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arXiv 2025年

作者： Zhang, Ying Tian, Guohui Zhang, Cui-Hua Hua, Changchun Ding, Weili Ahn, Choon Ki School of Electrical Engineering Hebei Key Laboratory of Intelligent Rehabilitation and Neuromodulation Yanshan University Qinhuangdao066004 China School of Control Science and Engineering Shandong University Jinan250061 China School of Electrical Engineering Korea University Seoul136-701 Korea Republic of

Service robots are increasingly entering the home to provide domestic tasks for residents. However, when working in an open, dynamic, and unstructured home environment, service robots still face challenges such as low intelligence for task execution and poor long-term autonomy (LTA), which has limited their deployment. As the basis of robotic task execution, environment modeling has attracted significant attention. This integrates core technologies such as environment perception, understanding, and representation to accurately recognize environmental information. This paper presents a comprehensive survey of environmental modeling from a new task-execution-oriented perspective. In particular, guided by the requirements of robots in performing domestic service tasks in the home environment, we systematically review the progress that has been made in task-execution-oriented environmental modeling in four respects: 1) localization, 2) navigation, 3) manipulation, and 4) LTA. Current challenges are discussed, and potential research opportunities are also highlighted. Copyright © 2025, The Authors. All rights reserved.

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A Remote Sensing Object Classification and Extraction Method Based on Improved U-Net 1

A Remote Sensing Object Classification and Extraction Method...

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1st IEEE International Conference on Data Intelligence and Innovative Application, DIIA 2024

作者： Wen, Lili Qu, Hui Wu, Man Lai, Junxiang Zhang, Wei Research Center for Carbon Sink and Low-Carbon Engineering Beibu Gulf of Guangxi Guangxi Academy of Marine Sciences line Nanning China National Marine Data and Information Service Tianjin China School of Electrical Engineering Guangxi University Nanning China Guangxi Academy of Sciences Guangxi Key Laboratory of Marine Environmental Science Nanning China Electric Power Research Institute of Guangxi Power Grid Co. Ltd. Guangxi Key Laboratory of Intelligent Control and Operation and Maintenance of Power Equipment Nanning China

ISBN: (纸本)9798331504434

This paper addresses the problems of low accuracy and poor adaptability to complex scenes in traditional remote sensing object classification methods, and proposes a remote sensing object classification extraction method based on an improved U-Net. We retained the U-shaped symmetrical structure of U-Net and introduced the Swin Transformer block with self-attention mechanism to replace the original convolutional operation, enhancing the model's ability to extract global features. We also constructed a Bottleneck structure between the encoder and decoder to learn deep feature representations and form a new U-shaped network based on Transformer, improving the network's image semantic segmentation ability in scenarios with small targets and complexity. Experimental results show that the method proposed in this paper has an improvement of more than 9% in evaluation indicators such as recognition accuracy and recall rate compared with existing U-Net and Res-UNet methods, has high semantic segmentation accuracy and robustness, and provides new ideas and technical support for remote sensing image object classification extraction. © 2024 IEEE.

关键词： Semantic Segmentation

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controllable four-wave mixing response in a dual-cavity hybrid optomechanical system

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Chinese Physics B 2021年第5期30卷 406-411页

作者： Lei Shang Bin Chen Li-Li Xing Jian-Bin Chen Hai-Bin Xue Kang-Xian Guo Department of Physics College of Physics and OptoelectronicsTaiyuan University of TechnologyTaiyuan 030024China Key Laboratory of Advanced Transducers and Intelligent Control System Ministry of Education and Shanxi ProvinceTaiyuan 030024China Department of Physics School of Physics and Material ScienceGuangzhou UniversityGuangzhou 510006China

We systematically investigate the four-wave mixing(FWM)spectrum in a dual-cavity hybrid optomechanical system,which is made up of one optical cavity with an ensemble of two-level atoms and another with a mechanical *** this work,we propose that the hybrid dual-cavity optomechanical system can be employed as a highly sensitive mass sensor due to the fact that the FWM spectrum generated in this system has a narrow spectral width and the intensity of the FWM can be easily tuned by controlling the coupling strength(cavity-cavity,atom-cavity).More fascinatingly,the dual-cavity hybrid optomechanical system can also be used as an all-optical switch in view of the easy on/off control of FWM signals by adjusting the atom-pump detuning to be positive or *** proposed schemes have great potential applications in quantum information processing and highly sensitive detection.

关键词： four-wave mixing dual-cavity optomechanical system atomic ensemble

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Minimal-time Deadbeat Consensus and Individual Disagreement Degree Prediction for High-order Linear Multi-agent Systems

arXiv

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arXiv 2023年

作者： Hu, Fu-Long Zhang, Hai-Tao Xu, Bowen Hu, Zhe Ren, Wei School of Artificial Intelligence and Automation Key Laboratory of Image Processing and Intelligent Control The State Key Laboratory of Digital Manufacturing Equipment and Technology Huazhong University of Science and Technology Wuhan430074 China School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University Xi’an710072 China Department of Electrical and Computer Engineering University of California Riverside92521 United States

In this paper, a Hankel matrix-based fully distributed algorithm is proposed to address a minimal-time deadbeat consensus prediction problem for discrete-time high-order multi-agent systems (MASs). Therein, each agent can predict the consensus value with the minimum number of observable historical outputs of its own. Accordingly, compared to most existing algorithms only yielding asymptotic convergence, the present method can attain deadbeat consensus instead. Moreover, based on the consensus value prediction, instant individual disagreement degree value of MASs can be calculated in advance as well. Sufficient conditions are derived to guarantee both the minimal-time deadbeat consensus and the instant individual disagreement degree prediction. Finally, both the effectiveness and superiority of the proposed deadbeat consensus algorithm are substantiated by numerical simulations. Copyright © 2023, The Authors. All rights reserved.

关键词： Multi agent systems

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A Fault Diagnosis Method For Hydraulic End Of Drilling Pump Based On Dynamic Fault Tree 12

A Fault Diagnosis Method For Hydraulic End Of Drilling Pump ...

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12th IEEE Global Reliability and Prognostics and Health Management, PHM-Nanjing 2021

作者： He, Xuan Li, Bo Hong, Tao Aircraft Swarm Intelligent Sensing And Cooperative Control Key Laboratory Of Sichuan Province School Of Aeronautics And Astronautics University Of Electronic And Technology Of China Chengdu China

ISBN: (纸本)9781665401302

As the heart of drilling engineering, drilling pump affects drilling engineering. Because of its working environment and nature, it is prone to failure. In order to avoid the decrease of work efficiency and the increase of cost caused by faults, it is necessary to diagnose faults quickly and accurately. At present, the traditional method of drilling pump fault diagnosis is fault tree analysis. However, the fault tree cannot represent the fault of a certain part of the drilling pump in a fixed order, resulting in inaccurate diagnosis results. Therefore, dynamic logic gate is introduced and dynamic fault tree is established to solve the dynamic problem of drilling pump fault. To improve the reliability and accuracy of the diagnosis results, the dynamic fault tree diagnosis model is studied and improved. Taking the hydraulic end of drilling pump as an example, the diagnosis results are consistent with the actual situation, which proves the effectiveness of the improved algorithm. © 2021 IEEE.

关键词： Bayesian networks

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Gaussian Mixture Model and Double-Weighted Deep Neural Networks for Data Augmentation Soft Sensing

Gaussian Mixture Model and Double-Weighted Deep Neural Netwo...

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Data Driven control and Learning Systems (DDCLS)

作者： Xiaoyu Jiang Le Yao Zeyu Yang Zhihuan Song Bingbing Shen State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou Zhejiang PR China School of Mathematics Hangzhou Normal University Hangzhou P. R. China Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou University Huzhou China

In practice, data-driven soft sensors often face data shortages in modeling. Data augmentation technology has offered a feasible solution for this problem in recent years. However, how to better use virtual data for data augmentation is still an open topic. In this paper, a novel data augmentation soft sensing method is proposed. It uses Gaussian mixture models (GMM) to generate virtual data for the training dataset, and developed a double-weighted neural network (dwDNN) for weighted regression modeling. On top of that, the Bayesian optimization algorithm is applied to the weight selection of dwDNN to further enhance the efficiency and effectiveness of GMM -dwDNN on virtual data. In the end, a real industrial case is used to illustrate the superiority of the proposed approach in soft sensing.

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The applied principles of EEG analysis methods in neuroscience and clinical neurology

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Military Medical Research 2024年第6期11卷 907-941页

作者： Hao Zhang Qing-Qi Zhou He Chen Xiao-Qing Hu Wei-Guang Li Yang Bai Jun-Xia Han Yao Wang Zhen-Hu Liang Dan Chen Feng-Yu Cong Jia-Qing Yan Xiao-Li Li School of Systems Science Beijing Normal UniversityBeijing 100875China College of Electrical and Control Engineering North China University of TechnologyBeijing 100041China School of Automation Science and Engineering South China University of TechnologyGuangzhou 510641China Department of Psychology the State Key Laboratory of Brain and Cognitive Sciencesthe University of Hong KongHong Kong SAR 999077China HKU-Shenzhen Institute of Research and Innovation Shenzhen 518057GuangdongChina Department of Health Technology and Informatics the Hong Kong Polytechnic UniversityHong Kong SAR 999077China Department of Rehabilitation Medicine the First Afliated Hospital of Nanchang UniversityNanchang 330006China Rehabilitation Medicine Clinical Research Center of Jiangxi Province Nanchang 330006China Beijing Key Laboratory of Learning and Cognition School of PsychologyCapital Normal UniversityBeijing 100048China School of Communication Science Beijing Language and Culture UniversityBeijing 100083China Institute of Electrical Engineering Yanshan UniversityQinhuangdao 066004HebeiChina School of Computer Science Wuhan UniversityWuhan 430072China School of Biomedical Engineering Faculty of Electronic Information and Electrical EngineeringDalian University of TechnologyDalian 116081LiaoningChina Guangdong Artifcial Intelligence and Digital Economy Laboratory(Guangzhou) Guangzhou 510335China.

Electroencephalography(EEG)is a non-invasive measurement method for brain *** to its safety,high resolution,and hypersensitivity to dynamic changes in brain neural signals,EEG has aroused much interest in scientific research and medical *** article reviews the types of EEG signals,multiple EEG signal analysis methods,and the application of relevant methods in the neuroscience feld and for diagnosing neurological ***,3 types of EEG signals,including time-invariant EEG,accurate event-related EEG,and random event-related EEG,are ***,5 main directions for the methods of EEG analysis,including power spectrum analysis,time-frequency analysis,connectivity analysis,source localization methods,and machine learning methods,are described in the main section,along with diferent sub-methods and effect evaluations for solving the same ***,the application scenarios of different EEG analysis methods are emphasized,and the advantages and disadvantages of similar methods are *** article is expected to assist researchers in selecting suitable EEG analysis methods based on their research objectives,provide references for subsequent research,and summarize current issues and prospects for the future.

关键词： Electroencephalogram analysis methods Applied principles Neuroscience Diagnosis Neurological diseases

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Robust Data-Driven control Against Actuator FDI for Unknown Linear Systems

Robust Data-Driven Control Against Actuator FDI for Unknown ...

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International Conference on control and automation (ICCA)

作者： Wenjie Liu Jian Sun Fang Deng Gang Wang Jie Chen National Key Lab of Autonomous Intelligent Unmanned Systems School of Automation Beijing Institute of Technology Beijing China Beijing Institute of Technology Chongqing Innovation Center Chongqing China Department of Control Science and Engineering Tongji University Shanghai China National Key Lab of Autonomous Intelligent Unmanned Systems Beijing Institute of Technology Beijing China

ISBN: (数字)9798350354409

ISBN: (纸本)9798350354416

This paper designs a data-driven controller for unknown linear systems whose actuators suffer from false data injection (FDI) attacks, using only noisy input-state data. To achieve this objective, a general FDI attack model is introduced, which imposes constraints only on the switching frequency of attack channels and the magnitude of attack matrices. A time-varying state feedback control law is designed based on offline and online input-state data, which adapts to the channel switching of FDI attacks. This is achieved by solving a data-based semi-definite programs (SDPs) on-the-fly such that stabilizing the set of subsystems consistent with both offline clean data and online attack-corrupted data. It is shown that under mild conditions on the attack and the noise, the feasibility of the proposed SDP guarantees that the controller stabilizes the attack-corrupted system. A numerical example is presented to validate the effectiveness of the proposed method.

关键词： Linear systems Switched systems Actuators State feedback Switching frequency Switches control systems

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Composite Nonlinear Trajectory Tracking control of Co-Driving Vehicles Using Self-Triggered Adaptive Dynamic Programming

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IEEE Transactions on Consumer Electronics 2025年

作者： Hu, Chuan Ge, Sicheng Shi, Yingkui Gao, Weinan Guo, Wenfeng Zhang, Xi Shanghai Jiao Tong University Intelligent Vehicle Institute School of Mechanical Engineering Shanghai200240 China Shanghai Jiao Tong University School of Mechanical Engineering Shanghai200240 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China Tsinghua University School of Vehicle and Mobility Beijing100190 China

This article presents a composite nonlinear feedback (CNF) control method using self-triggered (ST) adaptive dynamic programming (ADP) algorithm in a human-machine shared steering framework. For the overall system dynamics, a two-degrees-of-freedom (2-DOF) vehicle model is established and a two-point preview driver model is adopted. A dynamic authority allocation strategy based on cooperation level is proposed to combine the steering input of the human driver and the automatic controller. To make further improvements in the controller design, three main contributions are put forward. Firstly, the CNF controller is designed for trajectory tracking control with refined transient performance. Besides, the self-triggered rule is applied such that the system will update in discrete times to save computing resources and increase efficiency. Moreover, by introducing the data-based ADP algorithm, the optimal control problem can be solved through iteration using system input and output information, reducing the need for accurate knowledge of system dynamics. The effectiveness of the proposed control method is validated through Carsim-Simulink co-simulations in diverse driving scenarios. © 2025 IEEE. All rights reserved.

关键词： Steering

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Adaptive Dynamic Programming and Decentralized Optimal Output Regulation of Two-Time-Scale Interconnected Systems

Adaptive Dynamic Programming and Decentralized Optimal Outpu...

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International Conference on control, automation and Information Sciences (ICCAIS)

作者： Jianguo Zhao Chunyu Yang Linna Zhou Weinan Gao School of Information and Control Engineering China University of Mining and Technology Xuzhou China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China

In this paper, we address the decentralized optimal output regulation problem for two-time-scale (TTS) interconnected systems with unknown slow dynamics through adaptive dynamic programming (ADP). Firstly, singular perturbation theory is leveraged to decompose the TTS interconnected system into a slow subsystem and several interconnected fast time-scale subsystems. Secondly, we develop a decentralized optimal control scheme for the fast time-scale subsystems with interconnections. Also, we devise an off-policy ADP algorithm to learn the optimal control strategy for the slow time-scale subsystem with unknown dynamics. Moreover, we combine the slow and fast control strategies to design the composite decentralized optimal output regulator. Both optimality and stability of the closed-loop system are rigorously analyzed. Finally, simulation results are provided to validate the effectiveness of the proposed methodology.

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