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A Hybrid Data-Driven Granular Model for Battery Remaining Useful Life Prediction

IEEE Transactions on Instrumentation and Measurement 2025年 74卷

作者： Jing, TaiLong Du, Sheng Wang, Cong Wu, Chao Pedrycz, Witold Sharaf, Mohamed Li, ZhiWu Luoyang Institute of Science and Technology School of Electrical Engineering and Automation Luoyang471023 China China University of Geosciences School of Automation Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education Wuhan430074 China Xi’an710072 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6G 2R3 Canada Systems Research Institute Polish Academy of Sciences Warsaw00-901 Poland King Saud University College of Engineering Department of Industrial Engineering Riyadh11421 Saudi Arabia Institute of Systems Engineering Macau University of Science and Technology Macau China

Accurate and meaningful prediction of the remaining useful life (RUL) of lithium-ion batteries (LIBs) provides a significant guide for the maintenance and management of these LIBs. A singular numerical predictive result by numerous conventional models is insufficient to furnish decision-makers with comprehensive or nuanced insights. To address this issue, predictive RUL results in the form of information granules are essential. In this study, a hybrid data-driven granular model is established for battery RUL prediction with granular (interval-based) quantification for LIBs. This granular model is designed and formulated in a way that it produces comprehensive prediction outcomes realized as information granules, which are expressed in a more descriptive and comprehensive manner. Thus, the resulting information granules can effectively reflect and communicate the confidence associated with the predictions. The granular model is realized in two steps: 1) probabilistic prediction results are produced by a hybrid numeric predictive model and 2) a comprehensive granulation of the probabilistic results is achieved under the principle of information granularity. The granular predictive results are holistically evaluated and optimized by the two associated criteria of granularity (coverage and specificity). This transformation of information granules provides an intuitive and interpretable display for battery maintenance and safe work. The proposed granular model is verified by using an open-access LIBs dataset, and the experimental results demonstrate that the proposed granular model outperforms other comparative methods. © 2025 IEEE. All rights reserved.

关键词： Prediction models

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Dexterous Manipulation through Imitation Learning: A Survey

arXiv

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arXiv 2025年

作者： An, Shan Meng, Ziyu Tang, Chao Zhou, Yuning Liu, Tengyu Ding, Fangqiang Zhang, Shufang Mu, Yao Song, Ran Zhang, Wei Hou, Zeng-Guang Zhang, Hong School of Electrical and Information Engineering Tianjin University Tianjin300072 China School of Control Science and Engineering Shandong University Jinan250061 China State Key Laboratory of General Artificial Intelligence Beijing100086 China Department of Mechanical and Process Engineering ETH Zurich Zurich8092 Switzerland Department of Electronic and Electrical Engineering Southern University of Science and Technology Shenzhen518055 China School of Informatics University of Edinburgh EH8 9AB United Kingdom Department of Computer Science University of Hong Kong 999077 Hong Kong State Key Laboratory of Multimodal Artificial Intelligence Systems Institute of Automation Chinese Academy of Sciences Beijing100190 China School of Artificial Intelligence University of Chinese Academy of Sciences Beijing100049 China CASIA-MUST Joint Laboratory of Intelligence Science and Technology Institute of Systems Engineering Macau University of Science and Technology 999078 China

Dexterous manipulation, which refers to the ability of a robotic hand or multi-fingered end-effector to skillfully control, reorient, and manipulate objects through precise, coordinated finger movements and adaptive force modulation, enables complex interactions similar to human hand dexterity. With recent advances in robotics and machine learning, there is a growing demand for these systems to operate in complex and unstructured environments. Traditional model-based approaches struggle to generalize across tasks and object variations due to the high-dimensionality and complex contact dynamics of dexterous manipulation. Although model-free methods such as reinforcement learning (RL) show promise, they require extensive training, large-scale interaction data, and carefully designed rewards for stability and effectiveness. Imitation learning (IL) offers an alternative by allowing robots to acquire dexterous manipulation skills directly from expert demonstrations, capturing fine-grained coordination and contact dynamics while bypassing the need for explicit modeling and large-scale trial-and-error. This survey provides an overview of dexterous manipulation methods based on imitation learning (IL), details recent advances, and addresses key challenges in the field. Additionally, it explores potential research directions to enhance IL-driven dexterous manipulation. Our goal is to offer researchers and practitioners a comprehensive introduction to this rapidly evolving domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Grippers

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An introduction to CPU and DSP design in China

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Science China(Information sciences) 2016年第1期59卷 58-65页

作者： Weiwu HU Yifu ZHANG Jie FU State Key Laboratory of Computer Architecture Institute of Computing TechnologyChinese Academy of Sciences Loongson Technology Corporation Limited School of Computer and Control Engineering University of Chinese Academy of Sciences Institute of Computing Technology Chinese Academy of Sciences

In recent years, China has witnessed considerable achievements in the production of domesticallydesigned CPUs and DSPs. Owing to fifteen years of hard work that began in 2001, significant progress has been made in Chinese domestic CPUs and DSPs, primarily represented by Loongson and Shen Wei *** parts of the CPU design techniques are comparable to the world’s most advanced designs. A special issue published in Scientia Sinica I nf ormationis in April 2015, is dedicated to exhibiting the technical advancements in Chinese domestically-designed CPUs and DSPs. The content in this issue describes the design and optimization of high performance processors and the key technologies in processor development; these include high-performance micro-architecture design, many-core and multi-core design, radiation hardening design, highperformance physical design, complex chip verification, and binary translation technology. We hope that the articles we collected will promote understanding of CPU/DSP progress in China. Moreover, we believe that the future of Chinese domestic CPU/DSP processors is quite promising.

关键词： Chinese domestic CPUs and DSPs Loongson CPU Shen Wei CPU YHFT DSP BWDSP

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Parametric Estimation of Interconnected Nonlinear Systems Described by Input-output Mathematical Models

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International Journal of Automation and computing 2016年第4期13卷 364-381页

作者： Mourad Elloumi Samira Kamoun Laboratory of Sciences and Techniques of Automatic Control & Computer Engineering National Engineering School of Sfax (ENIS) University of Sfax Tunisia B.P. 1173 3038 Sfax Tunisia

In this paper, two types of mathematical models are developed to describe the dynamics of large-scale nonlinear systems, which are composed of several interconnected nonlinear subsystems. Each subsystem can be described by an input-output nonlinear discrete-time mathematical model, with unknown, but constant or slowly time-varying parameters. Then, two recursive estimation methods are used to solve the parametric estimation problem for the considered class of the interconnected nonlinear systems. These methods are based on the recursive least squares techniques and the prediction error method. Convergence analysis is provided using the hyper-stability and positivity method and the differential equation approach. A numerical simulation example of the parametric estimation of a stochastic interconnected nonlinear hydraulic system is treated.

关键词： Large-scale nonlinear systems interconnected nonlinear systems deterministic systems stochastic systems input-outputmathematical models parametric estimation algorithm convergence analysis.

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Synthesis of churros-like V2Se9 nanostructures for ultrasensitive electrochemical detection of oxalic acid in urine with portable point-of-care diagnostic devices

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Biosensors and Bioelectronics 2025年 285卷 117589页

作者： Shi, Peizheng Ye, Chen Sun, Zhuang Sun, Kaiqiang Zhao, Ningbin Pourfakhr, Mahan Cai, Tao Nishimura, Kazuhito Jiang, Nan Tsai, Hsu-Sheng Wu, Lidong Wu, Wenqi Yu, Jiancheng Fu, Li Xu, Lixin Chen, Jun Lin, Cheng-Te College of Materials Science and Engineering Zhejiang University of Technology Hangzhou310014 China Qianwan Institute of CNITECH Ningbo315336 China State Key Laboratory of Advanced Marine Materials Ningbo Institute of Materials Technology and Engineering Chinese Academy of Sciences Ningbo315201 China Center of Materials Science and Optoelectronics Engineering University of Chinese Academy of Sciences Beijing100049 China Department of Bioengineering University of California Los AngelesCA90095 United States School of Physics Harbin Institute of Technology Harbin150001 China Key Laboratory of Control of Quality and Safety for Aquatic Products Chinese Academy of Fishery Sciences Beijing100141 China Department of Urology The Second Affiliated Hospital of Guangzhou Medical University Guangdong 510260 China Guangdong Key Laboratory of Urology Guangzhou Medical University Guangdong 511495 China Institute of Mass Spectrometry Zhejiang Engineering Research Center of Advanced Mass Spectrometry and Clinical Application Ningbo University Ningbo315211 China Zhenhai Institute of Mass Spectrometry Ningbo315211 China Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo315211 China College of Materials and Environmental Engineering Hangzhou Dianzi University Hangzhou310018 China

The binding of oxalic acid (OA) with metal ions, particularly calcium ions, is a key factor in kidney stone formation, as it produces insoluble calcium oxalate, which crystallizes and accumulates in the kidneys. Consequently, OA in urine is commonly considered a significant biomarker for clinical diagnosis and early screening of kidney stones. This study reports churros-like V2Se9 nanostructures synthesized using a solvothermal method (high pressure and temperature) for sensitive and selective electrochemical detection of OA in urine. The electrodes made of churros-like V2Se9 hybridized with reduced graphene oxide (rGO) for OA recognition, demonstrated a linear range from 20 nM to 4 mM, with an extremely low detection limit of 9.7 nM. To enable fast, user friendly, and accurate point-of-care testing (POCT) for OA in urine samples, we designed and developed a portable workstation integrated with smartphone for data visualization. This is the first study to utilize the V2Se9 system for highly sensitive biosensor applications, highlighting its potential to advance screening techniques for key indicators of kidney stones by incorporating novel transition metal chalcogenide nanomaterials in POCT devices. © 2025 Elsevier B.V.

关键词： Electrochemical biosensors

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A Sliding Mode Observer for Uncertain Nonlinear Systems Based on Multiple Models Approach

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International Journal of Automation and computing 2017年第2期14卷 202-212页

作者： Kd s Hfa edh Karim Dahech Tarak Damak Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering (Lab-STA) National School of Engineers of Sfax University of Sfax B.P. 1173 3038 Sfax Tunisia

This paper presents a method of state estimation for uncertain nonlinear systems described by multiple models approach. The uncertainties, supposed as norm bounded type, are caused by some parameters＇ variations of the nonlinear system. Linear matri~ inequalities （LMIs） have been established in order to ensure the stability conditions of the multiple observer which lead to determine the estimation gains. A sliding mode gain has been added in order to compensate the uncertainties. Numerical simulations through a state space model of a real process have been realized to show the robustness of the synthesized observer.

关键词： Uncertain nonlinear system norm bounded uncertainty multiple models approach multiple observer sliding modeobserver.

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Power optimisation of wind energy conversion system using T-S fuzzy approach

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International Journal of Industrial and Systems engineering 2021年第1期38卷 35-58页

作者： Abderrahim, Sahbi Allouche, Moez Chaabane, Mohamed Research Laboratory Numerical Control of Industrial Processes National School of Engineers of Gabes University of Gabes Tunisia Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering National School of Engineers of Sfax University of Sfax Tunisia

This paper presents a maximum power point tracking control for a wind energy conversion system (WECS) using a squirrel cage induction generator (SCIG) which supplies a DC load through AC/DC converter. The goal of the developed fuzzy control law is to track the reference signals corresponding to the maximum power generation that leads to an optimal operation of the induction generator. First, the dynamics behaviour of the WECS is represented by a Takagi-Sugeno (T-S) fuzzy model. Then, a H∞ observer-based fuzzy controller is proposed to achieve a maximum power point tracking even when we consider a varying wind speed profile. The controller and observer gains are calculated by solving linear matrix inequalities (LMIs). Finally, simulation results are presented to illustrate the effectiveness of the proposed approach. Copyright © 2021 Inderscience Enterprises Ltd.

关键词： Linear matrix inequalities

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Low-cost video-based air quality estimation system using structured deep learning with selective state space modeling

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Environment International 2025年 199卷 109496页

作者： Ahmed, Maqsood Zhang, Xiang Shen, Yonglin Ahmed, Tanveer Ali, Shahid Ali, Ayaz Gulakhmadov, Aminjon Nam, Won-Ho Chen, Nengcheng National Engineering Research Center of Geographic Information System School of Geography and Information Engineering China University of Geosciences Wuhan430074 China Department of Computer Science School of Engineering and Digital Sciences Nazarbayev University Astana Kazakhstan Software College Northeastern University Shenyang110169 China Department of Cybernetics Nanotechnology and Data Processing Faculty of Automatic Control Electronics and Computer Science Silesian University of Technology Akademicka 16 Gliwice44-100 Poland Research Center of Ecology and Environment in Central Asia Xinjiang Institute of Ecology and Geography Chinese Academy of Sciences Urumqi830011 China Institute of Water Problems Hydropower and Ecology of the National Academy of Sciences of Tajikistan Dushanbe734042 Tajikistan School of Social Safety and Systems Engineering Institute of Agricultural Environmental Science National Agricultural Water Research Center Hankyong National University Anseong Korea Republic of

Air quality is crucial for both public health and environmental sustainability. An efficient and cost-effective model is essential for accurate air quality predictions and proactive pollution control. However, existing research primarily focuses on single static image analysis, which does not account for the dynamic and temporal nature of air pollution. Meanwhile, research on video-based air quality estimation remains limited, particularly in achieving accurate multi-pollutant outputs. This study proposes Air Quality Prediction-Mamba (AQP-Mamba), a video-based deep learning model that integrates a structured Selective State Space Model (SSM) with a selective scan mechanism and a hybrid predictor (HP) to estimate air quality. The spatiotemporal forward and backward SSM dynamically adjusts parameters based on input, ensures linear complexity, and effectively captures long-range dependencies by bidirectional processing of spatiotemporal features through four scanning techniques (row-wise, column-wise, and their vertical reversals), which allows the model to accurately track pollutant concentrations and air quality variations over time. Thus, the model efficiently extracts spatiotemporal features from video and simultaneously performs regression (PM2.5, PM10, and AQI), and classification (AQI) tasks, respectively. A high-quality outdoor hourly air quality dataset (LMSAQV) with 13,176 videos collected from six monitoring stations in Lahore, Pakistan, was utilized as the case study. The experimental results demonstrate that the AQP-Mamba significantly outperforms several state-of-the-art models, including VideoSwin-T, VideoMAE, I3D, VTHCL, and TimeSformer. The proposed model achieves strong regression performance (PM2.5: R2 = 0.91, PM10: R2 = 0.90, AQI: R2 = 0.92) and excellent classification metrics: accuracy (94.57 %), precision (93.86 %), recall (94.20 %), and F1-score (93.44 %), respectively. The proposed model delivers consistent, real-time performance with a latency

关键词： Air quality

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Predicting circRNA-Disease Associations by Using Multi-Biomolecular Networks Based on Variational Graph Auto-Encoder with Attention Mechanism

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Chinese Journal of Electronics 2024年第6期33卷 1526-1537页

作者： Jing YANG Xiujuan LEI Yi PAN School of Computer Science Shaanxi Normal University Faculty of Computer Science and Control Engineering Shenzhen Institute of Advanced TechnologyChinese Academy of Sciences Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology

CircRNA-disease association(CDA) can provide a new direction for the treatment of diseases. However,traditional biological experiment is time-consuming and expensive, this urges us to propose the reliable computational model to predict the associations between circRNAs and diseases. And there is existing more and more evidence indicates that the combination of multi-biomolecular information can improve the prediction accuracy. We propose a novel computational model for CDA prediction named MBCDA, we collect the multi-biomolecular information including circRNA, disease, miRNA and lncRNA based on 6 databases, and construct three heterogeneous network among them, then the multi-heads graph attention networks are applied to these three networks to extract the features of circRNAs and diseases from different views, the obtained features are put into variational graph auto-encoder(VGAE) network to learn the latent distributions of the nodes, a fully connected neural network is adopted to further process the output of VGAE and uses sigmoid function to obtain the predicted probabilities of circRNA-disease *** a result, MBCDA achieved the values of AUC and AUPR under 5-fold cross-validation of 0.893 and 0.887. MBCDA was applied to the analysis of the top-25 predicted associations between circRNAs and diseases, these experimental results show that our proposed MBCDA is a powerful computational model for CDA prediction.

关键词： Knowledge engineering Attention mechanisms Computational modeling Biological system modeling Predictive models Feature extraction Heterogeneous networks Topology Data mining Diseases

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X-scPAE: An explainable deep learning model for embryonic lineage allocation prediction based on single-cell transcriptomics revealing key genes in embryonic cell development

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computers in Biology and Medicine 2025年 188卷 109787-109787页

作者： Liao, Kai Yan, Bowei Ding, Ziyin Huang, Jian Fan, Xiaodan Wu, Shanshan Chen, Changshui Li, Haibo The Central Laboratory of Birth Defects Prevention and Control The Affiliated Women and Children's Hospital of Ningbo University Ningbo315021 China Ningbo Key Laboratory for the Prevention and Treatment of Embryogenic Diseases The Affiliated Women and Children's Hospital of Ningbo University Ningbo315021 China Ningbo Key Laboratory of Genomic Medicine and Birth Defects Prevention The Affiliated Women and Children's Hospital of Ningbo University Ningbo315021 China MOE Engineering Research Center of Gene Technology School of Life Sciences Fudan University Shanghai200433 China Institutes of Biomedical Sciences Fudan University Shanghai200433 China Center for Reproductive Medicine The Affiliated Women and Children's Hospital of Ningbo University Ningbo315021 China College of Computer Science Chongqing University Chongqing400044 China

In single-cell transcriptomics research, accurately predicting cell lineage allocation and identifying differences between lineages are crucial for understanding cell differentiation processes and reducing early pregnancy miscarriages in humans. This paper introduces an explainable PCA-based deep learning attention autoencoder model, X-scPAE (eXplained Single Cell PCA - Attention Auto Encoder), which is built on the Counterfactual Gradient Attribution (CGA) algorithm. The model is designed to predict lineage allocation in human and mouse single-cell transcriptomic data, while identifying and interpreting gene expression differences across lineages to extract key genes. It first reduces dimensionality using Principal Component Analysis (PCA) and ranks the importance of principal components. An autoencoder is then employed for feature extraction, integrating an attention mechanism to capture interactions between features. Finally, the Counterfactual Gradient Attribution algorithm calculates the importance of each feature. The model achieved an accuracy of 0.945 on the test set and 0.977 on the validation set, with other metrics such as F1-score, Precision, and Recall all reaching 0.94. It significantly outperformed both baseline algorithms (XGBoost, SVM, RF, and LR) and advanced approaches like F-Score-SVM, CV2-LR, scChrBin, and TripletCell. Notably, the explainability analysis uncovered key lineage predictor genes for both humans and mice and identified crucial genes distinguishing between developmental stages and lineages. A logistic regression model built using the extracted key genes still achieved an AUROC of 0.92, surpassing the performance of other feature extraction methods, including F-Score, CV2, PCA, random feature selection, and the interpretability method Shapley. Lastly, ablation studies demonstrated the effectiveness of each model component. © 2025 The Authors

关键词： Mammals

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