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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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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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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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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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Inverse Compensation Feedforward control Strategy for a Vertical Pneumatic Artificial Muscle System

Inverse Compensation Feedforward Control Strategy for a Vert...

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IEEE International Conference on control Science and Systems Engineering (CCSSE)

作者： Huai Xiao Qingxin Meng Xuzhi Lai Ze Yan Shiying Zhao 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 Hubei China

This paper proposes an inverse compensation feed-forward control strategy for a vertical pneumatic artificial muscle (PAM) system. Firstly, we conduct a preliminary experiment on the vertical PAM system, and on this basis, we analyze the motion characteristics of the system. Moreover, we clarify the control objective of this paper. Then, we establish a model that can describe the hysteresis characteristics of the system, and we further construct an inverse compensation feedforward controller by inversing this established model. Finally, we carry out some tracking control experiments based on the vertical PAM experimental platform to verify the effectiveness and superiority of the proposed control strategy,

关键词： Artificial muscles Tracking control systems Real-time systems Modeling Feedforward systems Hysteresis

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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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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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Capturing Spatial Information for sEMG-Based Gesture Recognition Using Graph Attention Networks

Capturing Spatial Information for sEMG-Based Gesture Recogni...

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Pervasive and intelligent Computing (PICom), IEEE Conference on

作者： Tong Xia 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 Japan

ISBN: (数字)9798331522742

ISBN: (纸本)9798331522759

Surface electromyography (sEMG) signals, known for their rich encapsulation of motion intention, have emerged as a promising avenue for human-computer interaction. Despite the advances in gesture recognition through machine learning, the omission of spatial information in sEMG signals has been identified as a limitation, often resulting in diminished accuracy and robustness of recognition systems. In this paper, we introduce the application of graph attention networks (GATs) to harness the spatial dynamics within sEMG signals. Initially, the sEMG signals are transformed into a graph-based structure through a meticulous preprocessing phase, where each electrode is represented as a node, and the spatial interplay among them is meticulously captured. Subsequently, the GATs are employed to conduct gesture recognition on this structured graph, with the networks adeptly adjusting the significance of weights between channels through the attention mechanism. This adaptive approach effectively captures the intricate spatial and temporal correlations embedded within the sEMG signals. On the CapgMyo dataset DB-a, our model demonstrates a consistent recognition accuracy of approximately 88.8%, surpassing previous machine learning models such as support vector machines (SVMs) and random forests (RFs) in recognition performance.

关键词： Electrodes Support vector machines Adaptation models Accuracy Computational modeling Gesture recognition Graph neural networks Electromyography Data models Topology

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Interpretable analysis of feature importance and implicit correlation based on sEMG grayscale. images

Interpretable analysis of feature importance and implicit co...

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Industrial Cyber-Physical Systems (ICPS)

作者： Xiaohu AO Feng Wang Juan Zhao Jinhua She School of Automation China University of Geosciences Wuhan China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex System 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

For patients requiring upper limb rehabilitation, the hand rehabilitation robot assists the patient in completing movements within a certain training trajectory to achieve therapeutic results. There have been studies based on deep learning to convert surface electromyography (sEMG) signals into sEMG images for motion intention analysis. Although good recognition accuracy has been achieved, the working principle of neural networks and the processing of image features by the networks are not well explained. The interpretability of deep neural networks determines human confidence in neural network decisions. In this paper, we design a method based on feature importance and implicit correlation for hand motion intention recognition, experimentally explored that convolutional neural networks have implicit definitions for sEMG grayscale images of the same hand gesture action, and verified the effectiveness of the designed method.

关键词： Deep learning Analytical models Correlation Image recognition Neural networks Thumb Gray-scale

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Bit Bounce Detection for Drilling Process Based on Multi-Feature Graph and Graph Convolutional Network

Bit Bounce Detection for Drilling Process Based on Multi-Fea...

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Annual Conference of Industrial Electronics Society

作者： Peng Zhang Wenkai Hu Weihua Cao Min Wu Jiandong Wang 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 College of Electrical Engineering and Automation Shandong University of Science and Technology Qingdao Shandong Province China

Prompt detection of bit bounce can prevent serious incidents and is of great importance for safe and efficient deep geological drilling. In the early stage of bit bounce, signal changes are relatively weak. In addition, there are differences in the topological relationships of samples at different time instances in normal state and bit bounce. These factors present a challenge to timely and accurate bit bounce detection. Therefore, this paper proposes a bit bounce detection method based on multi-feature graph and graph convolution networks. A multi-feature graph construction method using process variables, mean value, Mahalanobis distance, and Euclidean distance is proposed, and a two-layer graph convolutional network is designed to realize deep feature extraction and incident detection. The effectiveness and superiority of the proposed method are demonstrated by a real drilling industrial case.

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