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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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Economic Research on User-Side Photovoltaic Energy Storage System Considering Shared Energy Storage

Economic Research on User-Side Photovoltaic Energy Storage S...

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International Conference on Power and Energy Technology (ICPET)

作者： Kaiwen Chen Qingyi Wang Kai Kang Hao Wang 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 Wuhan China PowerChina Hubei Electric Engineering co. LTD Wuhan China Wuhan Second Ship Design and Research Institute Wuhan China

Based on the background of photovoltaic development in the whole county and the demand for energy storage on the user-side, this paper establishes an economic evaluation model of user-side photovoltaic energy storage system considering shared energy storage. Firstly, three schemes of no energy storage, independent energy storage and shared energy storage are proposed, and the influence of photovoltaic energy storage system loss on annual power generation is considered. Secondly, based on the whole life cycle theory, the cost and benefit model of the user-side photovoltaic energy storage system is established, and the unit electricity cost of the user is used as the evaluation index. Finally, the relevant data of user in hubei area are analyzed as an example to verify the effectiveness of the model and provide reference for the investment of related projects.

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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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A Multi-Time-Scale Prediction Method for Blast Furnace Gas Utilization Rate Based on Causality Analysis

A Multi-Time-Scale Prediction Method for Blast Furnace Gas U...

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Chinese automation Congress (CAC)

作者： Guoyu Zhao Jianqi An Yunpeng Guo Chenglin Yang Yong 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

Blast furnace operating parameters regulate the gas utilization rate (GUR), and different operating parameters affect the GUR on different time scales. However, the existing methods only analyze and model the prediction on a single-time scale, and the accuracy of the model needs to be improved. In this paper, a multi-time-scale prediction method of blast furnace gas utilization rate based on causality is proposed. Firstly, according to the production characteristics of the blast furnace, burden and blast supply are the operation means to regulate the blast furnace on different time scales. Then, this paper presents a blast furnace operation parameter extraction method fusing correlation coefficient and information entropy, and selects two most informative parameters to characterize the operation. On this basis, the causal analysis method is used to prove that the selected operation parameters affect the development trend of GUR. Finally, the long and short time scale prediction models of GUR are established, which are verified by the data of the actual blast furnace production process, improving the prediction accuracy of the GUR efficiently.

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Q-learning-based feedback linearization method for unknown dynamics

Q-learning-based feedback linearization method for unknown d...

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2023 IEEE International Conference on Mechatronics (ICM)

作者： Yipu Sun Xin Chen Wenpeng He Luo Wang Edwardo F. Fukushima 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 Geo-Exploration. Ministry of Education Wuhan China School of Engineering Tokyo University of Technology Tokyo Japan

This paper present a feedback linearization technique for affine nonlinear systems that is independent of system dynamics. First, a input-output feedback linearization correction framework is described, and a interference estimator is employed to guarantee the stability of plant during the learning process. Then, a model-free Q-learning algorithm is presented to solve the feedback linearized controller. Finally, the position control of a single-link flexible joint manipulator system is used as an example to demonstrate the effectiveness of the method.

关键词： Linear systems Q-learning Mechatronics System dynamics Simulation Neural networks Position control

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Skeleton-Based Multi-Stream Adaptive Graph Convolutional Network for Indoor Scene Action Recognition

Skeleton-Based Multi-Stream Adaptive Graph Convolutional Net...

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2023 China automation Congress, CAC 2023

作者： Li, Jiazhuo Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru The School of Automation The Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Wuhan430074 China The Department of Electrical and Computer Engineering University of Alberta EdmontonABT6G 2R3 Canada The Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland The Department of Computer Engineering Istinye University Sariyer Istanbul34396 Turkey The Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798350303759

With the rapid advances in computer vision, human action recognition has gradually received attention, but the current methods still exhibit some problems in indoor environments. The human skeleton, as the framework of human motion, contains high-quality actional feature information, and the skeleton-based action recognition method effectively avoid the interference of interior background noise and has advantages in indoor action recognition. The outstanding effect of graph convolutional networks on graph structure data processing has led to its rapid development and wide application in skeleton-based action recognition. Second-order skeletal information also contains a large number of actional features but is not effectively utilized. The artificial predefined topology of the human skeleton map has limitations, and cannot reflect the interaction between limbs. To solve the above problems, this article designs an adaptive weighted multi-stream graph convolutional network (AM-GCN) based on skeletal information, using an attention mechanism to enhance the network's ability to extract actional features, and an adaptive layer to make the construction graph more flexible, incorporating second-order skeletal features through a dual-stream architecture. In this article, the NTU-RGB+D dataset has been used for the experiments, the results show that the method in this article has good results. © 2023 IEEE.

关键词： Convolution

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Plate Shape Prediction Based on Data-Driven in Roll Quenching Process

Plate Shape Prediction Based on Data-Driven in Roll Quenchin...

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2023 China automation Congress, CAC 2023

作者： Hu, Liu Chen, Luefeng Hu, Jie Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems The Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland Istinye University The Department of Computer Engineering Istanbul Sariyer34396 Turkey Tokyo Institute of Technology Tokyo Yokohama226-8502 Japan

ISBN: (纸本)9798350303759

In the process of steel plate production, predicting the plate shape is of great significance for producing high-quality and consistently stable plate shapes. This paper presents a model that predicts both the defect types and flatness of the plate, providing theoretical support for setting process parameters in roller quenching production. First, the parameters of the quenching process are analyzed to identify their characteristics. Then, the K-Means clustering algorithm and correlation analysis are employed to process the quenching process parameters. A gradient boosting decision tree (GBDT) model is used to predict the defect types and flatness of the steel plates. Finally, industrial production data is utilized for experimental validation. The obtained experimental results verify the reliability of the proposed method. © 2023 IEEE.

关键词： Decision trees

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An Online Education Effect Assessing Method Based on YOLOv8 and Vision Transformer

An Online Education Effect Assessing Method Based on YOLOv8 ...

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Chinese automation Congress (CAC)

作者： Bo-Yang Zhang Zhen-Tao Liu Xiang-Yu Zeng 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

Due to the rapid growth of online education worldwide, assessing the learning effectiveness of students during online classes has become increasingly challenging for teachers. In this paper, a method of assessing online education effect based on YOLOv8 and Vision Transformer is proposed. Firstly, the drowsiness state of students in an online teaching unconstraint environment is estimated by using YOLOv8, and then the gaze direction of awake students is estimated by using Vision Transformer to assess the students' attention levels during online education. The results from YOLOv8 are determined based on the calculated probabilities of drowsiness or wakefulness. And then gaze estimation method proposed in this paper was compared with state-of-the-art methods on the MPIIFaceGaze and Gaze360 datasets in which the angular errors of gaze estimation are 4.58° and 12.27°, respectively. We conducted experiments and analysis on a self-made dataset, from which the results demonstrate the feasibility of our method in an unconstrained environment.

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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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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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