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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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ensemble learning Based Collaborative Filtering with Instance Selectionand Enhanced Clustering

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Computers, Materials & Continua 2022年第5期71卷 2419-2434页

作者： G.Parthasarathy S.Sathiya Devi Anna University Chennai600025India University College of Engineering BIT CampusAnna UniversityTiruchirappalli620024India

Recommender system is a tool to suggest items to the users from the extensive history of the user’s ***,it is an emerging research area concerning academics and industries,where it suffers from sparsity,scalability,and cold start *** paper addresses sparsity,and scalability problems of model-based collaborative recommender system based on ensemble learning approach and enhanced clustering algorithm for movie *** this paper,an effective movie recommendation system is proposed by Classification and Regression Tree(CART)algorithm,enhanced Balanced Iterative Reducing and Clustering using Hierarchies(BIRCH)algorithm and truncation *** this research paper,a new hyper parameters tuning is added in BIRCH algorithm to enhance the cluster formation process,where the proposed algorithm is named as enhanced *** proposed model yields quality movie recommendation to the new user using Gradient boost classification with broad *** this paper,the proposed model is tested on Movielens dataset,and the performance is evaluated by means of Mean Absolute Error(MAE),precision,recall and *** experimental results showed the superiority of proposed model in movie recommendation compared to the existing *** proposed model obtained 0.52 and 0.57 MAE value on Movielens 100k and 1M ***,the proposed model obtained 0.83 of precision,0.86 of recall and 0.86 of f-measure on Movielens 100k dataset,which are effective compared to the existing models in movie recommendation.

关键词： Clustering ensemble learning feature selection gradient boost tree instance selection truncation parameter

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ensemble learning Based on GBDT and CNN for Adoptability Prediction

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Computers, Materials & Continua 2020年第11期65卷 1361-1372页

作者： Yunfan Ye Fang Liu Shan Zhao Wanting Hu Zhiyao Liang School of Design Hunan UniversityChangsha410082China College of Computer National University of Defense TechnologyChangsha410073China Canvard College Beijing Technology and Business UniversityBeijing100037China School of Science and Technology Macao University999078Macao

By efficiently and accurately predicting the adoptability of pets,shelters and rescuers can be positively guided on improving attraction of pet profiles,reducing animal suffering and *** prediction methods usually only used a single type of content for ***,many pets contain not only textual content,but also *** make full use of textual and visual information,this paper proposed a novel method to process pets that contain multimodal *** employed several CNN(Convolutional Neural Network)based models and other methods to extract features from images and texts to obtain the initial multimodal representation,then reduce the dimensions and fuse ***,we trained the fused features with two GBDT(Gradient Boosting Decision Tree)based models and a Neural Network(NN)and compare the performance of them and their *** evaluation result demonstrates that the proposed ensemble learning can improve the accuracy of prediction.

关键词： Adoptability of pets multimodal representation CNN GBDT ensemble learning

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ensemble learning HMM for motion recognition and retrieval by Isomap dimension reduction

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Journal of Zhejiang University-Science A(Applied Physics & Engineering) 2006年第12期7卷 2063-2072页

作者： XIANG Jian, WENG Jian-guang, ZHUANG Yue-ting, WU Fei (School of Computer Science, Zhejiang University, Hangzhou 310027, China) School of Computer Science Zhejiang University Hangzhou China

Along with the development of motion capture technique, more and more 3D motion databases become available. In this paper, a novel approach is presented for motion recognition and retrieval based on ensemble HMM (hidden Markov model) learning. Due to the high dimensionality of motion’s features, Isomap nonlinear dimension reduction is used for training data of ensemble HMM learning. For handling new motion data, Isomap is generalized based on the estimation of underlying eigen- functions. Then each action class is learned with one HMM. Since ensemble learning can effectively enhance supervised learning, ensembles of weak HMM learners are built. Experiment results showed that the approaches are effective for motion data recog- nition and retrieval.

关键词： Feature Isomap HMM (hidden Markov model) ensemble learning Motion recognition and retrieval

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ensemble learning prediction of soybean yields in China based on meteorological data

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Journal of Integrative Agriculture 2023年第6期22卷 1909-1927页

作者： LI Qian-chuan XU Shi-wei ZHUANG Jia-yu LIU Jia-jia ZHOU Yi ZHANG Ze-xi Agricultural Information Institute Chinese Academy of Agricultural SciencesBeijing 100081P.R.China Beijing Engineering Research Center for Agricultural Monitoring and Early Warning Beijing 100081P.R.China Aerospace Information Research Institute Chinese Academy of SciencesBeijing 100094P.R.China The Department of Mathematics Columbia UniversityNY 10027USA Key Laboratory of Agricultural Monitoring and Early Warning Technology Ministry of Agriculture and Rural AffairsBeijing 100081P.R.China

The accurate prediction of soybean yield is of great significance for agricultural production, monitoring and early *** previous studies have used machine learning algorithms to predict soybean yield based on meteorological data,it is not clear how different models can be used to effectively separate soybean meteorological yield from soybean yield in various regions. In addition, comprehensively integrating the advantages of various machine learning algorithms to improve the prediction accuracy through ensemble learning algorithms has not been studied in depth. This study used and analyzed various daily meteorological data and soybean yield data from 173 county-level administrative regions and meteorological stations in two principal soybean planting areas in China(Northeast China and the Huang–Huai region), covering 34 *** effective machine learning algorithms(K-nearest neighbor, random forest, and support vector regression) were adopted as the base-models to establish a high-precision and highly-reliable soybean meteorological yield prediction model based on the stacking ensemble learning framework. The model's generalizability was further improved through 5-fold crossvalidation, and the model was optimized by principal component analysis and hyperparametric optimization. The accuracy of the model was evaluated by using the five-year sliding prediction and four regression indicators of the 173 counties, which showed that the stacking model has higher accuracy and stronger robustness. The 5-year sliding estimations of soybean yield based on the stacking model in 173 counties showed that the prediction effect can reflect the spatiotemporal distribution of soybean yield in detail, and the mean absolute percentage error(MAPE) was less than 5%. The stacking prediction model of soybean meteorological yield provides a new approach for accurately predicting soybean yield.

关键词： meteorological factors ensemble learning crop yield prediction machine learning county-level

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DeepM6ASeq-EL:prediction of human N6-methyladenosine(m^(6)A)sites with LSTM and ensemble learning

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Frontiers of Computer Science 2022年第2期16卷 27-33页

作者： Juntao CHEN Quan ZOU Jing LI Institute of Fundamental and Frontier Sciences University of Electronic Science and Technology of ChinaChengdu 610051China Hainan Key Laboratory for Computational Science and Application Hainan Normal UniversityHaikou 571158China College of Intelligence and Computing Tianjin UniversityTianjin 300350China

N6-methyladenosine(m^(6)A)is a prevalent methylation modification and plays a vital role in various biological processes,such as metabolism,mRNA processing,synthesis,and *** studies have suggested that m^(6)A modification is related to common diseases such as cancer,tumours,and ***,accurate prediction of methylation sites in RNA sequences has emerged as a critical issue in the area of ***,traditional high-throughput sequencing and wet bench experimental techniques have the disadvantages of high costs,significant time requirements and inaccurate identification of *** through the use of traditional experimental methods,researchers have produced many large databases of m^(6)A *** the support of these basic databases and existing deep learning methods,we developed an m^(6)A site predictor named DeepM6ASeq-EL,which integrates an ensemble of five LSTM and CNN classifiers with the combined strategy of hard *** to the state-of-the-art prediction method WHISTLE(average AUC 0.948 and 0.880),the DeepM6ASeq-EL had a lower accuracy in m^(6)A site prediction(average AUC:0.861 for the full transcript models and 0.809 for the mature messenger RNA models)when tested on six independent datasets.

关键词： N6-methyladenosine site prediction LSTM CNN ensemble learning

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Data-driven Transient Stability Assessment Model Considering Network Topology Changes via Mahalanobis Kernel Regression and ensemble learning

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Journal of Modern Power Systems and Clean Energy 2020年第6期8卷 1080-1091页

作者： Xianzhuang Liu Xiaohua Zhang Lei Chen Fei Xu Changyou Feng State Grid Corporation of China State Grid Jibei Electric Power Company the State Key Laboratory of Control and Simulation of Power System and Generation Equipment Department of Electrical EngineeringTsinghua University

Transient stability assessment(TSA)is of great importance in power system operation and *** of the usual tasks in TSA is to estimate the critical clearing time(CCT)of a given fault under the given network topology and pre-fault power ***-driven methods try to obtain models describing the mapping between these factors and the CCT from a large number of ***,the influence of network topology on CCT is hard to be analyzed and is often ignored,which makes the models inaccurate and *** this paper,a novel data-driven TSA model combining Mahalanobis kernel regression and ensemble learning is proposed to deal with the *** model is a weighted sum of several *** sub-model only uses the data of one topology to construct a kernel *** weights are determined by both the topological similarity and numerical similarity between the *** similarities are decided by the parameters in Mahalanobis distance,and the parameters are to be *** reduce the model complexity,sub-models within the same topology category share the same *** estimating CCT,the model uses not only the sub-model which the sample topology belongs to,but also other ***,it avoids the problem that there may be too few data under some *** also efficiently utilizes information of data under all the ***,its decision-making process is clear and understandable,and an effective training algorithm is also *** results on both the IEEE 10-machine 39-bus and a real system verify the effectiveness of the proposed model.

关键词： Transient stability assessment critical clearing time network topology change Mahalanobis kernel regression ensemble learning data-driven

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Electricity Theft Detection Method Based on ensemble learning and Prototype learning

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Journal of Modern Power Systems and Clean Energy 2024年第1期12卷 213-224页

作者： Xinwu Sun Jiaxiang Hu Zhenyuan Zhang Di Cao Qi Huang Zhe Chen Weihao Hu the School of Mechanical and Electrical Engineering University of Electronic Science and Technology of ChinaChengduChina Southwest University of Science and TechnologyChengduChina

With the development of advanced metering infrastructure(AMI),large amounts of electricity consumption data can be collected for electricity theft ***,the imbalance of electricity consumption data is violent,which makes the training of detection model *** this case,this paper proposes an electricity theft detection method based on ensemble learning and prototype learning,which has great performance on imbalanced dataset and abnormal data with different abnormal *** this paper,convolutional neural network(CNN)and long short-term memory(LSTM)are employed to obtain abstract feature from electricity consumption *** calculating the means of the abstract feature,the prototype per class is obtained,which is used to predict the labels of unknown *** the meanwhile,through training the network by different balanced subsets of training set,the prototype is *** with some mainstream methods including CNN,random forest(RF)and so on,the proposed method has been proved to effectively deal with the electricity theft detection when abnormal data only account for 2.5%and 1.25%of normal *** results show that the proposed method outperforms other state-of-the-art methods.

关键词： Electricity theft detection ensemble learning prototype learning imbalanced dataset deep learning abnormal level

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Utilizing interpretable stacking ensemble learning and NSGA-Ⅲ for the prediction and optimisation of building photo-thermal environment and energy consumption

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Building Simulation 2024年第5期17卷 819-838页

作者： Yeqin Shen Yubing Hu Kai Cheng Hainan Yan Kaixiang Cai Jianye Hua Xuemin Fei Qinyu Wang School of Arts Jiangsu UniversityZhenjiang212013China School of Architecture and Urban Planning Nanjing UniversityNanjing210093China School of Computer Science and Communication Engineering Jiangsu UniversityZhenjiang212013China

This study develops an approach consisting of a stacking model integrated with a multi-objective optimisation algorithm aimed at predicting and optimising the ecological performance of *** integrated model consists of five base models and a meta-model,which significantly improves the prediction ***,the R2 value was improved by 9.19% and the error metrics MAE,MSE,MAPE,and CVRMSE were reduced by 69.47%,79.88%,67.32%,and 57.02%,respectively,compared to the single prediction *** to the research on interpretable machine learning,adding the SHAP value gives us a deeper understanding of the impact of each architectural design parameter on the *** the multi-objective optimisation part,we used the NSGA-Ⅲ algorithm to successfully improve the energy efficiency,daylight utilisation and thermal comfort of the ***,the optimal design solution reduces the energy use intensity by 31.6 kWh/m^(2),improves the useful daylight index by 39%,and modulated the thermal comfort index,resulting in a decrement of 0.69℃ for the summer season and an enhancement of 0.64℃ for the winter season,***,this study provides building designers and decision makers with a tool to make better design decisions at an early stage to achieve a better combination of energy efficiency,daylight utilisation and thermal comfort optimisation in an integrated manner,providing an important support for achieving sustainable building design.

关键词： building ecological performance ensemble learning multi-objective optimisation sustainable design explainable machine learning

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Dynamic Loading Vulnerability Detection for Android Applications Through ensemble learning

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Chinese Journal of Electronics 2017年第5期26卷 960-965页

作者： YANG Tianchang CUI Haoliang NIU Shaozhang Beijing Key Lab of Intelligent Telecommunication Software and Multimedia Beijing University of Posts and Telecommunications

Valid authentication and security protection measures are not provided for external code and resources executed by dynamic loading technology during the runtime in *** this paper,a new method of detecting vulnerabilities related to dynamic loading technology is *** phases are included in the detection *** analysis phase determines the location information of the loading point and extracts the feature vector for each loading *** phase classifies the extracted feature vector by means of constructed multilabel classification ensemble learning *** to the examination result on 4464 Android applications,37.8% of all applications use the dynamic loading technology,and more than 12% of total test applications are detected with related security *** result shows that the detection method can detect vulnerabilities of dynamic loading effectively and is more comprehensive.

关键词： Dynamic loading Android application Vulnerability ensemble learning

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