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intelligent systems, International Workshop on

作者： Ruoyu Jiang 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 Tokyo Japan

This paper presents a high-precision control method based on the model-following control (MFC) and equivalent-input-disturbance (EID) approaches. The MFC approach ensures that the output of the plant tracks the output of a desired nominal model. The EID estimates and eliminates an external disturbance. The combination of these two methods effectively achieves high-precision tracking. The validity of this method is demonstrated by a vibration-suppression example through simulations.

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Optimal State Synchronization for Discrete-Time Nonlinear Multi-Agent systems Under Switching Communication Graph

Optimal State Synchronization for Discrete-Time Nonlinear Mu...

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Industrial Cyber-Physical systems (ICPS)

作者： Wenpeng He Xin Chen Sun Yipu Akinori Sekiguchi 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 Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan

This paper proposes a new method to solve the optimal consensus control of discrete-time nonlinear multi-agent systems (MASs) under switching topology. To get a time-invariant control object for the switching MASs, a new augment local neighborhood tracking error is defined. Then the hierarchical control strategy is used to decompose the nonlinear consensus problems into two sub-problems, i.e., the tracking problems between individuals and the reference models, and the distributed consensus control among reference models and the leader. for the former, a robust ADHDP method is used to make the states of the follower track that of the reference model. for the latter, the value iteration method is used to derive the distributed optimal reference control. A simulation is given to verify the effectiveness of the proposed method.

关键词： Switches Transforms Consensus control Cyber-physical systems control systems Topology Synchronization

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Industrial Cyber-Physical systems (ICPS)

作者： Xiangxiang Zhang Wenkai Hu Ahmad W. Al-Dabbagh Weihua Cao 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 The University of British Columbia Kelowna Canada

In industrial facilities, alarm systems are essential for process monitoring. However, due to the simplicity of alarm configuration and the poor performance of the alarm system, alarm floods often happen. Similarity analysis of alarm floods compares alarm flood sequences to look for common patterns. These patterns can offer information that is helpful in identifying root causes of alarm floods. Existing methods for alarm flood similarity analysis can only conduct similarity measures based on match operations for alarms represented by text strings; as a result, the match operations ignore the correlations between alarm occurrences. In this work, a new alarm flood similarity analysis method based on word embedding and Move-Split-Merge (MSM) distance is proposed, in order to reveal sequence similarity from a new perspective. The contributions are mainly twofold: 1) The correlations of alarm occurrences are considered in the alarm encoding via word embedding; 2) The MSM distance is calculated to analyze the similarity of alarm flood sequences of different lengths. Then, clustering results are compared with the original true labels of the alarm floods. The effectiveness of the proposed method is demonstrated by a case study with alarm data generated by a public industrial model of the Vinyl Acetate Monomer process.

关键词： Process monitoring Correlation Clustering algorithms Alarm systems Cyber-physical systems Industrial facilities Data models

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Adaptive Equivalent-input-disturbance Approach to Improving Disturbance-rejection Performance

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International Journal of automation and computing 2020年第5期17卷 701-712页

作者： Ze-Wen Wang Jin-Hua She Guang-Jun Wang School of Automation China University of GeosciencesWuhan 430074China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074China School of Engineering Tokyo University of TechnologyHachioji 192-0982Japan

This paper presents an adaptive equivalent-input-disturbance(AEID)approach that contains a new adjustable gain to improve disturbance-rejection performance.A linear matrix inequality is derived to design the parameters of a control *** adaptive law for the adjustable gain is presented based on the combination of the root locus method and Lyapunov stability theory to guarantee the stability of the AEID-based *** adjustable gain is limited in an allowable range and the information for adjusting is obtained from the state of the *** results show that the method is effective and robust.A comparison with the conventional EID approach demonstrates the validity and superiority of the method.

关键词： Adaptive control disturbance rejection equivalent input disturbance(EID) linear matrix inequality(LMI) Lyapunov stability root locus

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Exchange Strategies for Multi-Colony Ant Algorithms in Dynamic Environments

Exchange Strategies for Multi-Colony Ant Algorithms in Dynam...

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Congress on Evolutionary Computation

作者： Michalis Mavrovouniotis Changhe Li Danial Yazdani Diofantos Handjimitsis ERATOSTHENES Centre of Excellence Limassol Cyprus Department of Civil Engineering and Geomatics Cyprus University of Technology Limassol Cyprus Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems School of Automation China University of Geosciences Wuhan China Faculty of Engineering & Information Technology University of Technology Sydney Sydney Australia

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

In dynamic optimization problems where optimal solutions change over time, traditional ant colony optimization (ACO) algorithms face limitations. This study explores the adaptation of multi-colony ACO algorithms, known for their enhanced search capabilities in stationary problems, to tackle optimization problems in dynamic environments. Various strategies for exchanging information between colonies, which is a critical factor influencing algorithm performance, are investigated. Using the dynamic traveling salesman problem as a foundation, we generate test cases to reflect real-world complexities. Our results on a set of problem instances reveal that the choice of communication strategy between colonies significantly impacts the adaptability and efficiency of multi-colony ACO algorithms in tracking moving optimum.

关键词： Schedules Satellites Processor scheduling Heuristic algorithms Evolutionary computation Traveling salesman problems Search problems

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A Reinforcement Learning Approach for Robust Restoration of Generators in Power System

A Reinforcement Learning Approach for Robust Restoration of ...

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Chinese automation Congress (CAC)

作者： Huimiao Li Xiaozhou Yang Chao Zhai School of Automation China University of Geosciences Wuhan Campus Hubei Key Laboratory of Advanced Control and Intelligent Automation fo Complex Systems Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China Singapore-ETH Centre CREATE Campus Future Resilient Systems Singapore

In the past decades, cascading blackouts have caused serious damages to power systems and affected the normal operation of society, so it is crucial to quickly restore the damaged power system to normal state. In this paper, a reinforcement learning (RL) approach is developed to achieve the robust restoration of generators in power systems. Firstly, the performance recovery of damaged power system is modeled as markov decision process, and constraints such as line voltage and active power output of generators are considered. Secondly, a RL algorithm is proposed to search the optimal control strategy for generator units recovery. Based on the proposed restoration approach, Q-learning algorithm is employed to obtain the optimal strategy for power supply of generaors in power grids. Finally, numerical simulations are carried out on IEEE 9-bus system under different scenarios of external disturbances and certainties. The simulation results demonstrate the effectiveness and feasibility of the proposed approach.

关键词： Q-learning Power supplies Simulation Power system protection Generators Explosions Power systems

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A fuzzy control and neural network based rotor speed controller for maximum power point tracking in permanent magnet synchronous wind power generation system

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Global Energy Interconnection 2023年第5期6卷 554-566页

作者： Min Ding Zili Tao Bo Hu Meng Ye Yingxiong Ou Ryuichi Yokoyama School of Automation China University of GeosciencesWuhan 430074P.R.China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074P.R.China Global Energy Interconnection Group Co. LtdSTATE GRID Corporation of ChinaBeijing 100032P.R.China School of Environment and Energy Engineering Waseda UniversityTokyo169-8050Japan

When the wind speed changes significantly in a permanent magnet synchronous wind power generation system,the maximum power point cannot be easily determined in a timely *** study proposes a maximum power reference signal search method based on fuzzy control,which is an improvement to the climbing search method.A neural network-based parameter regulator is proposed to address external wind speed fluctuations,where the parameters of a proportional-integral controller is adjusted to accurately monitor the maximum power point under different wind speed ***,the effectiveness of this method is verified via Simulink simulation.

关键词： Maximum wind power tracking Fuzzy control Neural network

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Dynamics Modeling Method of Offshore Drilling Experimental System Based on Double-Layer Stewart Parallel Mechanism

Dynamics Modeling Method of Offshore Drilling Experimental S...

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Chinese control Conference (CCC)

作者： Chenxuan Wang Zhejiaqi Ma Chengda Lu Sheng Du Min Wu 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

Establishing the dynamics model of the offshore drilling experimental system can better complete the offshore drilling test in the laboratory environment and reduce the cost of testing. A dynamical modeling method for the offshore drilling experimental system built on the double-layer Stewart parallel mechanism is proposed. Firstly, the kinematic and dynamical characteristics of the double-layer Stewart parallel mechanism are combined with the Lagrange method and the virtual work method to establish the dynamics model of the mechanism. Then a parameter identification scheme is designed using a nonlinear gray system estimation method based on the trust-domain reflection algorithm, and the model parameters are identified. The model is downscaled to improve the feasibility of the identification scheme and the accuracy of the identified parameters. The actual experimental system data verify this model's correctness and the model parameters' accuracy.

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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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Reducing the Influence of Individual Variability in sEMG-Based Fatigue Detection Using Transfer Learning

Reducing the Influence of Individual Variability in sEMG-Bas...

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Renwei Lin Feng Wang Seiichi Kawata Juan Zhao Jinhua She Daisuke Chugo 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 School of Engineering Kwansei Gakuin University Sanda Hyogo Japa

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

In the field of sEMG fatigue detection, deep learning algorithms have emerged as prominent tools due to their unparalleled capability to automatically learn discriminative features from extensive datasets. However, the practicality of fatigue detection in everyday life remains limited, as it demands a substantial effort from users to generate vast amounts of sEMG data. In this paper, we propose the hypothesis that by aggregating sEMG data from multiple users, it becomes feasible to extract general informative features from the resulting large dataset, thus enhancing the ability to detect fatigue. To achieve this, transfer learning is employed on the aggregated data from various users, leveraging the power of deep learning algorithms to learn discriminative features from large datasets. The approach involves initially training the model using one individual's sEMG data, followed by fine-tuning using minimal data from other individuals, allowing for efficient personalized adaptation with significantly less time compared to retraining from scratch. This study designs a transfer learning model using sEMG data from the Tokyo Institute of Technology and demonstrates noteworthy efficiency improvements, reducing the required time by an average of 74.31%, while achieving an average accuracy of 95.32%. These experimental results emphasize the potential of transfer learning to enhance the practicality and widespread applicability of sEMG fatigue detection in real-world applications.

关键词： Training Deep learning Adaptation models Accuracy Transfer learning Muscles Fatigue Feature extraction Data models Optimization

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