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2024 International Conference on Advanced Robotics and Intelligent Systems, ARIS 2024

作者： Tsai, Fu-Yu Song, Kai-Tai Institute of Electrical and Control Engineering College of Electrical and Computer Engineering National Yang Ming Chiao Tung University 1001 Ta Hsueh Road Hsinchu Taiwan

ISBN: (纸本)9798350362572

This paper presents a novel path generation method for robotic surface polishing based on point cloud data. In this method, an initial path is first planned to create waypoints via the point cloud. The method estimates the contact area to dynamically adjust initial path points to ensure uniform trajectory coverage on the surface. In this paper, the bilateral tool path generation (BTPG) algorithm is proposed based on a dual-thread strategy to expedite polishing path generation. Further, we introduce a method that generates smooth postures to enhance the stability of the robotic polishing pose. In this work, hybrid force/velocity control scheme is adopted to track polishing path and apply the assigned force on the workpiece freeform surface. Practical experimental results show that the proposed method achieves 95% improvement in path planning time and 85% in smoothness of the robot fourth joint's posture compared with the baseline slicing method. © 2024 IEEE.

关键词： Robot programming

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Comparison of Two AC Current De-tumbling Methods for Uncooperative Space Target through Electromagnetic Characteristic Analysis 1

Comparison of Two AC Current De-tumbling Methods for Uncoope...

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1st Aerospace Frontiers Conference, AFC 2024

作者： Du, Lei Chen, Zhen Hu, Hengzai Liu, Xiangdong Zhang, Qiang Zhang, Kemo Liu, Hui School of Automation Beijing Institute of Technology Beijing100081 China Beijing Institute of Control Engineering Beijing100191 China Beijing Institute Computer for Application Beijing100089 China

ISBN: (纸本)9781510681613

A large number of space debris with rotational motion occupy scarce orbital resources, thus, a de-tumbling phase may be necessary prior to the capturing phase to reduce the risk of collision. A new contactless AC current de-tumbling method based on the principle of electromagnetic eddy current is proposed in this paper. Firstly, an analysis is conducted on external disturbances affecting the attitude of the debris and de-tumbling device, such as magnetic field perturbations. Compared to the electromagnetic forces acting on de-tumbling device, the influence of the geomagnetic force on detumbling device can be negligible. However, the geomagnetic torque acting on de-tumbling device cannot be ignored. Then, the advantages and disadvantages of the two AC current cases are compared and analyzed, and the method to increase the de-tumbling torque can be obtained by adjusting the frequency of the three phase AC current. Finally, the three-dimensional attitude dynamics of the uncooperative space target is established taking a spinning cylinder as the hypothetical target of de-tumbling. Simulation shows that the de-tumbling method is effective and the de-tumbling rate can be greatly improved by adjusting the frequency of the three phase AC current. © 2024 SPIE.

关键词： Space debris

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A novel fractional physics-informed neural networks method for solving the time-fractional Huxley equation

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Neural Computing and Applications 2024年第30期36卷 19097-19119页

作者： Shi, Jieyu Yang, Xiaozhong Liu, Xinlong School of Control and Computer Engineering North China Electric Power University Beijing102206 China Institute of Information and Computing School of Mathematics and Physics North China Electric Power University Beijing102206 China

The neural network methods in solving differential equations have significant research importance and promising application prospects. Aimed at the time-fractional Huxley (TFH) equation, we propose a novel fractional physics-informed neural networks (fPINNs) method. By integrating the physical information of the TFH equation into neural networks, the fPINNs are trained as a precise approximation model for solving the TFH equation. The fPINNs method involves calculating the Caputo fractional derivative using the L1 formula and estimating the integer-order derivative through the chain rule. The Adam algorithm is employed to optimize two kinds of loss functions constructed by hard and soft constraints, respectively. Through an analysis of the impact of fPINNs parameters on training results, we identify the optimal parameter configuration for solving both one-dimensional and two-dimensional (2D) TFH equations. Numerical examples validate the efficiency and robustness of the fPINNs method in solving TFH equations. Furthermore, numerical simulation of the 2D TFH problem demonstrates the method’s practical applicability to engineering problem. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Numerical methods

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Point Cloud Classification Using Content-Based Transformer via Clustering in Feature Space

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IEEE/CAA Journal of Automatica Sinica 2024年第1期11卷 231-239页

作者： Yahui Liu Bin Tian Yisheng Lv Lingxi Li Fei-Yue Wang 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 100190 IEEE the Transportation and Autonomous Systems Institute(TASI)and the Department of Electrical and Computer Engineering Purdue School of Engineering and TechnologyIndiana University-Purdue University Indianapolis(IUPUI)Indianapolis 46202 USA

Recently, there have been some attempts of Transformer in 3D point cloud classification. In order to reduce computations, most existing methods focus on local spatial attention,but ignore their content and fail to establish relationships between distant but relevant points. To overcome the limitation of local spatial attention, we propose a point content-based Transformer architecture, called PointConT for short. It exploits the locality of points in the feature space(content-based), which clusters the sampled points with similar features into the same class and computes the self-attention within each class, thus enabling an effective trade-off between capturing long-range dependencies and computational complexity. We further introduce an inception feature aggregator for point cloud classification, which uses parallel structures to aggregate high-frequency and low-frequency information in each branch separately. Extensive experiments show that our PointConT model achieves a remarkable performance on point cloud shape classification. Especially, our method exhibits 90.3% Top-1 accuracy on the hardest setting of ScanObjectN N. Source code of this paper is available at https://***/yahuiliu99/PointC onT.

关键词： Content-based Transformer deep learning feature aggregator local attention point cloud classification

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Multi-scale network for single image deblurring based on ensemble learning module

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Multimedia Tools and Applications 2025年第11期84卷 9045-9064页

作者： Wu, WenBo Pan, Yun Su, Na Wang, JingTao Wu, ShaoChuan Xu, ZeKun Yu, YouJian Liu, YaPeng State Key Laboratory of Media Convergence and Communication Communication University of China Beijing100024 China School of Computer and Cyberspace Security Communication University of China Beijing100024 China School of Electronics and Information Engineering Harbin Institute of Technology Harbin150080 China College of Computer and Control Engineering Northeast Forestry University Harbin150040 China School of Computer and Information Engineering Tianjin Chengjian University Tianjin300384 China School of Computer Science and Technology Anhui University Hefei230601 China

In this paper, we have identified two primary issues with current multi-scale image deblurring methods. On the one hand, the blurring scale is ignored. On the other hand, the context information of images is not fully utilized, and the spatial detail information of images is lost. To better solve the above problems, on top of that, this paper proposes a multi-scale network for single image deblurring. The designed network contains two parts: 1) Ensemble Learning Model: The Weak Learning Model in this module connects multiple weak learners in series to increase network depth. Moreover, through multi-scale feature ensemble module, the output features and intermediate features of different weak learner are integrated, and finally strong learner is used to enhance the features with semantic information at different scales;2) Spatial Feature Enhancement Module: This module can preserve spatial detail information of images and compensate for the disadvantages of losing image spatial details due to continuous downsampling in the Ensemble Learning Model structure. Our proposed deblurring model has been extensively evaluated on several benchmark datasets, and the model achieves superior performance compared to the most advanced deblurring methods. In particular, the peak signal-to-noise ratio (PSNR) and structural similarity index measure (SSIM) values are 33.57 dB and 0.965 on the GoPro dataset, respectively, with PSNR and SSIM improving by 0.49 dB and 0.002 over NAFNet, and 0.65 dB and 0.004 over Restormer. © The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature 2024.

关键词： Learning systems

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On convergence of covariance matrix of empirical Bayes hyper-parameter estimator

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control Theory and Technology 2024年第2期22卷 149-162页

作者： Yue Ju Biqiang Mu Tianshi Chen School of Data Science and Shenzhen Research Institute of Big Data The Chinese University of Hong KongShenzhenShenzhen518172GuangdongChina Division of Decision and Control Systems School of Electrical Engineering and Computer ScienceKTH Royal Institute of Technology10044StockholmSweden Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and System ScienceChinese Academy of SciencesBeijing100190China

Regularized system identification has become the research frontier of system identification in the past *** related core subject is to study the convergence properties of various hyper-parameter estimators as the sample size goes to *** this paper,we consider one commonly used hyper-parameter estimator,the empirical Bayes(EB).Its convergence in distribution has been studied,and the explicit expression of the covariance matrix of its limiting distribution has been ***,what we are truly interested in are factors contained in the covariance matrix of the EB hyper-parameter estimator,and then,the convergence of its covariance matrix to that of its limiting distribution is *** general,the convergence in distribution of a sequence of random variables does not necessarily guarantee the convergence of its covariance ***,the derivation of such convergence is a necessary complement to our theoretical analysis about factors that influence the convergence properties of the EB hyper-parameter *** this paper,we consider the regularized finite impulse response(FIR)model estimation with deterministic inputs,and show that the covariance matrix of the EB hyper-parameter estimator converges to that of its limiting ***,we run numerical simulations to demonstrate the efficacy of ourtheoretical results.

关键词： Regularized system identification Hyper-parameter estimator Empirical Bayes Convergence of covariance matrix

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PSRMER: Proactive Services Recommendation Driven-by Multimodal Emotion Recognition

PSRMER: Proactive Services Recommendation Driven-by Multimod...

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2023 IEEE International Conference on Web Services, ICWS 2023

作者： Liu, Zhizhong Huang, Guangyu Chu, Dianhui Sun, Yuhang Yantai University School of Computer and Control Engineering Yantai China Harbin Institute of Technology School of Computer Science and Technology Weihai China

ISBN: (纸本)9798350304855

In the past three years, global COVID-19 pandemic not only impacted people's physical health but also significantly affected their mental health, which resulting in rapid increase of psychological problems. Emotions are a common manifestation of psychological changes, and some services (such as music, video, or psychological counseling services) can help users to adjust their emotions in a timely manner, thus to avoid bringing extreme events (e.g., running away from home or committing suicide). Therefore, how to perceive users' real-time emotions and then recommend the most appropriate services to users has become a challenge. To address this issue, this work proposes an approach for proactive services recommendation driven-by multimodal emotion recognition (named as PSRMER). Specifically, PSRMER first actively identifies a user's emotion with a multimodal emotion recognition model based on BiGRU and Transformer;Then, considering the user's emotion and preferences, PSRMER selects the optimal services based on an index-graph linking different emotions and various services;Finally, PSRMER proactively recommends the selected optimal service to the user. Extensive experiments have been conducted and the effectiveness of our proposed method have been proved. Moreover, the proposed method can also be used in smart education, smart transportation, smart elderly care and other modern industry fields. © 2023 IEEE.

关键词： COVID-19

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Multi-objective Optimization of the Interior Permanent Magnet Synchronous Motor for Electric Vehicles 6

Multi-objective Optimization of the Interior Permanent Magne...

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6th IEEE International Electrical and Energy Conference, CIEEC 2023

作者： Huang, Changchuang Kou, Baoqaun Zhao, Xiaokun Harbin Institute of Technology Electrical Engineering and Automation Heilongjiang China Northeast Forestry University Computer and Control Engineering Heilongjiang China

ISBN: (纸本)9798350346671

Permanent magnet synchronous motors (PMSMs) are widely used in various fields due to their high efficiency, high power factor and small volume. In this paper, a segmented permanent magnet interior permanent magnet synchronous motor (IPMSM) is proposed and optimized. First, the topology of the IPMSM is introduced. Second, multi-objective optimization is performed by non-dominated sorting genetic algorithm-II (NSGA-II) using rotor structure parameters as optimization variables, saliency ratio as constraint, electromagnetic torque and constant-power speed range as objective functions. Then, the electromagnetic characteristics of the initial IPMSM and the optimized IPMSM are analyzed and compared. The results show that the torque and constant-power speed range of the optimized motor are improved, which verifies the validity of the optimization method. The proposed motor is suitable for electric vehicles, which require large torque at low speeds and wide constant-power speed range at high speeds. © 2023 IEEE.

关键词： Multiobjective optimization

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Interference Suppression and Jitter Elimination Ability-Based Adaption Tracking Guidance for Robotic Fishes

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IEEE/CAA Journal of Automatica Sinica 2025年第1期12卷 126-137页

作者： Dongfang Li Jie Huang Rob Law Xin Xu Limin Zhu Edmond Q.Wu IEEE the School of Electrical Engineering and Automation Fuzhou University the Key Laboratory of System Control and Information Processing Ministry of Education the School of Electrical Engineering and Automation Fuzhou University 5G+Industrial Internet Institute Fuzhou University the University of Macau the College of Mechatronics and Automation National University of Defense Technology the School of Mechanical Engineering Shanghai Jiao Tong University the Department of Computer Science and Engineering Shanghai Jiao Tong University

This work presents an adaptive tracking guidance method for robotic fishes. The scheme enables robots to suppress external interference and eliminate motion jitter. An adaptive integral surge line-of-sight guidance rule is designed to eliminate dynamics interference and sideslip issues. Limited-time yaw and surge speed observers are reported to fit disturbance variables in the model. The approximation values can compensate for the system's control input and improve the robots' tracking ***, this work develops a terminal sliding mode controller and third-order differential processor to determine the rotational torque and reduce the robots' run jitter. Then, Lyapunov's theory proves the uniform ultimate boundedness of the proposed method. Simulation and physical experiments confirm that the technology improves the tracking error convergence speed and stability of robotic fishes.

关键词： Adaptive integral surge line-of-sight approximation robotic fish speed observer

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Supremal and Robust control against Actuator Jamming Attacks in Cyber-Physical Systems

IEEE Transactions on Intelligent Vehicles

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IEEE Transactions on Intelligent Vehicles 2023年第9期9卷 1-13页

作者： Duo, Wenli Wang, Shouguang Zhou, MengChu You, Dan Wisniewski, Remigiusz Bazydlo, Grzegorz Albeshri, Aiiad Institute of Systems Engineering Macau University of Science and Technology Macau China School of Information and Electronic Engineering Zhejiang Gongshang University Hangzhou China Department of Electrical and Computer Engineering New Jersey Institute of Technology Newark NJ USA Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Department of Computer Science King Abdulaziz University Jeddah Saudi Arabia

Cyber-physical systems, such as unmanned aerial vehicles and connected and autonomous vehicles, are vulnerable to cyber attacks, which can cause significant damage to society. This paper examines the attack issue in cyber-physical systems within the framework of discrete event systems. Specifically, we consider a scenario where a malicious intruder injects a jamming signal into an actuator channel. It disrupts the transmission of control commands and prevents an actuator from receiving them. This is termed an actuator jamming attack. In the paper, we first analyze the closed-loop system behavior under such an attack. An attack structure is constructed to illustrate how an intruder exploits a jamming attack to drive a system into unsafe states. Then, we study the supervisory control problem for a system exposed to such an attack. The problem is reduced to a basic supervisory control one in discrete event systems by introducing the concept of dynamically controllable language. A solution to this problem is explored, where we establish an existence condition for a supremal and robust supervisor that is capable of defending against actuator jamming attacks, and design an algorithm to derive it. Finally, the effectiveness of our method is illustrated by an intelligent automated guided vehicle system. IEEE

关键词： Discrete event simulation

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