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An FE-S-BiLSTM and Heatmap-Based State-of-Health Estimation Method for Lithium-Ion Batteries

IEEE Sensors Journal 2025年第10期25卷 17727-17738页

作者： Kui Shao Chao Zhai Chaolong Zhang Yigang He Bolun Du Ji Wu School of Future Technology China University of Geosciences Wuhan China School of Automation China University of Geosciences Wuhan China College of Intelligent Science and Control Engineering Jinling Institute of Technology Nanjing China China Electric Power Research Institute Wuhan China School of Electrical Engineering and Automation Wuhan University Wuhan China School of Automotive and Transportation Engineering Hefei University of Technology Hefei Anhui China

Accurate battery state-of-health (SOH) estimation can improve battery reliability and ensure its safe and efficient operation. Therefore, this study proposes a novel method for battery SOH estimation. First, a thermocouple temperature sensor monitors the battery’s operating temperature and provides feedback to the thermostat for precise temperature control during experiments. The battery’s charging voltage and current are measured using voltage transmitters and Hall current sensors, respectively, and the two are fused to obtain the battery’s charging power. Next, 1-D charging power data are converted into 2-D heatmaps using image encoding techniques. The heatmap corresponding to the first cycle is selected as the reference image, and the difference between the heatmaps of subsequent cycles and the reference image is quantified using structural similarity (SSIM). The final results serve as an indicator for battery health. In addition, this study proposes a novel battery SOH estimation model, the feature enhancement-simplification-bidirectional long short-term memory (FE-S-BiLSTM). The feature enhancement layer in the FE-S-BiLSTM model enriches global static features through enhancement learning. Based on the model’s bidirectional long short-term memory (BiLSTM) layer and simplification layer, dynamic features in the time-space domain are double captured. Finally, this study utilizes six batteries and designed a variety of experiments to validate the effectiveness of the proposed method. The experimental design comprises three tasks: SOH estimation based on full-charging data, SOH estimation based on random SOC interval charge data, and cross-battery SOH estimation. The experimental results demonstrate that the proposed SOH estimation method for batteries exhibits significant potential for practical applications.

关键词： Batteries Estimation Heating systems Sensors Aging Voltage Feature extraction Temperature measurement Image coding Voltage measurement

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Average Filtering Error-Based Event-Triggered Fuzzy Filter Design with Adjustable Gains for Networked control Systems

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IEEE Transactions on Fuzzy Systems 2025年

作者： Pan, Yingnan Huang, Fan Li, Tieshan Lam, Hak-Keung Qingdao University School of Automation Shandong Qingdao266071 China Bohai University College of Control Science and Engineering Liaoning Jinzhou121013 China University of Electronic Science and Technology of China School of Automation Engineering Sichuan Chengdu611731 China King's College London Strand Department of Engineering LondonWC2R 2LS United Kingdom

This paper investigates the event-triggered H∞ filtering problem for interval type-2 fuzzy networked control systems with data loss. The goal of this paper is to design an event-triggered fuzzy filter with adjustable gains to further improve the H∞ performance of the filtering error system (FES) while saving communication resources. Firstly, an adaptive event-triggered mechanism (ETM) that utilizes the average filtering error to adaptively adjust the triggered threshold is proposed to mitigate network congestion. In contrast to the existing filtering achievements, the proposed adaptive ETM considers the impact of filtering error on data transmission which can achieve the effect of saving more communication resources. Then, an interval stability theory is applied to adjust the filter gains for achieving the purposes of changing the convergence speed of the FES and improving the Hinfty performance. Moreover, under the conditions of imperfect communication links, sufficient criteria for designing the H∞ filter with adjustable gains are obtained based on the interval stability theory and linear matrix inequalities technique. Finally, the simulation results are utilized for demonstrating the feasibility of the proposed method. © 1993-2012 IEEE.

关键词： Fuzzy filters

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SDSFusion: A Semantic-Aware Infrared and Visible Image Fusion Network for Degraded Scenes

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IEEE transactions on image processing : a publication of the IEEE Signal Processing Society 2025年 PP卷 PP页

作者： Jun Chen Liling Yang Wei Yu Wenping Gong Zhanchuan Cai Jiayi Ma 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 Faculty of Engineering China University of Geosciences Wuhan China School of Computer Science and Engineering Macau University of Science and Technology Taipa Macau China Electronic Information School Wuhan University Wuhan China

A single-modal infrared or visible image offers limited representation in scenes with lighting degradation or extreme weather. We propose a multi-modal fusion framework, named SDSFusion, for all-day and all-weather infrared and visible image fusion. SDSFusion exploits the commonality in image processing to achieve enhancement, fusion, and semantic task interaction in a unified framework guided by semantic awareness and multi-scale features and losses. To address the disparity between infrared and visible images in degraded scenes, we differentiate modal features in a unified fusion model. Unlike existing joint fusion methods, we propose an adversarial generative network that refines the reconstruction of low-light images by embedding fused features. It provides feature-level brightness supplementation and image reconstruction to refine brightness and contrast. Extensive experiments in degraded scenes confirm that our approach is superior to state-of-the-art approaches in visual quality and performance, demonstrating the effectiveness of interaction improvement. The code will be posted at: https://***/Liling-yang/SDSFusion.

关键词： Degradation Image fusion Semantics Training Visualization Meteorology Feature extraction Lighting Image segmentation Brightness

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Subspace Deep Learning of Independent Component Regression Model for Ensemble Spectroscopic Calibration

引用

IEEE Sensors Journal 2025年

作者： Zheng, Junhua Zhou, Hansong Liu, Xinyu Lyu, Yuting Yang, Zeyu Ge, Zhiqiang Zhejiang University of Science and Technology School of Automation and Electrical Engineering Hangzhou China Huzhou University Huzhou Key Laboratory of Intelligent Sensing and Optimal Control for Industrial Systems School of Engineering Huzhou313000 China Southeast University School of Mathematics Nanjing210096 China

Independent component regression (ICR) has become as an important spectroscopic calibration modeling method, due to its advantages in extracting non-Gaussian and high-order statistic features. While the calibration performance of ICR was recently improved by its deep learning form, the result is unstable which is subjected to different random seeds in the model initialization stage. This, however, will make the model less trustworthy in practical industrial applications. In this paper, a subspace deep learning framework is formulated to combine the ideas of both deep learning and ensemble learning, in order to improve the prediction stability of the ICR model. Meanwhile, with an appropriate design of the ensemble learning strategy in the subspace modeling framework, the calibration performance can be further enhanced. For performance evaluation, a detailed benchmark case study on the Corn dataset is carried out. Compared to ordinary and deep residual ICR models, both of the calibration performance and prediction stability have been significantly improved by the subspace deep learning approach. © 2001-2012 IEEE.

关键词： Linear regression

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Event-Triggered Neural Asynchronous control of Nonhomogeneous Markov Jump Power Systems with Hybrid Cyberattacks and Unknown States

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IEEE Transactions on Power Systems 2025年

作者： Luo, Wende Chen, Haiyang Zong, Guangdeng Zhao, Xudong Qufu Normal University School of Engineering Rizhao276826 China Tiangong University School of Control Science and Engineering Tianjin300387 China Nanjing Institute of Technology School of Automation Nanjing211167 China Dalian University of Technology Faculty of Electronic Information and Electrical Engineering Dalian116024 China

This paper studies the observer-based eventtriggered neural asynchronous control problem of nonhomogeneous Markov jump power systems (MJPSs) under hybrid cyberattacks. Both periodic denial-of-service attacks and deception attacks (DAs) are involved because of the openness of networks. An event-triggered mechanism is established to ease the network load. Besides, neural network (NN) approaches are adopted to suppress the adverse effects of DAs on nonhomogeneous MJPSs, and the hidden Markov model (HMM) is employed to capture the asynchronous phenomenon between the system mode and the controller mode. With NN approaches and HMM, an observer-based NN asynchronous controller is designed. By constructing the mode- and parameter-dependent Lyapunov function, sufficient criteria ensuring boundedness in probability for nonhomogeneous MJPSs are obtained. Then, a design algorithm is shown to simultaneously obtain the controller gains, observer gains, and event-triggering weight matrices based on the established conditions. Finally, the single-machine infinitebus power system and 3-machine 6-bus power system are applied to justify the validity of the proposed algorithm. © 1969-2012 IEEE.

关键词： Lyapunov functions

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Lightning Current Sweep Damage Test System Based on Energy Conversion 19th

Lightning Current Sweep Damage Test System Based on Energy C...

引用

19th Annual Conference of China Electrotechnical Society, ACCES 2024

作者： Duan, Zemin Tong, Chen Qu, Minglei Huang, Yeyuan School of Electrical Engineering and Automation Hefei University of Technology Hefei China National Key Laboratory of Electromagnetic Information Control and Effects Hefei China Key Laboratory of Strong Electromagnetic Environment Protection Technology Aviation Science and Technology Hefei China Key Laboratory of Aircraft Lightning Protection of Anhui Province Hefei China

ISBN: (纸本)9789819608966

To solve the problem of difficulty in simulating dynamic sweep damage of lightning current, this paper established a new lightning current sweep damage test system using motors and launch wheels. The speed of the test piece and the temperature during the damage process were measured by high-speed cameras and infrared thermometers. The speed of the test piece was 34 m/s and the temperature exceeded 600 °C. At the same time, a new lightning current component C generator topology and control method were proposed. The load current and inductor current were used to form a double closed-loop PI negative feedback controller to ensure that the load current meets the standard output requirements. The study in this paper has certain guiding significance for the direct effect sweep damage assessment of composite materials and the design of lightning current generators. © Beijing Paike Culture Commu. Co., Ltd. 2025.

关键词： High speed cameras

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Exploring the Evolution of Physics Cognition in Video Generation: A Survey

arXiv

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arXiv 2025年

作者： Lin, Minghui Wang, Xiang Wang, Yishan Wang, Shu Dai, Fengqi Ding, Pengxiang Wang, Cunxiang Zuo, Zhengrong Sang, Nong Huang, Siteng Wang, Donglin School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China School of Engineering Westlake University Hangzhou China School of Control Science and Engineering Shandong University Jinan China Tsinghua University Beijing China Zhejiang University Hangzhou China

Recent advancements in video generation have witnessed significant progress, especially with the rapid advancement of diffusion models. Despite this, their deficiencies in physical cognition have gradually received widespread attention - generated content often violates the fundamental laws of physics, falling into the dilemma of "visual realism but physical absurdity". Researchers began to increasingly recognize the importance of physical fidelity in video generation and attempted to integrate heuristic physical cognition such as motion representations and physical knowledge into generative systems to simulate real-world dynamic scenarios. Considering the lack of a systematic overview in this field, this survey aims to provide a comprehensive summary of architecture designs and their applications to fill this gap. Specifically, we discuss and organize the evolutionary process of physical cognition in video generation from a cognitive science perspective, while proposing a three-tier taxonomy: 1) basic schema perception for generation, 2) passive cognition of physical knowledge for generation, and 3) active cognition for world simulation, encompassing state-of-the-art methods, classical paradigms, and benchmarks. Subsequently, we emphasize the inherent key challenges in this domain and delineate potential pathways for future research, contributing to advancing the frontiers of discussion in both academia and industry. Through structured review and interdisciplinary analysis, this survey aims to provide directional guidance for developing interpretable, controllable, and physically consistent video generation paradigms, thereby propelling generative models from the stage of "visual mimicry" towards a new phase of "human-like physical comprehension". A comprehensive list of papers studied in this survey is available at here. Copyright © 2025, The Authors. All rights reserved.

关键词： Economic and social effects

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Adaptive Critic Attitude Learning control for Hypersonic Morphing Vehicles without Backstepping

引用

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Li, Shihong Shao, Xingling Wang, Hongyu Liu, Jun Zhang, Qingzhen School of Instrument and Electronics North University of China Taiyuan030051 China State Key Laboratory of Instrumentation Science & Dynamic Measurement Taiyuan030051 China School of Electrical and Control Engineering North University of China Taiyuan030051 China School of Automation Science and Electrical Engineering Beihang University Beijing100191 China

This paper proposes an adaptive critic attitude learning control for hypersonic morphing vehicles (HMVs) under large uncertainties and deformation. Firstly, to remove the recursive design complexity caused by backstepping, an attitude-morphing coupled model in Brunovsky form is created by introducing a coordinate transformation. Secondly, a disturbance compensation controller is designed without using backstepping, wherein a resource-saving yet efficient unknown system dynamics estimator (USDE) is introduced to estimate the lumped uncertainties by a simple filtering operation. Then, a near optimal regulator capable of online learning is developed under a critic-only adaptive © 2022 IEEE.

关键词： Backstepping

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Contrastive Conditional Adversarial Autoencoder with Class-specific Forces for Imbalanced Open-set Fault Detection

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IEEE Transactions on automation science and engineering 2025年 22卷 15757-15767页

作者： Zuo, Zhonglin Meng, Tao Li, Zheng Zhang, Hao Liu, Tong Chen, Zhansheng Pang, Zhibo Zhejiang University College of Control Science and Engineering Hangzhou310027 China Zhejiang University School of Aeronautics and Astronautics Hangzhou310027 China Hainan Institute of Zhejiang University Sanya572025 China Chongqing University School of Automation Chongqing400044 China University of Sheffield Department of Computer Science SheffieldS1 4DP United Kingdom Shanghai Aerospace Space Technology Co. Ltd Shanghai201109 China ABB Corporate Research Sweden Department of Automation Technology Vasteras Sweden KTH Royal Institute of Technology Department of Intelligent Systems Stockholm Sweden

In real-world industrial scenarios, fault detection faces the widely recognized challenge of data imbalance, which not only refers to the scarcity of fault data but also includes the imbalance in healthy data. This article is concerned with imbalanced open-set fault detection (IOSFD), a practical yet challenging scenario in industrial applications where multiple healthy operating conditions and multiple fault types are imbalanced. In this article, we propose a new contrastive conditional adversarial autoencoder for IOSFD. It constructs an end-to-end unified model based on multi-class known healthy and faulty data to address the reliance of traditional methods on fault samples, while optimizing with class-specific weighted forces to ensure equal attention to imbalanced known classes. Input and feature reconstruction conditioned on operating modes are utilized to learn a compact decision plane and achieve both unknown fault detection and known data classification. Significantly, we formulate the optimization objective of conditional reconstruction based on contrastive learning and introduce adversarial training to further enhance the model’s performance. The effectiveness of the proposed method is validated through real-world pipeline leak detection and Tennessee-Eastman multi-fault detection. © 2004-2012 IEEE.

关键词： Contrastive Learning

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Secure Data Reconstruction: A Direct Data-Driven Approach

arXiv

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arXiv 2025年

作者： Yan, Jiaqi Markovsky, Ivan Lygeros, John School of Automation Science and Electrical Engineering Beihang University Beijing100191 China Barcelona Spain Barcelona Spain Automatic Control Laboratory ETH Zurich Switzerland

This paper addresses the problem of secure data reconstruction for unknown systems, where data collected from the system are susceptible to malicious manipulation. We aim to recover the real trajectory without prior knowledge of the system model. To achieve this, a behavioral language is used to represent the system, describing it using input/output trajectories instead of state-space models. We consider two attack scenarios. In the first scenario, up to k entries of the collected data are malicious. On the other hand, the second scenario assumes that at most k channels from sensors or actuators can be compromised, implying that any data collected from these channels might be falsified. For both scenarios, we formulate the trajectory recovery problem as an optimization problem and introduce sufficient conditions to ensure successful recovery of the true data. Since finding exact solutions to these problems can be computationally inefficient, we further approximate them using an 1-norm and group Least Absolute Shrinkage and Selection Operator (LASSO). We demonstrate that under certain conditions, these approximation problems also find the true trajectory while maintaining low computation complexity. Finally, we extend the proposed algorithms to noisy data. By reconstructing the secure trajectory, this work serves as a safeguard mechanism for subsequent data-driven control methods. © 2025, CC BY.

关键词： Spatio-temporal data

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