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3rd International Conference on Image Analysis and Signal Processing, IASP 2011

作者： Cai, Rongtai Wu, Qingxiang Wang, Ping Zhang, Xuguang Hu, Shuo School of Physics Optics Electronic Science and Technology Fujian Normal University Fuzhou 350108 China Key Lab. of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066000 China

ISBN: (纸本)9781612848808

This paper analyzes the performance of object tracking algorithms. The issues that affect precision in object tracking are analyzed first and how the parameters in tracking algorithms affect the precision in object tracking are introduced. Then, the time complexity of those object-tracking algorithms is discussed. At last, the holistic analysis and evaluation of these object-tacking algorithms are parsed from application range, tracking precision and time complexity. © 2011 IEEE.

关键词： Tracking (position)

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Hydraulic System Reliability Modelling and Analysis Based on Convex Model Bayesian Network

Hydraulic System Reliability Modelling and Analysis Based on...

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2015 International Conference on Fluid Power and Mechatronics

作者： Dongning Chen Yuliang Zhang Chengyu Yao Shijun Lv Key Lab of Heavy Machinery Fluid Power Transmission and Control of Hebei Province Yanshan University Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University

Compared with the probabilistic method, the nonprobabilistic convex model only requires a small number of samples and experiences to obtain the variation bounds of the ambiguous and imprecise parameters, and whereby makes the reliability analysis very convenient. In this paper, hydraulic lifting system reliability model is established and the reliability of the system is analyzed by convex model Bayesian network, then the reliability indices are described in the form of interval parameters. Using ellipsoid model to constrain interval model, the more accurate reliability indices are obtained. Finally the convex model Bayesian network is compared with traditional Bayesian network. All the reliability indices obtained by convex model Bayesian network provide the basis for condition-based maintenance, fault diagnosis and reliability design.

关键词： reliability analysis hydraulic system Bayesian network convex model

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Hydraulic System Reliability Modelling and Analysis Based on Convex Model Bayesian Network

Hydraulic System Reliability Modelling and Analysis Based on...

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International Conference of Fluid Power and Mechatronics

ISBN: (纸本)9781479987719

Compared with the probabilistic method, the non-probabilistic convex model only requires a small number of samples and experiences to obtain the variation bounds of the ambiguous and imprecise parameters, and whereby makes the reliability analysis very convenient. In this paper, hydraulic lifting system reliability model is established and the reliability of the system is analyzed by convex model Bayesian network, then the reliability indices are described in the form of interval parameters. Using ellipsoid model to constrain interval model, the more accurate reliability indices are obtained. Finally the convex model Bayesian network is compared with traditional Bayesian network. All the reliability indices obtained by convex model Bayesian network provide the basis for condition-based maintenance, fault diagnosis and reliability design.

关键词： Reliability analysis Hydraulic system Bayesian network Convex model

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Two-Stage Force Particle Swarm Optimization Algorithm and Application in Hydraulic System Reliability Optimization

Two-Stage Force Particle Swarm Optimization Algorithm and Ap...

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2015 International Conference on Fluid Power and Mechatronics

作者： Chengyu Yao Xueyun Tan Dongning Chen Shijun Lv Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Key Lab of Heavy Machinery Fluid Power Transmission and Control of Hebei Province Yanshan University

In view of the particle swarm optimization(PSO) algorithm's shortages of easily trapping into local optimum and premature convergence, an improvement research from the perspective of interaction mechanism among particles is made in this paper. Consider advantages and disadvantages of the current improved PSO algorithms from the aspect of force, the two-stage force particle swarm optimization(TFPSO) algorithm is proposed. The proposed algorithm, which employs staged search strategy to achieve a trade-off between global exploration and local exploitation abilities, divides search process into two stages and constructs corresponding force rules combining with the idea of attractive and repulsive forces in artificial physics. In order to demonstrate the performance of the proposed algorithm in solving optimization problems, TFPSO algorithm is compared with some well-known PSO algorithms in reliability optimization for hydraulic system. Comparison results show that TFPSO algorithm obtains the best optimization results and enhances the performance of PSO in terms of accuracy of the optimal solution and local search ability.

关键词： PSO algorithm force rule TFPSO algorithm reliability optimization hydraulic system

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Behavioral Recognition of Mice Based on a Deep Network 11

Behavioral Recognition of Mice Based on a Deep Network

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11th IEEE Annual International Conference on CYBER Technology in Automation, control, and Intelligent Systems, CYBER 2021

作者： Wang, Xingqi Du, Chen Wang, Ying Hu, Sheng Zhao, Yuliang Key Lab of Industrial Computer Control Engineering of Hebei Province School of Electrical Engineering Yanshan University Qinhuangdao066004 China School of Biomedical Engineering Beijing University of Aeronautics and Astronautics at Beijing Beijing100000 China School of Control Engineering Northeastern University at Qinhuangdao Qinhuangdao066004 China

ISBN: (纸本)9781665425278

In recent years, mouse behavior recognition has been widely used in animal behavior experiments, which is great significance to study ethology, research of animal learning and memory and analysis of drug action. However, how to quickly and accurately determine and recording the behavior of mice is still a challenge. Using manual record of mouse behavior is not only time consuming but also low accuracy;the model obtained by training C3D convolutional neural network is used for prediction and the accuracy of the obtained results cannot meet the requirements. In this paper, according to an 'open field' experiment scheme in animal behavior experiments, an image preprocessing method that integrates multi-Temporal mouse action pictures is proposed. With the theoretical research of deep learning convolutional neural network, the target of mouse behavior classification is finally completed. The preliminary analysis results show that this method has the characteristics of accurate classification, high accuracy and low cost. This method can obtain mouse behavior sequence by continuous batch recognition, which greatly improves the recognition efficiency. © 2021 IEEE.

关键词： Image fusion

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Efficient human action recognition using accumulated motion image and support vector machines

Efficient human action recognition using accumulated motion ...

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International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Cao, Wei Zhang, Xuguang Cao, Shukai Zhang, Jie Wang, Mingjia Han, Guangliang Key Lab. of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China

Vision-based human action recognition provides an advanced interface, and research in this field of human action recognition has been actively carried out. This paper describes a scheme for recognizing human actions from a video sequences. The proposed method is an extension of the Motion History Image(MHI) method based on the ordinal measure of accumulated motion, which is robust to variations of appearances. We define the accumulated motion image(AMI) using image differences firstly. Then the AMI of the video sequencesis resized to a M×N regulation following the standard of training phases. Finally, we employ Support Vector Machine(SVM) as a classifier to distinguish the current activity in target video sequences. In a word, our proposed algorithm not only outperforms the state of art on public available KTH data set and Weizmann data set, but also proves practical to some real world applications, in addition, this method is computationally simple and able to achieve a satisfactory accuracy.

关键词： Support vector machines

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Fuzzy Q-learning obstacle avoidance algorithm of humanoid robot in unknown environment 37

Fuzzy Q-learning obstacle avoidance algorithm of humanoid ro...

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37th Chinese control Conference, CCC 2018

作者： Wen, Shuhuan Chen, Jianhua Li, Zhen Rad, Ahmad B. Mohammed Othman, Kamal Key Lab of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao066004 China School of Engineering Sciences Simon Fraser University SurreyBCV3T 0A3 Canada

ISBN: (纸本)9789881563941

This paper proposes a fuzzy Q-learning (FQL) algorithm to solve the problem of the robot obstacle avoidance in unknown environment. FastSLAM algorithm is used to localize the position of the robot. Traditional Q-learning algorithm, optimized Q-learning algorithm, FQL algorithm are compared. The simulation results show that FQL algorithm has a faster learning speed than other two algorithms and the results demonstrate that the fuzzy Q-learning obstacle avoidance algorithm is effective. © 2018 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Anthropomorphic robots

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Approach for detecting crowd panic behavior based on fluid kinematic features and entropy

Approach for detecting crowd panic behavior based on fluid k...

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International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011

作者： Cao, Shukai Zhang, Xuguang Cao, Wei Liu, Chunxia Li, Ying Li, Peng Cheng Key Lab. of Industrial Computer Control Engineering of Hebei Province Institute of Electrical Engineering Yanshan University Qinhuangdao 066004 China Changchun Institute of Optics Fine Mechanics and Physics Chinese Academy of Sciences Changchun 130033 China

Crowd panic behavior detection is an important task in video analysis and event recognition, whose purpose is to detect when the panic behavior happened and alarming the abnormal event timely. In this paper, the crowd is regard as a fluid, and the crowd motion is described by four fluid kinematic features (divergence, vorticity, gradient tensor invariant and rotation tensor invariant). To discriminate the panic event from normal crowd behavior, an information entropy is calculated as a high level feature based on the fluid kinematic features. Experimental results show that the entropy raised dramatically once a panic event happened.

关键词： Kinematics

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Effects and solution of electric power steering system on vehicle handling stability

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Journal of Computational Information Systems 2012年第1期8卷 65-72页

作者： Zang, Huaiquan Geng, Jia Key Lab of Industrial Computer Control Engineering of Hebei Province Institute of Electrical and Engineering Yanshan University Qinhuangdao 066004 China

To research the effects of electric power steering (EPS) system on vehicle handling stability and to improve it, this paper derives the expressions of road feeling and steering sensitivity, regards which as the performance targets that estimates the vehicle handling stability in addition. Finally, a dynamic compensation controller is presented for the EPS system. The simulation results show the compensated EPS system takes account of a good steering feeling and a rapid response of steering sensitivity. Its handing stability is better than the EPS vehicle which only considers the power effect. © 2012 Binary Information Press January 2012.

关键词： Steering

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control strategy and experimental research on girder hoister steering system based on RBF neural network PID

Control strategy and experimental research on girder hoister...

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9th International Conference on Electronic Measurement and Instruments, ICEMI 2009

作者： Bingyu, Sun Jingyi, Zhao Zhiyong, Wang Chengyu, Yao Rui, Guo Hebei Key Laboratory of Heavy Machinery Fluid Power Transmission and Control Yanshan University Qinhuangdao 066004 China Qinhuangdao Tianye Tolian Heavy Industry Co. Ltd. Qinhuangdao 066100 China Key Lab. of Industrial Computer Control Engineering of Hebei Province Yanshan University Qinhuangdao 066004 China

ISBN: (纸本)9781424438624

n order to meet the needs of construction of high-speed railway passenger dedicated line, 900t rubber-tyred girder hoister is researched and manufactured. Steering system of the hoister is controlled by electro-hydraulic proportional displacement control system, and segment-PID control strategy with dead zoon is used. However with the passage of time, steering performance of girder hoister begin to get worse and worse. Then, valve control cylinder model, equilibrium equation of the steering actuator and system model is built up. Through analyzing and referring advantage of the neural network, one kind of steering control strategy based on RBF neural network PID is presented. Based on similarity principle, this control strategy is used in the steering system of 100t ship yard transporter. The experiment has been lasted 3 months, and the result shows that the system settling time reduced by 5%, over shoot doesn't change, and performance doesn't get worse. Validity of the control strategy is validated. ©2009 IEEE.

关键词： Three term control systems

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