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2019 2nd International Conference on advanced Materials, intelligent Manufacturing and automation, AMIMA 2019

作者： Hu, Cong Xia, Wei Chen, Peng Huang, Shun Guo, Hui Jin, Xing School of Automation China University of Geosciences Wuhan430074 China Hubeikey Laboratory of Advanced Control and Intelligent Automation for Complex System Wuhan430074 China

In order to improve the robustness of the correlation filtering (CF) tracking algorithm and overcome the problem that the traditional correlation filtering method can not deal with the target's scale and deformation change, a feature-adaptive scale adaptive correlation filter tracking algorithm is proposed. Firstly, the target is characterized;then the output filter is calculated by using the correlation filter;finally, the image blocks of different scales are intercepted from the target position of the current frame, and the optimal estimation of the target scale is obtained by the adaptive scale pool model. The experiment selects multiple video sequences for testing and compares the proposed algorithm with other target tracking methods. The experimental results show that the average performance is better than the comparison method. © Published under licence by IOP Publishing Ltd.

关键词： Deformation

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SPP-CNN: An Efficient Framework for Network Robustness Prediction

arXiv

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arXiv 2023年

作者： Wu, Chengpei Lou, Yang Wang, Lin Li, Junli Li, Xiang Chen, Guanrong The College of Computer Science Sichuan Normal University Chengdu610066 China The Key Laboratory of System Control and Information Processing Ministry of Education Shanghai200240 China The Department of Computer Science National Yang Ming Chiao Tung University Hsinchu300 Taiwan The Department of Automation Shanghai Jiao Tong University Shanghai200240 China The Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous Systems Tongji University Shanghai201210 China The Department of Control Science and Engineering Tongji University Shanghai200240 China The Department of Electrical Engineering City University of Hong Kong Hong Kong

This paper addresses the robustness of a network to sustain its connectivity and controllability against malicious attacks. This kind of network robustness is typically measured by the time-consuming attack simulation, which returns a sequence of values that record the remaining connectivity and controllability after a sequence of node- or edge-removal attacks. For improvement, this paper develops an efficient framework for network robustness prediction, the spatial pyramid pooling convolutional neural network (SPP-CNN). The new framework installs a spatial pyramid pooling layer between the convolutional and fully-connected layers, overcoming the common mismatch issue in the CNN-based prediction approaches and extending its generalizability. Extensive experiments are carried out by comparing SPP-CNN with three state-of-the-art robustness predictors, namely a CNN-based and two graph neural networks-based frameworks. Synthetic and real-world networks, both directed and undirected, are investigated. Experimental results demonstrate that the proposed SPP-CNN achieves better prediction performances and better generalizability to unknown datasets, with significantly lower time-consumption, than its counterparts. © 2023, CC BY.

关键词： complex networks

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Improved potential field method path planning based on genetic algorithm

Improved potential field method path planning based on genet...

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第三十九届中国控制会议

作者： Feng Liu Hualing He Zhihua Li Zhi-Hong Guan Hua O Wang School of Automation China University of Geosciences Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems College of Automation Huazhong University of Science and Technology Department of Mechanical Engineering Boston University

In order to overcome the problem of path planning failure in traditional potential field method, an improved potential field path planning method based on genetic algorithm is proposed in this paper. Firstly, for the problem of inaccessibility of the target, a factor method is added to the traditional gravity function to solve it;then the local minimum point problem of the traditional artificial potential field method is solved to ensure that the robot can reach the target point. At the same time, in order to get the optimal path, genetic algorithm is used to optimize the combined potential field function of gravity and repulsion, to find the lowest point of potential energy directly, so as to determine the step size and moving direction of the robot. Simulation is carried out to verify the effectiveness of the method.

关键词： Path planning artificial potential field gravitation function genetic algorithms

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A Novel Multi-objective Evolutionary Algorithm Based on Space Partitioning 11th

A Novel Multi-objective Evolutionary Algorithm Based on Spac...

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11th International Symposium on Intelligence Computation and Applications, ISICA 2019

作者： Wu, Xiaofang Li, Changhe Zeng, Sanyou Yang, Shengxiang School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China School of Computer Science and Informatics De Montfort University LeicesterLE1 9BH United Kingdom

ISBN: (纸本)9789811555763

To design an effective multi-objective optimization evolutionary algorithms (MOEA), we need to address the following issues: 1) the sensitivity to the shape of true Pareto front (PF) on decomposition-based MOEAs;2) the loss of diversity due to paying so much attention to the convergence on domination-based MOEAs;3) the curse of dimensionality for many-objective optimization problems on grid-based MOEAs. This paper proposes an MOEA based on space partitioning (MOEA-SP) to address the above issues. In MOEA-SP, subspaces, partitioned by a k-dimensional tree (kd-tree), are sorted according to a bi-indicator criterion defined in this paper. Subspace-oriented and Max-Min selection methods are introduced to increase selection pressure and maintain diversity, respectively. Experimental studies show that MOEA-SP outperforms several compared algorithms on a set of benchmarks. © 2020, Springer Nature Singapore Pte Ltd.

关键词： Multiobjective optimization

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A Low-Cost Enhanced Heterogeneous Barrier Construction Method for Multi-Target Intrusion Monitoring

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IEEE Sensors Journal 2025年

作者： Ye, Jing Xiong, Yonghua Dong, Haobin Yu, Anjun She, Jinhua China University of Geosciences School of Automation Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China Jiangxi Ganyue Expressway Company Ltd Nanchang330200 China Tokyo University of Technology School of Engineering Tokyo192-0982 Japan

In multi-target intrusion monitoring, heterogeneous nodes may overlap due to different sensing ranges. Additionally, excessively long barriers will increase the cost of barriers and reduce the number of barriers. To address these issues, this article proposes a distributed method for constructing weighted heterogeneous barriers using depth-first search and learning automata (WHBDLA). It designs a dynamically weighted coverage graph based on the non-overlap rate and horizontal displacement. The proposed method considers multiple heterogeneous properties. It uses several intrusion targets to identify the boundaries of local sub-regions while avoiding the cost increase and quantity decrease caused by long barriers. WHBDLA combines the advantages of depth-first search and the reinforcement learning mechanism to adaptively construct heterogeneous barriers in sub-regions with unknown faults. The algorithm initially searches for potential barrier path nodes and then activates nodes with sufficient energy and high weights. Finally, it monitors and repairs the barriers. Comparative experiments and analyses indicate that the proposed method increases the number of barriers, reduces node costs, and decreases running time. © 2001-2012 IEEE.

关键词： Reinforcement learning

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Fractional-order Virtual Model control for Trotting Motion of Quadruped Robot

Fractional-order Virtual Model Control for Trotting Motion o...

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第32届中国控制与决策会议

作者： JiangBo Zhao Shicheng Ma Shanshuai Niu Junzheng Wang Key Laboratory of Complex System Intelligent Control and Decision School of AutomationBeijing Institute of Technology Key Laboratory of Servo Motion System Drive and Control Ministry of Industry and Information TechnologySchool of AutomationBeijing Institute of Technology

Virtual model control(VMC) is the most widely-used method for motion control of quadruped *** could decrease the abrupt ground reaction force and realize compliant interaction between foot and ground at the cost of control *** improve the robustness and precision of VMC,we integrate a fractional-order virtual damper with the classical VMC and propose a fractional-order VMC(FOVMC) for quadruped robot trotting *** we provide the implementation details about optimization-based FOVMC parameter tuning method and carry out the trotting experiments on the quadruped robot *** experiment results demonstrate that FOVMC could improve the precision of single-leg motion control and achieve a better whole-body trotting performance than classical VMC.

关键词： Fractional-order calculus Virtual model control Quadruped robot Trotting

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Active Vibration Isolation for A Parallel Wheel-legged Robot Based on Dynamic Model

Research Square

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Research Square 2020年

作者： Guo, Fei Wang, Shoukun Yue, Binkai Wang, Junzheng State Key Laboratory of Intelligent Control and Decision of Complex System School of Automation Beijing Institute of Technology Beijing100081 China

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A Unified Framework for Finite-/Fixed-Time Synchronization of Memristor Chaotic systems and Its Application in Image Encryption

Research Square

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Research Square 2021年

作者： Wang, Leimin Jiang, Shan Ge, Ming-Feng Hu, Junhao China University of Geosciences China School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China School of Mechanical Engineering and Electronic Information China University of Geosciences Wuhan430074 China College of Mathematics and Statistics South-Central University for Nationalities Wuhan430074 China

This paper proposes a sliding-mode-based unified control framework to solve the synchronization problem of memristor chaotic systems. Both finite- and fixed-time synchronization of the memristor chaotic systems can be obtained in the uniform framework. According to the Lyapunov stability and finite-time stability theories, we demonstrate that the trajectories of error system reach the presented sliding-mode surface and converge to the origin along the surface in a finite/fixed time. Moreover, an image encryption algorithm is developed based on the presented control framework. Finally, the numerical simulations and the statistical performance analyses are discussed to illustrate the correctness of synchronization results, the effectiveness of the proposed encryption algorithm, and its potential applications in the scope of secure communication. © 2021, CC BY.

关键词： Chaotic systems

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Early Forest Fire Segmentation Based on Deep Learning

Early Forest Fire Segmentation Based on Deep Learning

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Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS), CAA Symposium on

作者： Mengna Li Youmin Zhang Jing Xin Lingxia Mu Ziquan Yu Han Liu Guo Xie Shangbin Jiao Yingmin Yi Shaanxi Key Laboratory of Complex System Control and Intelligent Information Processing Xi’an University of Technology Xi’an Shaanxi China Concordia University Montreal Quebec Canada College of Automation Engineering Nanjing University of Aeronautics and Astronautics Nanjing 211106 China

ISBN: (纸本)9781665401166

Fire segmentation is very important for fire rescue. It can make firefighters get the information on fire area, spread direction and so on, and then help them make quick and effective fire-fighting plan. Therefore, this paper proposes an early forest fire segmentation algorithm based on a deep learning model, named F-Unet, which mainly uses the architecture idea of Unet for reference. F-Unet consists of contraction path, feature fusion layer and expansion path. The contraction path is composed of the first 13 layers of VGG16, which is used to obtain feature maps with different scales. In order to improve the segmentation accuracy of the model, the feature fusion network proposed in this paper is added to the Unet architecture to fuse these feature maps with different scales. The expansion path is used for upsampling these feature maps to restore the size of the original input image and obtain the fire segmentation results. The experimental testing results on the FLAME dataset show that F-Unet can significantly improve the fire segmentation precision, and also prove that the proposed feature fusion network is effective to improve the performance of fire segmentation.

关键词： Deep learning Image segmentation Fuses Fault detection Fires Forestry Safety

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Dynamic Modeling for Dielectric Elastomer Actuators Based on LSTM Deep Neural Network

Dynamic Modeling for Dielectric Elastomer Actuators Based on...

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International Conference on advanced Robotics and Mechatronics (ICARM)

作者： Huai Xiao Jundong Wu Wenjun Ye Yawu Wang School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China University of Geosciences Wuhan Hubei China Gina Cody School of Engineering and Computer Science Concordia University Montreal Quebec Canada

ISBN: (数字)9781728164793

ISBN: (纸本)9781728164809

This paper proposes a dynamic model for dielectric elastomer actuators (DEAs) based on the long-short term memory (LSTM) deep neural network. The fabrication of the DEA and the framework of the experimental platform are introduced firstly. The behaviors of the DEA are analyzed through several sets of experiments, which shows the DEA has obvious memory behavior (i.e., the hysteresis behavior and creep behavior), where the hysteresis behavior is a symmetry and rate-dependence. Considering that the traditional neural network is difficult to describe the memory property, the LSTM deep neural network is constructed as the dynamic model of the DEA. Then, such neural network is trained according to the experimental data. Finally, the comparation results of the experimental data and the model output verify the effectiveness as well as the generalization ability of the dynamic model.

关键词： Mathematical model Neural networks Hysteresis Actuators Logic gates Soft robotics

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