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Output-Feedback Adaptive Neural Network control for Uncertain Nonsmooth Nonlinear Systems With Input Deadzone and Saturation

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IEEE Transactions on Cybernetics 2023年第9期53卷 5957-5969页

作者： Zong, Guangdeng Xu, Qian Zhao, Xudong Su, Shun-Feng Song, Limei Tiangong University School of Control Science and Engineering The Tianjin Key Laboratory of Intelligent Control of Electrical Equipment Tianjin300387 China Tiangong University School of Control Science and Engineering Tianjin300387 China Qufu Normal University School of Engineering Rizhao276826 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China National Taiwan University of Science and Technology Department of Electrical Engineering Taipei106 Taiwan

Nonsmooth nonlinear systems can model many practical processes with discontinuous property and are difficult to be stabilized by classical control methods like smooth nonlinear systems. This article considers the output-feedback adaptive neural network (NN) control problem for nonsmooth nonlinear systems with input deadzone and saturation. First, the nonsmooth input deadzone and saturation is converted to a smooth function of affine form with bounded estimation error by means of the mean-value theorem. Second, with the help of approximation theorem and Filippov's differential inclusion theory, the given nonsmooth system is converted to an equivalent smooth system model. Then, by introducing a proper logarithmic barrier Lyapunov function (BLF), an output-feedback adaptive NN strategy is set up by constructing an appropriate observer and adopting the adaptive backstepping technique. A new stability criterion is established to guarantee that all the signals in the closed-loop system are semiglobally uniformly ultimately bounded (SGUUB). Finally, comparative simulations through Chua's oscillator are offered to verify the effectiveness of the proposed control algorithm. © 2013 IEEE.

关键词： Stability criteria

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Visual Management based on Integrated Convolution Neural Network 2

Visual Management based on Integrated Convolution Neural Net...

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2nd International Conference on Machine Vision, Image Processing and Imaging technology, MVIPIT 2024

作者： Gao, Jie Shi, Dongqiang Zhang, Ying Quality Manage Department Beijing Aerospace Automatic Control Institute Beijing China Project Manage Department Beijing Aerospace Automatic Control Institute Beijing China National Key Laboratory of Science and Technology on Aerospace Intelligent Beijing Aerospace Automatic Control Institute Peking University Beijing China

ISBN: (纸本)9798331543037

Using convolutional neural network technology for model compression, the convolutional layer operation speed is improved through multiple row-column parallel computing units in the convolution operation acceleration module. At the same time, it is proposed that a convolution operation acceleration module should be used to build a multi-way parallel architecture of the convolution layer, saving time and improving management efficiency. By utilizing IP core development and efficient management of algorithm hardware, image big data pre-processing and post-processing calculations are realized through PS-side instruction scheduling. Using a dedicated neural network processor architecture and a dedicated algorithm acceleration core, computing power support is provided, the autonomy of the visual image management system is improved, and the dependence on measurement and control equipment is reduced, thereby realizing the rapid deployment of neural networks. ©2024 IEEE.

关键词： Convolutional neural networks

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Dual-view Label and Feature Supervision Network for Semi-supervised Medical Image Segmentation 24

Dual-view Label and Feature Supervision Network for Semi-sup...

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7th International Conference on Signal Processing and Machine Learning, SPML 2024

作者： Zhou, Zhen Li, Zuoyong Wang, Zhengyuan Teng, Shenghua Wang, Tao College of Electronic and Information Engineering Shandong University of Science and Technology Qingdao China Fujian Provincial Key Laboratory of Information Processing and Intelligent Control School of Computer and Big Data Minjiang University Fuzhou China

ISBN: (纸本)9798400717192

Semi-supervised learning has garnered significant attention, particularly in medical image segmentation, owing to its capacity to leverage a large number of unlabeled data and a limited amount of labeled data to improve performance. However, most existing semi-supervised segmentation methods exhibit shortcomings in supervising unlabeled data, both in the label space due to potential noise in pseudo-labels, and the feature space due to indistinct class boundaries. To address the issues above, we propose Dual-view Label and Feature Supervision Network, termed DLFS-Net, to enhance label and feature supervision for unlabeled data. Our approach is based on a dual-view learning strategy that incorporates two modules: the Confidence Fusion Supervision Module (CFS) and the Dual-view Prototype Learning Module (DPL). Specifically, the CFS module generates weight matrices to integrate complementary information from two model outputs, producing more accurate pseudo-labels in the label space. The DPL module reduces intra-class variations in the feature space from two different views through prototype learning to generate clear class boundaries. Experiments on the LA and Pancreas-CT datasets demonstrate that our framework show solid gains (e.g.,1.76% Dice and 2.37% Jaccard improvement on Pancreas-CT dataset with 20% labeled data) compared with the state-of-the-art methods. © 2024 ACM.

关键词： Self-supervised learning

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An open dataset for intelligent recognition and classification of abnormal condition in longwall mining

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Coal International 2024年第4期272卷 22-35页

作者： Yang, Wenjuan Zhang, Xuhui Ma, Bing Wang, Yanqun Wu, Yujia Yan, Jianxing Liu, Yongwei Zhang, Chao Wan, Jicheng Wang, Yue Huang, Mengyao Li, Yuyang Zhao, Dian School of Mechanical Engineering Xi’an University of Science and Technology No.58 Mid-Yanta Road Xi’an710054 China Shaanxi Key Laboratory of Mine Electromechanical Equipment Intelligent Detection and Control No.58 Yanta Road Xi’an710054 China MARCO automatic control system development Co. LTD No.20 Fenghui South Road Xi’an710054 China

The underground coal mine production of the fully mechanised mining face exists many problems, such as poor operating environment, high accident rate and so on. Recently, the intelligent autonomous coal mining is gradually replacing the traditional mining process. The artificial intelligence technology is an active research area and is expected to identify and warn the underground abnormal conditions for intelligent longwall mining. It is inseparable from the construction of datasets, but the downhole dataset is still blank at present. This work develops an image dataset of underground longwall mining face (DsLMF+), which consists of 138004 images with annotation 6 categories of mine personnel, hydraulic support guard plate, large coal, towline, miners’ behaviour, and mine safety helmet. All the labels of dataset are publicly available in YOLO format and COCO format. The availability and accuracy of the datasets were reviewed by experts in coal mine field. The dataset is open access and aims to support further research and advancement of the intelligent identification and classification of abnormal conditions for underground mining. © 2024 Tradelink Publications Ltd.. All rights reserved.

关键词： Longwall mining

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Practical Distance-Based Formation Stabilization and Tracking of Nonholonomic Multi-agent Systems 7th

Practical Distance-Based Formation Stabilization and Tracki...

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7th Chinese Conference on Swarm Intelligence and Cooperative control, CCSICC 2023

作者： Tang, Xiangyu Yu, Jianglong Dong, Xiwang Yang, Xiangyu Ren, Zhang School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Institute of Artificial Intelligence Beihang University Beijing100191 China Science and Technology on Complex System Control and Intelligent Agent Cooperation Laboratory Beijing100074 China

ISBN: (纸本)9789819733279

This paper investigates practical distance-based formation stabilization and tracking issues for nonholonomic multi-agent systems (MASs). Firstly, the agent’s nonholonomic kinematics is represented as a left invariant system on a Lie group. Through the introduction of an auxiliary state and a virtual control guided by the transverse function method, the original system is effectively converted into a fully actuated one. Secondly, under the assumption of infinitesimal and minimal rigidity in the graph, a gradient-descent-based virtual controller is devised for the transformed MASs, facilitating practical rigid formation stabilization. Thirdly, a discontinuous distributed velocity estimator is designed for formation tracking. The nonholonomic MASs achieve the desired formation shape while effectively tracking the reference trajectory. Finally, the effectiveness of the proposed method is verified by numerical examples. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Multi agent systems

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Data Security Risk Assessment Method of intelligent and Connected Vehicles Based on Data Security Classification and Grading 8

Data Security Risk Assessment Method of Intelligent and Conn...

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8th IEEE International Conference on intelligent Transportation Engineering, ICITE 2023

作者： Wang, Liyong Jin, Long Ji, Haojie Wang, Jingyan Chen, Yupeng Fang, Junzhe State Key Laboratory of Intelligent Vehicle Safety Technology Chongqing China Ministry of Education Beijing Information Science & Technology University Key Laboratory of Modern Measurement & Control Technology Beijing China State Key Laboratory of Intelligent Vehicle Safety Chongqing China China Automotive Engineering Research Institute Co. Ltd Chongqing China

ISBN: (纸本)9798350314212

The rapid development of intelligent and connected vehicle (ICV) has brought increasing communication data and valuable data assets. However, the diversity and complexity of ICV data make it hard to carry out data security risk assessment. Aiming at the problem, this paper proposes an effective analytical method for the data security risk assessment method of ICVs. Considering the typical scenario for the generated data of ICVs, a data security classification and grading framework is constructed from the aspects of in-vehicle data, personal data, and external environment data. The data security risk assessment model is built for ICVs and includes functional modules such as asset identification, threat identification, impact assessment, vulnerability identification, and risk assessment. This work provides methods and security management and control strategy support for ICV data security risk assessment. © 2023 IEEE.

关键词： data classification and grading data security intelligent and connected vehicles risk assessment

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Magnetic Microrobots Fabricated by Photopolymerization and Assembly

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Cyborg and Bionic Systems 2023年第1期4卷 41-46页

作者： Xiyue Liang Yue Zhao Dan Liu Yan Deng Tatsuo Arai Masaru Kojima Xiaoming Liu Key Laboratory of Biomimetic Robots and Systems Ministry of EducationState Key Laboratory of Intelligent Control and Decision of Complex SystemBeijing Advanced Innovation Center for Intelligent Robots and Systemsand School of Mechatronical EngineeringBeijing Institute of TechnologyBeijing 100081China Center for Neuroscience and Biomedical Engineering The University of Electro-CommunicationsTokyo 182-8585Japan Department of Materials Engineering Science Osaka UniversityOsaka 560-8531Japan

Magnetic soft microrobots have great potential to access narrow spaces and conduct multiple tasks in the biomedical *** now,drug delivery,microsurgery,disease diagnosis,and dredging the blocked blood vessel have been realized by magnetic soft microrobots in vivo or in ***,as the tasks become more and more complex,more functional units have been embedded in the body of the developed magnetic *** magnetic soft microrobots with complex designed geometries,mechanisms,and magnetic orientation are now greatly challenging the fabrication of the magnetic *** this paper,we propose a new method combining photopolymerization and assembly for the fabrication of magnetic soft *** the micro-hand assembly system,magnetic modules with different shapes and materials are firstly arrayed with precise position and orientation ***,the developed photopolymerization system is employed to fix and link these modules with soft *** on the proposed fabrication method,3 kinds of soft magnetic microrobots were fabricated,and the fundamental locomotion was *** believe that the presented fabrication strategy could help accelerate the clinical application of magnetic microrobots.

关键词： Magnetic system precise

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Distributed algorithm for solving variational inequalities over time-varying unbalanced digraphs

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control Theory and technology 2024年第3期22卷 431-441页

作者： Yichen Zhang Yutao Tang Zhipeng Tu Yiguang Hong School of Artificial Intelligence Beijing University of Posts and TelecommunicationsBeijing 100876China Network Technology Laboratory Huawei Technologies Co.Ltd.Beijing 100095China Department of Control Science and Engineering Tongji UniversityShanghai 200092China Shanghai Research Institute for Intelligent Autonomous Systems Tongji UniversityShanghai 200092China

In this paper,we study a distributed model to cooperatively compute variational inequalities over time-varying directed ***,each agent has access to a part of the full mapping and holds a local view of the global set *** virtue of an auxiliary vector to compensate the graph imbalance,we propose a consensus-based distributed projection algorithm relying on local computation and communication at each *** show the convergence of this algorithm over uniformly jointly strongly connected unbalanced digraphs with nonidentical local *** also provide a numerical example to illustrate the effectiveness of our algorithm.

关键词： Variational inequality Distributed computation Multi-agent system Weight-unbalanced graph

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Attention-Based Residual Dense Shrinkage Network for ECG Denoising

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Computer Modeling in Engineering & sciences 2024年第3期138卷 2809-2824页

作者： Dengyong Zhang Minzhi Yuan Feng Li Lebing Zhang Yanqiang Sun Yiming Ling Hunan Provincial Key Laboratory of Intelligent Processing of Big Data on Transportation Changsha University of Science and TechnologyChangsha410114China School of Computer and Communication Engineering Changsha University of Science and TechnologyChangsha410114China School of Computer and Artificial Intelligence Huaihua UniversityHuaihua418000China School of Computer National University of Defense TechnologyChangsha410073China New Energy Centralized Control Center CHN Energy Hunan Power New Energy Co.Ltd.Changsha410000China

Electrocardiogram(ECG)signal is one of the noninvasive physiological measurement techniques commonly usedin cardiac ***,in real scenarios,the ECGsignal is susceptible to various noise erosion,which affectsthe subsequent pathological ***,the effective removal of the noise from ECG signals has becomea top priority in cardiac diagnostic *** at the problem of incomplete signal shape retention andlow signal-to-noise ratio(SNR)after denoising,a novel ECG denoising network,named attention-based residualdense shrinkage network(ARDSN),is proposed in this ***,the shallow ECG characteristics are extractedby a shallow feature extraction network(SFEN).Then,the residual dense shrinkage attention block(RDSAB)isused for adaptive noise ***,feature fusion representation(FFR)is performed on the hierarchicalfeatures extracted by a series of RDSABs to reconstruct the de-noised ECG *** on the MIT-BIHarrhythmia database and MIT-BIH noise stress test database indicate that the proposed scheme can effectively resistthe interference of different sources of noise on the ECG signal.

关键词： Electrocardiogram signal denoising signal-to-noise ratio attention-based residual dense shrinkage network MIT-BIH

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Experimental Study on the Damage Effect of HPM on LNA

Experimental Study on the Damage Effect of HPM on LNA

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International Conference on Vacuum Electronics

作者： Jiawei Huang Bicheng Zhang Ruilong Song Mingwen Zhang Chunguang Ma Yong Luo School of Electronic Science and Engineering University of Electronic Science and Technology of China Chengdu Sichuan China State Key Laboratory of Electromagnetic Space Cognition and Intelligent Control Technology Beijing China

ISBN: (数字)9798350348705

ISBN: (纸本)9798350348712

This paper mainly introduces the damage effect experiment of high power microwave (HPM) on GaAs low noise amplifier (LNA). In HPW injection mode, the influence of high power continuous wave and pulses with different repetition rates on the damage threshold of LNAs was studied. The damage mechanism of LNAs was compared and analyzed, which provides an insight into the protection and reinforcement of LNAs.

关键词： Low-noise amplifiers Semiconductor devices Gallium arsenide Microwave amplifiers Dielectric breakdown Vacuum electronics Electron sources Protection Electromagnetics Electric fields

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