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第43届中国控制会议

作者： Xiaomin Mao Chenglin Liu Liang Shan Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Institute of AutomationJiangnan University School of Automation Nanjing University of Science and Technology

ISBN: (数字)9789887581581

ISBN: (纸本)9798350366907

To enhance the robustness of the permanent magnet synchronous motor(PMSM) speed control system, and reduce the response time of the control system, a composite terminal sliding mode control strategy is proposed. Firstly, a integral terminal sliding surface and a fast reaching law are designed based on the mathematical model of the PMSM to avoid singularity issues,enhance quickness, and reduce chattering. Then, the stability and finite-time convergence of the control system are proved by constructing a Lyapunov function. Considering the system's susceptibility to disturbances, the lumped disturbance is considered in the mathematical model. An extended state observer(ESO) is designed to observe the lumped disturbance and compensate for the lumped disturbance to the controller. Finally, through simulation and experimentation, it is demonstrated that the designed controller exhibits advantages such as fast response, no overshoot, and strong robustness.

关键词： Permanent Magnet Synchronous Motor Speed control System Terminal Sliding-mode control Extended State Observer

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LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel Attention-Aided U-Net Model for Microscopic Medical Image Segmentation 9

LEECAU-Net: A Latent Entropy-Quantized and Efficient Channel...

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9th IEEE International Conference on Progress in Informatics and Computing, PIC 2024

作者： Das, Sanchita Maity, Gouranga Mandal, Diptarka Kaplun, Dmitrii Voznesensky, Alexander Sarkar, Ram Jadavpur University Dept. of Cse Kolkata India Defence Instituite of Advanced Technology Department of Applied Mathematics Pune India Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China St. Petersburg Electrotechnical University 'LETI' Dept. of Automation and Control Process St. Petersburg Russia

ISBN: (纸本)9798350363203

Microscopic medical image segmentation is essential for detailed cellular-level analysis in computational pathology, offering insights into disease mechanisms. However, this task is particularly challenging due to the high similarity between different cellular classes and variability within each class, especially in densely packed or overlapping cell regions. To this end, we present LEECAU-Net (Latent Entropy-quantized and Efficient Channel Attention-aided U-Net), a U-Net-based model designed to improve segmentation accuracy through a dual-attention mechanism. Built on an Inceptionv4 backbone, LEECAU-Net's encoder integrates Latent Entropy-quantized Channel Attention (LECA) and Efficient Channel Attention (ECA) modules. The ECA module enhances the network's ability to focus on essential channel dependencies, while LECA leverages entropy quantification to prioritize channels rich in information, leading to better differentiation between similar structures. We have tested LEECAU-Net on three challenging publicly available datasets: Triple Negative Breast Cancer (TNBC), Multi-organ Nuclei Segmentation (MoNuSeg), and Cervical Nucleus Segmentation (CNS). Across these datasets, our model achieves higher performance metrics than numerous state-of-the-art approaches reported in the literature. The improved results underscore the effectiveness of LEECAU-Net's architecture in handling the complexity of microscopic medical images, making it a valuable tool for advancing automated cellular analysis in biomedical research. The source code and more results are made available at https://***/Cmatermedicalimageanalysis/LEECAU-Net. © 2024 IEEE.

关键词： Image segmentation

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Identification of Abnormal Operating Conditions in Fused Magnesium Furnaces Using a Deep Multiview TSK Fuzzy System

Identification of Abnormal Operating Conditions in Fused Mag...

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2024 China automation Congress, CAC 2024

作者： Xi, Chao Wu, Gaochang Fan, Zizhu Xu, Feng Wang, Hui Tang, Xiaokun School of Electrical and Automation Engineering East China Jiaotong University Nanchang China State Key Laboratory of Synthetical Automation for Process Industries Northeastern University Shenyang China College of Computer Science and Technology Shanghai University of Electric Power Shanghai China Key Laboratory of Advanced Control and Optimization of Jiangxi Province East China Jiaotong University Nanchang China

ISBN: (纸本)9798350368604

The smelting process of the fused magnesium furnace(FMF) is prone to semi-molten abnormal conditions, which may lead to low product quality and production efficiency, and is also prone to safety accidents. At present, the operating conditions of FMF mainly rely on manual observation of the furnace shell features for diagnosis, and it is hard to timely and effectively identify abnormalities in the smelting process. Given the difficulty in identifying abnormal operating conditions of FMF, this paper proposes a deep multiview fuzzy classification method(DMFC) that can automatically identify abnormal operating conditions. First, based on the three-phase current data, CNN and Transformer are used to extract features as multiple views. Secondly, the Takagi-Sugeno-Kang(TSK) fuzzy model is used as a single-view model, and the evidence theory is used for dynamic fusion of multiple views. This method can effectively fuse multiple deep features, and has strong scalability and a certain degree of interpretability. Through verification of the real historical data of multiple FMFs, our model has relatively high accuracy, false detection rate(FDR), and missed detection rate(MDR). © 2024 IEEE.

关键词： Smelting

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Robust PD-type iterative learning control design for uncertain batch processes subject to nonrepetitive disturbances 41

Robust PD-type iterative learning control design for uncerta...

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第41届中国控制会议

作者： Robert Maniarski Wojciech Paszke Shoulin Hao Hongfeng Tao Institute of Automation Electronic and Electrical EngineeringUniversity of Zielona Góra Key Laboratory of Intelligent Control and Optimization for Industrial Equipment of Ministry of Education Dalian University of Technology Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Jiangnan University

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

This paper develops PD-type iterative learning control schemes for a class of uncertain batch processes subject to nonrepetitive *** means of two-dimensional/repetitive setting,the sufficient conditions for batch-to-batch error convergence and H disturbance attenuation are formulated and ***,the procedure for computing the desired control law matrices is formulated in terms of solvability of linear matrix *** proposed control law is able to fulfil the imposed design specifications,i.e.,they are suitable for the batch processes with time-varying uncertainties as well as non-repetitive *** illustrative example is used to validate the proposed control scheme and demonstrates a possible applicability of the developed results.

关键词： iterative learning control repetitive processes batch processes robust control nonrepetitive disturbances

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Multimodal process Monitoring Based on Dynamic Extended K-means-VAE-RF

Multimodal Process Monitoring Based on Dynamic Extended K-me...

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第35届中国控制与决策会议

作者： Shiwei Su Kaixiang Peng Xueyi Zhang Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical Engineering University of Science and Technology Beijing

With the development of customization and flexibility of manufacturing industry,intelligent manufacturing process gradually presents multimodal characteristics,which are reflected in the strong coupling,nonlinear,multi working conditions and other characteristics of the *** this paper,a multi-modal process monitoring method based on dynamic expansion is *** view of the dynamic characteristics of process data,the process data is dynamically expanded through the amplification matrix,and then the multi-modal process data is modal divided through the improved K-means *** divided multi-modal process data is monitored using Variational Autoencoder,and relevant statistics and control limits are ***,the multi-modal process monitoring is constructed by introducing the method of random forest for modal *** validity of the algorithm is verified by real data of steel rolling process.

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Exponential Weighted Moving Average-Based Variational Mode Decomposition Method for Fault Diagnosis of Vehicle Batteries 13

Exponential Weighted Moving Average-Based Variational Mode D...

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13th IEEE Data Driven control and Learning Systems Conference, DDCLS 2024

作者： Wu, Penghao Tao, Hongfeng Tian, Engang Yin, Tangwen Chen, Hongtian Chen, Yiyang School of Mechanical and Electrical Engineering Soochow University Suzhou215137 China Jiangnan University Key Laboratory of Advanced Process Control for Light Industry of Ministry of Education Wuxi214122 China School of Optical-Electrical and Computer Engineering University of Shanghai for Science and Technology Shanghai200093 China Shanghai Jiao Tong University Department of Automation Shanghai200240 China

ISBN: (纸本)9798350361674

In recent years, the rapid advancement of electric vehicles has heightened concerns regarding the safety of high-energy batteries. Consequently, there has been a significant focus on the development of fault diagnosis technologies for batteries. Intelligent fault diagnosis (IFD), particularly data-driven approaches, represents a crucial methodology in addressing this concern. This paper proposes a data-driven approach, for IFD in high-energy batteries utilized in electric vehicles. The proposed Exponentially Weighted Average Variational Mode Decomposition (EWVMD) method overcomes these limitations by employing the exponential weighted moving average (EWMA) technique to smooth collected time-series voltage data. This process reduces noise, accentuates abnormal change trends, and yields secondary data with enhanced features. Subsequently, variational mode decomposition (VMD) is applied to the secondary data. The resulting decomposed sub-mode curves (intrinsic mode functions - IMFs) and the change trends of the residual curve, in conjunction with a fault threshold tailored to actual working conditions, are utilized to ascertain the occurrence of faults. Design verification experiments, the C42MSA electric vehicle power supply battery undergoes charging and discharging tests with injected faults at fixed intervals. The collected fault signals are then subjected to fault diagnosis using EWVMD. Results demonstrate the algorithm's superior detection effectiveness and increased sensitivity to fault signals compared to traditional methods. © 2024 IEEE.

关键词： Variational mode decomposition

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Robustness of gross primary production estimation from long-term reconstructed solar-induced chlorophyll fluorescence varies with greenness on a global scale

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Journal of Applied Remote Sensing 2025年第1期19卷

作者： Fu, Lijiang Xia, Qian Tang, Hao Chen, Junqing Tan, Jinglu Guo, Ya Jiangnan University Ministry of Education Key Laboratory of Advanced Process Control for Light Industry Wuxi China Changshu Institute of Technology School of Electrical Engineering and Automation Changshu China University of Missouri Department of Chemical and Biomedical Engineering ColumbiaMO United States

Long-term reconstructed solar-induced chlorophyll fluorescence (SIF) derived from raw gridded SIF has been used for the estimation of gross primary production (GPP), but the robustness of the spatial relationship may vary from location to location. We examined the often-used linear relationship between GPP and SIF in terms of R2 values for varied locations globally using three GPP datasets (FLUXCOM, VPM, PML) and three long-term reconstructed monthly SIF datasets (CSIF, SIF005, and RTSIF). The results show that the R2 value is a concave function of vegetation greenness level (NDVI) on an annual or seasonal basis. The average R2 is over 0.8 in areas where the annual average NDVI is in the range of 0.4 to 0.6, whereas the R2 is much lower where the annual average NDVI is less than 0.2 or greater than 0.8. Prediction of GPP or SIF by three methods from five major environmental variables revealed greater uncertainties in GPP and/or SIF at low or high greenness levels as an apparent cause of the low R2. The results offer useful insights into how global GPP may be effectively estimated from multi-satellite measured SIF. © 2025 Society of Photo-Optical Instrumentation Engineers (SPIE).

关键词： Optical correlation

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Non-Monotonic Lyapunov-Krasovskii Functional Approach to Stability Analysis and Stabilization of Discrete Time-Delay Systems

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IEEE/CAA Journal of Automatica Sinica 2020年第3期7卷 752-763页

作者： Younes Solgi Alireza Fatehi Ala Shariati Advanced Process Automation and Control Research Group Industrial Control Center of ExcellenceFaculty of Electrical EngineeringK.N.Toosi University of TechnologyTehran 16317-14191Iran IEEE

In this paper, a novel non-monotonic Lyapunov-Krasovskii functional approach is proposed to deal with the stability analysis and stabilization problem of linear discrete time-delay systems. This technique is utilized to relax the monotonic requirement of the Lyapunov-Krasovskii theorem. In this regard, the Lyapunov-Krasovskii functional is allowed to increase in a few steps, while being forced to be overall decreasing. As a result, it relays on a larger class of Lyapunov-Krasovskii functionals to provide stability of a state-delay system. To this end, using the non-monotonic Lyapunov-Krasovskii theorem, new sufficient conditions are derived regarding linear matrix inequalities(LMIs)to study the global asymptotic stability of state-delay ***, new stabilization conditions are also proposed for time-delay systems in this article. Both simulation and experimental results on a p H neutralizing process are provided to demonstrate the efficacy of the proposed method.

关键词： Linear matrix inequality(LMI) non-monotonic Lyapunov-Krasovskii functional(NMLKF) pH neutralization plant stabilization state-delay system time-delay system

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Tracking performance limitations of MIMO discrete-time networked control systems with multiple constraints

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Science China(Information Sciences) 2023年第8期66卷 267-268页

作者： Xiaowei JIANG Bin ZHANG Xiangyong CHEN Huaicheng YAN School of Automation China University of Geosciences Key Laboratory of Industrial Internet of Things & Networked Control Ministry of Education Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education School of Automation and Electrical Engineering Linyi University Key Laboratory of Smart Manufacturing in Energy Chemical Process of the Ministry of Education

Dear editor,Networked control systems(NCSs) have attracted widespread attention in some fields because they offer the advantages of reduced cabling, greater resource sharing,and ease of installation and maintenance [1]. However, in an unreliable network environment, network constraints can significantly limit the performance of NCSs.

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Multimodal process Monitoring Based on Dynamic Extended K-means-VAE-RF

Multimodal Process Monitoring Based on Dynamic Extended K-me...

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Chinese control and Decision Conference, CCDC

作者： Shiwei Su Kaixiang Peng Xueyi Zhang University of Science and Technology Beijing Key Laboratory for Advanced Control of Iron and Steel Process of Ministry of Education School of Automation and Electrical EngineeringBeijingPeople's Republic of China100083

With the development of customization and flexibility of manufacturing industry, intelligent manufacturing process gradually presents multimodal characteristics, which are reflected in the strong coupling, nonlinear, multi working conditions and other characteristics of the process. In this paper, a multi-modal process monitoring method based on dynamic expansion is proposed. In view of the dynamic characteristics of process data, the process data is dynamically expanded through the amplification matrix, and then the multi-modal process data is modal divided through the improved K-means algorithm. The divided multi-modal process data is monitored using Variational Autoencoder, and relevant statistics and control limits are designed. Finally, the multi-modal process monitoring is constructed by introducing the method of random forest for modal identification. The validity of the algorithm is verified by real data of steel rolling process.

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