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Multi-model ensemble learning for battery state-of-health estimation:Recent advances and perspectives

Journal of Energy Chemistry 2025年第1期100卷 739-759页

作者： Chuanping Lin Jun Xu Delong Jiang Jiayang Hou Ying Liang Zhongyue Zou Xuesong Mei State Key Laboratory for Manufacturing Systems engineering Xi'an Jiaotong University Shaanxi Key Laboratory of Intelligent Robots School of Mechanical Engineering Xi'an Jiaotong University Department of Electrical engineering and automation Luoyang Institute of Science and Technology School of Mechanical engineering Hangzhou Dianzi University

The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ens

关键词： Lithium-ion battery State-of-health estimation Data-driven Machine learning Ensemble learning Ensemble diversity

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Luminescence: a new perspective for investigating perovskite precursors

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Science China Materials 2025年第01期 312-313页

作者： Haofeng Zheng Shaocong Hou School of Electrical engineering and automation Wuhan University Wuhan University Shenzhen Research Institute

In the past two decades, perovskite materials have made significant progress in optoelectronic semiconductors, emerging as formidable candidates for the next generation of commercial solar cells, light-emitting diodes (LEDs), and *** the fundamental solution chemistry and photophysics in solution-processed perovskite devices is crucial for comprehending to achieve highly efficient optoelectronic devices.

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Stability and Stabilization of Sampled-Data Based LFC for Power Systems:A Data-Driven Method

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IEEE/CAA Journal of Automatica Sinica 2025年第1期 291-293页

作者： Yu-Long Fan Chuan-Ke Zhang Yong He the School of automation China University of Geosciences the Hubei Key Laboratory of Advanced Control and Intelligent automation for Complex Systems the engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education IEEE

Dear Editor,This letter is concerned with stability analysis and stabilization design for sampled-data based load frequency control(LFC) systems via a data-driven method. By describing the dynamic behavior of LFC systems based on a data-based representation, a stability criterion is derived to obtain the admissible maximum sampling interval(MSI) for a given controller and a design condition of the PI-type controller is further developed to meet the required MSI. Finally, the effectiveness of the proposed methods is verified by a case study.

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Endoscopy-assisted lightweight diagnosis system based on transformers for colon polyp detection

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Optoelectronics Letters 2025年第1期21卷 57-64页

作者： FAN Weiming YU Jiahui JU Zhaojie School of automation and Electrical engineering Shenyang Ligong University Department of Biomedical engineering Zhejiang University School of Computing University of Portsmouth

The integration of endoscopy has significantly propelled the diagnosis and treatment of gastrointestinal diseases, with colonoscopy establishing itself as the primary method for early diagnosis and preventive care in colorectal cancer(CRC). Although deep learning holds promise in mitigating missed polyp rates, modern endoscopy examinations pose additional challenges, such as image blurring and atomizing. This study explores lightweight yet powerful attention mechanisms, introducing the spatial-channel transformer(SCT), an innovative approach that leverages spatial channel relationships for attention weight calculation. The method utilizes rotation operations for inter-dimensional dependencies, followed by residual transformation, encoding inter-channel and spatial information with minimal computational overhead. Extensive experiments on the CVC-Clinic DB polyp detection dataset, addressing endoscopy pitfalls, underscore the superiority of our SCT over other state-of-the-art methods. The proposed model maintains high performance, even in challenging scenarios.

关键词： Endoscopy

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Characterization of ultraviolet communication in a mixed aerosol environment

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Optoelectronics Letters 2025年第2期 77-84页

作者： ZHAO Taifei SUN Yuxin WANG Wuxin ZHANG Haijun PAN Feixiang WU Hao Artificial Intelligence Key Laboratory of Sichuan Province Sichuan University of Science & Engineering Faculty of automation and Information engineering Xi'an University of Technology

Wireless ultraviolet (UV) has strong scattering characteristics and can communicate through non-direct *** UV signals are transmitted in the atmosphere,they are affected by the absorption and scattering effects of atmospheric particles and atmospheric turbulence,resulting in attenuation of UV signal energy and reduced reliability of the communication *** paper focuses on the channel model of UV non-direct-view single scattering communication,and simulates and analyzes the communication characteristics of UV light in atmospheric turbulence and mixed aerosol environment under horizontal,vertical and oblique range communication *** results show that at equal relative humidity,the wireless UV non-directive scattering communication performance for vertical communication scenarios is more affected by the mixed aerosol environment and the communication performance is worse.

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Targets capture by distributed active swarms via bio-inspired reinforcement learning

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Science China(Physics,Mechanics & Astronomy) 2025年第1期Mechanics & Astronomy) .卷 210-221页

作者： Kun Xu Yue Li Jun Sun Shuyuan Du Xinpeng Di Yuguang Yang Bo Li Institute of Biomechanics and Medical engineering Applied Mechanics Laboratory Department of Engineering MechanicsTsinghua University Institute of Nuclear and New Energy Technology Tsinghua University Shanghai Aerospace Control Technology Institute Shanghai Key Laboratory of Aerospace Intelligent Control Technology College of automation engineering Nanjing University of Aeronautics and Astronautics

Natural swarms arranged from cells to herds are usually decentralized but display intriguing collective intelligence in coordinating individuals across large scales to efficiently achieve their common goals. Learning from nature may provide new strategies for controlling collective dynamics of synthetic swarms to accomplish specific functions. Here, we present a bioinspired computational framework that steers distributed active swarms to collectively capture and merge targets via reinforcement learning. We exploit collective milling structures of natural herds to cage the targets, and adopt a switching control policy inspired by sperms' chiral dynamics to optimize the trajectories of individuals, through which the active swarms can selforganize to enclose single or multiple distant targets in a dynamical, adaptive and scalable manner. There exists a critical swarm size, beyond which the excessive competition between agents would generate large mechanical forces, leading to capture instability but enabling the transition from short-distance to long-distance merging capture of multiple targets. This work provides physical insights into distributed active swarms and could offer a multilevel, decentralized strategy toward controlling swarm robotics in wide applications such as bio-medical devices, machine immunity, and target clearance.

关键词： swarm intelligence robotics multi-tasks artificial intelligence bionics

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A Novel Self-Supervised Learning Network for Binocular Disparity Estimation

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Computer Modeling in engineering & Sciences 2025年第1期142卷 209-229页

作者： Jiawei Tian Yu Zhou Xiaobing Chen Salman A.AlQahtani Hongrong Chen Bo Yang Siyu Lu Wenfeng Zheng Department of Computer Science and engineering Major in Bio Artificial IntelligenceHanyang UniversityAnsan-si15577Republic of Korea School of Electrical and Computer engineering Louisiana State UniversityBaton RougeLA 70803USA Department of Computer engineering College of Computer and Information SciencesKing Saud UniversityRiyadh11574Saudi Arabia School of automation University of Electronic Science and Technology of ChinaChengdu610054China

Two-dimensional endoscopic images are susceptible to interferences such as specular reflections and monotonous texture illumination,hindering accurate three-dimensional lesion reconstruction by surgical *** study proposes a novel end-to-end disparity estimation model to address these *** approach combines a Pseudo-Siamese neural network architecture with pyramid dilated convolutions,integrating multi-scale image information to enhance robustness against lighting *** study introduces a Pseudo-Siamese structure-based disparity regression model that simplifies left-right image comparison,improving accuracy and *** model was evaluated using a dataset of stereo endoscopic videos captured by the Da Vinci surgical robot,comprising simulated silicone heart sequences and real heart video *** results demonstrate significant improvement in the network’s resistance to lighting interference without substantially increasing ***,the model exhibited faster convergence during training,contributing to overall performance *** study advances endoscopic image processing accuracy and has potential implications for surgical robot applications in complex environments.

关键词： Parallax estimation parallax regression model self-supervised learning Pseudo-Siamese neural network pyramid dilated convolution binocular disparity estimation

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Methods of data processing in multi-wavelength thermometry

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Journal of Harbin Institute of Technology(New Series) 2006年第4期13卷 421-426页

作者：孙晓刚赵巍原桂彬戴景民 dept. of automation Measurement &Control Harbin Institute of Technology dept. of automation Control Harbin Engineering University

Three kinds of methods for processing the data of the multi-wavelength pyrometer are presented in this paper and are named curve auto-search method, curve auto-regression method and neural network method. Tbe experimental results indicate that the calculated temperature and the spectral emissivity compared with the true target temperature and spectral emissivity have significant deviation using the curve auto-search and the curve auto-regression methods. However, the calculated temperature and the spectral emissivity with higher accuracy can be obtained using the neural network method.

关键词： multi-wavelengtb thermometry curve auto-search method curve auto-regression method neural network

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Study on information utility

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Journal of Systems engineering and Electronics 2005年第3期16卷 579-582,644页

作者：张东戈 dept..of Computer Science and command automation Inst .of Command AutomationPeople’s Liberation Army Univ .of Science and Technology

Information has two aspects. One aspect is the objective one; another aspect is the subjective one. Shannon has discussed the objective aspect of information in information theory. But the subjective aspect of information has not been fully discussed. Someone use “Bayesian approaches” to evaluate the value of information. But in some cases it does not meet the information user＇s need. This paper is focus on the subjective aspect of objectively measurable information and gives formal definitions for information, information utility, and marginal information utility, normalized calculation of information utility. The information discussed in the paper has interdisciplinary nature. This work can be the foundation of many application areas.

关键词： analysis definition of information analysis definition of information utility analysis definition of marginal information utility normalized calculation of information utility mic^nomics analysis expression of information utility information utility functions C^4ISR system evaluation.

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Sensor management based on fisher information gain

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Journal of Systems engineering and Electronics 2006年第3期17卷 531-534页

作者： Tian Kangsheng Zhu Guangxi dept. of Telecommunication engineering Huazhong Univ. of Science & TechnologyWuhan 430074 P. P. China dept. of command automation engineering Airforce Radar Academy Wuhan 430019 P. R. China

Multi-sensor system is becoming increasingly important in a variety of military and civilian applications. In general, single sensor system can only provide partial information about environment while multi-sensor system provides a synergistic effect, which improves the quality and availability of information. Data fusion techniques can effectively combine this environmental information from similar and/or dissimilar sensors. Sensor management, aiming at improving data fusion performance by controlling sensor behavior, plays an important role in a data fusion process. This paper presents a method using fisher information gain based sensor effectiveness metric for sensor assignment in multi-sensor and multi-target tracking applications. The fisher information gain is computed for every sensor-target pairing on each scan. The advantage for this metric over other ones is that the fisher information gain for the target obtained by multi-sensors is equal to the sum of ones obtained by the individual sensor, so standard transportation problem formulation can be used to solve this problem without importing the concept of pseudo sensor. The simulation results show the effectiveness of the method.

关键词： data fusion sensor management fisher information gain linear programming.

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