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第43届中国控制会议

作者： He Yueyang Liang Xiuxia Xia Manman Liang Tao School of Artificial Intelligence and Data Science Hebei University of Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

Batch process is an indispensable part of modern industrial production. Because of its flexibility and high efficiency,it is widely used in biopharmaceutical,wastewater treatment,fine chemical and other fields. Due to the improvement of modern equipment and the expansion of the scale of the factory,the chance of accidents and failures also showed an exponential increase trend. Therefore,fault diagnosis is very important to ensure the stability and safety of chemical processes. In order to improve the safety and reliability of batch process,an improved Genetic Algorithm is proposed to optimize the parameters of Separable Convolution Network and Temporal Convolutional Network(SeparableConv1D-TCN) for fault diagnosis of batch process. Firstly,the SeparableConv1D-TCN network is constructed to extract features from the original intermittent data and perform fault diagnosis by standardizing the intermittent data. In order to find the network parameters with high diagnostic accuracy,the genetic algorithm with good point set method and optimal domain search is used to optimize. The effectiveness of the method is verified by simulation experiments and comparative experiments with penicillin experimental data.

关键词： Batch Process Fault Diagnosis SeparableConv1D-TCN Genetic Algorithm

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Transverse Velocity Field Measurement of Solar High-resolution Images Based on Unsupervised Deep Learning

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Research in Astronomy and Astrophysics 2025年第3期25卷 236-248页

作者： Zhen-Hong Shang Long Chen Zhen-Ping Qiang Yi Bi Run-Xin Li Faculty of Information Engineering and Automation Kunming University of Science and Technology Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and Technology College of Big Data and Intelligent Engineering Southwest Forestry University Yunnan Observatories Chinese Academy of Sciences

Measuring the transverse velocity field in high-resolution solar images is essential for understanding solar *** paper introduces an innovative unsupervised deep learning optical flow model designed to calculate the transverse velocity field,addressing the challenges of missing optical flow labels and the limited accuracy of velocity field measurements in high-resolution solar *** proposed method converts the transverse velocity field computation problem into an optical flow computation problem,using two forward propagations of features to get rid of the reliance on optical flow ***,it reduces the impact of the“Brightness Consistency”constraint on optical flow accuracy by identifying and handling optical flow *** apply this method to compute the transverse velocity fields of high-resolution solar image sequences from the Hαand TiO bands,observed by the New Vacuum Solar *** experiments with several wellestablished optical flow methods,including those based on supervised deep learning models,show that our approach outperforms the comparison methods according to key evaluation metrics such as Residual Map Mean,Residual Map Variance,Cross Correlation,and Structural Similarity Index ***,since optical flow captures the fundamental motion information in image sequences,the proposed method can be applied to a variety of research areas,including solar image registration,sequence alignment,image super-resolution,magnetic field calibration,and solar activity *** code is available at https://***/jackie-willianm/Transverse-Velocity-Field-Measurement-of-Solar-High-Resolution-Images.

关键词： methods: data analysis techniques: image processing Sun: fundamental parameters

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Semantic-aware entity alignment for low resource language knowledge graph

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Frontiers of Computer science 2024年第4期18卷 97-106页

作者： Junfei TANG Ran SONG Yuxin HUANG Shengxiang GAO Zhengtao YU Faculty of Information Engineering and Automation Kunming University of Science and TechnologyKunming 650500China Yunnan Key Laboratory of Artificial Intelligence Kunming University of Science and TechnologyKunming 650500China

Entity alignment(EA)is an important technique aiming to find the same real entity between two different source knowledge graphs(KGs).Current methods typically learn the embedding of entities for EA from the structure of KGs for *** EA models are designed for rich-resource languages,requiring sufficient resources such as a parallel corpus and pre-trained language ***,low-resource language KGs have received less attention,and current models demonstrate poor performance on those low-resource ***,researchers have fused relation information and attributes for entity representations to enhance the entity alignment performance,but the relation semantics are often *** address these issues,we propose a novel Semantic-aware Graph Neural Network(SGNN)for entity ***,we generate pseudo sentences according to the relation triples and produce representations using pre-trained ***,our approach explores semantic information from the connected relations by a graph neural *** model captures expanded feature information from *** results using three low-resource languages demonstrate that our proposed SGNN approach out performs better than state-of-the-art alignment methods on three proposed datasets and three public datasets.

关键词： graph neural network knowledge graph entity alignment low-resource language

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Adaptive Prescribed-Time Distributed Nash Equilibrium Seeking for Networked Games with Heterogeneous Dynamics and Unknown Uncertainties

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IEEE Transactions on Control of Network Systems 2025年第2期12卷 1744-1755页

作者： Chen, Yiyang Hua, Yongzhao Feng, Zhi Dong, Xiwang Beihang University School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Shenyuan Honors College Beijing100191 China Beihang University School of Artificial Intelligence Institute of Artificial Intelligence Beijing100191 China Beihang University School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China Beihang University Institute of Unmanned System School of Artificial Intelligence Institute of Artificial Intelligence School of Automation Science and Electrical Engineering Science and Technology on Aircraft Control Laboratory Beijing100191 China

This paper investigates the adaptive prescribed-time distributed Nash equilibrium (NE) seeking problems for networked games with heterogeneous dynamics and unknown uncertainties. The proposed algorithms are based on the two-layer structure, namely the NE seeking part and the tracking control part. For the players without uncertainties, the adaptive parameters are utilized in the seeking part to avoid the use of the global information. The auxiliary variables are constructed to seek for the NE point within the prescribed time and serve as the reference signals for the tracking control part. Then the state feedback control is designed to drive the strategies of all the players to the expected NE point in the prescribed time. Furthermore, the approximation theory is introduced to deal with the unknown nonlinear uncertainties. The exponential parameters are involved in the designed estimates to accelerate the convergence rate. The Lyapunov method is utilized to show the prescribed-time convergence property of the algorithms. Besides, although the time-varying piecewise function is involved in the algorithms, the uniform boundedness of the control input can be ensured by carefully selecting the initial values of the auxiliary parameters. Finally, the simulation is given to show the effectiveness of the proposed algorithms. © 2025 IEEE.

关键词： Lyapunov methods

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A Long Short-Term Memory Network-Based Approach for Predicting the Trends in the S&P 500 Index

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Journal of The Institution of Engineers (India): Series B 2024年第1期105卷 19-26页

作者： Siddesh, G.M. Sekhar, S. R. Mani Srinivasa, K.G. Department of Artificial Intelligence & Data Science M S Ramaiah Institute of Technology Bangalore India Department of Information Science and Engineering M S Ramaiah Institute of Technology Bangalore India Data Science and Artificial Intelligence Programme International Institute of Information Technology Naya Raipur India

The price level of a considered security in the stock market is largely determined by perceived consumer demand. Variations in price level in the stock market are essentially a manifestation of public psychology aiming to either cut losses or maximize profits. The assumption that market movements are controlled by a guiding trend is proved by establishing that the time-series variations of daily S&P 500 indices are a non-stationary process using the Dickey–Fuller test. This paper presents a long short-term memory (LSTM) network adaptation of an autoregressive predictive model for S&P 500 indices. Later, an LSTM network-based model is devised to learn the underlying patterns that are responsible for driving the trends in market dynamics. The proposed model accepts as input at each time-step a vector of prior S&P indices over the past two weeks and estimates the current market index. The network weights assume the correct settings by training the model using daily S&P indices over the past ten years. The proposed model decisively captures subtleties in market trends, which are in turn verified using the Granger causality approach. The result demonstrates that the proposed model achieved an accuracy of 82.6%, whereas random forest results in 79.12%. The results obtained are truly the latest in this line of predictive models and reaffirm that LSTM networks are powerful learning models capable of deciphering intricate underlying patterns. © 2023, The Institution of Engineers (India).

关键词： Long short-term memory

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Performance-based Assistance Control for Upper Limb Robotic Mirror Therapy

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Journal of Bionic Engineering 2024年第5期21卷 2291-2301页

作者： Sixian Fei Qing Sun Yichen Zhang Huanian Cai Shuai Guo Xianhua Li School of Mechatronic Engineering and Automation Shanghai UniversityShanghai200444China School of Artificial Intelligence Anhui University of Science and TechnologyHuainan232001China

As an effective therapy for treating unilateral neglect,Mirror Therapy(MT)is employed in the upper limb motor function rehabilitation of hemiplegic ***,traditional MT has a serious limitation—the Impaired Limb(IL)doesn't actually *** this study,a novel performance-based assistance strategy suitable for Robotic Mirror Therapy(RMT)based on MT is proposed.A guiding assistance based on the progress difference HL and IL is constructed in trajectory guidance,and a multi-stiffness region correction force field based on trajectory tracking error is designed to constrain IL's deviation from the intended path in trajectory correction *** validate the presented strategy,a series of experiments on a RMT system based on the end-effector upper limb rehabilitation robot are *** results verify the performance and feasibility of this strategy.

关键词： Rehabilitation robot Stroke Robotic mirror therapy Upper limb Assistance control

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Coupled Urban Risks:A Complex Systems Perspective with a People-Centric Focus

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工程（英文） 2025年第1期44卷 44-50页

作者： Min Ouyang Zekai Cheng Jiaxin Ma Hongwei Wang Stergios Aristoteles Mitoulis School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Key Laboratory of Image Processing and Intelligent Control Ministry of EducationHuazhong University of Science and TechnologyWuhan 430074China School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China School of Management Huazhong University of Science and TechnologyWuhan 430074China School of Engineering University of BirminghamBirmingham B15 2TTUK

The complexity of coupled risks,which refer to the compounded effects of interacting uncertainties across multiple interdependent objectives,is inherent to cities functioning as dynamic,interdependent systems.A disruption in one domain ripples across various urban systems,often with unforeseen *** to this complexity are people,whose behaviors,needs,and vulnerabilities shape risk evolution and response *** cities as complex systems centered on human needs and behaviors is essential to understanding the complexities of coupled urban *** paper adopts a complex systems perspective to examine the intricacies of coupled urban risks,emphasizing the critical role of human decisions and behavior in shaping these *** focus on two key dimensions:cascading hazards in urban environments and cascading failures across interdependent exposed systems in *** risk assessment models often fail to capture the complexity of these processes,particularly when factoring in human *** tackle these challenges,we advocate for a standardized taxonomy of cascading hazards,urban components,and their *** its core is a people-centric perspective,emphasizing the bidirectional interactions between people and the systems that serve *** on this foundation,we argue the need for an integrated,people-centric risk assessment framework that evaluates event impacts in relation to the hierarchical needs of people and incorporates their preparedness and response *** leveraging real-time data,advanced simula-tions,and innovative validation methods,this framework aims to enhance the accuracy of coupled urban risk *** effectively manage coupled urban risks,cities can draw from proven strategies in real complex ***,given the escalating uncertainties and complexities associated with climate change,prioritizing people-centric strategies is *** approach will empower cities to bui

关键词： Coupled urban risks People-centric Risk management

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Finite-Time Topology Identification of Delayed Complex Dynamical Networks and Its Application

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Cyborg and Bionic Systems 2024年第1期5卷 653-664页

作者： Yu Chen Zhi-Wei Liu Yuzhen Qin The School of Artificial Intelligence and Automation Huazhong University of Science and TechnologyWuhan 430074China Department of Artificial Intelligence Donders Institute for BrainCognition and BehaviourRadboud UniversityNijmegen 6525 GDNetherlands

To understand the functional behaviors of systems built on networks,it is essential to determine the uncertain topology of these *** synchronization-based topology identification methods generally converge asymptotically or exponentially,resulting in their inability to give timely identification *** finite-time stability theory is adopted in this paper with the aim of addressing the problem of fast identification of uncertain topology in networks.A novel finite-time topology observer is proposed to achieve finite-time topology identification and synchronization of general complex dynamical networks with time delay and second-order dynamical networks with time delay and nonlinear *** addition,the proposed finite-time identification method is applied to power grids to address the problem of fast detection of line ***,2 numerical experiments are provided to demonstrate the effectiveness and rapidity of the proposed finite-time identification method.

关键词： finite time topology identification understand functional behaviors power grids finite time stability theory delayed complex dynamical networks synchronization based topology identification line outages topology identification

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A Diffusion Model for Traffic data Imputation

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IEEE/CAA Journal of Automatica Sinica 2025年第3期12卷 606-617页

作者： Bo Lu Qinghai Miao Yahui Liu Tariku Sinshaw Tamir Hongxia Zhao Xiqiao Zhang Yisheng Lv Fei-Yue Wang the School of Artificial Intelligence University of Chinese Academy of Sciences Baidu Inc. IEEE the State Key Laboratory for Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Meituan Guangdong University of Technology the Department of Transportation Engineering School of Transportation Science and Technology Harbin Institute of Technology

Imputation of missing data has long been an important topic and an essential application for intelligent transportation systems(ITS) in the real world. As a state-of-the-art generative model, the diffusion model has proven highly successful in image generation, speech generation, time series modelling *** now opens a new avenue for traffic data imputation. In this paper, we propose a conditional diffusion model, called the implicit-explicit diffusion model, for traffic data imputation. This model exploits both the implicit and explicit feature of the data simultaneously. More specifically, we design two types of feature extraction modules, one to capture the implicit dependencies hidden in the raw data at multiple time scales and the other to obtain the long-term temporal dependencies of the time series. This approach not only inherits the advantages of the diffusion model for estimating missing data, but also takes into account the multiscale correlation inherent in traffic data. To illustrate the performance of the model, extensive experiments are conducted on three real-world time series datasets using different missing *** experimental results demonstrate that the model improves imputation accuracy and generalization capability.

关键词： data imputation diffusion model implicit feature time series traffic data

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Research on the Influence of Signal Sampling Frequency on Soft Fault Diagnosis Accuracy of DC/DC Converters

CPSS Transactions on Power Electronics and Applications

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CPSS Transactions on Power Electronics and Applications 2023年第1期8卷 33-41页

作者： Miao, Jigui Liu, Yang Yin, Quan Zhang, Guoqing Yuan, Yining School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan430074 China

As a key component of power system, DC/DC converter needs to operate normally. Related DC/DC converter fault diagnosis technology has been widely proposed, among which the output voltage is often used to indicate the health status of the converter system. This paper mainly studies the influence of various sampling frequencies of output voltage on soft fault diagnosis accuracy of DC/DC converters. A buck converter is used as an example, and its output voltage signal is collected at different frequencies. Based on the obtained output voltage samples, various machine learning models are applied to diagnose converter soft faults. The diagnosis accuracy of these models under different output voltage sampling frequencies is studied. The appropriate sampling frequency of the models is investigated. This research provides a reference for the selection of signal sampling frequency for soft fault diagnosis of DC/DC converters in practical engineering applications, because the implementation cost of high-frequency sampling circuits may be high. © 2017 CPSS.

关键词： Machine learning

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