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2024 International Conference on Machine Learning, Pattern Recognition and automation engineering, MLPRAE 2024

作者： Rao, Jinjun Wang, Cong Liu, Mei Lei, Jingtao Giernacki, Wojciech Department of Precision Mechanical Engineering School of Mechatronic Engineering and Automation Shanghai University China Institute of Robotics and Machine Intelligence Faculty of Automatic Control Robotics and Electrical Engineering Poznan University of Technology Poland

ISBN: (纸本)9798400709876

Aiming at the problem that the combination of self-play (SP) and deep reinforcement learning (DRL) only involves two-party games and the policy learning of each party is limited, a multi-party asymmetric self-play algorithm (MASP) is proposed. Firstly, by improving the ELO scoring system, the ELO scoring takes into account each party of the unmanned surface vehicle (USV) clusters, and the imbalance of the number of USV clusters, so that the frequency of exchanging strategies of all USV clusters in the confrontation process is equal. Secondly, it ensures that USV clusters have a balanced combat ability, and at the same time ensures that the combat ability of all parties is strong and weak and the gap is not too wide. In addition, the parameters are dynamically set to reduce the update frequency of the policy of the stronger party. The experimental results show that the MASP can make the USV clusters learn more effective policies, have a shorter game time, and obtain higher rewards and ELO scores in the simple 2v2 adversarial game scenario and the three-party game scenario of a warship escort mission. © 2024 ACM.

关键词： Deep reinforcement learning

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Semi-supervised learning with GAN for Semantic Segmentation of Wood Defect image

Semi-supervised learning with GAN for Semantic Segmentation ...

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2024 China automation Congress, CAC 2024

作者： Zheng, Kunxin Zheng, Song Xu, Zhezhuang Lin, Ye Zhu, Yuhang College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Automatic defect detection on wood surface is essential for ensuring product quality. Semantic segmentation methods have shown outstanding performance in wood defect detection. However, it is costly to acquire correct pixel-level labels of wood surface defects due to the complicated characteristics. To mitigate the cost of acquiring high-quality datasets, this study proposes a semi-supervised defect detection approach based on generative adversarial networks, which contains a generator and a discriminator. The generator is utilized to generate pixel-level segmentation results of wood defect images. To improve the segmentation performance, the discriminator learns against the generator by judging the goodness of the prediction results and generates supervised signals for the prediction results of unlabeled images. With this learning model, the proposed method can enhance segmentation performance using unlabeled images and alleviate the burden of data annotation. We validate the effectiveness of the proposed approach on a collected dataset of wood defects. Experimental results demonstrate the remarkable performance of our method even when utilizing only a small amount of annotated data. © 2024 IEEE.

关键词： Generative adversarial networks

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A new appearance-based gaze estimation via multi-modal fusion 3

A new appearance-based gaze estimation via multi-modal fusio...

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3rd International Conference on Neural Networks, Information and Communication engineering, NNICE 2023

作者： Yang, Huijie Yang, Zuoyun J., Liu J., Chi School of Automation and Electrical Engineering University of Science and Technology Beijing Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Engineering Research Center of Intelligence Perception and Autonomous Control Ministry of Education Beijing China

ISBN: (纸本)9798350335972

Appearance-based gaze estimation has gained more and more attention because of its generality, robustness, and subject independence. Deep learning, which has made a great deal of success in computer vision, has also greatly improved the accuracy of appearance-based gaze estimation. To further reduce the error in gaze estimation, we focus on extracting better feature information from eye and face images. In this paper, we propose a novel multimodal fusion gaze estimation model based on ConvNext and dilated convolution. In this model, the eye image and face image are used as input, and the ConvNext network is used to extract the features of the face image and the eye features are extracted by a dilated convolution-based network, and the feature map of the two images are fused using the fully connected layer to perform gaze estimation. In the experimental part, the designed model is verified on the public dataset MPIIGaze, and compared the proposed model with other gaze estimation models. The experimental results show that our proposed method has greatly improved the accuracy of gaze estimation on the MPIIGaze dataset compared to other related works. Our proposed multimodal fusion gaze estimation model achieves state-of-the-art result on the MPIIGaze dataset. © 2023 IEEE.

关键词： Convolution

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Wood Surface Discoloration Defect Detection Based on Improved HRNet

Wood Surface Discoloration Defect Detection Based on Improve...

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2024 China automation Congress, CAC 2024

作者： Zhang, Haoran Xu, Zhezhuang Zhu, Yuhang Lin, Ye College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Discoloration defect detection is a significant task in the wood industry for ensuring the appearance and quality of wooden products. However, discoloration defect detection still faces some challenges due to the low contrast, blurred boundary, and slender shape of discoloration defects. To overcome these challenges, we propose a multi-branch feature encoding HRNet(MFE-HRNet) for wood surface discoloration defect detection. The network maintains high-resolution features and builds a parallel multi-resolution architecture to prevent the dilution of low-contrast discoloration defect features. Through repeated cross-fusion of multi-scale features, the network enhances the global representation of blurry features. In order to effectively preserve the detail features of the defect edge, a multi-branch feature encoding module (MFEM) is proposed as the encoding layer of the network. In the MFEM, multiple branches concurrently extract features, generating feature maps with complementary characteristics, thereby enhancing the ability of network to recognize fine details of defects. Experimental results verify the effectiveness of our method in detecting discoloration defects on wood surface. © 2024 IEEE.

关键词： Discoloration

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Analyzing the Correlation between Student Learning Behaviors and Academic Performance in Blended Learning Environments

Analyzing the Correlation between Student Learning Behaviors...

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2024 China automation Congress, CAC 2024

作者： Zhang, Ying Chen, Kang Xu, Zhezhuang Lin, Ye Zeng, Qingbo College of Electrical Engineering and Automation Fuzhou University Key Laboratory of Industrial Automation Control Technology and Information Processing Education Department of Fujian Province Fuzhou350000 China

ISBN: (纸本)9798350368604

Blended learning integrates online and face-to-face (FTF) teaching methods to offer students in higher education a flexible and customized educational experience. Despite its significant advantages in promoting academic achievement and increasing student participation, blended learning presents challenges for educators due to difficulties in collecting and analyzing learning behavior data. Existing research mainly focuses on single learning modes, and data collection methods primarily rely on interviews and questionnaires, which can be subjective. To address these challenges, this study developed a mobile software called EasyClass, integrated with an online Small Private Online Course(SPOC) platform, to automatically record student behaviors in a blended learning environment. Obtaining data from an undergraduate blended learning course, statistical methods are used to analyze the correlation between student behaviors and final grades, as well as to identify factors affecting grades. The results of multivariate linear regression models confirmed the feasibility and effectiveness of blended instruction. This study provides a new approach to identifying and disseminating effective data collection strategies in blended learning, enabling educators and educational institutions to better understand and assess student behaviors, and to further explore their impact on final academic achievement. © 2024 IEEE.

关键词： Contrastive Learning

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Steady-state Voltage Security-constrained Optimal Frequency control for Weak HVDC Sending-end AC Power Systems

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Journal of Modern Power Systems and Clean Energy 2024年第2期12卷 658-669页

作者： Qiangqiang Wang Liangzhong Yao Jian Xu Yuping Zheng Wei Li Wei Wang School of Electrical Engineering and Automation Wuhan UniversityWuhan 430072China Hubei Engineering and Technology Research Center for AC/DC Intelligent Distribution Network(Wuhan University) Wuhan 430072China State Key Laboratory of Smart Grid Protection and Control NARI Group Corporation(State Grid Electric Power Research Institute)Nanjing 211106China

Due to the fact that a high share of renewable energy sources(RESs)are connected to high-voltage direct current(HVDC)sending-end AC power systems,the voltage and frequency regulation capabilities of HVDC sending-end AC power systems have *** has resulted in potential system operating problems such as overvoltage and overfrequency,which occur simultaneously when block faults exist in the HVDC *** this study,a steady-state voltage security-constrained optimal frequency control method for weak HVDC sending-end AC power systems is *** integrated virtual inertia control of RESs is employed for system frequency *** dynamic reactive power compensation devices are utilized to control the voltage of all nodes meet voltage security ***,an optimization model that simultaneously considers the frequency and steady-state voltage security constraints for weak HVDC sending-end AC power systems is *** optimal control scheme with the minimum total cost of generation tripping and additional dynamic reactive power compensation required is obtained through the optimization *** are conducted on a modified IEEE 9-bus test system and practical Qing-Yu line commutated converter based HVDC(LCC-HVDC)sending-end AC power system to verify the effectiveness of the proposed method.

关键词： Renewable energy source(RES) high-voltage direct current(HVDC) AC power system optimal frequency control steady-state voltage security dynamic reactive power compensation

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Adaptive Trajectory Tracking control for Nonholonomic Wheeled Mobile Robots:A Barrier Function Sliding Mode Approach

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IEEE/CAA Journal of Automatica Sinica 2024年第4期11卷 1007-1021页

作者： Yunjun Zheng Jinchuan Zheng Ke Shao Han Zhao Hao Xie Hai Wang the School of Mechanical Engineering Hefei University of TechnologyHefei 230009China the School of Science Computing and Engineering TechnologiesSwinburne University of TechnologyHawthorn VIC 3122Australia the School of Civil Aviation Northwestern Polytechnical UniversityXi’an 710072China the Shanghai Aerospace Control Technology Institute Shanghai 201109 the School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China the College of Science HealthEngineering and EducationMurdoch UniversityPerth WA6150Australia

The trajectory tracking control performance of nonholonomic wheeled mobile robots(NWMRs)is subject to nonholonomic constraints,system uncertainties,and external *** paper proposes a barrier function-based adaptive sliding mode control(BFASMC)method to provide high-precision,fast-response performance and robustness for *** with the conventional adaptive sliding mode control,the proposed control strategy can guarantee that the sliding mode variables converge to a predefined neighborhood of origin with a predefined reaching time independent of the prior knowledge of the uncertainties and disturbances *** advantage of the proposed algorithm is that the control gains can be adaptively adjusted to follow the disturbances amplitudes thanks to the barrier *** benefit is that the overestimation of control gain can be eliminated,resulting in chattering ***,a modified barrier function-like control gain is employed to prevent the input saturation problem due to the physical limit of the *** stability analysis and comparative experiments demonstrate that the proposed BFASMC can ensure the prespecified convergence performance of the NWMR system output variables and strong robustness against uncertainties/disturbances.

关键词： Adaptive sliding mode barrier function nonholonomic wheeled mobile robot(NWMR) trajectory tracking control

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EMI Noise Reduction with Magnetic Integrated LCL Filter in the Grid-Connected Inverter 49

EMI Noise Reduction with Magnetic Integrated LCL Filter in t...

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49th Annual Conference of the IEEE Industrial Electronics Society, IECON 2023

作者： Liu, Xingyu Liu, Yitao Jiang, Shiqi College of Mechatronics and Control Engineering Shenzhen University Shenzhen China School of Electrical Engineering and Automation Harbin Institute of Technology Harbin China

ISBN: (纸本)9798350331820

This paper presents a magnetic integration scheme of LCL filter with symmetrical structure. This magnetic integration scheme uses EIE core to realize the inductor integration of symmetrical LCL filter, and realizes the decoupling and integration of upper and lower E-cores. The effectiveness of the magnetic integrated symmetrical LCL filter is verified by simulation and experiment. A 600 W single-phase SiC high-frequency inverter was used as the experimental platform to give the output current harmonic analysis results and conducted EMI noise. The symmetrical LCL filter with integrated structure not only has good harmonic suppression effect, but also greatly improves EMI suppression ability. The integrated filter is significantly smaller weight, helping to increase system power density. © 2023 IEEE.

关键词： Noise abatement

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Deep reinforcement learning guidance with impact time control

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Journal of Systems engineering and Electronics 2024年第6期35卷 1594-1603页

作者： LI Guofei LI Shituo LI Bohao WU Yunjie School of Astronautics Northwestern Polytechnical UniversityXi’an 710072China Beijing Institute of Control and Electronic Technology Beijing 100038China School of Automation Science and Electrical Engineering Beihang UniversityBeijing 100191China

In consideration of the field-of-view(FOV)angle con-straint,this study focuses on the guidance problem with impact time control.A deep reinforcement learning guidance method is given for the missile to obtain the desired impact time and meet the demand of FOV angle *** basis of the framework of the proportional navigation guidance,an auxiliary control term is supplemented by the distributed deep deterministic policy gradient algorithm,in which the reward functions are developed to decrease the time-to-go error and improve the terminal guid-ance *** numerical simulation demonstrates that the missile governed by the presented deep reinforcement learning guidance law can hit the target successfully at appointed arrival time.

关键词： impact time deep reinforcement learning guidance law field-of-view(FOV)angle deep deterministic policy gradient

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Digital Twin Modeling for Aerospace CMG Based on Energy Analysis

Digital Twin Modeling for Aerospace CMG Based on Energy Anal...

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International Conference on Guidance, Navigation and control, ICGNC 2022

作者： Runhao, Cui Diyin, Tang Bin, Chen School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Beijing Institute of Control Engineering Beijing100094 China

ISBN: (纸本)9789811966125

This paper presents a digital twin modeling approach for aerospace control moment gyro (CMG). The proposed modeling approach is on the basis of energy balance theory with the physical mechanism in order to analyze the degradation of CMG components. Firstly, a CMG digital twin (DT) energy model is established, which combines the energy consumption of each CMG unit. Next, the CMG degradation parameters are extracted through detailing the power loss of subsystems. Finally, by using CMG simulation data, the effectiveness and validity of the digital twin model are verified. The proposed digital twin model can be a useful tool for analyzing CMG degradation mechanism, and supporting anomaly detection and remaining useful life (RUL) prediction. © 2023, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Energy utilization

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