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第40届中国控制会议

作者： Sheng Gao Guangfu Ma Yanning Guo Wei Zhang Department of Control Science and Engineering Harbin Institute of Technology State Key Laboratory of Robotics Shenyang Institute of AutomationChinese Academy of Sciences Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences

For the problem of previous fault-estimation studies that the so-called observer-matching condition should be satisfied,as well as the difficulty to estimate actuator and sensor faults simultaneously,an augmented system fault-estimation observer is proposed for a sort of nonlinear systems subject to process and sensor *** essential idea is to construct an augmented system using a filter and to further create an augmented state vector by considering the system states and simultaneous actuator and sensor *** nonlinearity,process,and sensor disturbances and multiple-fault problem of the actuator and sensor are all taken into ***,an augmented observer is designed for the augmented system by attenuating the disturbances and simultaneously estimating the system states and actuator and sensor *** asymptotic stability is confirmed via the Lyapunov stability theory,and the linear matrix inequality optimization technique is further utilized to solve the designed *** simulations of a single-link flexible-joint robot are performed to illustrate the effectiveness of the proposed robust fault-estimation scheme.

关键词： Augmented observer Fault diagnosis Fault estimation Nonlinear system Linear matrix inequality(LMI)

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EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for Edge Devices

EEG-GNet: GCN-Based EEG Signal Fatigue Detection Network for...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Fuquan Zhang Yu Wu Xinxin Xu Chuansheng Wang Minjia Ma Antoni Grau Jiayan Huang School of Computer and Big Data Minjiang University Fujian Provincial Key Laboratory of Information Processing and Intelligent Control Minjiang University Fuzhou China College of New Engineering Industry Putian University Putian China Department of Automatic Control Polytechnic University of Catalonia Barcelona Spain School of Robotics Xi’an Jiaotong-Liverpool University Suzhou China College of New Engineering Industry Putian University Fujian Ruituo Information Technology Limited Company Putian China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Fatigue driving is a common potential traffic safety hazard. To detect and analysis the driver Fatigue degree in real-time, more attention have been increasing paid to application research of EEG fatigue detection device. However, existing methods suffer from complex model structure or large-scale model parameters. In the other words, they requires high computational consumption and large memory footprint, which creates a contradiction between model and portable EEG machine with limited hardware resources. In this work, we take both time and frequency domains of EEG signal into account and design an lightweight and efficient driver fatigue detection network based on GCN (EEG-GNet), which is available edge-end device application. Experiments on 4th Raspberry Pi on a public baseline dataset show that our EEG-GNet outperforms the state-of-the-art methods and takes good tradeoff between detection accuracy and resource consumption. Our EEG-GNet provides a new potential opportunity for future applications in practical portable/wearable (P/W) devices.

关键词： Accuracy Computational modeling Image edge detection Frequency-domain analysis Fatigue Brain modeling Feature extraction Electroencephalography Real-time systems Time-domain analysis

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SLAM-based Joint Calibration of Multiple Asynchronous Microphone Arrays and Sound Source Localization

arXiv

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arXiv 2024年

作者： Wang, Jiang He, Yuanzheng Su, Daobilige Itoyama, Katsutoshi Nakadai, Kazuhiro Wu, Junfeng Huang, Shoudong Li, Youfu Kong, He The Shenzhen Key Laboratory of Control Theory and Intelligent Systems Southern University of Science and Technology No. 1088 Xueyuan Avenue Shenzhen China The College of Engineering China Agricultural University Beijing China The Department of Systems and Control Engineering Tokyo Institute of Technology Tokyo Japan The School of Data Science The Chinese University of Hong Kong Shenzhen China The Robotics Institute University of Technology Sydney Sydney Australia The Department of Mechanical Engineering City University of Hong Kong Hong Kong

Robot audition systems with multiple microphone arrays have many applications in practice. However, accurate calibration of multiple microphone arrays remains challenging because there are many unknown parameters to be identified, including the relative transforms (i.e., orientation, translation) and asynchronous factors (i.e., initial time offset and sampling clock difference) between microphone arrays. To tackle these challenges, in this paper, we adopt batch simultaneous localization and mapping (SLAM) for joint calibration of multiple asynchronous microphone arrays and sound source localization. Using the Fisher information matrix (FIM) approach, we first conduct the observability analysis (i.e., parameter identifiability) of the above-mentioned calibration problem and establish necessary/sufficient conditions under which the FIM and the Jacobian matrix have full column rank, which implies the identifiability of the unknown parameters. We also discover several scenarios where the unknown parameters are not uniquely identifiable. Subsequently, we propose an effective framework to initialize the unknown parameters, which is used as the initial guess in batch SLAM for multiple microphone arrays calibration, aiming to further enhance optimization accuracy and convergence. Extensive numerical simulations and real experiments have been conducted to verify the performance of the proposed method. The experiment results show that the proposed pipeline achieves higher accuracy with fast convergence in comparison to methods that use the noise-corrupted ground truth of the unknown parameters as the initial guess in the optimization and other existing frameworks. © 2024, CC BY.

关键词： Mapping

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Research on Distributed intelligent Protection Strategy of distribution network based on Reinforcement Learning 2

Research on Distributed Intelligent Protection Strategy of d...

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2nd IEEE International Conference on Information Technology, Big Data and Artificial Intelligence, ICIBA 2021

作者： Cui, Shijie Zeng, Peng Hu, Ende Shenyang Institute of Automation Chinese Academy of Sciences State Key Laboratory of Robotics Shenyang110016 China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110169 China University of Chinese Academy of Sciences Beijing100049 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China State Grid Shaoxing Power Supply Company Shaoxing312099 China

ISBN: (纸本)9781665428767

In the context of building a new power system with new energy as the main part, the operation mode of distribution network is more complex, and the traditional distribution network protection strategy can hardly meet the demand, so this paper proposes a distributed intelligent protection strategy for distribution network based on reinforcement learning. This paper analyzes the impact of new energy resources access on distribution network relay protection, and presents an intelligent protection algorithm model based on deep reinforcement learning. The structure of intelligent protection device is designed, and the distribution network distributed protection scheme based on intelligent protection device is proposed. Finally, the algorithm training process is given, and the experimental environment is built. © 2021 IEEE.

关键词： Reinforcement learning

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Analysis of electricity loss and electricity consumption law in low-voltage areas: A case study 2

Analysis of electricity loss and electricity consumption law...

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2nd International Conference on Advanced Electrical and Energy Systems, AEES 2021

作者： Chen, Xi Song, Chunhe Wang, Tianran Key Laboratory of Network Control System Chinese Academy of Sciences Shenyang110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang110016 China University of Chinese Academy of Sciences Beijing100049 China

Electric power is an important resource in energy. With the development of society, the consumption of electric power is more and more. Research on electricity consumption and power loss has become a hot *** this paper, we conduct statistical analysis on the power supply, power consumption and line loss rate of 395 power supply *** loss rate is an indicator to describe electricity *** found that the line loss rate decreased over the course of a *** overall distribution of line loss rate is approximately lognormal distribution. Total daily electricity supply and electricity consumption in the power supply area approximately obey exponential *** terms of electricity consumption, a statistical analysis was made on the daily electricity consumption of 100,000 users in three years, and it was found that the electricity distribution of users was in line with the power law distribution. © 2021 Institute of Physics Publishing. All rights reserved.

关键词： Statistical methods

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A DDOS Attack Traffic Classification Model for Industrial Internet Based on CNN-LSTM

A DDOS Attack Traffic Classification Model for Industrial In...

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Chinese Automation Congress (CAC)

作者： Weixuan Wei Xianda Liu Chuan Sheng Ansong Feng Shenyang Institute of Automation Chinese Academy of Sciences. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences College of Information Engineering. Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang University of Chemical Technology Shenyang China Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang Institute of Automation Chinese Academy of Sciences. Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang China College of Information Engineering. Shenyang University of Chemical Technology Shenyang China

With the continuous development and application of industrial Internet, the weakness of the security defense capability of industrial Internet has become increasingly prominent. This paper presents an end-to-end classification model combining Convolutional Neural Network (CNN) and Long Short-Term Memory (LSTM) network for DDoS attacks over industrial Internet. By self-learning the spatial and temporal characteristics of the original attack traffic, the model can avoid subjective errors that may occur due to manual selection of features. This method is a feasible means, with an accuracy rate of 99%.

关键词： Automation Neural networks Manuals Denial-of-service attack Internet Convolutional neural networks Security

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Fault Detection and Isolation Based on Unknown Input Set-membership Observer

Fault Detection and Isolation Based on Unknown Input Set-mem...

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第三十九届中国控制会议

作者： Xiaomin Fan Wei Zhang Xiaohang Li Laboratory of Intelligent Control and Robotics Shanghai University of Engineering Science

This paper proposes an actuator fault diagnosis method for a class of discrete-time linear dynamic systems subject to unknown but bounded disturbances and noises. The proposed unknown input observer has a new structure and the actuator faults of the system are taken as unknown inputs that provides more robust and more design degrees of freedom. A direct method based on zonotopes is used to estimate the threshold of residual generated by the unknown input observer. Based on the proposed method, a fault detection and isolation strategy using a set of unknown input set-membership observers are presented. The effectiveness of the proposed method is verified by numerical simulation of a flight control system.

关键词： unknown input observer set-membership estimation zonotope fault estimation and isolation

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Deployment Schemes of In-Band Management Mode Based on Industrial SDN Network

Deployment Schemes of In-Band Management Mode Based on Indus...

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6G Communications and IoT Technologies (6GIoTT), International Conference on

作者： Jinnan Piao Dong Li Ming Yang Jindi Liu Xueting Yu Zhibo Li Key Laboratory of Networked Control Systems Chinese Academy of Sciences Shenyang 110016 China Shenyang Institute of Automation Chinese Academy of Sciences Shenyang 110016 China Institutes for Robotics and Intelligent Manufacturing Chinese Academy of Sciences Shenyang 110169 China

When software defined network (SDN) is used to deploy the industrial control network, the deployment schemes are different in actual according to the different industrial production business, environment and capital cost. In this paper, we use the in-band management mode to deploy the SDN network in an industrial environment. We design four deployment schemes of the in-band management mode for the industrial SDN network according to the specific requirements of industrial environment and propose an algorithm for controller location selection in the in-band management network. For the four schemes and the algorithm, we use the controller and switches to build a simulation environment according to different schemes and compare the performance between the in-band management and out-of-band management, which shows that the in-band management mode can set up a normal SDN industrial network with applicable network scale and throughput.

关键词： 6G mobile communication Costs Industrial control Production Throughput control systems Internet of Things

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Gait-based Emotion Recognition Using Spatial Temporal Graph Convolutional Networks

Gait-based Emotion Recognition Using Spatial Temporal Graph ...

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Computer Information Science and Artificial Intelligence (CISAI), International Conference on

作者： Shiyi Yu Xinde Li Key Laboratory of Measurement and Control of CSE Ministry of Education School of Automation Southeast University Nanjing China Guangdong Intelligent Robotics Institute Dongguan China

ISBN: (纸本)9781665406932

Most of the existing gait emotion recognition methods use spatial convolution or temporal convolution separately. Without integrating these two dimensions, it is easy to loss valid information for emotion analysis. In order to solve this problem, we propose a supervised learning method using Spatial Temporal Graph Convolutional Networks (ST-GCN) to classify emotions into positive, neutral and negative patterns. In addition, we made a new gait dataset called EmoGait3d including 742 real gait skeleton sequences extracted from videos, also annotated with emotion labels. According to the experiment results, our method achieves the accuracy of 92.2% which outperforms current state-of-art algorithms for gait emotion recognition.

关键词： Emotion recognition Information science Convolution Fuses Supervised learning Linguistics Skeleton

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Design of a Novel Autonomous Docking System With a Supporting Robotic Fish

Design of a Novel Autonomous Docking System With a Supportin...

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IEEE International Conference on Real-time Computing and robotics (RCAR)

作者： Huijie Dong Chunyang Zhao Tiezhu Qiao Jian Wang Di Chen Yan Meng Junzhi Yu Ministry of Education Key Laboratory of Advanced Transducers and Intelligent Control System Taiyuan University of Technology Taiyuan China Institute of Automation Laboratory of Cognitive and Decision Intelligence for Complex System Chinese Academy of Sciences Beijing China Department of Advanced Manufacturing and Robotics College of Engineering State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China College of Electronics and Information Engineering Guangdong Ocean University Zhanjiang China

Underwater supporting robots serving as a relay of energy supplements and communication for other underwater equipment are promising for ocean exploration, development, and protection. This paper proposes a novel autonomous docking system centered on a designed supporting robotic fish named ‘CourierFish’. Specifically, CourierFish is capable of docking with a surface dock station for supplying itself and docking with a seafloor platform for supporting equipment in the platform. A visual navigation scheme integrating LED and ArUco markers is presented for accurate localization. The control approach for docking motion is also illustrated. Simulations and aquatic experiments are performed to verify the feasibility of the proposed docking system. The obtained results lay a solid foundation for the construction of various underwater equipment and robot networks.

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