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第41届中国控制会议

作者： Yifan Chen Liping Yan Yuanqing Xia Bo Xiao Key Laboratory of Intelligent Control and Decision of Complex Systems School of AutomationBeijing Institute of Technology School of Artificial Intelligence Beijing University of Posts and Telecommunications

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

With the improvement of hardware performance,semantic segmentation based on convolutional neural network has achieved wide concern for its' advantage of high ***,in practical application scenarios,such as autonomous driving,what we care about is not only the segmentation accuracy,but also the ability of the model to process information in real-time on edge *** this paper,a lightweight and efficient real-time semantic segmentation network was proposed,which is based on a two-way structure,aggregates detailed spatial information and high-level semantic information,integrates long-range dependencies and region-level *** proposed algorithm achieves good *** conducted experiments based on an NVIDIA Geforce RTX 2080 SUPER graphics card and achieved 73.8% accuracy on Cityscapes dataset with a speed of 211 FPS,and 72.8% accuracy on Camvid dataset.

关键词： Semantic Segmentation Real-time Semantic Segmentation Deep Learning Bilateral Neural Network

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Optimizing Polynomial-Time Solutions to a Network Weighted Vertex Cover Game

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IEEE/CAA Journal of Automatica Sinica 2023年第2期10卷 512-523页

作者： Jie Chen Kaiyi Luo Changbing Tang Zhao Zhang Xiang Li the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004 Yuxi Middle School Taizhou 317399China IEEE the College of Physics and Electronics Information Engineering Zhejiang Normal UniversityJinhua 321004China the College of Mathematics and Computer Science Zhejiang Normal UniversityJinhua 321004China the Adaptive Networks and Control Laboratory Department of Electronic EngineeringFudan UniversityShanghai 200433 the Institute of Complex Networks and Intelligent Systems Shanghai Research Institute for Intelligent Autonomous SystemsTongji UniversityShanghai 201210China

Weighted vertex cover(WVC)is one of the most important combinatorial optimization *** this paper,we provide a new game optimization to achieve efficiency and time of solutions for the WVC problem of weighted *** first model the WVC problem as a general game on weighted *** the framework of a game,we newly define several cover states to describe the WVC ***,we reveal the relationship among these cover states of the weighted network and the strict Nash equilibriums(SNEs)of the ***,we propose a game-based asynchronous algorithm(GAA),which can theoretically guarantee that all cover states of vertices converging in an SNE with polynomial ***,we improve the GAA by adding 2-hop and 3-hop adjustment mechanisms,termed the improved game-based asynchronous algorithm(IGAA),in which we prove that it can obtain a better solution to the WVC problem than using a the ***,numerical simulations demonstrate that the proposed IGAA can obtain a better approximate solution in promising computation time compared with the existing representative algorithms.

关键词： Game-based asynchronous algorithm(GAA) game optimization polynomial time strict Nash equilibrium(SNE) weighted vertex cover(WVC)

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Pore extraction method of rock thin section based on Attention U-Net

Pore extraction method of rock thin section based on Attenti...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Li, Mingjiang Zhang, Pan Hai, Tao School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

This paper proposes a solution to the shortcomings of traditional segmentation methods. The labeling method uses the incomplete labeling method in weakly supervised labeling to simplify labeling and combines transfer learning to initialize the weight of the network in advance. According to the above ideas, an end-to-end deep learning model is trained. The fine rock particles have a greater segmentation impact, and in addition to that, when compared with the popular deep learning semantic segmentation approaches, they also have a significant improvement. The next phase is to continue improving the network by optimizing the parameters, with the number of network layers and the total number of parameters remaining unaltered. This requirement must be satisfied before moving on to the next stage. The capability of generalization enhances the impact of segmentation on particles as well as their accuracy. Experiments show that this method is significantly better than the traditional method for segmenting rock flakes with manual operation and has better results in the segmentation and extraction of fine particles compared with the mainstream convolutional neural network. © Published under licence by IOP Publishing Ltd.

关键词： Semantics

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Multiscale Feature Fusion Module for Prohibited Item Detection 9

Multiscale Feature Fusion Module for Prohibited Item Detecti...

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9th International Conference on intelligent Computing and Signal Processing, ICSP 2024

作者： Xu, Ruiqi Zhang, Chirui School of Automation China University of Geosciences Hubei Wuhan430074 China Hubei key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Hubei Wuhan430074 China Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Hubei Wuhan430074 China

ISBN: (纸本)9798350376548

X-ray security check is a prevalent placement measure that plays a vital role in ensuring public safety. However, detecting prohibited items poses a complex challenge due to their variable sizes within X-ray images. In this study, we tackle this issue by curating a novel dataset through the integration of open-source data and introducing a Multiscale Feature Fusion Module. This module facilitates robust detection by amalgamating features across different scales. Extensive experiments verified that the superiority of our method compared to existing methods on the prohibited items test set constructed in this paper demonstrates extremely well the practical effectiveness of our proposed method. © 2024 IEEE.

关键词： component Environmental Security Prohibited Items Security Inspection X-ray Images

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Stability Analysis of a New Constant Current to Constant Voltage Power Supply System 42

Stability Analysis of a New Constant Current to Constant Vol...

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42nd Chinese control Conference, CCC 2023

作者： Song, Xiaoxian Fang, Zhijian Xia, Yuangeng Cai, Jie Wei, Zihao 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

ISBN: (纸本)9789887581543

A high-reliability constant current to constant voltage power supply system has the advantages of small volume of switching power supply, high power density, high efficiency was proposed. This paper use two controllers to control the shunt regulator(SR) circuit and single-end flyback converter part, and separate the two parts for small signal modeling and give the parameters to stabilize the closed loop. The state space average modeling idea was used to solve the state equations for the modes of the converters in a switching cycle. In order to ensure the stability of cascade system, this paper collaborative optimization of hardware filter parameters and the appropriate PI parameter design. The experimentals verify the correctness of our theory, and the system has good stability under closed-loop conditions. © 2023 Technical Committee on control Theory, Chinese Association of Automation.

关键词： Stability

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Nash Equilibrium Estimation of Non-Cooperative Games with Time-Varying Delay

Nash Equilibrium Estimation of Non-Cooperative Games with Ti...

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2023 China Automation Congress, CAC 2023

作者： Liu, Ziqi Zhai, Chao China University of Geosciences School of Automation Wuhan Campus China Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems China Ministry of Education Engineering Research Center of Intelligent Technology for Geo-Exploration Wuhan China

ISBN: (纸本)9798350303759

Time delay has great impacts on the stability and the reliability and real-time of the communication of multi-agent systems. In multi-agent communication network, due to network congestion, transmission distance and other factors, there are various communication delays. In this paper, we study the deviation of convergence value after adding time-varying delays under gradient descent method, and the upper bound related to delay time is estimated. This upper bound can be used to analyze the magnitude of deviation under different time delays and minimize the loss caused by delays, and provide more explicit information for system optimization and resource allocation. Numerical simulation is conducted to verify the proposed approach. © 2023 IEEE.

关键词： Time delay

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Roller Bearing Fault Diagnosis Using Deep Transfer Learning and Adaptive Weighting

Roller Bearing Fault Diagnosis Using Deep Transfer Learning ...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Liu, Yan Fan, Kaiyu School of Computer and Information Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

A fault diagnosis approach for roller bearings utilizing deep transfer learning and adaptive weighting is suggested to address the issue that extra fault state samples in the target domain data of roller bearings impair the fault diagnostic accuracy. CNN-LSTM is a network model proposed by Lecun et al., which has good performance in image processing and image processing. It can effectively apply predictive local perception of time series and weight sharing of CNN, which can greatly reduce the number of networks and improve the efficiency of model learning. The method first establishes a feature extraction module, and uses a deep convolutional neural network to map bearing samples to a high-dimensional feature space. Secondly, uses the transfer learning concept to design a weighted domain discriminator, and adaptively weights the samples;and finally, through the confrontation in the feature space, the bearing samples are classified. Training to increase the domain similarity of the healthy state samples shared by the target domain and the source domain. Then measuring the similarity between these samples based on the sample weight size, and setting a threshold to label the additional fault state samples of the target domains as unknown. The suggested technique is validated using gearbox bearing data, roller bearing data from Case Western Reserve University, and locomotive wheel bearing data. The diagnostic accuracy of the samples is less than 80%, suggesting that the suggested approach can successfully overcome the effects of extra fault state samples and diagnose roller bearing faults. © Published under licence by IOP Publishing Ltd.

关键词： Failure analysis

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United Impedance Stability Analysis for Multi-port Converter Considering Bidirectional Energy Flow 41

United Impedance Stability Analysis for Multi-port Converter...

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第41届中国控制会议

作者： Zhiguo Wei Zhijian Fang Fei Xie Yuangeng Xia Jie Cai School of Automation China University of Geosciences (Wuhan) Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Stability analysis is very important for multiport *** most stability analysis methods are published only for unidirectional energy flow conditions,which cannot be used for multiport *** thispaper,a stability analysis method is proposed for multi-port converter with bi-directional energy flow,which uses the unified impedance model of bidirectional converter to analyze the system ***,the bidirectional converter is modeled uniformly and the output impedance characteristics of bi-directional converter are *** the unified impedance criterion is used to judge its *** stability of the system under charging and discharging conditions is *** results show that the system stability is higher during charging than during discharging when the power of the battery is the ***,the correctness of the criterion is verified by simulation.

关键词： small signal model stability analysis bidirectional converter

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A Small Sample Photovoltaic Hot Spot Identification Method Based on Deep Transfer Learning

A Small Sample Photovoltaic Hot Spot Identification Method B...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Zhou, Jincheng Luo, Xiao School of Computer and Information Qiannan Normal University for Nationalities Guizhou Duyun558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Guizhou Duyun558000 China

A small-sample photovoltaic hot spot identification method based on deep transfer learning has been proposed as a solution to the problem that traditional deep learning models require a substantial amount of training data, whereas the number of hot spot effect samples is relatively low and difficult to collect. This issue can be resolved by the development of a solution that employs deep transfer learning. The Inception-v 4 model serves as the basis for building a deep transfer learning model. After finishing the training procedure using a small-sample hot spot dataset of negative sample multi-classification, a network model for hot spot detection is built. Experiments reveal that even when the number of accessible samples is restricted, the model trained using the deep transfer learning technique has great identification accuracy, a low false detection rate, and strong generalization capability. © Published under licence by IOP Publishing Ltd.

关键词： Classification (of information)

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A Non-Intrusive Load Decomposition Method Based on Generative Adversarial Networks and Asynchronous Federated Learning

A Non-Intrusive Load Decomposition Method Based on Generativ...

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International Conference on Emerging Electronic and Automation Technology 2022, ICEEAT 2022

作者： Wang, Dan Fan, Kaiyu School of Mathematics and Statistics Qiannan Normal University for Nationalities Duyun Guizhou558000 China School of Computer Sciences Baoji University of Arts and Sciences 721007 China Laboratory of Complex Systems and Intelligent Optimization of Guizhou Duyun Guizhou558000 China

The non-intrusive load decomposition method is an essential approach of appreciating the water requirements of consumers. Traditional techniques of load breakdown have a number of drawbacks, including a poor level of water identification precision. In order to do this, a model for load decomposition based on generative adversarial networks has been developed. By applying a convolutional autoencoder to the total power noise, the generative network constructs the power breakdown sequence of the given home appliance. The discriminant network is then utilized to determine whether or not the created sequence is legitimate. In order to achieve a more natural and authentic sequence, the participants compete against one another. In light of the shortcomings of the centralized model training method, the network model implementation scheme based on asynchronous federated learning is utilized to replace the traditional convolution in order to realize the model's lightweight nature. This allows the model to be applied to terminal devices such as smart water meters, and it is carried out in a cloud-edge collaborative manner. In addition, the model is carried out in a manner that is collaborative between the cloud and the edge. Training not only eases the strain of communication transmission but also protects the privacy of users and the integrity of their data. © Published under licence by IOP Publishing Ltd.

关键词： Generative adversarial networks

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