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Chinese control and Decision Conference, CCDC

作者： Zhanhong Wu Cuili Yang Faculty of Information Technology Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing University of Technology Beijing

ISBN: (纸本)9781665478977

In this paper, a new approach for optimizing the structure and prediction error of echo state network (ESN) is proposed. ESN is a kind of recurrent neural network with simple training and strong generalization ability. Reservoir is an important structure of ESN, which determine network performance. Thus, multi-objective optimization algorithm is used to optimize network structure and training error simultaneously. Moreover, a local search algorithm based on l 1 regularization is used to accelerate convergence. The experiment results of time series prediction and standard classification show that MESN can improve the network prediction performance while sparse network structure.

关键词： Training Recurrent neural networks Time series analysis Benchmark testing Prediction algorithms Reservoirs Classification algorithms

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Dynamic Random Route Mutation Mechanism for Moving Target Defense in SDN 6

Dynamic Random Route Mutation Mechanism for Moving Target De...

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6th International Symposium on Computer and Information Processing Technology, ISCIPT 2021

作者： Zhang, Bofeng Han, Li Sun, Shimin Tianjin University of Technology School of Computer Science and Engineering Tianjin China Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology National Engineering Laboratory for Computer Virus Prevention and Control Technology Tianjin China Tiangong University Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tianjin China

ISBN: (纸本)9781665441377

Moving Target Defense (MTD) is commonly used in changing the asymmetric situation between attack and defense in cyberspace. Route mutation is a vital branch of MTD. Current route mutation methods still have some limitations on unpredictability of mutating routing paths and time overhead. In this paper, we propose a Dynamic Random Route Mutation (DRRM) mechanism in order to maximize the unpredictability of mutation and reduce the time overhead. First, we adopt Jaccard distance matrix and temporal constraint to improve mutation space. Mutation space can be dynamically shifted to enhance the unpredictability. Then, we weaken attackers' capabilities of launching effective eavesdropping at a certain node by using route weight. Finally, we construct a pre-distribution strategy to reduce the transmission latency caused by route mutation. Simulation results show that DRRM not only raises the difficulty of network eavesdropping, but also ensures low mutation time overhead. © 2021 IEEE.

关键词： Dynamics

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A passive DDoS attack detection approach based on abnormal analysis in SDN environment 4

A passive DDoS attack detection approach based on abnormal a...

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2021 4th International Conference on Computer Information Science and Application Technology, CISAT 2021

作者： Sun, Shimin Zhang, Xinchao Huang, Wentian Xu, Aixin Wang, Xiaofan Han, Li Tianjin Key Laboratory of Autonomous Intelligent Technology and System School of Computer Science and Technology Tiangong University Tianjin300387 China National Engineering Laboratory for Computer Virus Prevention and Control Technology School of Computer Science and Engineering Tianjin University of Technology Tianjin300384 China

Most of the detection methods against DDoS attacks are based on periodic detection, which leads to high communication overhead, untimely detection, and slow attack response. This paper proposes a passive abnormality detection approach. First, we record the two flow-table characteristics of the regular switches. Then, based on dynamic threshold method and Grubbs outlier test method, we make a determination of abnormal switches. This method reduces the amount of data duplication and regular traffic collection. Moreover, we use Support Vector Machine (SVM) algorithm to evaluate the performance of the passive anomaly detection method. The experiment results show a better performance than active period DDoS attack detection approaches. © Content from this work may be used under the terms of the Creative Commons Attribution 3.0 Licence.

关键词： Support vector machines

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Vibration Transfer Function of Railway Vehicle system and its Spatial Coherence Under Vertical Track Irregularity Conditions

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Chinese Journal of Mechanical Engineering 2025年第2期 380-399页

作者： Peng Lu Xi Wang Yu Hou State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Frontiers Science Center for Smart High-speed Railway System Beijing Jiaotong University School of Mechanical Electronic and Control EngineeringBeijing Jiaotong University

Current research on rail vehicle system vibrations primarily relies on numerical methods, with vibration transfer functions commonly derived through data fitting. However, the physical mechanisms underlying these vibrations are not well understood. To clarify the vibration transfer function and its characteristics, four basic input vectors are defined, and an analytical method is proposed. The vibration transfer functions of the vehicle system are solved, and their spatial coherence is analyzed. The results show that there are two spatial scales and four coherent modes in the vehicle system. The track irregularity wavelengths are combined with two spatial scales to alter the proportions of basic input vectors and then show the characteristics of spatial coherence. Four coherent modes are involved in wheel-rail force and primary suspension force; two coherent modes are involved in bogie vertical motion; and their dominant modes vary with the input frequency. On the other hand, the coherent modes involved in the bogie pitching motion and vehicle body motion are single and fixed over the whole range of frequency. This study presents an analytical method for the rapid solution of dynamic responses in vehicle systems and systematically analyzes the coherence behavior of vibration transfer functions with respect to tracking irregularity wavelengths.

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A Hierarchy Early Warning Method based on RBF Neural Network for BOD in WWTP

A Hierarchy Early Warning Method based on RBF Neural Network...

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第34届中国控制与决策会议

作者： Junkai Zhang Wenjing Li Faculty of Information Technology Beijing University of Technology Beijing Laboratory of Smart Environmental Protection Beijing Key Laboratory of Computational Intelligence and Intelligent System Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education

As an indicator of the organic pollution of waste water,the early warning for the Biochemical Oxygen Demand(BOD) is vital and could prevent substandard waste water from environment *** paper presents a BOD early warning method combining multi-step time series prediction and hierarchy ***,an MIMO strategy based on an RBF neural network is designed to predict BOD concentration with multiple steps ***,a hierarchy alarm method based on a double-check from limit-value and tendency is proposed to recognize alarms from the predicted BOD time series ***,the BOD data from the Benchmark Simulation Model 1(BSM1) platform is used to test the effectiveness of the proposed *** experimental results show that the proposed method has a reliable early warning performance in different alarming level.

关键词： Early warning method Multi-step Prediction RBF neural network Biochemical Oxygen Demand(BOD) Waste Water Treatment Process

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Simultaneous Estimation of Human Motion Intention and Time-Varying Arm Stiffness for Enhanced Human-Robot Interaction

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IEEE Transactions on Cognitive and Developmental systems 2024年第3期17卷 510-524页

作者： Wu, Huayang Zhu, Chengzhi Cheng, Long Yang, Chenguang Li, Yanan University of Sussex Department of Engineering and Design BrightonBN1 9RH United Kingdom South China University of Technology Key Laboratory of Autonomous Systems and Networked Control College of Automation Science and Engineering Guangzhou510640 China Chinese Academy of Sciences State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Beijing100190 China University of Chinese Academy of Sciences School of Artificial Intelligence Beijing100049 China University of Liverpool Department of Computer Science LiverpoolL69 3BX United Kingdom

Recent advances in physiological human motor control research indicate that human endpoint stiffness magnitude increases linearly with grasp force. Based on these findings, a scheme was proposed in this paper to integrate the linear quadratic estimation (LQE) filter with the stiffness model inferred from grasp force, which can simultaneously estimate the human arm's stiffness and motion intention. Then, an online variable impedance controller (VIC) was designed based on previous estimations for physical human-robot interaction (pHRI). The proposed stiffness model and estimation method were validated through experiments using a planar robotic interface. In order to assess its performance in practical pHRI tasks, the implementation of human arm stiffness and intention estimation combining with VIC was extended to a teleoperation peg-in-hole and robot-assisted rehabilitation tasks. The experimental results demonstrate that the proposed method can effectively estimate human motion intention and arm stiffness simultaneously. Compared to existing methods, the proposed VIC enhances pHRI in terms of increased flexibility, effective guidance, and reduced human effort. © 2016 IEEE.

关键词： Human robot interaction

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Train Safety control Based on Deep Deterministic Policy Gradient with control Barrier Function

Train Safety Control Based on Deep Deterministic Policy Grad...

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IEEE International Conference on Networking, Sensing and control

作者： Guoteng Wang Jidong Lv Hongjie Liu Ming Chai Tao Tang Wentao Zhang School of Automation and Intelligence Beijing Jiaotong University Beijing China National Engineering Research Center of Rail Transportation Operation and Control System Beijing Jiaotong University Beijing China State Key Laboratory of Advanced Rail Autonomous Operation Beijing Jiaotong University Beijing China Beijing Zhengshi Environmental Engneering Company Beijing China

ISBN: (数字)9798350365221

ISBN: (纸本)9798350365238

Train Relative Distance Braking Mode (RDBM) has great potential to increase line capacity by enabling shorter following distances based on train-to-train communication in-formation. However, it is difficult for traditional train control methods to ensure avoidance of potential collision risks in RDBM while dealing with the nonlinearity and uncertainty of train dynamics. In this paper, a novel train safety control method combining Deep Deterministic Policy Gradient (DDPG) and control Barrier Function(Cbf)is proposed. Firstly, the safety constraint set for adjacent trains, considering their braking capabilities, is analyzed, and the uncertainty of train dynamics is modeled using a Gaussian process. Secondly, DDPG is used to explore the optimal control strategy under the action of uncertain train dynamics, and CBF provides compensation control input to ensure safety during the learning process. Then, a DDPG-CBF algorithm is presented to achieve efficient safety controller design and implementation. Finally, sufficient numerical simulations demonstrate that, compared with DDPG algorithm alone, the DDPG-CBF algorithm can not only ensure the safe and stable operation of trains, but also learn train control strategies expe-ditiously and accurately under different operating environments, with an average efficiency increase of 38.94 %.

关键词： Rails Uncertainty Heuristic algorithms Process control Optimal control Numerical simulation Stability analysis Safety Sensors Numerical stability

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Sensor-based Multi-agent Coverage control with Spatial Separation in Unstructured Environments

arXiv

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arXiv 2024年

作者： Wang, Xinyi Xu, Jiwen Gao, Chuanxiang Chen, Yizhou Zhang, Jihan Wang, Chenggang Ding, Yulong Chen, Ben M. Department of Mechanical and Automation Engineering The Chinese University of Hong Kong N.T. Shatin Hong Kong Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai200240 China Department of Control Science and Engineering Tongji University Shanghai China Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education China

Multi-robot systems have increasingly become instrumental in tackling coverage problems. However, the challenge of optimizing task efficiency without compromising task success still persists, particularly in expansive, unstructured scenarios with dense obstacles. This paper presents an innovative, decentralized Voronoi-based coverage control approach to reactively navigate these complexities while guaranteeing safety. This approach leverages the active sensing capabilities of multi-robot systems to supplement GIS (Geographic Information system), offering a more comprehensive and real-time understanding of environments like post-disaster. Based on point cloud data, which is inherently non-convex and unstructured, this method efficiently generates collision-free Voronoi regions using only local sensing information through spatial decomposition and spherical mirroring techniques. Then, deadlock-aware guided map integrated with a gradient-optimized, centroid Voronoi-based coverage control policy, is constructed to improve efficiency by avoiding exhaustive searches and local sensing pitfalls. The effectiveness of our algorithm has been validated through extensive numerical simulations in high-fidelity environments, demonstrating significant improvements in task success rate, coverage ratio, and task execution time compared with others. Copyright © 2024, The Authors. All rights reserved.

关键词： Multipurpose robots

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Adaptive Enhancement Technology for Screen Content Photos 17th

Adaptive Enhancement Technology for Screen Content Photos

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17th International Forum on Digital TV and Wireless Multimedia Communication, IFTC 2020

作者： Zhou, Yazhen Kyefondeme, Dakurah Collins Zhou, Huayi Zhao, Min Guo, Nan Wu, Li Faculty of Information Technology Beijing University of Technology Beijing China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing China Beijing Key Laboratory of Computational Intelligence and Intelligent System Beijing China Beijing Artificial Intelligence Institute Beijing China

ISBN: (纸本)9789811611933

Recent years have witnessed the widespread applications of mobiles and other portable electronic devices, involving a variety of screen content photos. Different from natural scene photos, screen content photos are composed of more lines, fewer colors and unique text messages. Thus, it is difficult to realize the satisfactory enhancement effect of screen content photos using traditional image enhancement technology since they are designed for the natural scene. In this paper, we develop a novel enhancement model for screen content photos by considering text and picture separately. To be specific, we first use a fully convolutional network to divide a screen content photo into three independent parts: picture region, foreground text region, and background. Second, an optimal modification of histogram is used to automatically enhance the picture region’s contrast, and the guided image filter is used to enhance the foreground text region. Third, the enhanced picture region, the enhanced foreground text region, and background are fused to obtain the final enhanced image. Experimental results show that our model has produced less noise and derived outstanding enhancement effect than the popular enhancement techniques. © 2021, Springer Nature Singapore Pte Ltd.

关键词： Image segmentation

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Recurrent Attentional Reinforcement Learning for Machinery Fault Diagnosis

SSRN

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SSRN 2024年

作者： Tang, Zhenhui Wang, Jingcheng Wu, Shunyu The Department of Automation The Key Laboratory of System Control and Information Processing Ministry of Education of China The Shanghai Engineering Research Center of Intelligent Control and Management Shanghai Jiao Tong University No.800 Dongchuan Road Shanghai200240 China The SJTU Sanya Yazhou Bay Institute of Deepsea Science and Technology Sanya572024 China The Autonomous Systems and Intelligent Control International Joint Research Center Xi’an Technological University Xi’an710021 China

Recognizing fault types of machinery system is a fundamental but challenging task in industrial application. Although remarkable progress has been attained by learning fault features and predicting the corresponded fault type with deep neural networks, the existing fault diagnosis pipelines usually learn non-discriminative information. Contextual sequence dependency between the localized time fragments is often ignored. To deal with these problem, a recurrent attention framework with reinforcement learning is proposed to find a series of informative features of local time fragments related to different faults in an iterative manner. Fault labels are further predicted on these time fragments. The proposed framework also explicitly models long-term dependencies among these attentional time fragments to capture historical fault information. Experiments on two typical machinery systems, bearing system and hydraulic system, show that the proposed framework achieves superior diagnosis performance and increases no computation burden on CPU. © 2024, The Authors. All rights reserved.

关键词： Reinforcement learning

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