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Asian Control Conference

作者： Chengyu Li Xin Chen Yang Zhou Wei Tan Wenpeng He School of Future Technology China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Automation China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

A disturbance suppression approach combining feedback linearization and equivalent input disturbance (EID) method is proposed to control nonlinear underwater robots' position. Firstly, the complex nonlinear model of the underwater robot is transformed into a linear model by feedback linearization. Then, to enhance the system's ability to reject unknown disturbances in the marine environment, the EID method is utilized for estimation and compensation. This helps improve the system's ability to handle disruptions. Additionally, a linear quadratic regulator (LQR) is employed to ensure system stability and achieve rapid convergence. Finally, the proposed approach is validated by demonstrating its effectiveness through simulation results.

关键词： Autonomous underwater vehicles Regulators Simulation Position control Mathematical models Stability analysis Feedback linearization

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Label Correlation Guided Feature Selection for Multi-label Learning 9th

Label Correlation Guided Feature Selection for Multi-label ...

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19th International Conference on Advanced Data Mining and Applications, ADMA 2023

作者： Zhang, Kai Liang, Wei Cao, Peng Yang, Jinzhu Li, Weiping Zaiane, Osmar R. Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China National Frontiers Science Center for Industrial Intelligence and Systems Optimization Shenyang110819 China School of Software and Microelectronics Peking University Beijing China Alberta Machine Intelligence Institute University of Alberta EdmontonAB Canada

ISBN: (纸本)9783031466731

Multi-label learning has received much attention due to its wide range of application domains. Multi-label data often has high-dimensional features, which brings more challenges to classification algorithms. Feature selection based on sparse learning is one of the most important approaches, which can select discriminative features to alleviate the curse of dimensionality. However, most of these methods do not consider feature redundancy and label correlation simultaneously. In this work, we propose a multi-label feature selection method that takes feature redundancy and label correlation into account. Specifically, the proposed method first divides features into groups according to feature correlation and then encourages competition within each group but relaxes competition between groups to eliminate redundant and irrelevant features. Moreover, we propose an approach to capture label correlation and exploit it to improve the feature selection. Finally, an iterative optimization algorithm is designed to obtain the feature weights for multi-label feature selection. Extensive experiments on various multi-label datasets demonstrate the superiority of the proposed method compared with some state-of-the-art multi-label feature selection methods. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Feature Selection

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Current Problems and Solutions of Industrial Control Network Intrusion Detection: A Brief Survey

Current Problems and Solutions of Industrial Control Network...

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2022 IEEE International Conference on Real-Time computing and Robotics, RCAR 2022

作者： Jiang, Tianyu Zhang, Chen Wang, Rui Chai, Senchun Institute of Automation Chinese Academy of Sciences and University of Science and Technology Beijing China Beijing Institute of Technology China Institute of Automation Chinese Academy of Sciences State Key Laboratory of Management and Control for Complex Systems China Institute of Intelligent Information Processing and Control Beijing Institute of Technology China

ISBN: (数字)9781665469838

ISBN: (纸本)9781665469838

With the advancement of industrial intelligence, the Industrial Internet has been widely used in energy, manufacturing and other important industries. In recent years, security incidents have occurred frequently in industrial control network, which means that maintaining industrial control network security has become more and more important. Intrusion detection technology can actively detect abnormal behavior in the network, and is an important means to ensure the security of industrial control networks. Presently, the intrusion detection technology of industrial control network has problems such as extreme imbalance of classes and redundant interference of traffic characteristics. This paper analyzes these problems and clarifies the existing solutions for different problems in principle. © 2022 IEEE.

关键词： Network security

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DeCASA in AgriVerse: Parallel Agriculture for Smart Villages in Metaverses

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IEEE/CAA Journal of Automatica Sinica 2022年第12期9卷 2055-2062页

作者： Xiujuan Wang Mengzhen Kang Hequan Sun Philippe de Reffye Fei-Yue Wang The State Key Laboratory for Management and Control of Complex Systems Chinese Academy of SciencesBeijing 100190 Beijing Engineering Research Center of Intelligent Systems and Technology Beijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100190 Department of Chromosome Biology Max Planck Institute for Plant Breeding ResearchBeijing 100049China Faculty of Biology LMU MunichGroßhaderner Str.2Planegg-Martinsried D-82152Germany AMAP Univ MontpellierCIRADCNRSINRAEIRDMontpellier F-34398France IEEE Macao Institute of Systems Engineering Macao University of Science and TechnologyMacao 999078China

The demand for food is tremendously increasing with the growth of the world population,which necessitates the development of sustainable agriculture under the impact of various factors,such as climate *** fulfill this challenge,we are developing Metaverses for agriculture,referred to as Agri Verse,under our Decentralized complex Adaptive systems in Agriculture(De CASA)project,which is a digital world of smart villages created alongside the development of Decentralized Sciences(De Sci)and Decentralized Autonomous Organizations(DAO)for Cyber-Physical-Social systems(CPSSs).Additionally,we provide the architectures,operating modes and major applications of De CASA in *** achieving sustainable agriculture,a foundation model based on ACP theory and federated intelligence is ***,we discuss the challenges and opportunities.

关键词： Parallel Agriculture Management and Control AgriVerse Agriculture CPSS ACP DAO-Based Platform Precision Agriculture

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A Novel Rate of Penetration Optimization Method for Vertical Wells Based on an Improved Particle Swarm Optimization Algorithm

A Novel Rate of Penetration Optimization Method for Vertical...

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Asian Control Conference

作者： Yang Zhou Peng Zhang Xiao Wu Xin Chen Min Wu School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China School of Future Technology China University of Geosciences Wuhan China

ISBN: (数字)9789887581598

ISBN: (纸本)9798331540845

The rate of penetration serves as a crucial indica-tor reflecting drilling rig efficiency. Maintaining high drilling speed is crucial in reducing drilling costs and non-drilling time. However, due to the complex nonlinearity inherent in the drilling process, optimizing and adjusting drilling speed faces high-dimensional variations and intricate constraints. This complex nonlinearity makes it challenging to obtain a suitable set of operating parameter values. To overcome these difficulties in rate of penetration optimization, we propose a novel rate of penetration optimization method aimed at addressing high-dimensional changes and complex constraints. First, the support vector regression method is introduced to formulate the drilling rate optimization problem. Then, an improved particle swarm optimization algorithm (IPSO) is developed to address the chal-lenge of high-dimensional variation in drilling rate optimization. Moreover, a method for analyzing vertical well longitudinal force is introduced to handle constraints. IPSO demonstrates superior global search capabilities compared to other algorithms in the IEEE CEC2015 benchmark functions. The developed drilling optimization method is validated using actual data. Results from two groups of experiments indicate that compared to manual adjustment, the developed method improved by 15.31 % and 15.38 %, respectively.

关键词： Drilling Support vector machines Simulation Optimization methods Process control Manuals Benchmark testing

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Modeling the Kinematics and Dynamics of the Pedaling Lower-Limb Rehabilitation Robot

Modeling the Kinematics and Dynamics of the Pedaling Lower-L...

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intelligent systems, International Workshop on

作者： Hao Zhang Juan Zhao Feng Wang Seiichi Kawata Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo- Exploration Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Hachioji Tokyo Japan

ISBN: (数字)9798350353303

ISBN: (纸本)9798350353310

Lower-limb rehabilitation robots are becoming increasingly prevalent in rehabilitation training. Accurate modeling helps to develop an effective rehabilitation program. This paper analyzes the physiological structure and joint motion characteristics of the human lower limbs using a pedaling lower-limb rehabilitation robot. We establish a simplified three-link model of the human lower limbs and apply the Denavit-Hartenberg (D-H) parametric method to analyze the joint range of motion, determining geometric parameters and boundary conditions. The forward kinematics analysis derives the expression for the end position of the three-link model, and inverse kinematics calculates joint angles, velocities, and accelerations. Using Lagrange's dynamical equations, we compute the torque required by each joint for different motion states, establishing the foundation for subsequent controller design. Finally, joint angles and torques were simulated in MATLAB. The results verified the feasibility of the model.

关键词： Training Analytical models Torque Computational modeling Dynamics Kinematics Assistive robots Robot sensing systems Mathematical models Optimization

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Prediction model of photovoltaic output power based on VMD-EMD-BiLSTM

Prediction model of photovoltaic output power based on VMD-E...

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Industrial Cyber-Physical systems (ICPS)

作者： Shaolong Zheng Danyun Li School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan China Wuhan China

The accuracy of photovoltaic (PV) power prediction is significantly influenced by the high complexity and volatility of the PV sequence. The existing methods for predicting photoelectric power are difficult to effectively mine and analyze the internal variation law of data. To improve the accuracy of PV power prediction, a new method is proposed that first performs variational mode decomposition (VMD) and empirical mode decomposition (EMD), and then establishes a bi-directional long and short-term memory neural network (BiLSTM) for PV output power prediction. The proposed method extracts the amplitude and frequency characteristics of the PV output power series through VMD. After that, the residual term with strong non-stationarity is generated, which still has more sequence characteristics. The residual term is then decomposed by EMD for the second time to extract more features. Finally, the BiLSTM model is established to conduct bidirectional mining for PV power data and predict PV output power. The actual PV data is used to test the experimental results, which show that the proposed VMD-EMD-BiLSTM prediction model has better prediction performance.

关键词： Photovoltaic systems Empirical mode decomposition Simulation Neural networks Predictive models Cyber-physical systems Prediction algorithms

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A Distributed Fixed-Time Neurodynamic Algorithm and Its Application in Multi-Autonomous Underwater Vehicle Collaborative Escorting

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IEEE Transactions on Network Science and Engineering 2025年

作者： Zhang, Peng He, Xing Yu, Junzhi Southwest University Chongqing Key Laboratory of Nonlinear Circuits and Intelligent Information Processing College of Electronic and Information Engineering Chongqing400715 China Peking University State Key Laboratory for Turbulence and Complex Systems Department of Advanced Manufacturing and Robotics College of Engineering Beijing100871 China Peking University Nanchang Innovation Institute Nanchang330224 China

In this paper, a distributed fixed-time neurodynamic algorithm (DFxTNA) is designed for solving distributed optimization problem with time-varying (TV) objective function and constraints. The DFxTNA consists of consensus part based on sliding-mode control technique and optimization part based on Hessian matrix and penalty function. In contrast to existing distributed TV algorithms, DFxTNA based on fixed-time sliding mode control can effectively improve the convergence performance of the algorithm and has better stability with the addition of TV disturbance. In addition, the consistency and fixed-time convergence of DFxTNA are proved by Lyapunov theory and fixed-time stability, respectively. The effectiveness of DFxTNA is verified by numerical experiments. Finally, a distributed TV optimization problem model is established for a multi-autonomous underwater vehicle (AUV) cooperative escort scenario, and DFxTNA is applied to solve the problem. Simulation results confirm the effectiveness and stability of DFxTNA in target tracking, formation control, and dynamic obstacle avoidance. © 2013 IEEE.

关键词： Lyapunov functions

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Human pose recognition based on multiple features and random forest algorithm 2

Human pose recognition based on multiple features and random...

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2nd International Conference on Computer Vision, Image, and Deep Learning

作者： Fang, Di Wang, Hong Meng, Fanglong School of Automation China University of Geosciences Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9781510646827

ISBN: (纸本)9781510646810

Human pose recognition based on bone node data collected by depth camera is a key problem in the field of human-computer interaction. To improve the accuracy of human pose recognition, a new algorithm based on multiple features and random forest model is proposed. Firstly, a 93-dimensional vector is defined, which contains the coordinate feature of the joint and the distance feature, and the distance feature is selected according to the spatial position of the joint. Then, in the process of body pose recognition, the random forest model is combined with Bagging algorithm to ensure the balance of samples, so as to improve the classification performance of the classifier for different samples. Finally, the performance test of the constructed classifier is carried out on the UTKinect-action3D Dataset. The experimental result shows that the algorithm can effectively identify a variety of human posture, and the recognition rate reaches more than 90%. The fusion of multiple features is of great significance to improve the accuracy of human posture recognition. © 2021 SPIE.

关键词： Decision trees

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Semi-supervised Anomaly Detection and Location Based on Generative Adversarial Network 6

Semi-supervised Anomaly Detection and Location Based on Gene...

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6th International Conference on Signal and Image Processing, ICSIP 2021

作者： Liu, Wei Gao, Mingqiang Duan, Shuaidong Wei, Longsheng China University of Geosciences School of Automation Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation For Complex Systems Wuhan China Engineering Research Center of Intelligent Technology For Geo-Exploration Ministry of Education Wuhan China

ISBN: (纸本)9780738133737

With the development of artificial intelligence, the anomaly detection plays more and more important role in security monitoring field. Because it is difficult to label abnormal data, most of the supervised methods consumed a lot of manpower and obtained low performance and generality. Inspired by this motivation, this paper proposes a semi-supervised method for anomaly detection in video frames based on GAN (Generative Adversarial Network), in which only normal data was used as the training sample. The quality gap between the predicted frame and the ground truth is used as the basis to determine whether it is abnormal. Moreover, the mathematical morphology approach was adopted to locate the anomaly area in the frames. Experiments show that our method can successfully detect abnormal frames in video and can also locate the area where abnormal behavior occurs in frames. © 2021 IEEE.

关键词： Generative adversarial networks

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