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Rapid diagnosis of power battery faults in new energy vehicles based on improved boosting algorithm and big data

Energy Informatics 2024年第1期7卷 1-25页

作者： Wang, Jiali Chen, Jia School of Intelligent Manufacturing and Information Engineering Shanghai Institute of Commerce & Foreign Languages Shanghai201399 China Co. Ltd Jinhua321200 China

In recent years, the new energy vehicle industry has developed rapidly. A fast diagnostic method based on boosting and big data is proposed to address the low accuracy and efficiency of fault diagnosis in new energy vehicle power batteries. boosting is a machine learning technique that combines multiple weak learners into a strong learner. Big data refers to large-scale, complex datasets that exceed traditional data processing capabilities. Firstly, analyze and preprocess the big data uploaded by the battery. Subsequently, the importance of indicators in the data was analyzed using the Random Forest algorithm (RF). Finally, three improved boosting algorithms were proposed, namely Light Gradient boosting Machine (LightGBM), eXtreme Gradient boosting Tree (XGBoost), and Gradient boosting Decision Tree (CatBoost). The experimental results indicate that the LightGBM model effectively detects anomalies in battery big data. The accuracy values of XGBoost, CatBoost, and LightGBM are 97.84%, 98.57%, and 99.16%, respectively. The recall rates of XGBoost, CatBoost, and LightGBM models are all 1. The F1 values of GBoost, CatBoost, and LightGBM are 0.873, 0.983, and 0.985, respectively. The power battery is the core component of new energy vehicles, and its safety performance directly affects the operational safety of the vehicle. Timely identification and diagnosis of battery faults can effectively reduce potential accidents such as battery overheating and short circuits. Research can achieve real-time monitoring and timely reminders of potential faults. By early detection of issues such as battery overheating and voltage imbalance, this method can effectively reduce the risk of serious safety accidents and improve the overall operational reliability of new energy vehicles during driving. © The Author(s) 2024.

关键词： boosting algorithm Fault diagnosis New energy vehicles Power battery RF

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Classification of Covid-19 Variants Using boosting algorithm 9

Classification of Covid-19 Variants Using Boosting Algorithm

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9th International Conference on Electrical Engineering, Computer Science and Informatics, EECSI 2022

作者： Muhammad, Izzudin Mukhlash, Imam Jamhuri, Mohammad Iqbal, Mohammad Irawan, Mohammad Isa Institut Teknologi Sepuluh Nopember Faculty of Science and Data Analytics Department of Mathematics Surabaya Indonesia Institut Teknologi Sepuluh Nopember UIN Maulana Malik Ibrahim Department of Mathematics Malang Indonesia

ISBN: (数字)9786239213565

ISBN: (纸本)9781665470438

COVID-19 is a disease caused by a virus from the coronavirus group, namely severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). The Sars-CoV-2 virus has 5 variants that are included in the variant of concern (VOC) namely Alpha, Beta, Delta, Gamma, and Omicron. The COVID-19 virus has infected more than 400 million people worldwide. This information causes a significant increase in data with the result that computations are needed to obtain knowledge (pattern) from the data. Machine learning is a tool that can facilitate the analysis of big data, one of which is classification. In this paper, we implement two boosting algorithms: eXtreme Gradient boosting (XGB) and Light Gradient boosting Machine (LGBM), to classify the Deoxyribonucleic acid (DNA) sequence data from the COVID-19 virus variants. Additionally, we utilized one-hot encoded method to encode data. The experiment results showed that XGB has better accuracy than LGBM, but LGBM has faster computation time than XGB. The highest accuracy is 0.992. © 2022 Institute of Advanced Engineering and Science (IAES).

关键词： boosting algorithm Classification COVID-19 DNA Sequencing One-Hot Encoded

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An Intelligent Intrusion Detection Scheme Powered by boosting algorithm 11

An Intelligent Intrusion Detection Scheme Powered by Boostin...

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11th International Conference on Cloud Computing, Data Science and Engineering (Confluence)

作者： SubhamDivakar Priyadarshini, Rojalina Barik, Rabindra Kumar Roy, Diptendu Sinha Persistent Syst Pune Maharashtra India CV Raman Global Univ Bhubaneswar Odisha India KIIT Deemed Univ Bhubaneswar Odisha India Natl Inst Technol Shillong Meghalaya India

ISBN: (纸本)9781665414517

Security is always a major concern in today's world. Due to the prevalent techniques like Internet of Things (IoT), Fog/Edge computing and the vast use of social networking, there is a significant increase in the generation of network traffic data. For this reason, proper and fast mechanisms are needed to monitorvariety of data to fight against the vulnerabilities and threats those may occur in the system. In the present article, a machine learning based Intrusion Detection Scheme (IDS) is being proposed. This system can monitor and analyze the incoming network traffic whether is normal. UNSW-NB 15 dataset is used to validate the machine learning model which is powered by boosting *** of the boosting algorithms such as Adaptive boosting (AdaBoost), Extreme Gradient boosting (XGBoost) and Gradient boosting Classifier (GBC) are trained over the six baseline models such as Support Vector Machine (SVM), Decision Tree (DT), K-Nearest Neighborhood (KNN), two variants of Random Forest Model, Gaussian Naive Bayes (GNB). The performance of the IDS is measured in terms of correct analysis of the network traffic as normal or abnormal and the time taken to detect it. As per the observed results the proposed IDS system is providing the best results for XGB model which gives 95.57 % of accuracy and the time taken to do it is come out as 3.03 seconds. The entire experiment is executed both in Central Processing Unit (CPU) and Graphical Processing UnitGPU) environment and a comparative analysis is done in terms of execution time.

关键词： Intrusion Detection System Security boosting algorithm

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Big multi-step ship motion forecasting using a novel hybrid model based on real-time decomposition, boosting algorithm and error correction framework

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OCEAN ENGINEERING 2022年 256卷

作者： Wei, Yunyu Chen, Zezong Zhao, Chen Chen, Xi Tu, Yuanhui Zhang, Chunyang Wuhan Univ Sch Elect Informat Wuhan 430072 Peoples R China China Ship Dev & Design Ctr Wuhan 430064 Peoples R China

Big multi-step ship motion forecasting plays an essential role as ship safety warning in maritime operations. However, big multi-step prediction will weaken the correlation of time series and introduce large error accumulation. Real-time prediction also requires a simple prediction model structure, data input-output relationship in line with the actual application, and a high prediction accuracy. To realize the above requirements, a hybrid big multi-step forecasting model is proposed based on real-time wavelet packet decomposition (RTWPD), outlier robust extreme learning machine (ORELM), boosting algorithm, and least squares support vector machine (LSSVM)based error correction method. Taking 10-step, 20-step, and 30-step as the research objects of big multi-step prediction, two sets of ship roll and pitch motion data obtained by practical experiment are provided to complete four comparison experiments, including the comparison of different decomposition algorithms, the comparison of different classical forecasting models, the comparison of different error correction methods and the comparison of the proposed model with different benchmark models. Taking the 10-step, 20-step, and 30-step prediction results of the pitch of dataset #2 as an example, the mean absolute errors (MAEs) of the proposed model are 0.1619 degrees, 0.1742 degrees, and 0.1808 degrees, respectively;and the minimum Diebold-Mariano (DM) value is 4.88, which is greater than the upper limit of the 1 % confidence level, indicating that the prediction results of the proposed model differ significantly from those of the other models. And the root mean square error (RMSE) of the proposed model is about 0.20 degrees for different data sets and different ship motions. The experimental results indicate that the proposed ***-LSSVM (RWOALS) model is stable and feasible in practical applications, and it can provide reliable guidance for maritime operations.

关键词： Big multi-step ship motion forecasting Real-time decomposition boosting algorithm Error correction

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Hourly PM_2.5 concentration multi-step forecasting method based on extreme learning machine, boosting algorithm and error correction model

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DIGITAL SIGNAL PROCESSING 2021年 118卷 103221-103221页

作者： Yin, Shi Liu, Hui Duan, Zhu Cent South Univ Inst Artificial Intelligence & Robot IAIR Key Lab Traff Safety Track Minist EducSch Traff & Transportat Engn Changsha 410075 Hunan Peoples R China

The multi-step forecasting of PM2.5 concentration is helpful to realize the early warning of air pollution, but the accurate multi-step forecasting of PM2.5 has certain difficulties. In this paper, a novel multistep forecasting method of hourly PM2.5 concentration is proposed. Two boosting algorithms, Modified *** and Gradient boosting, are used to enhance the extreme learning machine (ELM) for ensemble prediction of the PM2.5. Then two multi-step forecasting strategies, multiple-input multiple output (MIMO) and recursive, are used. Finally, through error correction model (ECM) the prediction error is corrected to obtain the hourly PM2.5 multi-step forecasting results. Corresponding experiments are carried out through the PM2.5 data sets of four cities, and the results show that: (1) the forecasting method proposed in this study can achieve a good multi-step forecasting effect of PM2.5, and changing the forecasting strategy or boosting algorithm has little influence on the forecasting effect;(2) the use of ECM can improve the PM2.5 forecasting accuracy of the model, and as the forecasting steps increase, the improvement effect of ECM is more significant;(3) the forecasting framework proposed in this paper is effective, and the forecasting accuracy of the proposed method is significantly better than the corresponding single models and the existing models. (C) 2021 Elsevier Inc. All rights reserved.

关键词： Hourly PM2.5 multi-step forecasting Extreme learning machine boosting algorithm Multi-step forecasting strategy Error correction model

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Shared bicycle dynamic distribution model based on boosting algorithm 31

Shared bicycle dynamic distribution model based on Boosting ...

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31st Chinese Control And Decision Conference (CCDC)

作者： Han, Lu Yu, Chongchong Chen, Yunbing Tang, Xiaojiang Beijing Technol & Business Univ Sch Comp & Informat Engn Beijing 100048 Peoples R China

ISBN: (纸本)9781728101057

The number of shared bicycles is large and the parking is not standardized. Accurate position prediction of the bicycle can effectively prevent the collision between the number of vehicles and the parking position. The traditional machine learning algorithm is slowly to process and can not meet the accuracy requirements. Therefore, the boosting algorithm which promote the weak learner to a strong learner is adopted to enhance running speed and accuracy. At the same time, the way of constructing feature groups is proposed, and the importance of features is evaluated. The comparison experiment compares the prediction capabilities of the three boosting algorithms, XGboost, LightGBM and Catboost. The example verification shows that the LightGBM algorithm has the highest test accuracy. and then this algorithm is used to predict the dynamic distribution of the Mobike in Beijing throughout the day. And show the heat distribution map of the prediction results of Mobike in different time periods.

关键词： Shared bicycle boosting algorithm Feature group Position prediction Dynamic distribution

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Data classification with binary response through the boosting algorithm and logistic regression

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EXPERT SYSTEMS WITH APPLICATIONS 2017年 69卷 62-73页

作者： de Menezes, Fortunato S. Liska, Gilberto R. Cirillo, Marcelo A. Vivanco, Mario J. F. Fed Univ Lavras UFLA Dept Phys DFI POB 3037 BR-37200000 Lavras MG Brazil Fed Univ Lavras UFLA Dept Stat DES POB 3037 BR-37200000 Lavras MG Brazil

The task of classifying is natural to humans, but there are situations in which a person is not best suited to perform this function, which creates the need for automatic methods of classification. Traditional methods, such as logistic regression, are commonly used in this type of situation, but they lack robustness and accuracy. These methods do not not work very well when the data or when there is noise in the data, situations that are common in expert and intelligent systems. Due to the importance and the increasing complexity of problems of this type, there is a need for methods that provide greater accuracy and interpretability of the results. Among these methods, is boosting, which operates sequentially by applying a classification algorithm to reweighted versions of the training data set. It was recently shown that boosting may also be viewed as a method for functional estimation. The purpose of the present study was to compare the logistic regressions estimated by the maximum likelihood model (LRMML) and the logistic regression model estimated using the boosting algorithm, specifically the Binomial boosting algorithm (LRMBB), and to select the model with the better fit and discrimination capacity in the situation of presence(absence) of a given property (in this case, binary classification). To illustrate this situation, the example used was to classify the presence (absence) of coronary heart disease (CHD) as a function of various biological variables collected from patients. It is shown in the simulations results based on the strength of the indications that the LRMBB model is more appropriate than the LRMML model for the adjustment of data sets with several covariables and noisy data. The following sections report lower values of the information criteria AIC and BIC for the LRMBB model and that the Hosmer-Lemeshow test exhibits no evidence of a bad fit for the LRMBB model. The LRMBB model also presented a higher AUC, sensitivity, specificity and accuracy

关键词： boosting algorithm Data classification Logistic regression Information criteria AIC BIC Selection of models Monte Carlo Simulation

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Short-term wind power forecasting using hybrid method based on enhanced boosting algorithm

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JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY 2017年第1期5卷 126-133页

作者： Jiang, Yu Chen, Xingying Yu, Kun Liao, Yingchen Hohai Univ Coll Energy & Elect Engn Nanjing 210098 Jiangsu Peoples R China

Day-ahead wind power forecasting plays an essential role in the safe and economic use of wind energy, the comprehending-intrinsic complexity of the behavior of wind is considered as the main challenge faced in improving forecasting accuracy. To improve forecasting accuracy, this paper focuses on two aspects: (1)proposing a novel hybrid method using boosting algorithm and a multi-step forecast approach to improve the forecasting capacity of traditional ARMA model;(2)calculating the existing error bounds of the proposed method. To validate the effectiveness of the novel hybrid method, one-year period of real data are used for test, which were collected from three operating wind farms in the east coast of Jiangsu Province, China. Meanwhile conventional ARMA model and persistence model are both used as benchmarks with which the proposed method is compared. Test results show that the proposed method achieves a more accurate forecast.

关键词： Hybrid method Multi-step-ahead prediction Wind power forecast boosting algorithm Time series model

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boosting algorithms for predicting end-point temperature in BOF steelmaking using big industrial datasets

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JOURNAL OF IRON AND STEEL RESEARCH INTERNATIONAL 2025年 1-13页

作者： Zhang, Jian-bo Ghalati, Maryam Khaksar Fu, Jun Yang, Xiao-an El-Fallah, G. M. A. M. Dong, Hong-biao Univ Leicester Sch Engn Leicester LE1 7RH England Nanjing Iron & Steel United Co Ltd Nanjing 210035 Jiangsu Peoples R China

The application of machine learning was investigated for predicting end-point temperature in the basic oxygen furnace steelmaking process, addressing gaps in the field, particularly large-scale dataset sizes and the underutilization of boosting algorithms. Utilizing a substantial dataset containing over 20,000 heats, significantly bigger than those in previous studies, a comprehensive evaluation of five advanced machine learning models was conducted. These include four ensemble learning algorithms: XGBoost, LightGBM, CatBoost (three boosting algorithms), along with random forest (a bagging algorithm), as well as a neural network model, namely the multilayer perceptron. Our comparative analysis reveals that Bayesian-optimized boosting models demonstrate exceptional robustness and accuracy, achieving the highest R-squared values, the lowest root mean square error, and lowest mean absolute error, along with the best hit ratio. CatBoost exhibited superior performance, with its test R-squared improving by 4.2% compared to that of the random forest and by 0.8% compared to that of the multilayer perceptron. This highlights the efficacy of boosting algorithms in refining complex industrial processes. Additionally, our investigation into the impact of varying dataset sizes, ranging from 500 to 20,000 heats, on model accuracy underscores the importance of leveraging larger-scale datasets to improve the accuracy and stability of predictive models.

关键词： Steelmaking Basic oxygen furnace Machine learning boosting algorithm

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A Theoretical Line Losses Calculation Method of Distribution System Based on boosting algorithm 12

A Theoretical Line Losses Calculation Method of Distribution...

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12th International Conference on Natural Computation, Fuzzy Systems and Knowledge Discovery (ICNC-FSKD)

作者： Wang, Yufei Wang, Chenlong Wang, Jing Zuo, Luo Shi, Yongyi Global Energy Interconnect Res Inst Beijing 102209 Peoples R China Datang Gangu Power Plant Tianshui 741211 Gansu Peoples R China China Mobile Grp Gansu Co Ltd Lanzhou 730030 Gansu Peoples R China Zhejiang Elect Power Co Hangzhou 310007 Zhejiang Peoples R China

ISBN: (纸本)9781509040933

Existing intelligent theoretical line losses calculation methods that prevalent on worse line calculation error, are all based on single learning algorithm. In order to overcome this defect, a novel intelligent calculation method based on boosting algorithm is proposed. In this calculation method, the theoretical line losses calculation is abstracted into function fitting problem, in addition, the sample set - which is structured by the lines' information of known theoretical line losses - is input to many single learning algorithms of boosting algorithm for training many sub-calculation model and constituting them as a sequence, which sequence is the final theoretical line losses calculation model. In the sub-calculation model training process, this intelligent method effectively reduces the calculation error by the boosting algorithm's internal mechanism that the large calculation error lines are constantly reinforcement training. Finally the experiment shows that this intelligent calculation method based on boosting algorithm has lower calculation error than traditions.

关键词： Distribution System Theoretical Line Losses boosting algorithm Function Fitting Calculation Error Data Quality

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