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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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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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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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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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Comprehensive Gear Shift Schedule Design for 2-AMT Electric Vehicles Based on Sparrow Search Algorithm

Comprehensive Gear Shift Schedule Design for 2-AMT Electric ...

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Chinese control Conference (CCC)

作者： Tianyi Wang Liling Ma Junzheng Wang State Key Laboratory of Intelligent Control and Decision of Complex Systems School of Automation Beijing Institute of Technology Beijing P.R. China

With the aim of 2-AMT electric vehicles, a comprehensive shift schedule that considers both power and economy is proposed. First, the objective function of the comprehensive shift schedule is constructed, which is the weighted sum of vehicle acceleration time and vehicle power consumption per unit distance. Second, Sparrow Search Algorithm is used to solve the objective function and obtain the comprehensive shift schedule. Finally, AVL Cruise simulation software is used to simulate vehicle dynamics and economy under an NEDC. The results show that the comprehensive shift schedule is similar to the optimal power shift schedule in terms of dynamic performance, and the economy is optimized by 3%. The feasibility of the comprehensive shift schedule is verified.

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Motion Semantic Enhancement and Autonomous Information Mining for Static-Dynamic Visual Emotion Recognition in Human-Robot Interaction

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IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jiang, Cheng-Shan Liu, Zhen-Tao Fukushima, Edwardo F. She, Jinhua China University of Geosciences School of Automation Wuhan 430074 China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan 430074 China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan 430074 China Tokyo University of Technology School of Engineering Hachioji 192-0982 Japan

The advancement of static-dynamic visual emotion recognition plays a pivotal role in the evolution of intelligent and empathetic machines. Currently, the progress of static visual emotion recognition (SVER) faces challenges, primarily the need to balance model efficiency with robust recognition performance. Moreover, obstacles such as noisy data and limited labeled datasets restrict models from effectively learning the appearance features and dynamic dependencies intrinsic to dynamic visual emotion recognition (DVER). In the realm of SVER, facial pixel and semantic representation are derived using a lightweight surface and landmark features embedding network, followed by neuron-energy-based feature fusion-filtering to enhance the fusion of semantic representations positively. For DVER, a Global Anchor-dependent Noisy Emotional Data Filtering (GADNEF) method is employed for noisy label learning, facilitating clip-wise filtering of ambiguous data via iterative computations of frame-wise attention statistics across batches. Furthermore, a self-supervised learning paradigm based on a unified appearance and motion-guided masked autoencoder is implemented, enabling large-scale knowledge transfer tailored for video-based facial expression analysis. Our approach has achieved accuracies of 97.07%, 98.90%, 61.39%, and 92.28% on SVER datasets including KDEF, RaFD, SFEW, and RAF-DB, respectively, while the weighted average recall (WAR) and unweighted average recall (UAR) on DVER datasets such as DFEW and MAFW were 75.12%, 64.37%, 55.26%, and 42.53%, respectively. A preliminary application experiment has also been conducted to validate the practical applicability of our method within human-robot interaction (HRI) scenarios. © 1963-2012 IEEE.

关键词： Human–robot interaction (HRI) noisy label learning (NLL) self-supervised training static-dynamic visual emotion recognition (DVER)

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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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Adaptive Finite-Time Almost Sure Tracking of Drill-Bits Driven by a Lévy Process

Adaptive Finite-Time Almost Sure Tracking of Drill-Bits Driv...

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第40届中国控制会议

作者： Liyan Chen Xiaofeng Zong School of Automation China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems

This paper studies the finite-time tracking control problem for the stochastic drill-bits system driven by a Lévy process with the bit-rock interaction. The finite-time tracking control problem of the stochastic drill-bits system driven by a Lévy process can be regarded as the finite-time stability analysis for the stochastic nonlinear equations driven by a Lévy process. So the Lyapunov-type finite-time stability theorem is firstly developed to obtain the finite-time almost sure stability for n-dimensional stochastic nonlinear equations driven by a Lévy process. Then based on finite-time stability theorem, the adaptive finite-time almost sure tracking of drill-b its is achieved. A drill-bit simulation is given to demonstrate the control effect.

关键词： Finite-time almost sure tracking Lévy process Drill-bits

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Collective Entity Alignment for Knowledge Fusion of Power Grid Dispatching Knowledge Graphs

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IEEE/CAA Journal of Automatica Sinica 2022年第11期9卷 1990-2004页

作者： Linyao Yang Chen Lv Xiao Wang Ji Qiao Weiping Ding Jun Zhang Fei-Yue Wang the State Key Laboratory for Management and Control of Complex Systems Institute of AutomationChinese Academy of SciencesBeijing 100190 the School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China the China Electric Power Research Institute Beijing 100192China IEEE the Qingdao Academy of Intelligent Industries Qingdao 256200China the School of Information Science and Technology Nantong UniversityNantong 226019China the School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China

Knowledge graphs(KGs)have been widely accepted as powerful tools for modeling the complex relationships between concepts and developing knowledge-based *** recent years,researchers in the field of power systems have explored KGs to develop intelligent dispatching systems for increasingly large power *** multiple power grid dispatching knowledge graphs(PDKGs)constructed by different agencies,the knowledge fusion of different PDKGs is useful for providing more accurate decision *** achieve this,entity alignment that aims at connecting different KGs by identifying equivalent entities is a critical *** entity alignment methods cannot integrate useful structural,attribute,and relational information while calculating entities’similarities and are prone to making many-to-one alignments,thus can hardly achieve the best *** address these issues,this paper proposes a collective entity alignment model that integrates three kinds of available information and makes collective counterpart *** model proposes a novel knowledge graph attention network(KGAT)to learn the embeddings of entities and relations explicitly and calculates entities’similarities by adaptively incorporating the structural,attribute,and relational ***,we formulate the counterpart assignment task as an integer programming(IP)problem to obtain one-to-one *** not only conduct experiments on a pair of PDKGs but also evaluate o ur model on three commonly used cross-lingual *** comparisons indicate that our model outperforms other methods and provides an effective tool for the knowledge fusion of PDKGs.

关键词： Entity alignment integer programming(IP) knowledge fusion knowledge graph embedding power dispatch

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