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7th CAA International Conference on Vehicular Control and Intelligence, CVCI 2023

作者： Liu, Xiangyong Yin, Fengfeng Jiang, Wei Fan, Siwen College of Automotive Studies Tongji University State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Department of Electromechanical Engineering Shanghai China State Key Laboratory of Process Automation in Mining & Metallurgy Beijing Key Laboratory of Process Automation in Mining & Metallurgy Bgrimm Technology Group Beijing China Linyi Engineering Branch China Railway Jinan Group Co Ltd Linyi China School of Mechatronical Engineering Shanghai China

ISBN: (纸本)9798350340488

The semantic segmentation of road objects is a prerequisite for autonomous driving. Rapid or bumpy movement of vehicles can lead to the images to be blurred, which reduces the safety of autonomous driving. This study combines the dynamic visual sensor (DVS) and RGB camera to improve the detection accuracy of road semantic segmentation. Firstly, based on DVS and RGB images, we develop two datasets of blurred images, including the stone and run datasets. Then, a double channels' detection network is designed with several Swin-transformer modules, and the two channels are designed to extract features from DVS and RGB images, respectively. In addition, a feature fusion module is designed to extract attention from DVS images. Finally, experiments demonstrate that our proposed double-channels' fusion method achieves the state-of-the-art segmentation performance for blurred images. © 2023 IEEE.

关键词： blurred images double channels' network dynamic visual sensors feature fusion semantic segmentation

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Bayesian Filtering for High-Dimensional State-Space Models With State Partition and Error Compensation

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IEEE/CAA Journal of Automatica Sinica 2024年第5期11卷 1239-1249页

作者： Ke Li Shunyi Zhao Biao Huang Fei Liu Key Laboratory of Advanced Process Control for Light Industry(Ministry of Education) Institute of AutomationJiangnan UniversityWuxi 214122China Department of Chemical and Materials Engineering University of AlbertaEdmonton AB T6G 2G6Canada

In the era of exponential growth of data availability,the architecture of systems has a trend toward high dimensionality,and directly exploiting holistic information for state inference is not always computationally *** paper proposes a novel Bayesian filtering algorithm that considers algorithmic computational cost and estimation accuracy for high-dimensional linear *** high-dimensional state vector is divided into several blocks to save computation resources by avoiding the calculation of error covariance with immense *** that,two sequential states are estimated simultaneously by introducing an auxiliary variable in the new probability space,mitigating the performance degradation caused by state ***,the computational cost and error covariance of the proposed algorithm are analyzed analytically to show its distinct features compared with several existing *** results illustrate that the proposed Bayesian filtering can maintain a higher estimation accuracy with reasonable computational cost when applied to high-dimensional linear systems.

关键词： filtering estimation error

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An Integrated Sensor Fault Estimation and Fault-Tolerant Control Design Approach for Continuous-Time Switched Systems

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Journal of Dynamic Systems, Measurement and Control 2025年第4期147卷 041006页

作者： Elouni, Mohamed Hamdi, Habib Rabaoui, Bouali Rodrigues, Mickael Braiek, Naceur Benhadj Advanced Systems Laboratory National Higher Engineering School of Tunis ENSIT University of Tunis La Marsa2078 Tunisia Advanced Systems Laboratory Tunisian Polytechnic School University of Carthage La Marsa2078 Tunisia Automation and Process Engineering Laboratory IUT Lyon1 University of Lyon CNRS UMR 5007 Lyon69100 France

This paper proposes a robust integrated fault estimation (FE) and fault-tolerant control (FTC) strategy for a class of continuous-time switched systems in the presence of unknown external disturbances and sensor faults. The main idea focuses on designing a switched descriptor observer to achieve simultaneous estimations of system states and sensor faults. With the acquired estimation information, an integrated development of the estimated state feedback fault-tolerant controller is constructed to compensate for the fault effects by stabilizing the switched closed-loop system. Based on the average dwell time (ADT) concept and the Lyapunov-Krasovskii functional method, new and less conservative stability conditions are established with a prescribed H∞ performance level in a single-step linear matrix inequality (LMI) formulation. Finally, both a numerical example and a highly maneuverable aircraft technology (HiMAT) vehicle are provided to illustrate the effectiveness of the developed strategy. Copyright © 2025 by ASME.

关键词： Continuous time systems

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Generative Adversarial Nets Model Based on Spatio-Temporal Graph Attention Network for Multi-Step Prediction

Generative Adversarial Nets Model Based on Spatio-Temporal G...

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2023 China automation Congress, CAC 2023

作者： Wang, Ling Wang, Rongxian Jia, Gaofeng School of Automation and Electrical Engineering Shunde Innovation School University of Science and Technology Beijing Key Laboratory of Knowledge Automation of Industrial Process of Ministry of Education Beijing China School of Automation and Electrical Engineering University of Science and Technology Beijing Key Laboratory of Knowledge Automation of Industrial Process of Ministry of Education Beijing China

ISBN: (纸本)9798350303759

The precise forecasting of traffic data holds significant importance for numerous practical applications in the real world. The challenge lies in the nonlinear spatio-temporal dependencies of the data, as well as rapid error accumulations for multi-step predictions. Therefore, we propose a Generative Adversarial Nets model based on a Spatio-Temporal Graph Attention Network (STGAT-GAN). Utilizing spatio-temporal graph attention proves to be an effective method for capturing the spatio-temporal dependencies, while GANs can learn a rich distribution over spatio-temporal data implicitly to produce realistic data adversarially. Experiments are carried out on two public traffic datasets. Comparative experimental analysis reveals that the proposed STGAT-GAN has better performance. © 2023 IEEE.

关键词： Generative adversarial networks

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Graph convolutional network soft sensor for process quality prediction

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Journal of process Control 2023年第1期123卷 12-25页

作者： Jia, Mingwei Xu, Danya Yang, Tao Liu, Yi Yao, Yuan Institute of Process Equipment and Control Engineering Zhejiang University of Technology Hangzhou310023 China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang110819 China Department of Chemical Engineering National Tsing Hua University Hsinchu30013 Taiwan

The nonlinear time-varying characteristics of the process industry can be modeled using numerous data-driven soft sensor methods. However, the intrinsic relationships among the variables, especially the localized spatial–temporal correlations that shed light on model behavior, have received little attention. In this study, a soft sensor based on a graph convolutional network is constructed by introducing the concept of graph to process modeling. The focus is on obtaining localized spatial–temporal correlations that aid in comprehending the intricate interactions among the variables included in the soft sensor. The model is trained by considering the regularization terms and it learns distinctive localized spatial–temporal correlations in an end-to-end manner. Furthermore, long-term dependence is established via temporal convolution. Thus, both the localized spatial–temporal correlations and time-series properties are captured. The feasibility of the proposed soft sensor is illustrated using two fermentation processes. The localized spatial–temporal correlations of this case study are visualized, and they demonstrate that the soft sensor is not a black-box model;instead, it is consistent with process knowledge. © 2023 Elsevier Ltd

关键词： Convolution

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Adaptive Optimal Output Regulation of Interconnected Singularly Perturbed Systems With Application to Power Systems

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 595-607页

作者： Jianguo Zhao Chunyu Yang Weinan Gao Linna Zhou Xiaomin Liu the Engineering Research Center of Intelligent Control for Underground Space Ministry of EducationChina University of Mining and TechnologyXuzhou 221116 the School of Information and Control Engineering China University of Mining and TechnologyXuzhou 221116 IEEE the State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819China

This article studies the adaptive optimal output regulation problem for a class of interconnected singularly perturbed systems(SPSs) with unknown dynamics based on reinforcement learning(RL).Taking into account the slow and fast characteristics among system states,the interconnected SPS is decomposed into the slow time-scale dynamics and the fast timescale dynamics through singular perturbation *** the fast time-scale dynamics with interconnections,we devise a decentralized optimal control strategy by selecting appropriate weight matrices in the cost *** the slow time-scale dynamics with unknown system parameters,an off-policy RL algorithm with convergence guarantee is given to learn the optimal control strategy in terms of measurement *** combining the slow and fast controllers,we establish the composite decentralized adaptive optimal output regulator,and rigorously analyze the stability and optimality of the closed-loop *** proposed decomposition design not only bypasses the numerical stiffness but also alleviates the *** efficacy of the proposed methodology is validated by a load-frequency control application of a two-area power system.

关键词： Adaptive optimal control decentralized control output regulation reinforcement learning(RL) singularly perturbed systems(SPSs)

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An Anti-Overlap Object Feature Weighted Fusion Detector for X-Ray Prohibited Item Detection 6

An Anti-Overlap Object Feature Weighted Fusion Detector for ...

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6th International Conference on Industrial Artificial Intelligence, IAI 2024

作者： Jia, Tong Jia, Wanying Tian, Yixin Ma, Bowen Wang, Hao Guo, Shiyi State Key Laboratory of Synthetical Automation for Process Industries Shenyang China College of Information Science and Engineering Northeastern University Shenyang China

ISBN: (纸本)9798350356618

Security checkpoints equipped with X-ray scanners play a crucial role in ensuring public safety by inspecting luggage for prohibited items. However, even trained professionals can easily make errors when manually inspecting X-ray images. To assist in manual inspection, direct application of object detection methods to X-rays can lead to false positives and false negatives due to overlapping objects. To address this issue, this paper proposes an object feature weighted fusion detector (OFWF-Detector) for X-ray prohibited item detection. The OFWF-Detector utilizes an object confidence mask selection (OCMS) mechanism to filter overlapping features, improving detection accuracy. Experimental results on the PIX ray dataset demonstrate that the proposed method effectively reduces false positives and false negatives, achieving superior performance compared to traditional methods. Specifically, the OFWF-Detector enhances the detection of small objects in overlapping scenarios by 12.9 % in terms of APs from Tab. I, showcasing its effectiveness in all the challenges posed by overlapping objects in X-ray images. These findings highlight the potential of the OFWF-Detector to enhance the efficiency and accuracy of security screening systems, contributing to improved public safety in high-traffic areas. © 2024 IEEE.

关键词： X ray detectors

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Latent-Enhanced Variational Adversarial Active Learning Assisted Soft Sensor

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IEEE Sensors Journal 2023年第14期23卷 15762-15772页

作者： Dai, Yun Yang, Chao Liu, Yi Yao, Yuan Zhejiang University of Technology Institute of Process Equipment and Control Engineering Hangzhou310023 China Northeastern University State Key Laboratory of Synthetical Automation for Process Industries Shenyang110819 China National Tsing Hua University Department of Chemical Engineering Hsinchu300044 Taiwan

As the acquisition of variables that measure quality is typically challenging, labeled samples for building a model for soft sensors are often inadequate. Additionally, owing to the installation of redundant sensors, high-dimensional process data with strong correlations are acquired. Therefore, in this study, a sample selection strategy for active learning (AL), referred to as latent-enhanced variational adversarial AL (LVAAL), is developed to enhance quality prediction performance with limited labeled data. The LVAAL method uses a minimax game to explore the latent representation of the original data. Specifically, a latent-enhanced variational autoencoder (LEVAE) is used to deceive the adversarial network into predicting all samples, both labeled and unlabeled, as labeled. However, the adversarial network attempts to indicate the difference between latent representations. Subsequently, Gaussian process regression (GPR) is adopted as a base model for LVAAL. With the proposed LVAAL selection strategy, informative unlabeled samples are incrementally selected, thereby enhancing the prediction performance. The application results for a numerical case and an industrial example demonstrate the advantages of LVAAL compared with existing AL strategies. © 2001-2012 IEEE.

关键词： Gaussian distribution

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RIS-assisted Channel Prediction Model for Mine V2X Systems 5

RIS-assisted Channel Prediction Model for Mine V2X Systems

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5th International Conference on Computer, Big Data and Artificial Intelligence, ICCBD+AI 2024

作者： Cheng, Yuanxun Hu, Qingsong Luo, Yujia Zhang, Yuansheng China University of Mining and Technology School of Information and Control Engineering Xuzhou China Beijing Key Laboratory of Process Automation in Mining & Metallurgy State Key Laboratory of Intelligent Optimized Manufacturing in Mining & Metallurgy Process Beijing China

ISBN: (纸本)9798331533991

The complex and dynamic environment of mines leads to time-variability and uncertainty in channel propagation, which affects the stability and reliability of Vehicle-to-Everything (V2X)communication in mine networks. Channel prediction can help mitigate these issues. Introducing reconfigurable intelligence surface (RIS) into the mine V2X system can enhance signal coverage and quality in the complex mine environment. To address the issue of long-term error accumulation in existing channel prediction models, this paper analyzes the prediction error of channel state information (CSI) in mine V2X systems. It was observed that the error distribution exhibits characteristics of an approximate normal distribution, revealing its predictability. Utilizing this characteristic, this paper proposes an error correction strategy to suppress long-term error accumulation during the prediction process. Furthermore, an adaptive method for optimizing RIS phases is designed based on the Snow Ablation Optimizer. Specifically, this paper utilizes a metaheuristic optimization algorithm and deep learning techniques to propose a mine V2X channel prediction model that incorporates the error correction strategy. Experimental results show that optimizing the RIS phase can effectively enhance channel gain, and the mine V2X channel prediction model incorporating the error correction strategy can effectively suppress cumulative errors during the prediction process, thereby improving prediction accuracy. © 2024 IEEE.

关键词： Normal distribution

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Fault Classification Method based on Self-paced Neighborhood Preserving Embedding 7

Fault Classification Method based on Self-paced Neighborhood...

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7th International Symposium on Autonomous Systems, ISAS 2024

作者： Gao, Hongrui Ma, Chuan Zhang, Yingwei Northeastern University College of Information Science and Engineering Shenyang China Northeastern University State Laboratory of Synthesis Automation of Process Industry Shenyang China

ISBN: (纸本)9798350363173

In view of the large amount of faulty data in industrial systems, strong correlation between data, large redundancy, and high dimensionality, traditional classification methods can easily lead to the phenomenon of "dimensionality disaster". On the basis of the Neighborhood Preserving Embedding algorithm (NPE), a fault classification algorithm based on Self-paced NPE is proposed. First, the neighbor point set and adjacency matrix of the fault sample points are obtained through the NPE algorithm. Secondly, the function of solving the projection matrix in the NPE algorithm is used as the loss function in the Self-paced Learning model to construct the objective function of the Self-paced Neighborhood Preserving Embedding (Self-paced NPE) algorithm. By designing the loss function in the Self-paced Learning model, gradually select from easy to difficult samples with smaller loss are selected, samples with larger loss are discarded, and relatively simple samples are used for training. Finally, the objective function of the Self-paced NPE algorithm is solved and the proposed algorithm is tested with TE process data. The experimental results show that, compared with the NPE algorithm, the Self-paced NPE algorithm can extract features of high-dimensional fault data more effectively, achieve dimension reduction, and improve the accuracy of fault classification. © 2024 IEEE.

关键词： Embeddings

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