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Rapid prediction of damaged ship roll motion responses in beam waves based on stacking algorithm

Journal of Hydrodynamics 2024年第2期36卷 394-405页

作者： Xin-ran Liu Ting-qiu Li Zi-ping Wang School of Naval Architecture Ocean and Energy Power EngineeringWuhan University of TechnologyWuhan430063China

Accurate modeling for highly non-linear coupling of a damaged ship with liquid sloshing in waves is still of considerable interest within the computational fluid dynamics(CFD)and AI *** paper describes a data-driven stacking algorithm for fast prediction of roll motion response amplitudes in beam waves by constructing a hydrodynamics model of a damaged ship based on the dynamic overlapping grid CFD *** general idea is to optimize various parameters varying with four types of classical base models like multi-layer perception,support vector regression,random forest,and hist gradient boosting *** offers several attractive properties in terms of accuracy and efficiency by choosing the standard DTMB 5415 model with double damaged compartments for *** is clearly demonstrated that the predicted response amplitude operator(RAO)in the regular beam waves agrees well with the experimental data available,which verifies the accuracy of the established damaged ship hydrodynamics *** high-quality CFD samples,therefore,implementation of the designed stacking algorithm with its optimal combination can predict the damaged ship roll motion amplitudes effectively and accurately(e.g.,the coefficient of determination 0.9926,the average absolute error 0.0955 and CPU 3s),by comparison of four types of typical base models and their various ***,the established stacking algorithm provides one potential that can break through problems involving the time-consuming and low efficiency for large-scale lengthy CFD simulations.

关键词： Ship motion damaged ship computational fluid dynamics(CFD) machine learning stacking algorithm

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Research on Total Electric Field PredictionMethod of Ultra-High Voltage Direct Current Transmission Line Based on stacking algorithm

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Computer Systems Science & Engineering 2024年第3期48卷 723-738页

作者： Yinkong Wei Mucong Wu Wei Wei Paulo R.F.Rocha Ziyi Cheng Weifang Yao Key Laboratory of Operation and Control of Terraced Hydropower Plants in Hubei Province Three Gorges UniversityYichang443002China School of Electricity and New Energy Three Gorges UniversityYichang443002China Marketing Service Center(Metering Center) State Grid Hubei Electric Power Co.Ltd.Wuhan430072China Centre for Functional Ecology-Science for People&the Planet Associate Laboratory TERRADepartment of Life SciencesUniversity of CoimbraCoimbra3000-456Portugal Research Institute of Electric Power Science State Grid Anhui Electric Power Company LimitedHefei230601China

Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic *** ground total electric field is considered a main electromagnetic environment indicator of UHV transmission lines and is currently employed for reliable long-term operation of the power ***,the accurate prediction of the ground total electric field remains a technical *** this work,we collected the total electric field data from the Ningdong-Zhejiang±800 kV UHVDC transmission project,as of the Ling Shao line,and perform an outlier analysis of the total electric field *** show that the Local Outlier Factor(LOF)elimination algorithm has a small average difference and overcomes the performance of Density-Based Spatial Clustering of Applications with Noise(DBSCAN)and Isolated Forest elimination ***,the stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite *** low prediction error of the stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines.

关键词： DC transmission line total electric field effective data multivariable outliers LOF algorithm stacking algorithm

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Employee Attrition Classification Model Based on stacking algorithm

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Psychology Research 2023年第6期13卷 279-285页

作者： CHEN Yanming LIN Xinyu ZHAN Kunye Shantou University South China Normal University Shenzhen University

This paper aims to build an employee attrition classification model based on the stacking *** algorithm is applied to address the issue of data imbalance and the Randomforest feature importance ranking method is used to resolve the overfitting problem after data cleaning and ***,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to ***,the stacking algorithm is used to establish the final classification *** model has practical and significant implications for both human resource management and employee attrition analysis.

关键词： employee attrition classification model machine learning ensemble learning oversampling algorithm Randomforest stacking algorithm

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