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Early prediction of battery degradation in grid-scale battery energy storage system using extreme gradient boosting algorithm

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RESULTS IN ENGINEERING 2024年 21卷

作者： Apribowo, Chico Hermanu Brillianto Hadi, Sasongko Pramono Wijaya, Franscisco Danang Setyonegoro, Mokhammad Isnaeni Bambang Sarjiya Univ Gadjah Mada Dept Elect & Informat Engn Yogyakarta 55281 Indonesia Univ Sebelas Maret Dept Elect Engn Surakarta 57126 Indonesia Univ Gadjah Mada Ctr Energy Studies Yogyakarta 55281 Indonesia

The growth of battery energy storage systems (BESS) is caused by the variability and intermittent nature of high demand and renewable power generation at the network scale. In the context of BESS, Lithium-ion (Li-ion) battery occupies a crucial position, although it is faced with challenges related to performance battery degradation over time due to electrochemical processes. This battery degradation is a crucial factor to account for, based on its potential to diminish the efficiency and safety of electrical system equipment, thereby contributing to increased system planning costs. This implies that the health of battery needs to be diagnosed, particularly by determining remaining useful life (RUL), to avoid unexpected operational costs and ensure system safety. Therefore, this study aimed to use machine learning models, specifically extreme gradient boosting (XGBoost) algorithm, to estimate RUL, with a focus on the temperature variable, an aspect that had been previously underemphasized. Utilizing XGBoost model, along with fine-tuning its hyperparameters, proved to be a more accurate and efficient method for predicting RUL. The evaluation of the model yielded promising outcomes, with a root mean square error (RMSE) of 90.1 and a mean absolute percentage error (MAPE) of 7.5 %. Additionally, the results showed that the model could improve RUL predictions for batteries within BESS. This study significantly contributed to optimizing planning and operations for BESS, as well as developing more efficient and effective maintenance strategies.

关键词： Battery energy storage system Battery degradation Remaining useful life extreme gradient boosting algorithm Hyperparameter tuning

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Research on four-bar linkage trajectory synthesis using extreme gradient boosting and genetic algorithm

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JOURNAL OF COMPUTATIONAL DESIGN AND ENGINEERING 2024年第2期11卷 1-21页

作者： Wang, Jianping Chen, Boyan Wang, Youchao Pu, Dexi Jia, Xiaoyue Xian Univ Technol Sch Mech & Precis Instrument Engn Xian 710048 Peoples R China

The current study on the synthesis problems of four-bar mechanism trajectories primarily relies on establishing a numerical atlas based on trajectory characteristics and employing neural networks to synthesize mechanism parameters. However, this approach has several shortcomings, including a vast database, inefficient retrieval, and challenges in maintaining accuracy. This paper presents a method for synthesizing a trajectory-generation mechanism that combines the extreme gradient boosting (XGBoost) algorithm with a genetic algorithm (GA). The purpose is to synthesize, based on a particular trajectory, the dimensions and installation position parameters of a four-bar mechanism. The paper classifies the trajectories according to their shape features and geometric center placements, thereby improving the accuracy of the XGBoost model for synthesizing mechanisms. The XGBoost algorithm is employed to synthesize the basic dimensional parameters for the mechanism, with the relative slopes of trajectories as input features. The synthesized basic dimensional parameters are turned into parameters for the actual mechanism by researching the scaling, translation, and rotation relationships between mechanisms and the trajectories they generate. The accuracy of the generated trajectories from the synthesized mechanism can be improved by applying GA to optimize the mechanism parameters. Five comparative examples are provided in this research for the different scenarios of given trajectory curves and trajectory points. The effectiveness and accuracy of the proposed approach in this study are validated in comparison to existing research methods by comparing errors between the generated trajectories and the given trajectories. Graphical Abstract

关键词： mechanism synthesis trajectory generation genetic algorithm extreme gradient boosting algorithm

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Machine learning in PEM water electrolysis: A study of hydrogen production and operating parameters

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COMPUTERS & CHEMICAL ENGINEERING 2025年 194卷

作者： Shomope, Ibrahim Al-Othman, Amani Tawalbeh, Muhammad Alshraideh, Hussam Almomani, Fares Amer Univ Sharjah Dept Chem & Biol Engn POB 26666 Sharjah U Arab Emirates Amer Univ Sharjah Coll Engn Energy Water & Sustainable Environm Res Ctr POB 26666 Sharjah U Arab Emirates Univ Sharjah Sustainable & Renewable Energy Engn Dept POB 27272 Sharjah U Arab Emirates Univ Sharjah Sustainable Energy & Power Syst Res Ctr RISE POB 27272 Sharjah U Arab Emirates Amer Univ Sharjah Dept Ind Engn PO Box 26666 Sharjah U Arab Emirates Jordan Univ Sci & Technol Ind Engn Dept POB 3030 Irbid Jordan Qatar Univ Dept Chem Engn POB 2713 Doha Qatar

Proton exchange membrane water electrolysis (PEMWE) powered by renewable energy stands out as a promising technology for the sustainable production of high-purity hydrogen. This study employed three machine learning (ML) algorithms, random forest (RF), support vector machine (SVM), and extreme gradient boosting (XGBoost), to predict hydrogen production in PEMWE. Model performance was evaluated using root mean squared error (RMSE), coefficient of determination (R2), and mean absolute error (MAE) metrics. The top-performing models, RF and XGBoost, were further refined through hyperparameter tuning. The final models demonstrated high reliability in predicting hydrogen production rates, with RF consistently outperforming XGBoost. The RF model achieved a predictive accuracy of R2 = 0.9898, RMSE = 19.99 mL/min, and MAE = 10.41 mL/min, while the XGBoost model achieved R2 = 0.9894, RMSE = 20.43 mL/min, and MAE = 11.50 mL/min. Partial dependency plots (PDPs) emphasized the critical role of optimizing both cell voltage and current to maximize hydrogen production in PEMWE. These insights provide valuable guidance for operational adjustments, ensuring optimal system performance for high efficiency and productivity. The study suggests further research on the impact of parameters like temperature and power density on hydrogen production, incorporating them for better optimization.

关键词： Water electrolysis Machine learning Hydrogen production Random forest Support vector machine extreme gradient boosting algorithm

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Void damage detection of rubber bearings in bridge structures under laboratory conditions using the active sensing method and BO-XGBoost algorithm

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STRUCTURAL HEALTH MONITORING-AN INTERNATIONAL JOURNAL 2025年

作者： Deng, Chubing Li, Yunfei Xiong, Feng Zeng, Yi Sichuan Univ Coll Architecture & Environm MOE Key Lab Deep Earth Sci & Engn Chengdu 610065 Peoples R China

In bridge engineering, void damage in rubber bearings refers to the phenomenon where gaps occur between the rubber bearing and the bridge structure due to factors such as aging, overloading, and earthquakes. Void damage can lead to abnormal stress distribution, bearing deformation failure, and uneven load transfer, thereby affecting the overall stability and safety of the bridge. Consequently, long-term monitoring of void damage is essential. However, methods such as manual inspection and computer vision have disadvantages, including high costs, lengthy processes, and limited monitoring scope. To address these challenges in void damage detection, this paper proposes a novel method that combines the active sensing approach with the Bayesian optimization (BO)-optimized extreme gradient boosting (XGBoost) algorithm. A total of 1200 sets of experimental data under different void conditions were obtained through the active sensing method, and a high-precision void damage detection model based on the BO-XGBoost algorithm was proposed. The study found that the proposed void damage detection model achieved an Accuracy value of 95.0% on the test set, indicating that the combination of the active sensing method with the BO-XGBoost algorithm can effectively solve the problem of void damage detection in laminated rubber bearings.

关键词： Laminated rubber bearing void damage active sensing method Bayesian optimization extreme gradient boosting algorithm

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Prediction of ultimate bearing capacity of single pile in composite formation based on HGS-XGBoost algorithm

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APPLIED GEOPHYSICS 2025年 1-18页

作者： Sun, Zong-jun Han, Ying-feng Jiang, Feng Wang, Gang Zheng, Wen Liu, Fei Wu, Yue Shandong Univ Sci & Technol Dept Civil Engn Qingdao 266590 Peoples R China Qingdao Ruihan Technol Grp Co Ltd Qingdao 266000 Peoples R China Fujian Univ Technol Coll Civil Engn Fuzhou 350118 Peoples R China Fujian HC Technol Internet Things Co Ltd Fuzhou 350004 Peoples R China

The inherent spatial heterogeneity of complex geological formations results in large differences in the ultimate bearing capacity of individual piles, and it is difficult to reliably quantify and assess the bearing capacity of individual piles. In this paper, based on the results of on-site static load test and sensitivity analysis method, eight sensitive factors are screened out, and the extreme gradient boosting algorithm (XGBoost) is used to predict the ultimate load carrying capacity of individual piles, however, the computed coefficient of determination is less than 0.9, and the prediction effect needs to be strengthened. On this basis, three kinds of swarm intelligent optimization algorithms are introduced to adaptively match the XGBoost hyper-parameters, and the results show that the HGS-XGBoost hybrid prediction model can more accurately calculate the ultimate bearing capacity of a single pile under the composite strata, and the prediction effect can satisfy the engineering requirements when using the HGS-XGBoost prediction model for the actual project.

关键词： composite formation sensitivity analysis extreme gradient boosting algorithm ultimate bearing capacity

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Prediction of cement specific surface area in the cement mill grinding process: A method combining dual-frequency principal component analysis and extreme gradient boosting

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PROCEEDINGS OF THE INSTITUTION OF MECHANICAL ENGINEERS PART B-JOURNAL OF ENGINEERING MANUFACTURE 2023年第1-2期237卷 69-80页

作者： Shi, Xin Xu, Qingquan Zheng, Lizhao Hao, Xiaochen Zhao, Yantao Yanshan Univ Sch Elect Engn 438 Hebei Ave Qinhuangdao 066004 Hebei Peoples R China

The cement specific surface area is an important indicator of cement quality. The accurate prediction of the cement specific surface area aims to guide operators to control the cement grinding process to improve product quality while reducing system energy consumption. However, due to the complexity of the cement grinding process, the process variables have coupling, time-varying delay, nonlinear characteristics, and different sampling frequency. Herein, we proposed the specific surface area prediction model, which combined dual-frequency principal component analysis and extreme gradient boosting (DF-PCA-XGB). In order to solve the problem of difficulty in modeling due to different sampling intervals of related data, this paper analyzes the low-frequency sampling data and high-frequency sampling data under multiple working conditions, and establishes prediction models respectively. Aiming at the data redundancy problem of high-frequency and low-frequency variable data in the introduced time window, a method based on the combination of principal component analysis (PCA) and extreme gradient boosting (XGB) cross-validation is proposed to reduce data redundancy while retaining most of the characteristics of the data. The final specific surface area prediction results were obtained by weighting the high-frequency data model and the low-frequency data model. The simulation results showed that the prediction method in this paper can improve the prediction accuracy of the specific surface area of the finished cement product under multiple working conditions with high stability and has promising application in the cement manufacturing process.

关键词： Cement grinding process specific surface area prediction different sampling frequency principal component analysis extreme gradient boosting algorithm

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A Hardware-Trojans Detection Approach Based on extreme gradient boosting 2

A Hardware-Trojans Detection Approach Based on eXtreme Gradi...

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2nd IEEE International Conference on Computer and Communication Engineering Technology (CCET)

作者： Chen, Jinghui Dong, Chen Zhang, Fan He, Guorong Fuzhou Univ Coll Math & Comp Sci Fuzhou Peoples R China Fujian Prov Key Lab Informat Secur Network Syst Fuzhou Peoples R China Fujian Prov Key Lab Network Comp & Intelligent In Fuzhou Peoples R China

ISBN: (纸本)9781728128719

As the core component of the electronic devices, the integrated circuit (IC) must be taken seriously with its security. The pre-silicon detection methods do not require gold chips, are not affected by process noise and are suitable for the safe detection of a very large scale integration (VLSI). Therefore, more and more researchers are paying attention to the presilicon detection method. In this paper, we propose a machine-learning-based hardware-Trojans detection method in gatelevel. First, by the analysis of the Trojan circuits, we put forward new Trojan-net features. After that, we use the scoring mechanism of the extreme gradient boosting (XGBoost) to set up a new effective feature set of 49 out of 56 features. Finally, the hardware-Trojan classifier was trained based on the effective feature set. The experimental results show that the proposed method can obtain the average Recall of 89.84%, the average F-measure of 87.75% and the average Accuracy of 99.83%. Furthermore, through the comparison experiments, it is proved that the features proposed in this paper can further improve the performance of detection.

关键词： hardware trojan gate-level netlist machine learning extreme gradient boosting algorithm

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Fast 3D-HEVC inter coding using data mining and machine learning

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IET IMAGE PROCESSING 2022年第11期16卷 3067-3084页

作者： Zhang, Ruyi Jia, Kebin Yu, Yuan Liu, Pengyu Sun, Zhonghua Beijing Univ Technol Fac Informat Technol 100 Pingleyuan Beijing 100124 Peoples R China

The Three-Dimensional High Efficiency Video Coding standard is a video compression standard developed based on the two-dimensional video coding standard HEVC and used to encode multi-view plus depth format video. This paper proposes an algorithm based on extreme gradient boosting to solve the problem of high inter-frame coding complexity in 3D-HEVC. Firstly, explore the correlation between the division depth of the inter-frame coding unit and the texture features in the map, as well as the correlation between the coding unit division structure between each map and each viewpoint. After that, based on the machine learning method, a fast selection mechanism for dividing the depth range of the inter-frame coding tree unit based on the extreme gradient boosting algorithm is constructed. Experimental results show that, compared with the reference software HTM-16.0, this method can save an average of 35.06% of the coding time, with negligible degradation in terms of coding performance. In addition, the proposed algorithm has achieved different degrees of improvement in coding performance compared with the related works.

关键词： coding performance reference software HTM-16.0 standard HEVC video compression standard tree codes video coding data mining two-dimensional video inter-frame coding unit data compression learning (artificial intelligence) fast 3D-HEVC inter coding fast selection mechanism inter-frame coding tree unit texture features three-dimensional high efficiency video coding standard machine learning method extreme gradient boosting algorithm coding unit division structure high inter-frame coding complexity

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A Haze Feature Extraction and Pollution Level Identification Pre-Warning algorithm

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Computers, Materials & Continua 2020年第9期64卷 1929-1944页

作者： Yongmei Zhang Jianzhe Ma Lei Hu Keming Yu Lihua Song Huini Chen School of Information Science and Technology North China University of TechnologyBeijing100144China Department of Electronic&Information Engineering The Hong Kong Polytechnic University00852Hong Kong School of Computer Information Engineering Jiangxi Normal UniversityNanchang330022China Department of Mathematics Brunel UniversityLondonUB83PHUK Department of Computer Science George Washington UniversityWashington DC20052USA

The prediction of particles less than 2.5 micrometers in diameter(PM2.5)in fog and haze has been paid more and more attention,but the prediction accuracy of the results is not *** prediction algorithms based on traditional numerical and statistical prediction have poor effects on nonlinear data prediction of *** order to improve the effects of prediction,this paper proposes a haze feature extraction and pollution level identification pre-warning algorithm based on feature selection and integrated *** Redundancy Maximum Relevance method is used to extract low-level features of haze,and deep confidence network is utilized to extract high-level *** gradient boosting algorithm is adopted to fuse low-level and high-level features,as well as predict *** PM2.5 concentration pollution grade classification index,and grade the forecast *** expert experience knowledge is utilized to assist the optimization of the pre-warning *** experiment results show the presented algorithm can get better prediction effects than the results of Support Vector Machine(SVM)and Back Propagation(BP)widely used at present,the accuracy has greatly improved compared with SVM and BP.

关键词： Deep belief networks feature extraction PM2.5 extreme gradient boosting algorithm haze pollution

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Machine Learning-Based Online Multi-Fault Diagnosis for IMs Using Optimization Techniques With Stator Electrical and Vibration Data

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IEEE TRANSACTIONS ON ENERGY CONVERSION 2024年第4期39卷 2412-2424页

作者： Hsu, Shih-Hsien Lee, Chien-Hsing Wu, Wen-Fang Jiang, Joe-Air Feng Chia Univ Dept Elect Engn Taichung 407 Taiwan Natl Cheng Kung Univ Dept Syst & Naval Mech Engn Tainan 701 Taiwan Natl Taiwan Univ Dept BioMechatron Engn Taipei 106 Taiwan

Induction motors (IMs) have been commonly applied to industrial fields since the past decades;thus, developing advanced fault diagnosis methods becomes vital for IM applications. This study proposed an online fault diagnosis system for IMs based on the Random Forest (RF) and extreme gradient boosting (XGBoost) algorithms to reduce the additional repair costs and prevent unexpected downtime. It focused on detecting healthy three-phase IMs and five common fault conditions of the IMs, involving broken rotor bars, rotor unbalance, and composite faults with short-circuited stator windings that combined two or three types of the faults, for practical purposes. The experimental results show that the model performance improved by 15% over the default model when train-test split ratios, feature selection, and hyperparameter optimization, notably in XGBoost, are considered. The proposed XGBoost model enables a high accuracy of 96.06% for RF to perform a motor fault diagnosis under six different motor conditions. Furthermore, the execution time required by the proposed fault diagnosis system is 57% less than the time required by existing motor fault diagnosis methods. These results successfully demonstrate the effectiveness of the methods proposed in this study for online motor diagnosis.

关键词： extreme gradient boosting algorithm machine learning motor failure diagnosis random forest algorithm

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