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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Liu, Wei Gao, Mingqiang Duan, Shuaidong Wei, Longsheng China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation For Complex Systems Wuhan China Engineering Research Center of Intelligent Technology For Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9780738133737

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames. © 2021 IEEE.

关键词： Generative adversarial networks

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Human pose recognition based on multiple features and random forest algorithm 2

Human pose recognition based on multiple features and random...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Fang, Di Wang, Hong Meng, Fanglong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition. © 2021 SPIE.

关键词： decision trees

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RGB-infrared fusion tracking algorithm based on Siamese network 2

RGB-infrared fusion tracking algorithm based on Siamese netw...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Li, Ruyou Cui, Wennan Wang, Zhiyong Zhang, Jingjing School of Automation China University of Geosciences Wuhan430074 China Key Laboratory of Intelligent Infrared Perception Chinese Academy of Sciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

The target tracking method based on RGB image is affected by many factors such as light and haze weather, so it is difficult to distinguish the tracking target from the background, and it is easy to lead to the drift or even loss of the tracking target. Target tracking based on infrared images is not affected by light, haze and other illumination factors, but the target's color, texture and other characteristic information will be missing. Therefore, in order to obtain the target's color, texture and other characteristic information in a poorly illuminated environment, while achieving accurate and fast tracking of the object, this paper proposes a RGB and infrared image fusion tracking method based on a deep convolutional network. Firstly, the fusion method of RGB and infrared images is studied;secondly, a target tracking network based on the Siamese network is established to extract the image convolution features of the target template and the current target;finally, the response map is calculated by the deep cross-correlation module. At last, the performance test of the target tracking algorithm is carried out on the VOT2019RGBT data set. Experimental results show that the algorithm can effectively solve the problem of target tracking when the target is partially occluded, the tracking scene has no suitable illumination or the light changes strongly, and it is of great significance to improve the accuracy of target tracking under complex backgrounds. © 2021 SPIE.

关键词： Image fusion

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Asymptotic stability of fractional linear discrete-time equations with arbitrary time delays

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The European Physical Journal Special Topics 2025年 1-18页

作者： Liu, Chong Wu, Guo-Cheng Hou, Hu-Shuang School of Mathematics and Statistics Guizhou University Guiyang People’s Republic of China Key Laboratory of Intelligent Analysis and Decision on Complex Systems Chongqing University of Posts and Telecommunications Chongqing People’s Republic of China School of Mathematical Sciences Sichuan Normal University Chengdu People’s Republic of China

This paper introduces a class of Caputo fractional difference equations with arbitrary time delays. It provides non-negative conditions of the Mittag-Leffler function solutions. Exact solutions of fractional linear difference equations are obtained by Picard’s method, and stability conditions are given by the Z-transform. Finally, such results are extended to h-fractional difference equations. This study reveals the time delay’s effect on the dynamics of fractional difference equations and the stable regions. It also provides necessary and sufficient conditions for neural networks and control.

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Cascade conditional generative adversarial nets for spatial-spectral hyperspectral sample generation

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Science China(Information Sciences) 2020年第4期63卷 81-96页

作者： Xiaobo LIU Yulin QIAO Yonghua XIONG Zhihua CAI Peng LIU School of Automation China University of Geosciences (Wuhan) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Computer Science China University of Geosciences (Wuhan) The Beibu Gulf Big Data Resources Utilization Laboratory Beibu Gulf University

Sample generation is an effective way to solve the problem of the insufficiency of training data for hyperspectral image classification. The generative adversarial network（GAN） is one of the popular deep learning methods, which utilizes adversarial training to generate the region of samples based on the required class label. In this paper, we propose cascade conditional generative adversarial nets for hyperspectral image complete spatial-spectral sample generation, named C;GAN. The C;GAN includes two stages. The stageone model consists of the spatial information generation with a window size that entails feeding random noise and the required class label. The second stage is the spatial-spectral information generation that generates spectral information of all bands in the spatial region by feeding the label regions. The visualization and verification of generated samples based on the Pavia University and Salinas datasets show superior performance, which demonstrates that our method is useful for hyperspectral image classification.

关键词： hyperspectral image sample generation generative adversarial nets(GAN) deep learning spatial-spectral information

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Transmission Optimization Based on Industrial Internet Information Model 4

Transmission Optimization Based on Industrial Internet Infor...

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4th International Conference on Machine Learning and Computer Application, ICMLCA 2023

作者： Feng, Haiwen Tian, Hao Wang, Ting Zhu, Jingyang Wang, Xintong School of Software College Shenyang University of Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China

ISBN: (纸本)9798400709449

With the standardized development of industrial Internet, the communication among industrial Internet platforms, devices and gateways is more frequent. Due to the problems of heterogeneous data and low data interoperability existing in different devices, the communication between industrial Internet is hindered. With the development of industrial Internet information model, on the basis of reducing data heterogeneity and improving interoperability, it is more difficult to transmit information due to the increase of information content. Therefore, this paper will design an information model transmission optimization method, through the information model XML file content optimization compression, while in the information model may exist in the Chinese information processing, so that it can better compress the Chinese-English information. The results show that the XML file used in information transmission can be compressed by using this optimization method, and it is proved that the proposed optimization method can improve the transmission efficiency. © 2023 ACM.

关键词： Interoperability

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Quantized control for Highly Nonlinear Stochastic systems with Multiple Delays under DoS Attacks

Quantized Control for Highly Nonlinear Stochastic Systems wi...

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第42届中国控制会议

作者： Hairui Zhao Dongyan Chen Jun Hu School of Science Harbin University of Science and Technology Heilongjiang Provincial Key Laboratory of Optimization Control and Intelligent Analysis for Complex Systems Harbin University of Science and Technology School of Automation Harbin University of Science and Technology

In this paper,the quantized control problem is discussed for a class of highly nonlinear stochastic systems with multiple delays under the DoS *** coefficients are allowed to be highly nonlinear,and the control input is subject to the quantization effects and the DoS *** aim is to deal with the stabilization problem for unstable highly nonlinear stochastic systems with multiple *** p-th moment exponential stability and almost surely exponential stability are discussed in light of the Lyapunov ***,an illustrative example is given to verify the validity of the theoretical results.

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Distributed Time-Varying Optimization with Equality Constraints

Distributed Time-Varying Optimization with Equality Constrai...

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International Conference on control and automation (ICCA)

作者： Zheng Yang Ji Ma Xiang Xu Department of Automation Xiamen University Xiamen Fujian China Shenzhen Key Laboratory of Control Theory and Intelligent Systems School of System Design and Intelligent Manufacturing Southern University of Science and Technology Shenzhen China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

In this paper, we consider the distributed time-varying optimization problem with coupled equality constraints over a connected undirected network. To address this issue, we design a novel distributed constraint optimization algorithm, and establish its ISS stability with external disturbances and tracking errors as the input and state, respectively. Moreover, the obtained result includes distributed constrained optimization with static objective functions as a special case. In comparison to existing relevant works, the proposed algorithm demonstrates exponential convergence for cases involving static objective functions. Finally, the theoretical results are validated via a numerical example.

关键词： Constraint optimization automation Linear programming Trajectory Optimization Numerical stability Convergence

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A-STC: auction-based spanning tree coverage algorithm for motion planning of cooperative robots

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Frontiers of Information Technology & Electronic Engineering 2019年第1期20卷 18-31页

作者： Guan-qiang GAO Bin XIN School of Automation Beijing Institute of Technology Key Laboratory of Intelligent Control and Decision of Complex Systems Beijing Advanced Innovation Center for Intelligent Robots and Systems

The multi-robot coverage motion planning(MCMP) problem in which every reachable area must be covered is common in multi-robot systems. To deal with the MCMP problem, we propose an efficient, complete, and off-line algorithm, named the "auction-based spanning tree coverage(A-STC)" algorithm. First, the configuration space is divided into mega cells whose size is twice the minimum coverage range of a robot. Based on connection relationships among mega cells, a graph structure can be obtained. A robot that circumnavigates a spanning tree of the graph can generate a coverage trajectory. Then, the proposed algorithm adopts an auction mechanism to construct one spanning tree for each robot. In this mechanism, an auctioneer robot chooses a suitable vertex of the graph as an auction item from neighboring vertexes of its spanning tree by heuristic rules. A bidder robot submits a proper bid to the auctioneer according to the auction vertexes' relationships with the spanning tree of the robot and the estimated length of its trajectory. The estimated length is calculated based on vertexes and edges in the spanning tree. The bidder with the highest bid is selected as a winner to reduce the makespan of the coverage task. After auction processes, acceptable coverage trajectories can be planned rapidly. Computational experiments validate the effectiveness of the proposed MCMP algorithm and the method for estimating trajectory lengths. The proposed algorithm is also compared with the state-of-the-art algorithms. The comparative results show that the A-STC algorithm has apparent advantages in terms of the running time and the makespan for large crowded configuration spaces.

关键词： Coverage motion planning Multi-robot system Auction algorithm Spanning tree coverage algorithm

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Consensus control of Leader-Following Multi-Agent systems in Directed Topology With Heterogeneous Disturbances

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IEEE/CAA Journal of Automatica Sinica 2021年第2期8卷 423-431页

作者： Qinglai Wei Xin Wang Xiangnan Zhong Naiqi Wu the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049and also with Qingdao Academy of Intelligent IndustriesQingdao 266109China the Department of Computer and Electrical Engineering and Computer Science Florida Atlantic UniversityBoca Raton FL33431 USA the Institute of Systems Engineering and Collaborative Laboratory for Intelligent Science and SystemsMacao University of Science and TechnologyMacao 999078China

This paper investigates the consensus problem for linear multi-agent systems with the heterogeneous disturbances generated by the Brown *** main contribution is that a control scheme is designed to achieve the dynamic consensus for the multi-agent systems in directed topology interfered by stochastic *** traditional ways,the coupling weights depending on the communication structure are static.A new distributed controller is designed based on Riccati inequalities,while updating the coupling weights associated with the gain matrix by state errors between adjacent *** introducing time-varying coupling weights into this novel control law,the state errors between leader and followers asymptotically converge to the minimum value utilizing the local *** the Lyapunov directed method and It?formula,the stability of the closed-loop system with the proposed control law is *** simulation results conducted by the new and traditional schemes are presented to demonstrate the effectiveness and advantage of the developed control method.

关键词： Consensus control directed topology external disturbance multi-agent(MA)systems

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