then candidates are selected based on an adaptive threshold for different illumination regions and a Support Vector Machine is adopted to construct a pixel-level classifier. The final binary decision for a test image is made based on the ratio of the obtained noise points to all candidates. With the purpose of expanding the diversity of content variation, a challenging image dataset is established. Experiments on three different datasets show that the proposed method is robust to sophisticated texture without auxiliary information about noise types and presents great generalization performance.

MonDIS-16 1605 Realization of 3D Reconstruction Algorithm Based on 2D Video Xin Wang Inner Mongolia Univ.Hui Zhang Inner Mongolia Univ.At present, how to realize 3D reconstruction from 2D video is a research hotspot. However but the reconstruction accuracy for areas with less or no texture is low. Thus, for the scene reconstruction with less texture, this paper proposes a 3D reconstruction method based on the depth map. The first is to extract the relevant feature points from the two-dimensional image frame. During this process, Harris-Sift algorithm is used to extract features of the image frame, and Kanade-Lucas-Tomasi (KLT) tracking algorithm is used to tracke and matche feature points. In this way, all adjacent image frames are reconstructed by 3D point cloud, and the recondtruction result is optimized through the bundle adjustment algorithm. Furthermore, the depth map is calculated, and the depth map of each frame in the video is obtained by using the planar scanning algorithm. Finally, the three-dimensional reconstruction of the object is realized by fusing the depth map, and point cloud conversion. As shown by the experimental results, the real scene is accurately restored by the relevant algorithm provided in this paper, and the problem of reconstruction of areas with less texture is solved.

MonDIS-17 532 Research on AUV Cooperative Positioning Algorithm Based on Innovation Correction Method Based Central Differential Kalman Filter Jianxiong Wei Harbin Inst. of Tech.Shanlin Chen Kunming department of Xi'an Research

Inst. of Precision MachineryYanyan Wang Harbin Inst. of Tech.Qingxin Wang Harbin Inst. of Tech.Fei Yu Harbin Inst. of Tech.Wenjun Huang Science and Tech. on

Near-Surface Detection LaboratoryReliable underwater positioning technology is a prerequisite and key technology for an autonomous underwater vehicle (AUV) to successfully complete its mission. Aiming at the strong nonlinear characteristics of the underwater vehicle cooperative positioning system and the unknown characteristics of underwater noise statistics, this paper proposes the innovation correction method based central differential Kalman filter (ICM-CDKF) algorithm, and establishes a Leader-Fellow cooperative positioning algorithm. The mathematical model realizes the high-precision positioning of the underwater AUV when the noise characteristics are unknown. Through simulation and ship navigation experiments on the lake, the effectiveness of the positioning method proposed in this paper is verified.

MonDIS-18 1073 An Improved CPS-PWM Method for Modular Multilevel Converter Yu Huang Wuhan Univ. of Science and Tech.

Technical Programmes CCDC 2021 Zhenxing Liu Wuhan Univ. of Science and Tech.Renjun Dian Wuhan Univ. of Science and Tech.

Dayu Electric Co. LtdPei Huang Wuhan Univ. of Science and Tech.Peng Wang Wuhan Univ. of Science and Tech.In this paper, an improved carrier phase shifted – pulse width modulation (CPS-PWM) method for half-bridge based modified modular multilevel converter (MMC) is proposed to increase the number of the output voltage levels and so as to reduce harmonics. Compared to the traditional MMC topology, new redundancy sub-modules are added in each bridge arm and the capacitive voltage of the new sub-modules is recursive half of the common. Based on the modified topology of MMC, an improved CPS-PWM control method is proposed by overlaying the pulse trigger signal of each sub-module in each phase, and the base wave component is removed from the superimposed signal, finally, the modulation wave signal is obtained to generating the trigger signal of the new sub-modules. With the proposed method, the number of ac side phase voltage levels increase exponentially. To verify the proposed method, the simulation is performed and the total harmonic distortion of the output voltage current has been reduced obviously from the simulation results.

MonDIS-19 1198 A Method for Quantitation and Screening of Bradykinesia in Parkinson’s Disease using Motion Capture Kelei Ding Nankai Univ.Yuanyuan Cheng Tianjin Huanhu HospitalYang Yu Tianjin Huanhu HospitalJianda Han Nankai Univ.Jialing Wu Tianjin Huanhu HospitalNingbo Yu Nankai Univ.Parkinson’s disease (PD) is a common neurodegenerative disease that is rapidly growing as population aging. Bradykinesia is the most characteristic clinical feature of Parkinson’s disease, and the correct assessment of bradykinesia is of great significance to the diagnosis and screening of PD. Regardless of the clinical standard - the Movement Disorders Society-Sponsored Revision of the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS) that has been used worldwide, the bradykinesia-related items show low agreement between different evaluators because of their dependence on subjective experience and judgements. Recent advancements in affordable optical motion capture (Mocap) systems, which have higher precision than general wearable inertial sensors, enable potentially growing clinical application of Mocap systems and call for quantified assessment methods. In this study, movements of PD patients and normal subjects were recorded with ethical approval from the hospital. A set of movement features related to the bradykinesia characteristics defined in the MDS-UPDRS were defined., Supervised learning algorithms were used to train an SVM classifier for discriminating PD patients from normal subjects. The results showed that our system and method achieved the accuracy of 83.33% for screening of PD patients from healthy subjects, and accuracy of 75% in the detailed classification of PD patients with different severity. This has validated the effeteness the proposed approach, and further, analysis of the confusion matrix indicated that more balanced clinical data are needed to enabler more elaborate classification models for future work.

MonDIS-20 1199 A Quantitative Plantar-Pressure Analysis Method for iNPH Tap Test and Surgery Assessment Yubo Sun Nankai Univ.Jingchao Wu Tianjin Huanhu HospitalYang Yu Tianjin Huanhu HospitalZhilin Shu Nankai Univ.Jianda Han Nankai Univ.Siquan Liang Tianjin Huanhu HospitalNingbo Yu Nankai Univ.Idiopathic normal pressure hydrocephalus (iNPH) is a common disease among elders, and is the most frequent cause of reversible dementia in aging. There is no golden-standard diagnostic or prognostic measure due to the limited understanding of the iNPH pathophysiology. A widely-taken diagnosis test is to observe symptoms improvements after removal of cerebrospinal fluid (CSF) by lumbar puncture, and is called CSF-TT. However, no consensus has been reached regarding CSF-TT analysis and post-surgery assessment measures. In this work, we proposed a quantitative gait analysis method based on plantar-pressure data. A pair of wireless force insoles were used to measure the plantar-pressure during a standard 10-meter walking test for the iNPH patient that went through CSF-TT and shunt implantation surgery. Five measurements were made at one day before CSF-TT, 8, 24 and 72 hours after CSF-TT, and one month follow-up after the neurosurgery, respectively. Kinematic and dynamic gait features were extracted, compared and analyzed. A dynamic gait feature, the normalized plantar-pressure variation index, well discriminated the iNPH impaired gait with the improved gait at 8 hours after CSF-TT, follow-up after surgery, and the healthy group. Therefore, the proposed plantar-pressure based gait analysis method and gait feature promise a solution for iNPH tap test and surgery responsiveness prediction.

MonDIS-21 4

Group Consensus Analysis of Multi-agent Systems with Cooperative-Competitive Interactions and Time Delays Jimin Yu Chongqing Univ. of Posts and TelecommunicationYousi Wang Chongqing Univ. of Posts and TelecommunicationDakang Teng Chongqing Univ. of Posts and TelecommunicationThis paper mainly studies the group consensus problem of multi-agent systems under the influence of different input and communication time delays. Based on the principle of competition and cooperation among agents, group consensus control protocols are designed for the first and second order multi-agent systems that agents are connected by a directed communication network. By using the knowledge of frequency domain control theory, the sufficient conditions for group consensus of multi-agent systems are obtained. It is found that the coupling weight between agents, the input time delay of each agent and the control parameters in the controller all affect the realization of group consensus of the multi-agent systems. The communication delays among agents do not affect the final convergence of the systems, but the communication delay will affect the dynamic performance of the systems. The research shows that the smaller the time delay, the faster the system can achieve asymptotic stability. Numerical simulation results verify the correctness of the conclusion.

MonDIS-22 24 An Efficient Adaptive Weights Update Scheme for a Gaussian Mixture Filter Li Cun The 41st Research Inst. of China Electronics Tech. Group

CorporationThis paper proposes an adaptive weight update scheme of the Gaussian components for the Gaussian mixture filter in the time update stage. This method contributes to obtaining a better approximation of the posterior probability density function, which is constrained by large uncertainty in the measurements or ambiguity in the model. The Gaussian mixture filter is improved through combination with the Cubature Kalman Filter (CKF). The Gaussian components are predicted and updated using a CKF with the results merged and weighted. A series of extensive trails were run to assess the estimation precision offered by various algorithms. The results based on the Unmanned Underwater Vehicle (UUV) lake trial data demonstrate the superiority of the proposed algorithm through better accuracy and stability compared with the conventional navigation algorithms, with are reasonable computational time to meet real-time navigation requirements.

MonDIS-23 38 PSO Based Variable Structure Control of Submarine at Near Surface Depth with Wave Action Kun Hu Navy Submarine AcademyXiaonan Pang Navy Submarine AcademyLei Zheng Navy Submarine AcademyJianhua Zhang Navy Submarine AcademyAs the submarine depth control near surface within wave action is characterized by nonlinear, uncertain interference and slow time variation, this paper proposes a kind of variable structure controller (VSC) by improved sliding reaching control law for keeping submarine depth is proposed. The controller parameter of VSC in a submarine depth control system is optimized by using particle swarm optimization (PSO) aiming at one type of submarine. Based on Simulink, the depth control of submarine is simulated without disturbance and near the surface with wave action. The results of simulation shows that the VSC controller is suitable for depth control precision, also has low rudder range, non-chattering characteristic. The VSC controller based on PSO has good control quality and strong robustness. The VSC controller provides a feasible approach to the design of submarine autopilot.

MonDIS-24 52 Model-Free Off-Policy Iterative Adaptive Dynamic Programming for Nitrate-Nitrogen Concentration Control Ruyue Yang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Beijing Inst. of Artificial Intelligence,Ding Wang Beijing University of Technology

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Beijing Inst. of Artificial Intelligence,Junfei Qiao Beijing University of Technology

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Beijing Inst. of Artificial Intelligence,To solve the optimal control problem of the nitrate-nitrogen concentration in the wastewater treatment plant (WWTP), a model-free off-policy iterative adaptive dynamic programming algorithm using online data is proposed. Under the actor-critic structure, the developed algorithm approximates the optimal Q-function by minimizing the temporal difference and improves the control law through the policy gradient method. Neural networks are utilized in the proposed scheme. Besides, the experience replay buffer is involved in the off-policy iteration of neural networks. Finally, simulation examples for a nonlinear system and WWTP are presented to verify the effectiveness of the proposed method.

Technical Programmes CCDC 2021 MonDIS-25 98 Used Mobile Phone Recognition Method Based on Differential Evolution – Deep Forest Zixuan Wang Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Jian Tang Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemChengyu Cui Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Honggui Han Beijing Univ. of Tech.Beijing Key Laboratory of Computational Intelligence

and Intelligent SystemWith the continuous development of the mobile phone industry, used mobile phone (UMP) recycling has become a hot topic. At present, few experts research UMP recognization (UMPR) methods based on UMP recycling equipment. Deep forest (DF) identification model of UMPs for intelligent recycling equipment has been proposed, which has many parameters to be manually adjusted. Aim at the above problems, this paper combines differential evolution (DE) algorithm and DF model to propose a method for identifying UMPs. First, the candidate multi-scale feature parameters and DF parameters are given out. Next, we use DE to find the optimal parameters with DF accuracy as the evaluation criterion. Finally, we feed the obtained optimal parameters into the DF model to construct the final UMPR model. Simulation results based on mobile phone pictures of the Ministry of Industry and Information Technology prove the effectiveness of the method.

MonDIS-26 121 The Research of Anime Character Portrait Generation Based on Optimized Generative Adversarial Networks Zhentong Yi Jianghan UniversityGui Wu Jianghan UniversityXueliang Pan Jianghan UniversityJun Tao Jianghan UniversityAnime Character Portrait Generation is an interesting but a challenging job. At present, most of the existing methods are solved by using generative adversarial networks. However, the styles of anime character portrait are quite different and Generative Adversarial Networks is also prone to cause problems including mode collapse, which makes it hard to generate good anime character portrait samples. In this paper, DRAGAN model will be optimized and the improved version of the basic structure of Conv-BN-Relu-Pooling will be applied reasonably. The optimized DRAGAN is trained by using alternating gradient updates procedure to achieve high-quality generation samples. At the same time, through the experimental comparison with other generative adversarial networks of deep learning, it is finally verified that the optimized DRAGAN performs better than the experimental comparison group in terms of image visual quality, FID and gradient penalty terms. The mode collapse problem has also been alleviated finally.

MonDIS-27 227 Pavement crack detection based on deep learning Rui Zhang Shenyang Jianzhu Univ.Yixuan Shi Shenyang Jianzhu Univ.Xiaozheng Yu Shenyang Jianzhu Univ.Effective and timely crack identification is essential to repair and limit road aging.So far, most crack detection follows manual testing rather than automatic detection based on image, which make the whole process is expensive and time-consuming.In this study, we proposed a deep learning network,that use YOLO v3 and adaptive spatial feature fusion (ASFF) strategies to enhance, label, and learn crack images.Realizing the precise classification and identification of pavement cracks.At the same time, the optimization method for identifying cracks is proposed.We chose 2000 images used in the training,which obtained from the data. 500 road images were used in the testing. The feasibility of the proposed detector is measured by precision and speed. The successful application of this study will help to identify abnormal roads which require emergency repairs, thereby improving the performance of monitoring systems for civil infrastructure.

MonDIS-28 235 Adaptive Switching Control Algorithm Design based on Particle Swarm optimization Lili Wang Qingdao Univ. of Science and Tech.Ling Xin Qingdao Univ. of Science and Tech.In view of the nonlinearity and time variability of industrial control systems, as well as the poor transient response in traditional adaptive control, this paper presents a neural network multi-model switching adaptive control method basing on particle swarm optimization. Firstly, the PSO algorithm was used to adjust the neural network weights to achieve the optimal value. Based on the BPNN and multiple models was designed with an adaptive control strategy. The optimal controller can be selected to control the system through the constructed rational switching rules. The good approximation ability of neural network can improve the

performance of adaptive control. The performance through PSO optimization are studied through simulation methods using Matlab, which verifies that the proposed method can significantly improve the overall performance of the system: fast convergence, high precision, good network generalization and approximation ability, and can precisely track the output of the control system.

MonDIS-29 255 IFCM clustering segmentation based on genetic algorithm Meiju Liu Shenyang Jianzhu Univ.Xiaozheng Yu Shenyang Jianzhu Univ.Yixuan Shi Shenyang Jianzhu Univ.In the early diagnosis of breast cancer(BC), computer aided design (CAD) is particularly important, and the accurate detection of breast mass in mammography plays an important role Objective: In order to distinguish the mass region from other background regions, an effective segmentation scheme was proposed in Mammographic Image Analysis Society (MIAS) segmentation Methods: First, the initial clustering center of intuitionistic fuzzy C-means (IFCM) is determined by genetic algorithm (GA), and then the image is segmented by IFCM algorithm to turn the random initial clustering center into a purpose-selected one, so as to ensure the optimal result of the final clustering center results: The average segmentation precision of MIAS.I images with noise level of 5%,7% and 9% were 90.15% and 86.85% and 87.31%.Conclusion: This method combines the advantages of the two algorithms to segment the location of mass more accurately and quickly.

MonDIS-30 265 3D Path Planning based on MMVO Siyue Liang Jiuquan Satellite Launch CentreRui Zhang Jiuquan Satellite Launch CentreYun Bai Jiuquan Satellite Launch CentreAiming at the problems of difficult parameter determination and poor quality of crawling path planning, this paper proposes a 3D path planning method which uses the complex field to model the environment and the modified MultiVerse Optimizer algorithm (MMVO) to optimize the gravitational factor of the virtual potential field. On the basis of ameliorating the potential field navigation function, the complex number is used to carry the information of elevation and obstacle area in the environment, so as to tackle the problem of large data scale and low efficiency of path planning in traditional environment modeling. In order to enhance the quality of path planning model, fitness function is designed and Multi-Verse Optimizer algorithm (MVO) is improved from the aspect of travel distance rate, which can solve the problems of slow convergence speed and easy to fall into local extremum in the process of path planning parameter selection of common optimization algorithms, and get rid of the limitations of artificial potential field itself. The simulation results show that the path planning method based on MMVO artificial potential field has better modeling effect and can reduce the complexity of the model, which verifies the effectiveness, progressiveness and real time performance of the method.

MonDIS-31 342 Track Control Design Of Towed Helicopter And Towed Body Position Correction Strategy Yu Li AVIC Shanxi Dongfang Aviation Instrument Co.Pei Zhou Northwestern Polytechnical Univ.The helicopter has obvious advantages over towed ship when performing mine clearance and risk removal tasks at sea, due to its mobility and flexibility. When the helicopter towing system performs search and sweep operations at sea, under the environment of ocean currents and sea breeze, the track of the towed body will deviate from the prescribed course, which will affect the efficiency of the helicopter towing system. Therefore, it is necessary to design the track control law for the helicopter towing system and the position correction strategy for the towed body to ensure its stably flight and make the towed body operate along the prescribed route. The research in this paper is of great significance for promoting the related research and engineering application of helicopter towing system.

MonDIS-32 636 Novel Global Harmony Search Algorithm for Nonlinear Two-Point Boundary Value Problem Longquan Yong Shaanxi Univ. of Tech.

Shaanxi Key Laboratory of Industrial AutomationNonlinear two-point boundary value problem (BVP) is studied. After discretization of BVP by finite difference method, the system of nonlinear equations are formed, and solved by a recently proposed algorithm named novel global harmony search (NGHS). NGHS algorithm can overcome disadvantage of Newton’s iterative methods. Numerical experiment shows that the NGHS algorithm has a higher rate of convergence than the other HS-variants.

MonDIS-33 641 Occlusion-aware On-road Autonomous Driving: A Path Planning Method in Combination with Honking Decision Making

Technical Programmes CCDC 2021 Bai Li Hunan Univ.Tankut Acarman Galatasaray Univ.Youmin Zhang Concordia Univ.Qi Kong JD.com American Technologies CorporationOn-road path planning evaluates and tests an autonomous vehicle’s perception-reaction particularly when possible hazards occur suddenly on its path. Occlusion-aware path planning is about generating a path that is robust and adaptable to the underlying risks originated from the occluded areas. Most of the existing methods are focused on the cases where typical traffics are occluded. However, suddenly emerged pedestrians on the road, although being educated about how to pass the road, may not be treated in the same way as on-road traffic, because pedestrian motion dynamics, failures to sense approaching vehicles along with their reaction logics are not easy to model. In terms of liability and active safety, a naive way to handle the complexity of the pedestrians is to consider the worst-case situation, which makes a planner overcautious. Following the predominant first-search-then-optimize framework, this study defines a potential-field-based cost function to measure the underlying appearance of a pedestrian from an occluded region. By requesting the ego vehicle to take the honking operation when it feels risky, we make the honking decisions in combination with the path planning process, thereby avoiding driving overcautiously.

MonDIS-34 704 Research on Path Optimization of Automated Warehouse Based on Heuristic Ant Colony Algorithm Jiankun L Shandong Univ. of Finance and EconomicsRui Wang Shandong Univ. of Finance and EconomicsSiyuan Wen Shandong Univ. of Finance and EconomicsAGV (Automatic Guided Vehicle), as an important part of the automated warehouse, plays an important role in the efficient and normal operation of the warehouse. The heuristic ant colony algorithm used in this paper can avoid the local optimal phenomenon that traditional ant colony algorithm easily appears, and shorten the optimal route. At the same time, the heuristic ant colony algorithm is extended from the common two-dimensional planar path optimization to the three-dimensional space of the warehouse, and the distance between two points in the ant colony algorithm is converted into horizontal and vertical polylines, so as to optimize the route for AGV storing goods among multiple rows of three-dimensional shelves. Through the verification of examples, the results show that the heuristic ant colony algorithm can plan the moving path of AGV in the three-dimensional space of multiple rows of shelves, and improve the efficiency and speed of access.

MonDIS-35 747 Improved dark channel prior single image defogging Tongying Guo Shenyang Jianzhu Univ.Na Li Shenyang Jianzhu Univ.Chao Zhang Shenyang Jianzhu Univ.The foggy sea image has low contrast, color distortion, blurred edges and other problems. Sea fog brings great difficulties to ocean observation. In response to these problems, this paper proposes an improved single sea fog defogging method based on domain decomposition and dark and bright channel priors. First, it is proposed to segment the sky area through gradient information and edge tracking, and combine the dark channel of the sky area to determine the atmospheric light value. Secondly, it is proposed to optimize the transmission map based on the bright channel of the sky area and the dark channel of the non-sky area, and use the guided filtering to optimize the edge. Finally, the fog-free image is obtained by combining the atmospheric scattering model. Evaluate the performance of our algorithm from a comprehensive test. Experiments on images under different sea fog scenes show that the algorithm can not only effectively overcome the sky color distortion and halo phenomenon, but also restore the details of non-sky areas.

MonDIS-36 809 Application of a combined forecasting model in the prediction of hull outer plate surface forming Chengwei Ge Jiangsu Univ. of Science and Tech.Liang Qi Jiangsu Univ. of Science and Tech.Chaochun Yu Jiangsu Univ. of Science and Tech.Yue Huang Jiangsu Univ. of Science and Tech.Jie Sun Jiangsu Univ. of Science and Tech.The curved surface forming process of the hull shell is a commonly used method for the construction of hull shells.However, due to the highly complex deformation process of the hull outer plate curved surface forming process.Moreover, there are many processing parameters that affect the deformation results of the steel plate, and there are complex nonlinear relationships between the processing parameters.In order to be able to quickly and accurately predict the deformation of the hull outer plate surface forming, this paper uses the Simpson integral formula to optimize the gray system combined support vector machine model.Use the Simpson integral formula to improve the background value of the gray model, and select the steel plate thickness, Hydrogen-oxygen flame heat source flow, heating speed, heat source radius, and heat source efficiency as the main influencing factors of the deformation result.Five factors that affect the deformation results are used as the input of the gray system and the support vector machine at the same time. The standard deviation method is used to determine the weight information of

the model to construct a combination prediction model of gray prediction and support vector machine.Comparing and analyzing the prediction results, it can be clearly seen that compared with a single model, the prediction accuracy of the combined model is higher than that of the single model, the prediction error is smaller, and it is more in line with actual needs.

MonDIS-37 812 Application and Research of Particle Swarm optimization in window anti-pinch test system Anyu Cheng Chongqing Univ. of Posts and TelecommunicationsHao Yan Chongqing Univ. of Posts and TelecommunicationsYibo Peng Chongqing Univ. of Posts and TelecommunicationsChenchen Xu Chongqing Univ. of Posts and TelecommunicationsThere are many redundancies in the manually-written power window anti-pinch algorithm test cases, and the anti-pinch algorithm cannot be fully covered, and the test purpose is not achieved.This paper analyzes the window anti-pinch control algorithm, uses the particle swarm optimization algorithm(PSO) to optimize the test path, introduces the weight array and pile insertion technology to speed up the iterative update of the algorithm, uses the branch function superposition method to count the path coverage, finds the optimal solution is the end of the algorithm, then outputs the test case set.Through the functional test of the window regulator Electronic Control Unit(ECU) product of a certain vehicle, the efficiency of using PSO to generate test cases is 16.7% higher than that of random algorithm(RA), which basically realizes the full coverage of anti-pinch algorithm and ensures the reliability of the anti-pinch algorithm.

MonDIS-38 816 Research on Control Strategy of Electric Vehicle Heat Pump Air Conditioning System in Low Temperature Environment Anyu Cheng Chongqing Univ. of Posts and TelecommunicationsChenchen Xu Chongqing Univ. of Posts and TelecommunicationsTianci Long Chongqing Univ. of Posts and TelecommunicationsMost electric vehicle air conditioners use Positive Temperature Coefficient (PTC)or heat pump to make heat in low temperature environment, but high PTC power consumption will affect the mileage of electric vehicle. Heat pump in low temperature environment, low heating efficiency, can not meet the need of heating time. This text under low temperature environment, heat pump and PTC mixed heating mode are adopted to solve the problem of high PTC power consumption and low heating efficiency of heat pump. The actual working condition verification shows that when the environment temperature is between -10~-5, the heating time of heat pump and PTC mixing mode is shortened by 2 minutes compared with that of single heat pump, and the heating effect meets the demand. Heat pump and PTC mixing mode save about 7.1% energy compared with single heating.

MonDIS-39 823 Many-objective Evolutionary Algorithm Based on Distance Dominance Relation QingHua Gu Xi’an Univ. of Architecture and Tech.QingSong Xu Xi’an Univ. of Architecture and Tech.There are two main aspects of research in multi-objective optimization algorithm, namely, convergence and diversity. While, it is difficult for original algorithms to maintain the diversity of solutions in the high-dimensional objective space. In order to enhance the diversity of algorithms in many-objective optimization problems, a new distance dominance relation is proposed in this paper. First, in order to ensure the convergence of the algorithm, the distance dominance relation calculates the distance from the candidate solution to the ideal point as the fitness value, and selects the candidate solution with good fitness value as the non-dominant solution. Then, in order to enhance the diversity of the algorithm, the distance dominance relation sets each candidate solution to have the same niche and ensures that only one optimal solution is retained in the same territory. Finally, the VaEA algorithm is improved based on the proposed distance dominance relation. On the DTLZ and IDTLZ test of 5-15 dimensional targets, the improved algorithm is compared with six commonly used algorithms. The experimental results show that the improved algorithm is highly competitive and can significantly enhance the diversity of the algorithm.

MonDIS-40 1104 Optimization of Test Sequence in Radio Block Center Based on Simulated Annealing Algorithm Weiqi Wang China Academy of Railway Sciences Corporation

LimitedRadio Block Center (RBC) is an indispensable part of the CTCS-3 system, and is mainly responsible for sending the information needed for train operation control. Before the RBC equipment is put into actual operation, a large number of tests are required to ensure that the equipment can meet the needs of the train control system. Generating a reasonable test sequence and being able to fully cover the system specifications are the key issues in RBC system testing. This paper proposes a test sequence optimization approach for RBC function testing. Based on the analysis of the function feature of RBC and the test

Technical Programmes CCDC 2021 sequence construction method, the optimization generation problem of the test sequence is transformed into solving the Traveling Salesman Problem (TSP) with the asymmetric distance. An improved simulated annealing (SA) algorithm is adopted to solve the proposed model. The results of the case study shows that the TSP model and SA could achieve optimization of test sequences successfully and reduce time of the test program.

MonDIS-41 1110 Algorithm Research Based on Network Iterative Control Lei Wang Wuhan Univ. of Tech.Huajun Zhang Wuhan Univ. of Tech.Lishou liu Wuhan Univ. of Tech.Since it is increasingly necessary to control machines in industry, the goal of controlled objects with repetitive operating characteristics is to ideally track the desired input to meet the needs of industrial production. For the current research situation in practice, a new research direction of iterative learning control has been developed in control science and technology. In this paper, the basic concepts of forgetting factors and iterative control algorithms and learning laws of related simple system types are first explained, and then for linear stationary systems, a proof of PD type iterative learning control tracking expected output convergence with forgetting factors is given. Among them, the PD-type forgetting factor learning law is used to expand the narrative and algorithm proof, and then use MATLAB to simulate the expected trajectory tracking of a single input single output linear stationary system to ensure the correctness of the conclusion. Finally, the system is summarized and analyzed for different iterations and whether or not the control algorithm is forgotten.

MonDIS-42 1145 Overview of the Application of Artificial Intelligence in Several Bao Liu Xueqing Wang Lei Gao The development of water conservancy is the cornerstone and fundamental to the development of all mankind. With the continuous expansion of water conservancy projects and the study of smart sensing technology, water conservancy data also presents the characteristics of big data which undoubtedly poses a greater challenge to the solution of water conservancy related issues. The development of artificial intelligence has also provided a powerful research approach for water quality analysis and prediction, water resources scheduling, water pollution prevention, hydrological forecasting and other fields. We mainly summarize the application of artificial intelligence in water resource management, water quality safety management and water resource utilization. Based on the inadequacy of the application of artificial intelligence technology in water conservancy, the deficiencies and problems of the application of artificial intelligence technology in water conservancy were discussed in depth. What’s more, the paper also proposes the development direction of smart water conservancy and solutions to related problems.

MonDIS-43 1189 Research on Carbon Monoxide Content Prediction Based on Improved Particle Swarm Optimization SVM Jianyun Ni Tianjin Key Laboratory for Control Theory

& Applications in Complicated SystemsMingyang Zhao Tianjin Univ. of Tech.Yong Wang Tianjin Univ. of Tech.In the petroleum processing industry, the carbon monoxide (CO) content of the flue gas emitted by the heating furnace is predicted according to the various gases that will be generated in the on-site environment of the heating furnace. Therefore, a Particle Swarm Optimization Support Vector Machine (PSO-SVM) model is proposed for the prediction of carbon monoxide content. When optimizing the parameters of SVM for particle swarm optimization, it is easy to fall into the problem of local optimization and premature convergence. An improved particle swarm optimization (IPSO) algorithm is proposed: in the optimization process, adding a passive aggregation term to improve the speed formula of the PSO algorithm can make the particles reach the global optimal state. Finally, the carbon monoxide content is predicted by the improved PSO optimization support vector machine. The experimental results show that the improved particle swarm algorithm is used to fully explore the potential of the SVM model, and compared with the experimental results of other prediction models, it is proved that the model has the advantages of high accuracy.

MonDIS-44 1200 Photovoltaic power prediction model based on parallel dendritic neural model Hao Li Nanjing Univ. of Posts and TelecommunicationsTengfei Zhang Nanjing Univ. of Posts and TelecommunicationsYang Yu Nanjing Univ. of Posts and TelecommunicationsChen Peng Shanghai Univ.Dendritic neural model (DNM) has characteristics of a simple structure and a fast convergence speed. However, when a single DNM is applied to a scene with a large data set, the number of branch layers often needs

to be increased, which makes the structure of DNM larger and leads to a poor prediction accuracy. From this perspective, this paper proposes a parallel-structure based DNM with multiple sub-networks, which uses a fuzzy C-means clustering (FCM) algorithm to divide the data set. The FCM algorithm can effectively reduce the amount of data required for the training of each sub-network. Consequently, actual photovoltaic data simulation results verify that the accuracy of the photovoltaic power prediction model can be further improved, and the proposed model is effective and efficiency.

MonDIS-45 1235 Object Detection in unmanned vehicle with End-to End Edge-Enhanced GAN and Object Detector Network Shuangjian Zhang Shandong Univ.Yong Song Shandong Univ.This paper proposes an efficient method for image detection for unmanned cars based on vision, and solves the problem of false localization for unmanned cars. The current SR methods based on deep learning have shown remarkable comparative advantages but remain unsatisfactory in recovering the high-frequency edge details of the images in noise-contaminated imaging conditions, we add Edge enhancement network (EEN) to GAN network to recover the high-frequency edge details. For the problem of false localization, we build a model of the bounding box of YOLOv3 with a Gaussian parameter and redesign the loss function. By using the predicted localization uncertainty and edge enhancement network, during the detection process, the proposed schemes can significantly reduce the FP and recover the high-frequency edge details. Compared to a conventional YOLOv3, the proposed algorithm, End-to-End Edge-Enhanced GAN and Object Detector Network improves the mean average precision by 4.2 on the COCO datasets.

MonDIS-46 1274 Ship Target Recognition and Positioning Based on Aerial Photos Xuan Jia Jiangsu Univ. of Science and Tech.Bangyu Li Inst. of Automation, Chinese Academy of Sciences.Liang Qi Jiangsu Univ. of Science and Tech.Zhu Lu Jiangsu Univ. of Science and Tech.Chaochun Yu Jiangsu Univ. of Science and Tech.Jiaye Gu Jiangsu Univ. of Science and Tech.Jie Sun Jiangsu Univ. of Science and Tech.In order to obtain the ship information in the domestic canals and territorial sea more conveniently, a method of acquiring ship type information and geographical position information by aerial photography is proposed. In this paper, the YOLOv4 target detection algorithm is used to identify ships. The method of data enhancement and adversarial training is used to improve the robustness of ship target detection model. According to the data of the inertial navigation system and the attitude angle data of the high-definition camera, the pixel coordinates of the ship target are converted to the longitude and latitude coordinates of WGS-84. The positioning experiment of ship target on water surface is designed. And then the positioning experimental data were calculated and analyzed. The positioning accuracy of ship target is improved by analyzing the error factors of experiment. Experiments show that the proposed method can meet the requirements of practical work and lay a foundation for fast and accurate acquisition of ship information.

MonDIS-47 1364 Research on speed optimization model of pickling and tandem cold rolling mill based on particle swarm optimization algorithm Xiaoyan Zhu Shenyang Jianzhu Univ.YongLIiu Shenyang Jianzhu Univ.Shibang Zhang Shenyang Jianzhu Univ.Songhua Li Shenyang Jianzhu Univ.Jiao-zhao Cao Shenyang Jianzhu Univ.Yunjian Hu Shenyang Jianzhu Univ.In the combined continuous pickling and tandem cold rolling line, the unstable speeds of four sections will not only decrease production efficiency, but also cause equipment wear. In order to solve the problem of great speed fluctuations in production line, the pickling section and rolling section are studied as a whole in this paper. The specific works include the analysis of the speed characteristics of each section and design of the objective function. Then, the optimized speed of each section is calculated by a particle swarm optimization algorithm. According to the speed comparison before and after optimization, it is found that the optimized speed does not fluctuate sharply, the acceleration and deceleration of the production line are stable, and the abundance values of loopers are controlled within a reasonable range. Practical application shows that the optimized speeds can fluctuate well within a certain range, which reduces equipment wear and improves production efficiency. The proposed speed optimization model is suitable for industrial promotion.

MonDIS-48 1462 End-point Static Prediction of Basic Oxygen Furnace (BOF) Steelmaking Based on INPSVR and WOA Liming Liu Univ. of Science and Tech. Liaoning

Technical Programmes CCDC 2021 Ping Li Univ. of Science and Tech. LiaoningMaoxiang Chu Univ. of Science and Tech. LiaoningBasic oxygen furnace (BOF) steelmaking plays a significant role in steelmaking process. Therefore, it is necessary to study the modeling of BOF steelmaking. In order to realize the end-point prediction of converter steelmaking, improve the yield of target product and realize energy saving and emission reduction, an improved nonparallel support vector regression (INPSVR) algorithm is proposed in this paper. Meanwhile, in order to speed up the modeling, whale optimization algorithm (WOA) is used to optimize the parameters of INPSVR model. This has some guiding significance for small and medium converter enterprises to ensure tapping quality, improve production efficiency and reduce cost. Experiments results show that the proposed prediction model has perfect performance in accuracy and efficiency.

MonDIS-49 1468 Direct Torque Sensorless Control of PMSM Based on Dual Extended Kalman Filter Hongwei Zhang Henan Polytechnic Univ.Di Jiang Henan Polytechnic Univ.Xinhuan Wang Henan Polytechnic Univ.Mingren Wang Wuxi Leili Electronic Control Tech. Co. LTDExtended Kalman filter algorithm for direct torque sensorless control of traditional permanent magnet synchronous motor has time delay, which affects the estimation accuracy. To solve this problem, an improved Extended Kalman Filter algorithm is proposed, which is connected by two Extended Kalman Filters in parallel. The Taylor firstorder linearization of the system state transition matrix is carried out with the estimated value of the current time to solve the delay characteristic of the traditional Extended Kalman Filter. Firstly, the mathematical model of PMSM in twophase static coordinate system is established, and the state equation of traditional Extended Kalman Filter is established and linearized. Then, the Dual Extended Kalman Filter algorithm is designed. The estimated state variables in EKFI are taken as the input values of EKFII state variables, and the obtained EKFII state variables are input into EKFI to calculate the Taylor first-order linear approximation of the current EKFI state transition matrix. Finally, the improved algorithm is applied to PMSM direct torque sensorless control, and the simulation results show that the dual Extended Kalman Filter has better estimation accuracy than the traditional Extended Kalman Filter.

MonDIS-50 1491 Fall Detection Algorithm of the Elderly Based on BP Neural Network Qiushi Xiong Shenyang Aerospace Univ.Danhong Chen Shenyang Aerospace Univ.Ying Zhang Shenyang Aerospace Univ.Zhen Gong Shenyang Aerospace Univ.With the rapid development of population aging, falling is a very serious problem for the elderly. Real-time detection of whether the elderly has fallen can minimize the damage caused by falling. Therefore, this paper proposes a fall detection method based on BP neural network. The algorithm uses a three-layer BP reverse neural network to collect human motion data by wearing a three-axis acceleration sensor (MMA7660FC). After feature extraction of the data, network training is carried out, and the neuron weight and learning rate are adjusted to the training process so that it can realize the function of fall detection. Experimental results show that the algorithm can identify falls well, and its accuracy rate can reach 99.44%.

MonDIS-51 1560 A whale optimization of vapor compression refrigeration system Dehao Kong Qingdao Univ. of Science & Tech.Xiaohong Yin Qingdao Univ. of Science & Tech.Ning Fang Shandong Inst. of Commerce and Tech.In this paper, a model-based optimization strategy for vapor compressor refrigeration system is proposed to reduce system energy consumption. More accurate components’ models of the vapor compression refrigeration system are established. An improved whale algorithm with fast convergence rate is proposed to solve the optimization problem and obtain the optimal set-points for control settings. Compared with the traditional strategy, the energy consumption of vapor compression refrigeration system in the proposed optimization strategy is reduced by 15.28%. Meanwhile, the energy saving potential of the optimal control strategy is more remarkable for low cooling load. The proposed optimal control strategy can work well for applications in control and energy efficiency improvement of the existing system.

MonDIS-52 1606 A Global Dynamic Path Planning Algorithm Based on Optimized A* Algorithm and Improved Dynamic Window Method Changwu Li Wuhan Univ. of Tech.Danhong Zhang Wuhan Univ. of Tech.In order to solve the path planning and real-time obstacle avoidance problems of unmanned surface vehicles (USV), this paper proposes a global dynamic path planning algorithm based on improved A* algorithm and optimized dynamic window method. An optimized A* algorithm is formed to solve the global path planning problem by optimizing the

heuristic function, filtering out the global optimal key turning points in the path, and reducing the search directions to rise the calculational speed. The introduction of global optimal path using a key turning point in the dynamic window method solves the problem of local path planning. The optimized A* algorithm and the improved dynamic window method are combined to form a fusion algorithm. Simulation analysis in multiple environments proves that the fusion algorithm is superior to the traditional A* algorithm in global path planning questions, improving the smoothness of the planned path and solving the problem of planning the path in a dynamic environment.

MonDIS-53 1686 Study on Non-Uniformity of Cooling in Secondary Cooling Zone of Continuous Casting Slab Zhaofeng Wang Bohai UniversityJiahui Zhang Bohai UniversityYichi Zhang Bohai UniversityThe quality of continuous casting slab is very important for the continuous casting production. In the heat exchange process of liquid-solid transformation, the cooling and solidification in the secondary cooling zone can be considered as the central link of the whole solidification process. The secondary cooling intensity and cooling uniformity have an important influence on the output of caster and the quality of slab. The non-uniformity of slab wide surface cooling in the secondary cooling zone is discussed and studied in this paper. The results verify the effectiveness of the uniformity model optimization and can provide theoretical support and help for further improving the slab production quality.

MonDIS-54 41 Research on Strategy of breaking through multi-UAV defense Changbiao Yu Qilu Univ. of Tech.Gaizhen Chang Qilu Univ. of Tech.Xinyu Zhang Qilu Univ. of Tech.Wenwen Xu Qilu Univ. of Tech.With the development of intelligent systems, multi-UAV combat will be one of the main combat styles in the future battlefield. This paper mainly studies the strategies of blue UAV breaking through red UAV defense under the condition of the width of ABCD in rectangular region determined and changed. When the channel width is determined, the intercepted state of blue UAV is analyzed by using Apollonius circle, and the non-turning model aiming at the shortest time of blue UAV reaching the boundary and the turning model aiming at the highest escape success rate are established respectively, and the corresponding interception escape strategy is proposed. Through the simulation of the initial state of the blue UAV, it is concluded that the blue UAV can escape the red UAV intercepted safety area. When the channel width changes, place the centers of the red drone cluster at D and C respectively, and use their vertical displacement to reflect the change in channel width, and the horizontal meeting reflects the meeting of the red and blue UVA in the plane. The nonlinear relationship between the channel width and the red initial heading is analyzed by MATLAB, the lower limit of the channel width and the shortest penetration strategy are obtained.

MonDIS-55 58 Optimal Control of pH Value in Wet Flue Gas Desulfurization Process Based on Model Predictive Control Jiawei Dong Beijing Univ. of Tech.Xiaoli Li Beijing Univ. of Tech.

Beijing Key Laboratory of Computational Intelligenceand Intelligent System

Engineering Research Center of Digital CommunityKang Wang Beijing Univ. of Tech.Yang Li Communication Univ. of ChinaIn the flue gas desulfurization control system, the pH value of the absorption tower slurry plays a vital role in the desulfurization efficiency of the system. Aiming at the non-linearity and large lag of the slurry pH change process in the wet flue gas desulfurization (WFGD) process, a model predictive control (MPC) method based on the dynamic matrix control (DMC) algorithm is designed to realize the precise tracking control of the slurry pH value. The simulation results show that, compared with the traditional PID control algorithm, the MPC method based on the DMC algorithm has better tracking ability and higher control accuracy for the setting inputs and fluctuations of the slurry pH value. This method could further satisfies the system real-time control requirements. In addition, the model predictive control method can effectively suppress the stochastic interference in the WFGD process, and enhance the stability and anti-interference ability of the system.

MonDIS-56 95 Research On The Technology Of Electric-Heat Hybrid System To Improve Wind Power Consumption Xiu Ji Changchun Inst. Of Tech.

National and Local Joint Engineering ResearchCenter for Measurement, Control and SafeOperation of Intelligent DistributionNetworks

Guilin Dong Changchun Inst. Of Tech.Hui Wang Changchun Inst. Of Tech.

Technical Programmes CCDC 2021 National and Local Joint Engineering Research

Center for Measurement, Control and SafeOperation of Intelligent DistributionNetworks

Guangye Xue Changchun Inst. Of Tech.In view of the large-scale and rapid development of wind power in China’s “Three Norths” area, a large number of wind abandonment issues have been studied, based on the combined consumption and abandonment strategy of electric boilers and thermal storage tanks, and a comprehensive benefit analysis model was established. Taking actual engineering as an example, this paper proposes the technical scheme and mathematical model of the electric-heat hybrid system for wind elimination and abandonment, and the genetic algorithm is used to optimally solve the model. It is of great significance to promote wind power consumption, decoupling thermal power units, and increase the space of wind power grid connection.

MonDIS-57 153 Capacity Optimization Model for Seawater Desalination Plant Junci Tang Shenyang Univ. of Tech.

State Grid Liaoning Electric Power Co. Ltd.Shuai Chu Shenyang Univ. of Tech.Shitan Zhang Northeast Electric Power Univ.Weichun Ge Shenyang Univ. of Tech.

State Grid Liaoning Electric Power Co. Ltd.Dai Cui Shenyang Univ. of Tech.

State Grid Liaoning Electric Power Co. Ltd.Chuang Liu Northeast Electric Power Univ.The capacity optimization model of the seawater desalination plant is established in this paper. This model is adaptable to the high energy consumption characteristics of desalination plants. The optimal construction scale can be calculated on the basis of the maximum profit rate throughout the lifecycle of the desalination plant. The results show the minimum number of water production units needed by seawater reverse osmosis (SWRO) plants. The economic loss caused by the large difference in electricity prices at different times can be resolved by operating during off-peak hours.

MonDIS-58 640 Fast Trajectory Planning for AGV in the Presence of Moving Obstacles: A Combination of 3-dim A* Search and QCQP Bai Li Hunan Univ.Youmin Zhang Concordia Univ.Yakun Ouyang Hunan Univ.Yi Liu Xiaopeng Automobile Tech. Co., LtdXiang Zhong Hunan Univ.Hangjie Cen Hunan Univ.Qi Kong JD.com American Technologies CorporationThis paper concerns about the automatic guided vehicle (AGV) trajectory planning scheme. Nominally it should be formulated as an optimal control problem (OCP) and solved via numerical methods. The concrete procedures to solve an OCP numerically include discretizing it into a mathematical programming (MP) problem and solving the MP via an appropriate solver. However, most of the predominant MP solvers only derive local optima because global optimization takes too long. As the predominant MP solvers only find local optima, the solution quality relies on the homotopy class of the initial guess, i.e. the starting point of an optimization process. A* search in the abstracted x-y-time state space is adopted to find a suitable initial guess, which directly plans a coarse trajectory rather than a path. With the initial guess, an MP in the form of a quadratically constrained quadratic program (QCQP) is solved easily. Simulation results show that the average CPU time spent on the first-A*-then-QCQP method is only 1.4035 seconds in MATLAB.Source codes are provided at https://github.com/libai1943/AGV_Motion_Plannin g_with_Moving_Obstacles.

MonDIS-59 646 Optimization and Analysis for Hypersonic Steady-State Cruise Trajectory Hesong Li National Univ. of Defense Tech.Yi Wang National Univ. of Defense Tech.Yunfan Zhou National Univ. of Defense Tech.Shangcheng Xu National Univ. of Defense Tech.Kai An National Univ. of Defense Tech.Xiaoqiang Fan National Univ. of Defense Tech.Steady-state cruise refers to cruise at a constant altitude and speed. Aiming at the optimal fuel consumption of hypersonic steady-state cruise, a two-level optimization process, a revised Breguet Range Equation and a graphic analysis method are explored in this paper. Particle Swarm Optimization (PSO) algorithm is improved firstly. A two-level optimization method which combines improved PSO and Sequential Quadratic Programming (SQP) is developed and implemented on the basis of HL-20 aircraft model, where cruise altitude is the unique optimization variable for the whole process, and result shows that the optimal steady-state cruise trajectory could be obtained quickly. Considering the large Mach number, a revised Breguet Range Equation is deduced to apply in hypersonic flight based on the optimization result. Finally, a graphic method for steady-state cruise trajectory design with the constraints of maximum dynamic pressure and heating rate is explored based on the analysis of the two constraints.

MonDIS-60 1418 Optimal Control for Markov Jump Linear System with Multiplicative Noise and Input Delay Xueyang Li, Univ. of JinanChunyan Han Univ. of JinanThis paper investigates the finite-horizon quadratic optimal control problem of discrete-time Markovian jump linear systems subject to multiplicative noises and input delays. A new stochastic Markovian jump maximum principle is developed for dealing this control problem. Applying this principle to the case of stochastic jumping parameter systems with one-step input delay, a necessary and sufficient condition for the problem admitting a unique solution is given. Under this condition, an explicit analytical optimal controller and the corresponding optimal cost are presented in terms of two generalized jumping coupled difference equations with the same dimension as the original state. The key technique is to establish relations between the optimal costate and the state.

MonDIS-61 1516 Research on Path Planning of AGV Based on Improved Ant Colony Optimization Algorithm Jiuxiang Sun Qingdao Univ. of Science and Tech.Ya’nan Yu Qingdao Univ. of Science and Tech.Ling Xin Qingdao Univ. of Science and Tech.Path planning is a key problem in the motion control of mobile robot. In order to solve the problem that the traditional storage mode of automatic container terminal affects the overall operation efficiency, this paper puts forward a matrix yard storage mode, which is transformed into grid map model, and then uses ant colony optimization algorithm to plan the path of AGV. Aiming at the shortcomings of traditional ant colony optimization algorithm (ACO) in global path planning, such as slow convergence speed and weak optimization ability, an improved ant colony path planning algorithm is proposed. Firstly, the grid map is established, and the fruit fly optimization algorithm (FOA) is used for fast pre-search on the grid map to generate the original pheromone distribution required by the ant colony optimization algorithm, and then the ant colony optimization algorithm is used for global path planning. At the same time, in order to solve the problem of many path turning angles and large cumulative turning angles in the planning, the path smoothing is carried out. The simulation results show that the improved algorithm has fewer turns and smoother path, and the improved ant colony algorithm has a greater improvement in path search speed and accuracy than the traditional algorithm.

MonDIS-62 1151 Big Data Sampling Algorithm Based on Peak Detection Mengyu Liu Beijing Aerospace Automatic Control Inst.Yuhang Wang Beijing Aerospace Automatic Control Inst.Ruishi Lin Beijing Aerospace Automatic Control Inst.Shenhang Wang Beijing Aerospace Automatic Control Inst.Wei Zheng Beijing Aerospace Automatic Control Inst.Domestic mass data processing system in aerospace field uses big data simple sampling algorithm for data specification in the data preprocessing stage. This paper analyzes the data curve distortion caused by this algorithm, and proposes an optimization method for that. Finally, a big data sampling algorithm based on peak detection is adopted to achieve the purpose of quickly viewing the fidelity and complete picture of massive historical data, while ensuring the correctness of the data interpretation after data preprocessing at the same time. Through the using of real test data for verification, in the data preprocessing stage of the domestic mass data processing system, the large data sampling algorithm based on peak detection is adopted to achieve the high fidelity of the data curve after sampling.

MonDIS-63 1626 Consensus Control of Small Unmanned Surface Vehicle with Event-triggered Communication Xiukun Ji Guangxi Univ. of Science and Tech.Jiayan Wen Guangxi Univ. of Science and Tech.Xinghua Liu Xi’an Univ. of Tech.Guangming Xie Guangxi Univ. of Science and Tech.

Peking Univ.Hongtao Ye Guangxi Univ. of Science and Tech.This paper presents a consensus control protocol for small unmaned surface vehicle (USV) based on eventtriggered control communication. The motion and force of USV are analyzed without considering the non-linear interference, and a second-order mathematical model of multi-USVs is established. A control protocol for USV systems is designed by considering the error between location and speed information of USV neighbors and their own state information. Event-triggered protocol based on location and speed information of USV neighbors is also designed. The proof that Zeno behavior does not occur in USV systems with event-triggered control algorithm is given. Numerical simulations are conducted to verify the effectiveness of the proposed control strategy.

MonDIS-64

Technical Programmes CCDC 2021 543 THz Super-Resolution Imaging Based on Complex Laplacian Prior Deconvolution Algorithm Ying Wang Northeastern Univ.Feng Qi Shenyang Inst. of Automation

Inst. for Robotics and Intelligent ManufacturingKey Laboratory of Opto-Electronic Information

ProcessingJinkuan Wang Northeastern Univ.Due to the long wavelength of the Terahertz (THz) wave, the imaging quality is seriously deteriorated with diffraction. To solve this problem, a simple but very effective approach based on prior knowledge and wave nature was introduced in this paper. In this prior, the image gradients are represented by Laplacian to constrain the gradient of the high-resolution image and the enhanced image when performing single image super-resolution and sharpness enhancement. Moreover, the deconvolution algorithm is expended to a complex dimension. Low-Resolution (LR) THz image was simulated by convolution the High-Resolution (HR) image with real-measured Point-Spread Function (PSF) to ensure the applicability. The numerical experiments illustrate the efficiency and effectiveness of the proposed method in terms of Peak Signal-to-Noise Ratio (PSNR), Mean-Square Error (MSE) and Structural Similarity (SSIM). Super-Resolution (SR) results show that the proposed method has good performance in convergence and suppressing ringing or jaggy artifacts.

MonDIS-65 1431 Capacitive Current Feedforward Control Strategy for a Three-Phase LCL-filter-based T-type Grid-connected converter with the Fuzzy-PR Chaoliang Dang Xi’an Univ. of Tech.Xiangqian Tong Xi’an Univ. of Tech.Weizhang Song Xi’an Univ. of Tech.Wang Fei Xi’an Univ. of Tech.A three-level T-type grid-connected inverter has been widely used in the current medium power distributed PV inverters, charging station and active power filter system with the advantages of high equivalent switching frequency, high efficiency, small filter inductance and grid current harmonic. However, the grid-connected inverter with LCL-filter is a third-order and multi-variable system, to meet the high-performance requirements for grid-connected current, claiming a higher requirement for the grid-current control system. Aiming at this, a new control strategy named capacitive current feedforward control based on the carrier-based PWM modulation scheme on is put forward. Further, to improve the power density, the capacitor current is generated indirectly calculated by the capacitor voltage. Finally, to verify the correctness and effectiveness of the proposed control strategy, a complete digitalsimulation model and experimental prototype platform is established, the detailed theoretical analysis and design method of the proposed control strategy are presented. The simulation and experimental results show that the steady and the transient performance of the grid current with the proposed control strategy are effectively improved, and no additional sensors are needed.

MonDIS-66 661 Low-Terahertz Radar Image Analysis for Road Obstacles Hongming Wu Northeastern Univ.Feng Qi Shenyang Inst. of Automation

Inst. for Robotics and Intelligent ManufacturingKey Laboratory of Opto-Electronic Information

ProcessingJinkuan Wang Northeastern Univ.At present, laser radar is main studied in the in automotive sensing. However, Driverless cars have higher request for different environment and light conditions. It is even difficult for lidar to find tiny targets on the road, such as pits and packets. In the paper, The low-terahertz high-resolution imaging radar system operating frequency is 94GHz, Using the frequency-modulated continuous-wave (FMCW) technique with 3mm wavelength. We simulated the pits of different depths on the road, analyzed the changes in the electromagnetic field, and used the low-terahertz radar to perform experiments.

MonDIS-67 1700 Integrated design of path planing and robust tracking flight control for unmanned aerial vehicle Chun-ru Li Nanjing Univ. of Aeronautics and AstronauticsMou Chen Nanjing Univ. of Aeronautics and AstronauticsAt present, with its many advantages, Unmanned Aerial Vehicle (UAV) has occupied a very important position in military and civil fields. No matter what what tasks are performed, the security obstacle avoidance flight of UAV is the premise to complete the target task. In this paper, the problem of safe obstacle avoidance flight of UAV is studied, mainly including the generation method of expected safe flight path based on path planning algorithm and the tracking control method of safe flight path for UAV. Aiming at the problem of UAV safe flight path generation, a path planning method combining A* algorithm and reinforcement learning is studied. Considering the problems such as too many inflection points and poor path trackability in A* algorithm, the generated path points are processed, and the performance constraints of UAV are transformed into the slope and curvature constraints of the path, so as to realize the

smooth processing of the path and obtain the safe and flightable flight flight path. On this basis, fully considering the complexity and uncertainty of UAV flight environment, A* algorithm and Q learning algorithm are organically combined to achieve effective path planning in dynamic environment. The simulation results show that the safety flight path planning of UAV is effective. Considering at the problem that the position tracking error in the flight process of UAV is often too large, collision will occur and thus affect the flight safety of obstacle avoidance, a safe tracking control method for UAVs with tracking error constraints is proposed. A preset performance function is introduced to limit the tracking error of UAV within the preset constraint range, and then a performance function transformation method is used to transform the constrained tracking control problem into an unconstrained problem. On this basis, combined with the disturbance observer technology, the UAV track safety tracking control method is designed to ensure the safe flight of UAV. The simulation results show that the designed controller can effectively track the UAV reference flight path, and the tracking error meets the given range in advance. Finally, the simulation studies are given to verify the effectiveness of the studied path planning and robust tracking flight control methods. The simulation results show that the designed path planing algorithm and the flight controller can effectively track the flight path and avoid obstacles safely.