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arXiv 2024年

作者： Shrinate, Aashi Tripathy, Twinkle Control and Automation specialization of the Department of Electrical Engineering Indian Institute of Technology Kanpur Uttar Pradesh Kanpur208016 India

In this work, we consider a strongly connected group of individuals involved in decision-making. The opinions of the individuals evolve using the Friedkin-Johnsen (FJ) model. We consider that there are two competing ‘influencers’ (stubborn agents) vying for control over the final opinion of the group. We investigate the impact of modifying the network interactions on their respective control over the final opinions (influence centrality). We use signal flow graphs (SFG) to relate the network interactions with the influence that each ‘influencer’ exerts on others. We present the sufficient conditions on the edge modifications which lead to the increase of the influence of an ‘influencer’ at the expense of the other. Interestingly, the analysis also reveals the existence of redundant edge modifications that result in no change in the influence centrality of the network. We present several numerical examples to illustrate these results. © 2024, CC BY-NC-ND.

关键词： Signal flow graphs

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Fault diagnosis of Electro-hydrostatic actuators based on CWT and CNN-Transformer 3

Fault diagnosis of Electro-hydrostatic actuators based on CW...

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3rd International Conference on Intelligent Mechanical and Human-Computer Interaction technology, IHCIT 2024

作者： Li, Jiatong Tu, Chaofan Wang, Xingjian Wang, Zhaoyang Xiu, Wanbo School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Laboratory of Science and Technology on Marine Navigation and Control China State Shipbuilding Corporation Tianjin300131 China China Tianjin Navigation Instrument Research Institute Tianjin300131 China

ISBN: (纸本)9781510683105

Electro-hydraulic actuator (EHA) system is a key component of the ship's electro-hydraulic control system, but the internal failure mechanism of EHA is extremely complex, and it is limited by insufficient detection methods in practical systems, making fault diagnosis of EHA a great challenge. To address this issue, this paper proposes a fault diagnosis method based on Continuous Wavelet Transform (CWT), Convolutional Neural Network (CNN) and Transformer. Firstly, the collected one-dimensional time series signal of the sensor is converted into a two-dimensional time-frequency map through CWT. Then, a hybrid module of CNN-Transformer is used to extract local and global fault features, and finally the classification results are output through softmax. Conduct EHA fault simulation experiments to validate the proposed method. The research results indicate that the proposed method has a fault diagnosis accuracy of 96.33%, achieving high-precision diagnosis of EHA faults. © 2024 SPIE.

关键词： Wavelet transforms

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Event-Triggered Energy Optimization of Wireless Sensor Networks 19

Event-Triggered Energy Optimization of Wireless Sensor Netwo...

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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Liu, Lu Song, Ruizhuo Wei, Qinglai School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China Institute of Automation Chinese Academy of Sciences The State Key Laboratory of Management and Control for Complex Systems Beijing China

ISBN: (纸本)9781665472432

Aiming at limited communication and energy re-sources in wireless sensor networks (WSN s), this paper proposes an energy management scheme of WSNs via adaptive dynamic programming (ADP) based on event-triggered mecha-nism (ETM). The optimal control strategy obtained by iteration can schedule the sensor nodes and make the nodes switch between working and sleeping situations, thus improving the energy utilization and extending the service life of the energy-constrained WSNs. Firstly, the mathematical model of WSNs is established, and the state is estimated by extended Kalman filter (EKF) algorithm to improve the measurement accuracy. Then, ADP solves the designed value function to achieve the scheduling plan. On the premise of system stability, ETM is applied to activate the controller on demand, which can reduce communication burden and save WSNs energy consumption. Finally, the simulation experiment reveals that the proposed algorithm can reduce the unnecessary triggering times of the controller effectively while ensuring the requirements, and avoid data congestion and interaction resource waste. © 2022 IEEE.

关键词： Energy utilization

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Sneak Circuit Analysis Covering Interface Circuits in Spacecraft Integrated Chips while Considering Faults

Sneak Circuit Analysis Covering Interface Circuits in Spacec...

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System Reliability and Safety Engineering (SRSE), International Conference on

作者： Weiming Liu Cen Chen Kaiwen Xiao Mingtao Feng Haonan Zhang Guofu Zhai Department of Electrical Engineering & Automation Harbin Institute of Technology Harbin China Beijing Aerospace Automatic Control Institute Beijing China Harbin Institute of Technology Harbin China

ISBN: (数字)9798350356083

ISBN: (纸本)9798350356090

Sneak circuit analysis is a crucial reliability design process. In the design of spacecraft electronic systems, it is challenging to identify sneak circuits related to integrated chip interface circuits, and there are few targeted researches. In extreme working conditions, the coupling sneak circuits caused by the failure of integrated chips are difficult to identify, affecting the design optimization. The paper proposes a typical interface information specification model for integrated chips to simulate the electrical information characteristics of interfaces. And the typical failure models of intrinsic components inside the chip are determined through multi-physics field analysis. Then, based on the digital model, a sneak circuit diagnosis process based on excitation and fault injection are proposed, and a sneak circuit screening method based on Python virtual prototyping is implemented in the case study. Finally, the design constraint clues for the chip’s external circuit are obtained, which provides a reference for related fault diagnosis and reliability optimization works.

关键词： Space vehicles Semiconductor device modeling Reliability engineering Topology Circuit faults Virtual prototyping Integrated circuit modeling Integrated circuit reliability Circuit analysis Stress

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Enhancing Robotic Grasping Detection Accuracy With the R2CNN Algorithm and Force-Closure

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Journal of Computing and Information Science in Engineering 2024年第6期24卷 061005-061005页

作者： Lin, Hsien-I Shodiq, Muhammad Ahsan Fatwaddin Chu, Hong-Qi Institute of Electrical and Control Engineering National Yang Ming Chiao Tung University Hsinchu30010 Taiwan Graduate Institute of Automation Technology National Taipei University of Technology Taipei30010 Taiwan

This study aims to use an improved rotational region convolutional neural network (R2CNN) algorithm to detect the grasping bounding box for the robotic arm that reaches supermarket goods. This algorithm can calculate the final predicted grasping bounding box without any additional architecture, which significantly improves the speed of grasp inferences. In this study, we added the force-closure condition so that the final grasping bounding box could achieve grasping stability in a physical sense. We experimentally demonstrated that deep model-treated object detection and grasping detection are the same tasks. We used transfer learning to improve the prediction accuracy of the grasping bounding box. In particular, the ResNet-101 network weights, which were originally used in object detection, were used to continue training with the Cornell dataset. In terms of grasping detection, we used the trained model weights that were originally used in object detection as the features of the to-be-grasped objects and fed them to the network for continuous training. For 2828 test images, this method achieved nearly 98% accuracy and a speed of 14–17 frames per second. © 2024 American Society of Mechanical Engineers (ASME). All rights reserved.

关键词： Object detection

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Few-shot working condition recognition of a sucker-rod pumping system based on a 4-dimensional time-frequency signature and meta-learning convolutional shrinkage neural network

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Petroleum Science 2023年第2期20卷 1142-1154页

作者： Yun-Peng He Chuan-Zhi Zang Peng Zeng Ming-Xin Wang Qing-Wei Dong Guang-Xi Wan Xiao-Ting Dong State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of SciencesShenyangLiaoning 110016China Key Laboratory of Networked Control Systems Chinese Academy of SciencesShenyangLiaoning 110016China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of SciencesShenyangLiaoning 110169China University of Chinese Academy of Sciences Beijing100049China Shenyang University of Technology ShenyangLiaoning 110870China School of Automation and Electrical Engineering Shenyang Ligong UniversityShenyangLiaoning 110159China

The accurate and intelligent identification of the working conditions of a sucker-rod pumping system is necessary. As onshore oil extraction gradually enters its mid-to late-stage, the cost required to train a deep learning working condition recognition model for pumping wells by obtaining enough new working condition samples is expensive. For the few-shot problem and large calculation issues of new working conditions of oil wells, a working condition recognition method for pumping unit wells based on a 4-dimensional time-frequency signature (4D-TFS) and meta-learning convolutional shrinkage neural network (ML-CSNN) is proposed. First, the measured pumping unit well workup data are converted into 4D-TFS data, and the initial feature extraction task is performed while compressing the data. Subsequently, a convolutional shrinkage neural network (CSNN) with a specific structure that can ablate low-frequency features is designed to extract working conditions features. Finally, a meta-learning fine-tuning framework for learning the network parameters that are susceptible to task changes is merged into the CSNN to solve the few-shot issue. The results of the experiments demonstrate that the trained ML-CSNN has good recognition accuracy and generalization ability for few-shot working condition recognition. More specifically, in the case of lower computational complexity, only few-shot samples are needed to fine-tune the network parameters, and the model can be quickly adapted to new classes of well conditions.

关键词： Few-shot learning Indicator diagram Meta-learning Soft thresholding Sucker-rod pumping system Time–frequency signature Working condition recognition

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Skeleton-based Action Recognition Using Two-stream Graph Convolutional Network with Pose Refinement 41

Skeleton-based Action Recognition Using Two-stream Graph Con...

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第41届中国控制会议

作者： Biao Zheng Luefeng Chen Min Wu Witold Pedrycz Kaoru Hirota 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 Department of Electrical and Computer Engineering University of Alberta Tokyo Institute of Technology

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the development of science and technology,graph convolutional network has made great progress in improving the accuracy of action ***,there still exists some deficiencies in current ***,the human skeleton point coordinates entering into the network are barely refined,which may cause large ***,the second-order information(the length and direction of bones),which can reflect action characteristics discriminatively,is rarely *** solve the above issues,a two stream graph convolutional network with pose refinement for skeleton based action recognition is ***,we use an adaptive block to to help improve the *** test our method on Kinetics dataset and the experiment show it can get better results than some recent methods,which plays a positive role in future research.

关键词： Graph convolutional network Pose refinement Skeleton based action recognition Adaptive block

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On extended state Kalman filter-based identification algorithm for aerodynamic parameters

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control Theory and technology 2024年第2期22卷 235-243页

作者： Wenyan Bai Ruizhe Jia Peng Yu Wenchao Xue Beijing Aerospace Automatic Control Institute Beijing100854China School of Automation and Electrical Engineering University of Science and Technology BeijingBeijing100083China Key Laboratory of Knowledge Automation for Industrial Processes Ministry of EducationBeijing100083China Key Laboratory of Systems and Control Institute of Systems ScienceAcademy of Mathematics and Systems ScienceChinese Academy of SciencesBeijing100190China

In this paper, the problem of time-varying aerodynamic parameters identification under measurement noises is studied. By analyzing the key aerodynamic parameters that affect the aircraft control system, a system model with extended states for identifying equivalent aerodynamic parameters is established, and error parameters are extended to the system state, avoiding the difficulty caused by the unknown dynamic in the system. Furthermore, an identification algorithm based on extended state Kalman filter is designed, and it is proved that the algorithm has quasi-consistency, thus, the estimation error can be evaluated in real time. Finally, the simulation results under typical flight scenarios show that the designed algorithm can accurately identify aerodynamic parameters, and has desired convergence speed and convergence precision.

关键词： Aerodynamic parameters Parameter identification Extended state Kalman filter

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Atten-DilatedNet: Fault diagnosis method of bearing for mine drilling rig

Atten-DilatedNet: Fault diagnosis method of bearing for mine...

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2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes, SAFEPROCESS 2023

作者： Sun, Guoqing Zou, Xiaoyu Pan, Jie Wang, Zhongbin Ji, Hongquan Xin, Dezhong China University of Mining and Technology School of Mechanical and Electrical Engineering Xuzhou China China University of Mining and Technology School of Information and Control Engineering Xuzhou China Shandong University of Science and Technology School of Electrical Engineering and Automation Qingdao China Coal Science and Industry Group Chongqing Research Institute of China Chongqing China

ISBN: (纸本)9798350337754

Currently, bearing fault signal diagnosis of mine drilling rig suffers from strong time dependency and serious signal noises, which leads to low diagnostic accuracy. Aiming at solving the problem of inadequate extraction of key features hidden in strong noise, a temporal attention embedding dilated convolutional neural network (Atten-DilatedNet) is designed for fault diagnosis of mine drilling rig bearing. The method takes the original vibration signals of different faulty bearings of drilling rigs as the input of the model. It adopts a one-dimensional dilated convolutional neural network to automatically extract features from the vibration signals, with larger receptive field and longer time range than traditional convolutional operator. A temporal attention mechanism is further embedded to the network, which adaptively assigns different weights to above feature sequences and model their time dependency. Thus, Atten-DilatedNet can enhance attention to critical-to-fault feature, and restrain interference of noise. Case studies on public data sets and experimental drilling rig demonstrate the validaty and superiority of the proposed Atten-DilatedNet based fault diagnosis method. It shows higher accuracy, better generalization performance, and stronger robustness than some traditional machine learning methods. © 2023 IEEE.

关键词： Failure analysis

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Analysis and Comparison of Different Rotor Torque of Arbitrary Multiphase Motor 18th

Analysis and Comparison of Different Rotor Torque of Arbitra...

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18th Annual Conference of China Electrotechnical Society, ACCES 2023

作者： Wang, Xiaocan Jiang, Huafeng Xiong, Zeliang Shi, Zhenchuan Xie, Wei School of Electrical Engineering and Automation Xiamen University of Technology Xiamen361000 China Xiamen Key Laboratory of Frontier Electric Power Equipment and Intelligent Control Xiamen University of Technology Xiamen361000 China Quanzhou Institute of Equipment Manufacturing Haixi Institutes China Academy of Sciences Quanzhou362000 China

ISBN: (纸本)9789819710676

The "arbitrary multiphase" squirrel-cage motor based on stator bars and squirrel-cage rotors has the ability to switch the number of poles in operation, and can choose the number of poles in operation according to the operating conditions, thus broadening the high-efficiency operating range of the motor. It is considered as a valuable alternative solution to the traditional drive motor, but it also has the defect of low torque density of induction motors. In order to evaluate the torque reduction caused by using squirrel-cage rotor for pole pairs switching of arbitrary multiphase motor, based on the same stator structure, this paper carries out a comparative study on the performance of arbitrary multiphase motor with different rotors, and considers the combination of this stator structure and permanent magnet rotor for the first time. Further clarifying the comparison between the advantages and disadvantages of arbitrary multiphase squirrel-cage motor and arbitrary multiphase permanent magnet motor is conducive to accurately evaluating the application range of arbitrary multiphase motor, which is a useful research on the selection of driving motor. © Beijing Paike Culture Commu. Co., Ltd. 2024.

关键词： Squirrel cage motors

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