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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Chen, Yi Sun, Xiao Zheng, Song Huang, Weibing Zeng, Sheng Chen, Dan Xu, Zhezhuang College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Education Department of Fujian Province Fuzhou350000 China Evomotion Co. Ltd. Shenzhen Guangdong518000 China

ISBN: (纸本)9798350315196

Reinforcement Learning (RL) has been applied to robotic arm control, which enables the agent to learn an effective policy to solve complex tasks. However, it requires constant interaction with the environment leading to low sample efficiency. In this paper, we propose a robotic arm control approach based on planning via lookahead search, which is a model-based RL algorithm to improve the sample efficiency. The approach builds an environment model in order to obtain the dynamics of the environment. Thus the model can be used to plan future actions by a tree-based search. The experiments show that our approach can solve the task of robotic arm control with less environmental samples. © 2023 IEEE.

关键词： Robotic arms

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Enhancing Scheduling Efficiency of Steel Logistics Parks by Optimized Storage Distribution

Enhancing Scheduling Efficiency of Steel Logistics Parks by ...

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2023 China automation Congress, CAC 2023

作者： Xu, Zhibin Zheng, Song Wang, Jinlong Xu, Zhezhuang Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming353000 China

ISBN: (纸本)9798350303759

The efficient management of storage has become a critical determinant of operational efficiency due to the growing and diverse demand for steel logistics parks. However, the current storage methods consistently result in significant imbalances in the loading of steel products and operating times across multiple yards. To address this challenge, we propose optimizing the distribution of steel products based on demand levels and retrieval efficiency. Firstly, we present an optimization problem that employs demand-driven storage principles to formulate the scheduling of steel product storage. Then, we introduce a novel algorithm called the Adaptive Preferred Evolutionary Heuristic (APEH) to tackle this problem. The results from our experiments indicate that this model effectively enhances the storage structure, and the proposed algorithm consistently yields optimal solutions within a reasonable timeframe. © 2023 IEEE.

关键词： Optimization

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Predicting the State of Crane Spreader for Steel Bar Delivery Based on Machine Learning 13

Predicting the State of Crane Spreader for Steel Bar Deliver...

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13th IEEE International Conference on CYBER Technology in automation, control, and Intelligent Systems, CYBER 2023

作者： Liu, Zibin Xu, Zhezhuang Chen, Jian Cui, Chen Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Fuzhou University Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd Fujian Sanming353000 China

ISBN: (纸本)9798350315196

Deducing the twisting pattern of spreader is very important for crane operators in steel warehouse, since crane spreaders have to be rotated repeatedly at steel bar transport sites. To improve the efficiency of the steel bar delivery, the state prediction for crane spreaders is necessary. In order to fulfill this requirement, the HTSK-LN-ReLU model prediction method based on Optuna optimization is applied in this paper. We deploy the IMU on the spreader surface, and then obtain the state data during the operation of the crane spreader by wireless transmission. The data are first denoised through fourier filtering, and then the Optuna framework is used to optimize the HTSK-LN-ReLU model's hyperparameters. The evaluation index of mean square error is used for model evaluation, and finally the optimal hyperparameter combination of the model is selected from 200 comparison experiments. Experimental results demonstrate superior performance of the HTSK-LN-ReLU model in predicting the state of crane spreader for steel bar delivery. © 2023 IEEE.

关键词： Forecasting

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Wood Broken Defect Detection With 3D Laser Scanning

Wood Broken Defect Detection With 3D Laser Scanning

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2022 Chinese automation Congress, CAC 2022

作者： Ai, Zhijie Lin, Ye Xu, Zhezhuang Zhu, Yuhang Wu, Lianxu Fuzhou University School of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9781665465335

Wood broken defects, such as cracks, penetrating dead knots and wanes, will seriously weaken the strength of the wood and destroy the structral integrity of the wood. Therefore, these defects should be inspected and then cut during the production. Although vision-based detection methods have been proposed to detect wood defect regions, they can not detect the defects that change in depth. To solve this problem, this paper focuses on the defect detection by measuring depth difference through 3D laser data. First, the 3D laser data is preprocessed to remove the noise. Secondly, we construct a support profile by multiple mean filtering at the line-level. Next, the residual components are obtained by subtracting the support profile from the profile data and the defect is determined by comparing the residual components and the setting threshold. Finally, we perform merging and filtering operation to determine the final position of defects. The experimental results verify the effectiveness of the proposed method. © 2022 IEEE.

关键词： Defects

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A data-driven disturbance estimation algorithm based on adaptive observer for MIMO linear systems

A data-driven disturbance estimation algorithm based on adap...

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第34届中国过程控制会议

作者： Xiaoyi Xu Hao Luo Xiao Zhang Department of Control Science and Engineering Harbin Institute of Technology Department of Automation Science and Electrical Engineering Beihang University

The adaptive observer is a well-developed datadriven algorithm for co-estimation of system parameters and ***,inevitable disturbances can affect the systems and lead to deterioration of the algorithm *** disturbance estimation methods require prior knowledge about model parameters and disturbance *** paper presents a robust adaptive observer method for multiple-inputmultiple-output linear systems subject to *** proposed method can perform joint estimation of unknown parameters,states and disturbances without any prior information about the model parameters and *** stability and convergence of the algorithm are rigorously proved to ensure that the estimation errors converge exponentially to *** results of a numerical example demonstrate the effectiveness of the algorithm.

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Modeling and Heuristic Solutions for Storage Scheduling of Steel Plates in Steel Logistics Park

Modeling and Heuristic Solutions for Storage Scheduling of S...

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2023 China automation Congress, CAC 2023

作者： Yang, Hao Wang, Jinlong Xu, Zhezhuang Chen, Boyu Zhang, Qingdong College of Electrical Engineering and Automation Key Laboratory of Industrial Automation Control Technology and Information Processing Fuzhou University Education Department of Fujian Province Fuzhou350000 China Sansteel Minguang Co. Ltd. Sanming353000 China

ISBN: (纸本)9798350303759

Steel plates are highly customized according to different customer demands. In this case, the outbound date, the specifications of the steel plates and the distribution of stacks must be simultaneously considered when the steel plates are stored in the yard. Improper storage of steel plates significantly increases the number of shuffles during outbound operations. To overcome the challenge, we propose an efficient steel plate storage scheme based on the actual scenario of existing steel plate distribution in the yard. Firstly, a model for steel plate storage scheduling is established to minimize the number of extra blocking plates. Then, three heuristic algorithms are designed to solve the problem. Comparative experiments are carried out to verify the effectiveness of the proposed algorithm. The experimental results show that the proposed method can sufficiently improve the efficiency of steel plates scheduling. © 2023 IEEE.

关键词： Heuristic algorithms

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Stabilization of Continuous-Time Systems Against Stochastic Network Uncertainties:Fundamental Variance Bounds

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Journal of Systems Science & Complexity 2021年第5期34卷 1858-1878页

作者： QI Tian CHEN Jie School of Automation Science and Engineering Key Laboratory of Autonomous Systems and Networked ControlSouth China University of TechnologyGuangzhou 510640China Department of Electrical Engineering City University of Hong KongHong KongChina

This paper studies the stabilizability and stabilization of continuous-time systems in the presence of stochastic multiplicative *** authors consider multi-input,multi-output(MIMO)linear time-invariant systems subject to multiple static,structured stochastic uncertainties,and seek to derive fundamental conditions to ensure that a system can be stabilized under a mean-square *** the stochastic control framework,this problem can be considered as one of optimal control under state-or input-dependent random noises,while in the networked control setting,a problem of networked feedback stabilization over lossy communication *** authors adopt a mean-square small gain analysis approach,and obtain necessary and sufficient conditions for a system to be meansquare stabilizable via output *** single-input,single-output(SISO)systems,the condition provides an analytical bound,demonstrating explicitly how plant unstable poles,nonminimum phase zeros,and time delay may impose a limit on the uncertainty variance required for mean-square *** MIMO minimum phase systems with possible delays,the condition amounts to solving a generalized eigenvalue problem,readily solvable using linear matrix inequality optimization techniques.

关键词： Mean-square small gain theorem multiplicative stochastic uncertainty networked control

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Position Errors and Interference Prediction-Based Trajectory Tracking for Snake Robots

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IEEE/CAA Journal of Automatica Sinica 2023年第9期10卷 1810-1821页

作者： Dongfang Li Yilong Zhang Ping Li Rob Law Zhengrong Xiang Xin Xu Limin Zhu Edmond Q.Wu IEEE School of Electrical Engineering and Automation Fuzhou UniversityFuzhou 350108 the Sino-US Global Logistics Institute Shanghai Jiao Tong UniversityShanghai 200240 the Key Laboratory for Agricultural Machinery Intelligent Control and Manufacturing of Fujian Education Institutions Fuzhou 350108China School of Biological Science and Medical Engineering and the Key Laboratory for Biomechanics and Mechanobiology of Ministry of Education Beihang UniversityBeijing 100191China University of Macao Macao 999078China School of Automation Nanjing University of Science and TechnologyNanjing 210094China College of Intelligence Science and Technology National University of Defence TechnologyChangsha 410073China School of Mechanical Engineering Shanghai Jiao Tong UniversityShanghai 200240China Department of Automation Shanghai Jiao Tong UniversityShanghai 200240China

This work presents a trajectory tracking control method for snake *** method eliminates the influence of time-varying interferences on the body and reduces the offset error of a robot with a predetermined *** optimized line-of-sight(LOS)guidance strategy drives the robot’s steering angle to maintain its anti-sideslip ability by predicting position errors and ***,the predictions of system parameters and viscous friction coefficients can compensate for the joint torque control *** compensation is adopted to enhance the compatibility of a robot within ever-changing *** and experimental outcomes show that our work can decrease the fluctuation peak of the tracking errors,reduce adjustment time,and improve accuracy.

关键词： Anti-sideslip compensation snake robot trajectory tracking

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Recognizing knee pathologies by using gait dynamics via kernel principal component analysis and deterministic learning theory

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Journal of Ambient Intelligence and Humanized Computing 2023年第11期14卷 15535-15543页

作者： Deng, Muqing Feng, Xiaoreng Zeng, Wei Cao, Jiuwen Zhang, Yu Zheng, Tongjia Institute of Information and Control Hangzhou Dianzi University Hangzhou China Department of Mechanical and Automation Engineering The Chinese University of Hong Kong Shatin Hong Kong Department of Orthopaedics and Traumatology Queen Mary Hospital the University of Hong Kong Pok Fu Lam Hong Kong College of Mechanical and Electrical Engineering Longyan University Longyan China Department of Orthopaedic Surgery Guangzhou General Hospital of Guangzhou Military Command Guangzhou China College of Engineering University of Notre Dame Notre DameIN United States

This study concentrates on dynamical motion characteristics of knees during treadmill walking, and describes the application of kernel principal component analysis and deterministic learning theory to the detection of abnormal gait dynamics induced by knee pathologies. First, twelve-dimensional knee joint rotation and translation parameters are measured by a marker-based motion analysis system during treadmill walking. Second, the dimension of the gait parameter signal is reduced based on kernel principal component analysis. Third, gait dynamics underlying time-varying gait parameter signal is captured by using localized radial basis function neural networks through deterministic learning mechanism. This kind of dynamics information represents the frame-to-frame temporal change of kinematics and kinetics modifications during treadmill walking, which is shown to be sensitive to the variance during knee pathologies. Gait patterns are represented as the gait dynamics underlying time-varying gait parameters. The training patters under different knee pathologies further constitute a uniform training dataset, containing all kinds of gait dynamics under different knee pathologies. By comparing the training patterns with a test knee gait pattern to be recognized, a set of recognition errors are generated. The average L1 norms of the errors are taken as the classification measure between the dynamics of the training gait patterns and the dynamics of the test pattern. A rapid recognition scheme can be achieved according to the smallest error principle. Experimental results show that encouraging recognition accuracy can be achieved. The proposed method is sensitive for the detection of knee pathologies and is capable of discharge of patients without knee pathologies. © 2018, Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Errors

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Optimal Linear Deception Attacks on Remote State Estimation with Constrained Alarm Rates: A Low-Dimensional Case 63

Optimal Linear Deception Attacks on Remote State Estimation ...

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63rd IEEE Conference on Decision and control, CDC 2024

作者： Shang, Jun Zhang, Hanwen Zhou, Jing Chen, Tongwen Tongji University Shanghai Research Institute for Intelligent Autonomous Systems National Key Laboratory of Autonomous Intelligent Unmanned Systems Frontiers Science Center for Intelligent Autonomous Systems Department of Control Science and Engineering Shanghai200092 China University of Science and Technology Beijing Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical Engineering Beijing100083 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 1H9 Canada

ISBN: (纸本)9798350316339

This study addresses linear attacks on remote state estimation within the context of a constrained alarm rate. Smart sensors, which are equipped with local Kalman filters, transmit innovations instead of raw measurements through a wireless communication network. This transmission is vulnerable to malicious data interception and manipulation by attackers. The aim of this research is to identify the optimal attack strategy that degrades the system performance while adhering to stealthiness constraints. A notable innovation of this paper is the direct association of the attack's stealthiness with the alarm rate, diverging from traditional approaches that rely on the covariance of the innovation or the Kullback-Leibler divergence, which are conventional metrics that have been extensively explored in previous studies. Our findings reveal that the optimal attack strategy exhibits some structural characteristics in systems of low dimensions. The performance of the proposed attack strategy is demonstrated through numerical examples. © 2024 IEEE.

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