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The TAO of Blockchain Intelligence for Intelligent Web 3.0

IEEE/CAA Journal of Automatica Sinica 2023年第12期10卷 2183-2186页

作者： Juanjuan Li Fei-Yue Wang IEEE the State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190China Faculty of Innovation Engineering Macao University of Science and TechnologyMacao 999078China

WE are in an exciting new intelligent era where various Web 3.0 systems emerge and flourish.[1]–[3].In this new epoch,the collaboration of data and knowledge,humans and machines,actual and virtual worlds is undergoing an unprecedented diversification and community-driven transformation,unveiling an open future full of boundless ***,the value of dispersed data extends far beyond passive storage and application.

关键词： Web TAO knowledge

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Decentralized Adaptive Super-Twisting control for Modular and Reconfigurable Robots with Uncertain Environment Contact 36

Decentralized Adaptive Super-Twisting Control for Modular an...

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第36届中国控制会议

作者： Bo Dong Yan Li Keping Liu Yuanchun Li Department of Control Science and Engineering Changchun University of Technology State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences

ISBN: (纸本)9781538629185

This paper presents a decentralized adaptive super-twisting control method for modular and reconflgurable robots（MRRs） with uncertain environment *** conventional methods that rely on robot-environment contact model or force/torque sensing,this paper addresses the problem of controlling MRRs in contact with uncertain environment that using only local dynamic information of each joint *** dynamic model of MRR is formulated as a synthesis of interconnected *** on the integral sliding mode control（ISMC） technique and the adaptive super-twisting algorithm（ASTA）,the decentralized controller is designed to compensate the model uncertainty in which the up-bound is *** stability of the MRR system is proved by using the Lyapunov *** last,simulations are conducted for 2-DOF MRRs with different configurations under the situations of dynamic contact and collision to investigate the advantage of the proposed approach.

关键词： Modular and reconfigurable robot Decentralized control strategy Uncertain environment contact Adaptive supertwisting algorithm Interconnected couplings

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False Noise Attack Detection for differentially-private distributed control of microgrids

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Automatica 2025年 179卷

作者： Feng Ye Xianghui Cao Lin Cai Mo-Yuen Chow School of Automation Southeast University Nanjing 210096 China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing 210096 China Department of Electrical and Computer Engineering University of Victoria Victoria BC V8W 3P6 Canada UM-SJTU Joint Institute Shanghai Jiao Tong University Shanghai 200240 China

Privacy preserving in distributed control is getting more attention, and differential privacy (DP) is the common tool to protect data privacy, in which additive noise is applied in the algorithm function. However, DP can be leveraged by false noise (FN) attacks because attack vectors can be disguised as artificial noise in DP. FN attacks are a concern as the stealth attacks are hard to detect. Moreover, DP in distributed control makes FN attack detection more difficult. Hence, detecting FN attacks in privacy-preserving distributed control is critical and challenging. In this paper, taking distributed energy management systems as the control object, we propose a novel peer-to-peer attack detection approach, named False Noise Attack Detection (FNAD). In FNAD, each device observes the power decisions of its neighbors based on the data from its two-hop neighbors, estimates the power decisions of its neighbors by a Kalman filter, and updates the detection index of each neighbor according to the residues of the Kalman filter at each iteration. The detection index is developed based on information entropy, without any prior knowledge of the FN attacks. If a device’s detection index is out of well-defined thresholds, its neighbors can perform a majority vote to decide whether it is malicious. We theoretically prove the detection effect of FNAD against three representative attacks in the literature and analyze the advantages of FNAD compared with the traditional methods. The effectiveness of FNAD is demonstrated by extensive simulations.

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A Spatial-Temporal Attention Model for Human Trajectory Prediction

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IEEE/CAA Journal of Automatica Sinica 2020年第4期7卷 965-974页

作者： Xiaodong Zhao Yaran Chen Jin Guo Dongbin Zhao the School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing 100083 the State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190China the Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing 100083China

Human trajectory prediction is essential and promising in many related applications. This is challenging due to the uncertainty of human behaviors, which can be influenced not only by himself, but also by the surrounding environment. Recent works based on long-short term memory(LSTM) models have brought tremendous improvements on the task of trajectory prediction. However, most of them focus on the spatial influence of humans but ignore the temporal influence. In this paper, we propose a novel spatial-temporal attention(ST-Attention) model,which studies spatial and temporal affinities jointly. Specifically,we introduce an attention mechanism to extract temporal affinity,learning the importance for historical trajectory information at different time instants. To explore spatial affinity, a deep neural network is employed to measure different importance of the neighbors. Experimental results show that our method achieves competitive performance compared with state-of-the-art methods on publicly available datasets.

关键词： Attention mechanism long-short term memory(LSTM) spatial-temporal model trajectory prediction

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Online Estimation of State of Healh for Lithium-ion Batteries Based on Attention-LSTM Netwrok Using Partial Charging Features 6

Online Estimation of State of Healh for Lithium-ion Batterie...

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6th International Conference on Energy, Electrical and Power engineering, CEEPE 2023

作者： Yao, Yue Wang, Ying Zhang, Jiangfeng Zhang, Kaifeng Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing China State Grid Zhejiang Electric Power Research Institute Zhejiang China

ISBN: (纸本)9798350348279

Accurate estimation for state-of-health (SOH) of lithium-ion batteries is critical to ensure the security and durability of battery operation. Accordingly, this paper proposes a SOH estimation method based on Attention-LSTM neural network using the partial charging features. First, features are extracted from partial constant-current charging curves. Second, Pearson coefficient analysis is employed to analysis the mapping relationship between extracted features and SOH. Third, an Attention-LSTM network is utilized to realize the SOH estimation. The attention mechanism can adaptively assign weights to the input features and time steps, thereby improving the estimation accuracy. Validation experiments are conducted on public dataset from NASA Ames Prognostics Center of Excellence. The experimental results highlight that the proposed method has higher accuracy and robustness compared with other approaches. © 2023 IEEE.

关键词： Lithium-ion batteries

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Image-Based Fast Finite-Time Target Tracking control of Onboard Inertially Stabilized Camera Platform

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IEEE Transactions on Industrial Electronics 2024年第12期71卷 16409-16418页

作者： Sun, Xunhong Zhu, Wenwu Du, Haibo Chen, Weile Chen, Chih-Chiang Hefei University of Technology School of Electrical Engineering and Automation Hefei230009 China Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Southeast University School of Automation Nanjing210096 China National Cheng Kung University Department of Systems and Naval Mechatronic Engineering Tainan70101 Taiwan

In this article, we investigate the problem of fast finite-time target tracking control of the inertially stabilized platform (ISP) with a camera that mounted on a mobile robot. At first, the kinematics of the onboard ISP target tracking system is established by the geographic coordinate and pinhole camera model, which includes the state-dependent uncertainties and external disturbances. Then, based on the image information captured by camera, a continuous image-based fast finite-time control algorithm is designed for target tracking. Rigorous analysis proves that the target can be tracked at the center of camera's view in finite time in the absence of disturbances. In the presence of disturbances, the tracking error can converge to a region around the origin in finite time. Simulation and experimental comparisons are presented to confirm the efficiency of the proposed method. © 1982-2012 IEEE.

关键词： Mobile robots

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A Pick-And-Throw Method for Enhancing Robotic Sorting Ability via Deep Reinforcement Learning 36

A Pick-And-Throw Method for Enhancing Robotic Sorting Abilit...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Fang, Zihan Hou, Yanxu Li, Jun Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education Nanjing210096 China Shenzhen Research Institute Southsast University Shenzhen518057 China

ISBN: (纸本)9781665437127

To promote the work capacity and efficiency in a weakly structured logistics sorting scene, a pick-And-Throw method based on reinforcement learning is proposed. First, a D N-based learning algorithm is used to obtain a picking feasibility distribution map for guiding an optimal picking action. Second, Deep Deterministic Policy Gradient (DDPG) algorithm is utilized to train a throwing policy. The throwing policy outputs a throwing velocity of the end-effector of a robot to throw the picked object to a target area. In addition, a memory pool optimization algorithm is also proposed to enhance the convergence of the throwing policy. Experiments are conducted in both simulation and real scenarios and results demonstrate that the proposed method can improve the sorting efficiency and expand the sorting space significantly. © 2021 IEEE.

关键词： Reinforcement learning

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A Dual-Motor Joint Model for Humanoid Robots

A Dual-Motor Joint Model for Humanoid Robots

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第25届中国控制与决策会议

作者： Jingtao Xue Xiaopeng Chen Ye Tian Zhangguo Yu Fei Meng Qiang Huang IRI School of Mechatronic Engineering Beijing Institute of Technology Key Laboratory of Biomimetic Robots and Systems Ministry of Education Key Laboratory of Intelligent Control and Decision of Complex System

ISBN: (纸本)9781467355339

To make humanoid robots walking fast, it's important to improve driving force of their leg joints. Usually, each joint of humanoid robots is driven by a single motor. Dual-motor joint, on the other hand, is one of the candidate solutions to meet the power requirement needed for fast walking. This paper proposed a new dual-motor control model. In the model, two motors are treated as a single control plant instead of two parallel control plants. With the usage of current distributor, the control model can pump different current to each motor freely so as to eliminate the unbalance of the load imposed on each motor. Simulation and experiment show that the proposed model works well under high joint load and it can be used on a fast walking humanoid robot.

关键词： Humanoid robot, dual motor, control model

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A Parallel Algorithm for Solving General Colored Traveling Salesman Problems 36

A Parallel Algorithm for Solving General Colored Traveling S...

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36th Youth Academic Annual Conference of Chinese Association of Automation, YAC 2021

作者： Ding, Penghui Xu, Xiangping Li, Jun Ministry of Education Southeast University Key Laboratory of Measurement and Control of Complex Systems of Engineering Nanjing210096 China Southeast University Shenzhen Research Institute Shenzhen518057 China

ISBN: (纸本)9781665437127

A colored traveling salesman problem (CTSP) is a generalization of the well-known multiple traveling salesman problem. It utilizes colors to differentiate the accessibility of its cities to ward its salesmen. All the existing algorithms for solving CTSP are serial ones. This paper presents for the first time a parallel algorithm framework for solving general CTSP. Then, an improved variable neighborhood search algorithm is proposed by using this algorithm framework. Finally, extensive simulations are conducted in various test cases. The results show that the algorithm proposed is superior to the compared serial algorithms in terms of convergence and solution quality. © 2021 IEEE.

关键词： Traveling salesman problem

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Optimization of Wide-Baseline Stereo Matching Based on Multi-view Geometry

Optimization of Wide-Baseline Stereo Matching Based on Multi...

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2024 China Automation Congress, CAC 2024

作者： Xu, Yinhui Shan, Shuo Han, Gongben Fang, Shixiong Zhang, Kanjian Wei, Haikun Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of Education School of Automation Southeast University Nanjing China Shenzhen Research Institute Southeast University Shenzhen China

ISBN: (纸本)9798350368604

Stereo vision plays a crucial role in scientific expeditions and facility maintenance. However, fixed-baseline stereo vision lacks accuracy for long-distance measurements, while wide-baseline stereo vision encounters occlusion issues at short distances. In this study, a wide-baseline stereo matching based on multi-view geometric constraints is proposed. Initially, the camera poses of multiple wide-baseline images are estimated, followed by image rectification and depth estimation. Subsequently, the disparity searching is constrained using geometric relationships among the views, resulting in more precise depth maps. Experiments were conducted to verify the superiority of the proposed method. The results indicate that the proposed method achieves an RMSE as low as 0.15 meters, even with invalid depth points. Compared to traditional stereo vision methods, accuracy is improved by approximately 35%, and robustness is significantly enhanced. In the aircraft runway scenario, overall depth map accuracy is higher, and occluded areas are significantly reduced. © 2024 IEEE.

关键词： Stereo vision

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