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检索条件"机构=China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex System"

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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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Surface Defect Detection of Sanitary Ceramic Product Utilizing High-resolution Image on Two Different Scales

Surface Defect Detection of Sanitary Ceramic Product Utilizi...

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第34届中国控制与决策会议

作者： Xing-ao Wang Jianqi An Yisheng Mei Xin Chen Min Wu School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

Surface defect detection of sanitary ceramic products is an important part of the production *** deep learning method is the mainstream research direction in the field of defect *** there are many small defects on the surface of sanitary ceramic products,it is an effective means to use a high-resolution camera to obtain *** detection speed of high-resolution pictures and the interference of background are the difficult problems in ***,this paper designs a two-scale detection framework to detect and locate defects of sanitary ceramics utilizing high-resolution *** two-scale detection framework speeds up the detection of high-resolution images and weakens the interference of the background by dividing the defect detection task into two parts,a workpiece region recognition task on large scale and a defect detection task on small *** experiments show that the two-scale detection method has a better detection speed and accuracy rate compared with the single-scale method,which proves the superiority of the proposed method.

关键词： Defect detection Two-scale detection Deep learning

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Anomaly Detection Based on Principle of Justifiable Granularity and Probability Density Estimation

Anomaly Detection Based on Principle of Justifiable Granular...

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2023 china automation Congress, CAC 2023

作者： Du, Sheng Ma, Xian Li, Xiang Wu, Min Cao, Weihua Pedrycz, Witold 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 Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University Faculty of Engineering and Natural Sciences Department of Computer Engineering Istanbul Sariyer Turkey

ISBN: (纸本)9798350303759

Anomaly detection is essential to ensure the safety of industrial processes. This paper presents an anomaly detection approach based on the probability density estimation and principle of justifiable granularity. First, time series data are transformed into a two-dimensional information granule by the principle of justifiable granularity. Then, the test statistic is constructed, and the probability density and cumulative distribution functions of the test statistic are calculated. Next, the confidence level determines the test threshold. Finally, the time series data of a key parameter in the sintering process is used as a case study. The experimental result demonstrates that the proposed approach can detect abnormal time series data effectively, providing an accurate and effective solution for detecting time series anomalies in industrial processes. © 2023 IEEE.

关键词： Time series

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Preassigned-Time Synchronization of Memristive Chaotic Circuit via Intermittent control

Preassigned-Time Synchronization of Memristive Chaotic Circu...

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Neuromorphic Computing (ICNC), International Conference on

作者： Qiming Wang Leimin Wang Genping 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 Wuhan Second Ship Design and Research Institute Wuhan China

In this paper, the preassigned-time synchronization (PTS) problem for a fifth-order memristive chaotic circuit (MCC) is investigated by designing a time-dependent intermittent controller. First, the dynamic characteristics of the MCC, especially the existence and occurrence of chaos, are investigated by simulation experiments. Besides, the stability of the equilibrium points is discussed by using Routh-Hurwitz criterion. Then, a time-dependent intermittent controller is designed and the PTS of MCC is realized via the presented controller. Finally, the effectiveness of theoretical results is verified by means of numerical simulations.

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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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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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A Contribution-based Resource Allocation Scheme for Multi-population Methods in Dynamic Environments 2

A Contribution-based Resource Allocation Scheme for Multi-po...

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2nd International Conference on Industrial Artificial Intelligence, IAI 2020

作者： Peng, Mai Li, Changhe Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China

ISBN: (数字)9781728182162

ISBN: (纸本)9781728182162

The multi-population method is a common method for solving dynamic optimization problems. However, to design an efficient multi-population method, one of the challenging issues is how to allocate computational resources between populations given a limited computing buget in dynamic environments. This paper designs a contribution-based resource allocation mechanism. In this mechanism, a contribution degree of a population is defined according to the performance of the population, which determines the probability of the population to obtain the computing resource. This mechanism is implemented in an adaptive multi-population method. Experimental results on the moving peaks benchmark show that the algorithm equipped with the resource allocation mechanism outperforms the original algorithms. © 2020 IEEE.

关键词： Resource allocation

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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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Self-Attention based Temporal Intrinsic Reward for Reinforcement Learning

Self-Attention based Temporal Intrinsic Reward for Reinforce...

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2021 china automation Congress, CAC 2021

作者： Jiang, Zhuo Tian, Daiying Yang, Qingkai Peng, Zhihong The School of Automation Beijing Institute of Technology Beijing China State Key Laboratory of Intelligent Control and Decision of Complex System Beijing China Peng Cheng Laboratory Guangdong Shenzhen China

ISBN: (纸本)9781665426473

This paper proposes a self-attention based temporal intrinsic reward model for reinforcement learning (RL), to synthesize the control policy for the agent constrained by the sparse reward in partially observable environments. This approach can solve the problem of temporal credit assignment to some extent and deal with the low efficiency of exploration. We first introduce a sequence-based self-attention mechanism to generate the temporary features, which can effectively capture the temporal property of the task for the agent. During the training process, the temporary features are employed in each sampled episode to elaborate the intrinsic rewards, which is combined with the extrinsic reward to help the agent learn a feasible policy. Then we use the meta-gradient to update this intrinsic reward model in order that the agent can achieve better performance. Experiments are given to demonstrate the superiority of the proposed method. © 2021 IEEE

关键词： Reinforcement learning

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