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IEEE International Conference on automation Science and Engineering (CASE)

作者： Fang Wang Gang Xiong Qihang Fang Zhen Shen Di Wang Li Wan Xisong Dong Fei-Yue Wang State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing China Beijing Engineering Research Center of Intelligent Systems and Technology Institute of Automation Chinese Academy of Sciences Beijing China Guangdong Engineering Research Center of 3D Printing and Intelligent Manufacturing Cloud Computing Center Chinese Academy of Sciences Dongguan China School of Mechanical and Automotive Engineering South China University of Technology Guangzhou China Beijing Ten Dimensions Technology Co. Ltd. Beijing China State Key Laboratory for Management and Control of Complex Systems Institute of Automation Chinese Academy of Sciences Beijing China

ISBN: (数字)9798350358513

ISBN: (纸本)9798350358520

3D printing technology has rapidly advanced, yet the variability in deployment environments poses significant challenges for monitoring systems, because the dynamic deployment environments such as changes in lighting and monitoring camera status, can severely impact the effectiveness and robustness of monitoring systems. To tackle this problem in monitoring system, this paper introduces a novel approach for deep neural network (DNN) adaptation to the dynamic environments through self-supervised learning and applies it to during in-situ monitoring. Specifically, we introduce a self-supervised learning strategy that leverages the auxiliary reconstruction task during in-situ monitoring, subsequently applying self-supervised fine-tuning to classification tasks with a new imbalanced-aware classification loss. Our methodology was rigorously evaluated using a real-world dataset for 3D printing defect detection. The experimental outcomes affirm the robustness of our approach, showcasing a higher defect detection accuracy rate than baselines. This substantially mitigates the adverse effects associated with printing defects, thereby increasing the reliability and quality of 3D printing processes.

关键词： Adaptation models Lighting Artificial neural networks Self-supervised learning Three-dimensional printing Feature extraction Robustness Monitoring Image reconstruction Defect detection

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Optimal Sequential False Data Injection Attack Scheme: Finite-Time Inverse Convergence

Optimal Sequential False Data Injection Attack Scheme: Finit...

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IEEE Conference on Decision and control

作者： Xiaoyu Luo Chongrong Fang Chengcheng Zhao Peng Cheng Jianping He The Department of Automation Key Laboratory of System Control and Information Processing Ministry of Education of China Shanghai Jiao Tong University Shanghai Engineering Research Center of Intelligent Control and Management Shanghai China The State Key Laboratory of Industrial Control Technology Institute of Cyberspace Research Zhejiang University China

In this paper, we explore the relationship between the injected attack signal and the attack selection strategy in networked control systems where the adversary desires to steer the system state to the expected malicious one. We construct a sequential attack framework, i.e., the injected false data varies with the sampling time in discrete-time systems, and then derive an optimal sequential FDI attack strategy. The optimal sequential FDI attack strategy reveals the strongly coupled relationship between the injected attack signal and the attack selection strategy. Furthermore, we prove the finite-time inverse convergence of the critical parameters in the injected optimal attack signal by discrete-time Lyapunov analysis, which enables the efficient off-line design of the attack strategy and saves computing sources. Extensive simulations are conducted to show the effectiveness of the injected optimal sequential attack and the relationship between the attack signal and the attack selection strategy.

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Multi-agent deep reinforcement learning for end—edge orchestrated resource allocation in industrial wireless networks

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Frontiers of Information Technology & Electronic Engineering 2022年第1期23卷 47-60页

作者： Xiaoyu LIU Chi XU Haibin YU Peng ZENG State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyang110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyang110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyang110169China University of Chinese Academy of Sciences Beijing100049China

Edge artificial intelligence will empower the ever simple industrial wireless networks(IWNs)supporting complex and dynamic tasks by collaboratively exploiting the computation and communication resources of both machine-type devices(MTDs)and edge *** this paper,we propose a multi-agent deep reinforcement learning based resource allocation(MADRL-RA)algorithm for end-edge orchestrated IWNs to support computation-intensive and delay-sensitive ***,we present the system model of IWNs,wherein each MTD is regarded as a self-learning ***,we apply the Markov decision process to formulate a minimum system overhead problem with joint optimization of delay and energy ***,we employ MADRL to defeat the explosive state space and learn an effective resource allocation policy with respect to computing decision,computation capacity,and transmission *** break the time correlation of training data while accelerating the learning process of MADRL-RA,we design a weighted experience replay to store and sample experiences ***,we propose a step-by-stepε-greedy method to balance exploitation and ***,we verify the effectiveness of MADRL-RA by comparing it with some benchmark algorithms in many experiments,showing that MADRL-RA converges quickly and learns an effective resource allocation policy achieving the minimum system overhead.

关键词： Multi-agent deep reinforcement learning End-edge orchestrated Industrial wireless networks Delay Energy consumption

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Fuzzy PID control for suppressing stick-slip vibration of drill-string system

Fuzzy PID control for suppressing stick-slip vibration of dr...

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2022 Chinese automation Congress, CAC 2022

作者： Hu, Shuaikang Lu, Chengda Wu, Min He, Zhaoqing Song, Haitao Li, Wangnian School of Automation China University of Geosciences Wuhan430074 China Hubei Key Laboratory of Advanced Control Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an Research Institute Co. Ltd. China Coal Technology and Engineering Group Xi’an710077 China

ISBN: (纸本)9781665465335

In this paper, a fuzzy PID (Proportional Integral Derivative) controller is introduced to suppress the stick-slip vibration present in the drilling process. First, the dynamical model of stick-slip vibration is composed of the multi-DOF (Degree-Of-Freedom) drill-string model and the Karnopp model describing the interaction of the drill bit with the rock. Then, stability analysis is made for the initial PID parameters of our controller. To suppress the stick-slip vibration and at the same time mitigate the effects of the stratigraphic environment variation on the control performance, the fuzzy adaptive algorithm is chosen as the method of optimization controller parameters. From the experimental results, it is clear that our controller has good control performance in the complex and changing stratigraphic environment. © 2022 IEEE.

关键词： controllers

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Data-driven control of consensus tracking for discrete-time multi-agent systems

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Journal of the Franklin institute 2023年第7期360卷 4661-4674页

作者： Xiufeng Zhang Gang Wang Jian Sun Faculty of Mechanical and Electrical Engineering Kunming University of Science and Technology Kunming 650504 China Yunnan International Joint Laboratory of Intelligent Control and Application of Advanced Equipment Kunming 650504 China School of Automation Beijing Institute of Technology Beijing 100081 China Key Lab of Intelligent Control and Decision of Complex Systems Beijing 100081 China

This paper investigates the consensus tracking problem of leader-follower multi-agent systems. Different from most existing works, dynamics of all the agents are assumed completely unknown, whereas some input-output data about the agents are available. It is well known from the Willems et al. Fundamental Lemma that when inputs of a linear time-invariant (LTI) system are persistently exciting, all possible trajectories of the system can be represented in terms of a finite set of measured input-output data. Building on this idea, the present paper proposes a purely data-driven distributed consensus control policy which allows all the follower agents to track the leader agent’s trajectory. It is shown that for a linear discrete-time multi-agent system, the corresponding controller can be designed to ensure the global synchronization with local data. Even if the data are corrupted by noises, the proposed approach is still applicable under certain conditions. Numerical examples corroborate the practical merits of the theoretical results.

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Finite-time robust control for transformerless H-bridge cascaded STATCOM with star configuration

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Journal of control and Decision 2021年第2期8卷 106-115页

作者： Xingyu Lv Bangjun Lei Shumin Fei Foreign Language Teaching Center Guizhou Institute of TechnologyGuiyangPeople’s Republic of China Key Laboratory of Power Dig Data of Guizhou Province School of Electrical and Information EngineeringGuizhou Institute of TechnologyGuiyang 550003People’s Republic of China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast UniversityNanjingPeople’s Republic of China

This article presents a finite-time robust control(FTRC)of a transformerless STATCOM based on a cascaded multilevel H-bridge converter(CMHC)with star *** FTRC is first proposed for the current loop control of a CMHC-based transformerless STATCOM by using the finite time robust control *** the parameters,perturbations and external disturbances into account and using coordinate transformation method,the nonlinear dynamic model of the CMHC-based transformerless STATCOM is transformed into a standard nonlinear port-controlled dissipative Hamiltonian(PCDH)*** on the PCDH structure,an FTRC is designed for the CMHC-based transformerless STATCOM to improve the transient stability and oscillation damping of power ***,the simulation results demonstrate that the FTRC has better dynamic performance and strong robustness in comparison with the passivity-based control of the CMHC-based transformerless STATCOM.

关键词： Cascaded H-bridge converter transformerless STATCOM finite time robust control oscillations damping transient stability

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Artificial intelligence in gastric cancer:applications and challenges

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Gastroenterology Report 2022年第1期10卷 227-242页

作者： Runnan Cao Lei Tang Mengjie Fang Lianzhen Zhong Siwen Wang Lixin Gong Jiazheng Li Di Dong Jie Tian School of Artificial Intelligence University of Chinese Academy of SciencesBeijingP.R.China CAS Key Laboratory of Molecular Imaging Beijing Key Laboratory of Molecular Imagingthe State Key Laboratory of Management and Control for Complex SystemsInstitute of AutomationChinese Academy of SciencesBeijingP.R.China Key Laboratory of Carcinogenesis and Translational Research(Ministry of Education/Beijing) Radiology DepartmentPeking University Cancer Hospital&InstituteBeijingP.R.China College of Medicine and Biological Information Engineering School Northeastern UniversityShenyangLiaoningP.R.China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine School of Engineering MedicineBeihang UniversityBeijingP.R.China Engineering Research Center of Molecular and Neuro Imaging of Ministry of Education School of Life Science and TechnologyXidian UniversityXi’anShaanxiP.R.China

Gastric cancer(GC)is one of the most common malignant tumors with high *** diagnosis and treatment decisions for GC rely heavily on human experts’careful judgments on medical ***,the improvement of the accuracy is hindered by imaging conditions,limited experience,objective criteria,and inter-observer ***,the developments of machine learning,especially deep-learning algorithms,have been facilitating computers to extract more information from data *** are exploring the far-reaching applications of artificial intelligence(AI)in various clinical practices,including ***,we aim to provide a broad framework to summarize current research on AI in *** the screening of GC,AI can identify precancerous diseases and assist in early cancer detection with endoscopic examination and pathological *** the diagnosis of GC,AI can support tumor-node-metastasis(TNM)staging and subtype *** treatment decisions,AI can help with surgical margin determination and prognosis ***,current approaches are challenged by data scarcity and poor *** tackle these problems,more regulated data,unified processing procedures,and advanced algorithms are urgently needed to build more accurate and robust AI models for GC.

关键词： gastric cancer artificial intelligence radiomics endoscopy computed tomography pathology

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PCDH-based nonlinear H∞control of VSC-HVDC with wind power integrated

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Journal of control and Decision 2021年第1期8卷 41-49页

作者： Xingyu Lv Bangjun Lei Shumin Fei Foreign language Teaching Center Guizhou Institute of TechnologyGuiyangPeople’s Republic of China Key Laboratory of Power Dig Data of Guizhou Province School of Electrical and Information EngineeringGuizhou Institute of TechnologyGuiyang 550003People’s Republic of China Key Laboratory of Measurement and Control of Complex Systems of Engineering Ministry of EducationSchool of AutomationSoutheast UniversityNanjingPeople’s Republic of China

Based on the port-controlled dissipative Hamiltonian(PCDH)system,a novel nonlinear(NN)H∞control is designed for the wind farm side(WFS)voltage source converter(VSC)of VSC-based high-voltage direct current(VSC-HVDC)transmission system connected large wind ***,the uncertain nonlinear dynamical model of WFS VSC in the VSC-HVDC is established by taking external disturbances and parameter variations into ***,the dissipative Hamiltonian structure of the WFS VSC system is constructed by means of variable ***,on the basis of the obtained dissipative Hamiltonian structure,a NN H∞control is put ***,the simulation results show that the proposed NN H∞control gains the advantage over the interconnection and damping assignment passivity-based(IDA-PB)control.

关键词： VSC-HVDC large wind farm Hamiltonian function method nonlinear H∞control

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GPR and SPSO-CG based gait pattern generation for subject-specific training

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Science China(Information Sciences) 2021年第8期64卷 244-246页

作者： Weiqun WANG Weiguo SHI Shixin REN Zeng-Guang HOU Xu LIANG Jiaxin WANG Liang PENG The State Key Laboratory of Management and Control for Complex Systems Institute of AutomationChinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences CAS Center for Excellence in Brain Science and Intelligence Technology

Dear editor,Gait training has been proved effective for recovery of walking ability for nerve injury patients caused by stroke, spinal cord injury(SCI), traumatic brain injury(TBI), etc. The traditional gait training methods, usually carried out by hands, are laborious and time-consuming, and hence, the training intensity is difficult to be maintained and the rehabilitation results are not satisfactory.

关键词： GPR and SPSO-CG based gait pattern generation for subject-specific training

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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IEEE International Conference on Industrial Technology (ICIT)

作者： Chengao Bao Luefeng Chen Min Li Min Wu Witold Pedrycz Kaoru Hirota School of Automation China University of Geosciences Wuhan China the Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation and the Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan China Department of Electrical and Computer Engineering University of Alberta Edmonton Canada Tokyo Institute of Technology Yokohama Japan Tokyo Institute of Technology Tokyo Japan

ISBN: (数字)9798331521950

ISBN: (纸本)9798331521967

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method.

关键词： Emotion recognition Visualization Accuracy Service robots Semantics Human-robot interaction Speech recognition Feature extraction Data mining Facial features

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