期刊文献+
任意字段
题名或关键词
题名
关键词
摘要
作者
第一作者
机构
刊名
分类号
参考文献
作者简介
基金资助
栏目信息
共找到12篇文章
< 1 >
每页显示 20 50 100
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
显示方式：
Prediction of Asphalt Pavement Rutting Depth Based on Multi-Model Fusion of Stacking Algorithm 认领 引用
1
作者 Chenhui Peng Jinbiao Tang Derun Zhang 《Structural Durability & Health Monitoring》 EI 2026年第3期156-171,共16页
Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support ... Rutting is a serious issue in asphalt pavement,which may reduce the pavement driving quality and safety.Accurately predicting rutting depth is a crucial task in pavement engineering,providing crucial decision support for asphalt pavement design and maintenance.However,accurate prediction of pavement rutting still remains a significant challenge for pavement engineers.This research first selects the loading number,temperature,dynamic modulus,asphalt layer thickness,and base layer type and thickness as candidate features.Data preprocessing,including outlier handling and feature selection,is then performed.Finally,based on the stacking algorithm,a multi-model fusion approach for predicting rutting depth in asphalt pavements is proposed,using ridge regression(RidgeR),K-nearest neighbor(KNN),multilayer perceptron(MLP),and random forest(RF)models as base models,and support vector machine(SVM)as a meta-model.The model is optimized using a Bayesian optimization model.Results demonstrate the feasibility of using correlation analysis for feature selection.Seven features,including axle weight,upper layer temperature,and middle layer modulus,were selected as predictive features.While all the basic models achieved good prediction accuracy,the stacking ensemble model exhibited lower variance and bias,demonstrating superior generalization capability.The asphalt pavement rutting depth prediction method based on the stacking algorithm multi-model fusion proposed in this research can accurately predict the rutting depth. 展开更多
关键词 Rutting prediction asphalt pavement stacking algorithm Bayesian optimization model
暂未订购 下载PDF
Research on Total Electric Field Prediction Method of Ultra-High Voltage Direct Current Transmission Line Based on Stacking Algorithm 认领 引用
2
作者 Yinkong Wei Mucong Wu +3 位作者 Wei Wei Paulo R.F.Rocha Ziyi Cheng Weifang Yao 《Computer Systems Science & Engineering》 2024年第3期723-738,共16页
Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The ground total electric field is considered a main electromagn... Ultra-high voltage(UHV)transmission lines are an important part of China’s power grid and are often surrounded by a complex electromagnetic environment.The 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 grid.Yet,the accurate prediction of the ground total electric field remains a technical challenge.In 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 data.We 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 algorithms.Moreover,the Stacking algorithm has been found to have superior prediction accuracy than a variety of similar prediction algorithms,including the traditional finite element.The low prediction error of the Stacking algorithm highlights the superior ability to accurately forecast the ground total electric field of UHVDC transmission lines. 展开更多
关键词 DC transmission line total electric field effective data multivariable outliers LOF algorithm Stacking algorithm
暂未订购 下载PDF
Employee Attrition Classification Model Based on Stacking Algorithm 认领 引用
3
作者 CHEN Yanming LIN Xinyu ZHAN Kunye 《Psychology Research》 2023年第6期279-285,共7页
This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling algorithm is applied to address the issue of data imbalance and the Randomforest feature importance rank... This paper aims to build an employee attrition classification model based on the Stacking algorithm.Oversampling 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 preprocessing.Then,different algorithms are used to establish classification models as control experiments,and R-squared indicators are used to compare.Finally,the Stacking algorithm is used to establish the final classification model.This model has practical and significant implications for both human resource management and employee attrition analysis. 展开更多
关键词 employee attrition classification model machine learning ensemble learning oversampling algorithm Randomforest stacking algorithm
暂未订购 下载PDF
Rapid prediction of damaged ship roll motion responses in beam waves based on stacking algorithm 认领 引用 被引量：2
4
作者 Xin-ran Liu Ting-qiu Li Zi-ping Wang 《Journal of Hydrodynamics》 SCIE EI CSCD 2024年第2期394-405,共12页
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 framework.This paper describes a da... 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 framework.This 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 technology.The 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 regression.This offers several attractive properties in terms of accuracy and efficiency by choosing the standard DTMB 5415 model with double damaged compartments for validation.It 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 model.Given 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 forms.Importantly,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. 展开更多
关键词 Ship motion damaged ship computational fluid dynamics(CFD) machine learning stacking algorithm
Prediction of geological characteristics from shield operational parameters by integrating grid search and K-fold cross validation into stacking classification algorithm 认领 引用 被引量：26
5
作者 Tao Yan Shui-Long Shen +1 位作者 Annan Zhou Xiangsheng Chen 《Journal of Rock Mechanics and Geotechnical Engineering》 SCIE CSCD 2022年第4期1292-1303,共12页
This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two le... This study presents a framework for predicting geological characteristics based on integrating a stacking classification algorithm(SCA) with a grid search(GS) and K-fold cross validation(K-CV). The SCA includes two learner layers: a primary learner’s layer and meta-classifier layer. The accuracy of the SCA can be improved by using the GS and K-CV. The GS was developed to match the hyper-parameters and optimise complicated problems. The K-CV is commonly applied to changing the validation set in a training set. In general, a GS is usually combined with K-CV to produce a corresponding evaluation index and select the best hyper-parameters. The torque penetration index(TPI) and field penetration index(FPI) are proposed based on shield parameters to express the geological characteristics. The elbow method(EM) and silhouette coefficient(Si) are employed to determine the types of geological characteristics(K) in a Kmeans++ algorithm. A case study on mixed ground in Guangzhou is adopted to validate the applicability of the developed model. The results show that with the developed framework, the four selected parameters, i.e. thrust, advance rate, cutterhead rotation speed and cutterhead torque, can be used to effectively predict the corresponding geological characteristics. 展开更多
关键词 Geological characteristics Stacking classification algorithm(SCA) K-fold cross-validation(K-CV) K-means++
暂未订购 下载PDF
Design Optimization of CFRP Stacking Sequence Using a Multi-Island Genetic Algorithms Under Low-velocity Impact Loads 认领 引用 被引量：3
6
作者 王宏晓 段玉岗 +1 位作者 ABULIZI Dilimulati ZHANG Xiaohui 《Journal of Wuhan University of Technology(Materials Science)》 SCIE EI CAS 2017年第3期720-725,共6页
A method to improve the low-velocity impact performance of composite laminate is proposed, and a multi-island genetic algorithm is used for the optimization of composite laminate stacking sequence under low-velocity i... A method to improve the low-velocity impact performance of composite laminate is proposed, and a multi-island genetic algorithm is used for the optimization of composite laminate stacking sequence under low-velocity impact loads based on a 2D dynamic impact finite element analysis. Low-velocity impact tests and compression-after impact（CAI） tests have been conducted to verify the effectiveness of optimization method. Experimental results show that the impact damage areas of the optimized laminate have been reduced by 42.1% compared to the baseline specimen, and the residual compression strength has been increased by 10.79%, from baseline specimen 156.97 MPa to optimized 173.91 MPa. The tests result shows that optimization method can effectively enhance the impact performances of the laminate. 展开更多
关键词 multi-island genetic algorithm low-velocity impact composite laminate stacking sequence
暂未订购 下载PDF
The RHSA Strategy for the Allocation of Outbound Containers Based on the Hybrid Genetic Algorithm 认领 引用 被引量：2
7
作者 Meilong Le Hang Yu 《Journal of Marine Science and Application》 2013年第3期344-350,共7页
Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems(CTMS).This ... Secure storage yard is one of the optimal core goals of container transportation;thus,making the necessary storage arrangements has become the most crucial part of the container terminal management systems(CTMS).This paper investigates a random hybrid stacking algorithm(RHSA)for outbound containers that randomly enter the yard.In the first stage of RHSA,the distribution among blocks was analyzed with respect to the utilization ratio.In the second stage,the optimization of bay configuration was carried out by using the hybrid genetic algorithm.Moreover,an experiment was performed to test the RHSA.The results show that the explored algorithm is useful to increase the efficiency. 展开更多
关键词 random hybrid stacking algorithm genetic algorithm container yard operation container stowage plan handling cost utilization ratio
暂未订购 下载PDF
STACKING SEQUENCE OPTIMIZA-TION OF LAMINATED COMPOSITE CYLINDER SHELL FOR MAXIMAL BUCKLING LOAD 认领 引用 被引量：5
8
作者 TANG Qian LIAO Xiaoyun GAO Zhan 《Chinese Journal of Mechanical Engineering》 SCIE EI CAS 2008年第4期31-34,共4页
A new optimization method for the optimization of stacking of composite glass fiber laminates is developed. The fiber orientation and angle of the layers of the cylindrical shells are sought considering the buckling l... A new optimization method for the optimization of stacking of composite glass fiber laminates is developed. The fiber orientation and angle of the layers of the cylindrical shells are sought considering the buckling load. The proposed optimization algorithm applies both finite element analysis and the mode-pursuing sampling （MPS）method. The algorithms suggest the optimal stacking sequence for achieving the maximal buckling load. The procedure is implemented by integrating ANSYS and MATLAB. The stacking sequence designing for the symmetric angle-ply three-layered and five-layered composite cylinder shells is presented to illustrate the optimization process, respectively. Compared with the genetic algorithms, the proposed optimization method is much faster and efficient for composite staking sequence plan. 展开更多
关键词 Composite Laminated cylindrical shell Stacking sequence optimization Buckling load Sampling algorithms
暂未订购 下载PDF
Soft-output stack algorithm with lattice-reduction for MIMO detection 认领 引用
9
作者 Yuan Yang Hailin Zhang Junfeng Hue 《Journal of Systems Engineering and Electronics》 SCIE EI 2010年第2期197-203,共7页
A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on t... A computationally efficient soft-output detector with lattice-reduction （LR） for the multiple-input multiple-output （MIMO） systems is proposed. In the proposed scheme, the sorted QR de- composition is applied on the lattice-reduced equivalent channel to obtain the tree structure. With the aid of the boundary control, the stack algorithm searches a small part of the whole search tree to generate a handful of candidate lists in the reduced lattice. The proposed soft-output algorithm achieves near-optimal perfor- mance in a coded MIMO system and the associated computational complexity is substantially lower than that of previously proposed methods. 展开更多
关键词 multiple-input multiple-output （MIMO） soft-output de- tection lattice-reduction stack algorithm.
暂未订购 下载PDF
Stochastic Hybrid Rumor Control:A Data-Driven Ensemble Learning Control Algorithm 认领 引用
10
作者 ZHONG Xiaojing ZENG Jiaxin +3 位作者 XIANG Wendi CARABALLO Tomás DENG Feiqi PENG Yuqing 《Journal of Systems Science & Complexity》 SCIE EI CSCD 2026年第4期1436-1462,共27页
To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via me... To explore the impact of various groups and methods on rumor propagation,the authors propose a‘Double-Refutation(DR)and Double-Blocking(DB)'rumor control strategy.This strategy combines external refutation via media reports,internal refutation by counteracting individuals,and both continuous and impulse blocking methods.By leveraging multi-synergy and aiming to minimize control costs,the authors propose stochastic optimal hybrid control strategies for rumor containment.Additionally,to enhance the response speed of the control strategy,the authors introduce an ensemble learning algorithm as a substitute for theoretical solutions.Numerical simulations demonstrate that the trained ensemble learning control algorithm can quickly identify sub-optimal control strategies for rumor spreading,with costs only 4.1%higher than those of the optimal control theory. 展开更多
关键词 Double refutation and blocking control mechanism dual-layer propagation model hybrid optimal control stacking ensemble learning control algorithm stochastic control
暂未订购 下载PDF
Combined prediction of rockburst based on multiple factors and stacking ensemble algorithm 认领 引用 被引量：10
11
作者 Hu Luo Yong Fang +4 位作者 Jianfeng Wang Yubo Wang Hang Liao Tao Yu Zhigang Yao 《Underground Space》 SCIE EI CSCD 2023年第6期241-261,共21页
Rockburst is a kind of common geological disaster in deep tunnel engineering.It has the characteristics of causing great harm and occurring at random locations and times.These characteristics seriously affect tunnel c... Rockburst is a kind of common geological disaster in deep tunnel engineering.It has the characteristics of causing great harm and occurring at random locations and times.These characteristics seriously affect tunnel construction and threaten the physical and mental health and safety of workers.Therefore,it is of great significance to study the tendency of rockburst in the early stage of tunnel survey,design and construction.At present,there is no unified method and selected parameters for rockburst prediction.In view of the large difference of different rockburst criteria and the imbalance of rockburst database categories,this paper presents a two-step rockburst prediction method based on multiple factors and the stacking ensemble algorithm.Considering the influence of rock physical and mechanical parameters,tunnel face conditions and excavation disturbance,multiple rockburst criteria are predicted by integrating multiple machine learning algorithms.A combined prediction model of rockburst criteria is established,and the results of each rockburst criterion index are weighted and combined,with the weight updated using the field rockburst record.The dynamic weight is combined with the cloud model to comprehensively evaluate the regional rockburst risk.Field results from applying the model in the Grand Canyon tunnel show that the rockburst prediction method proposed in this paper has better applicability and higher accuracy than the single rockburst criterion. 展开更多
关键词 Rockburst Stacking ensemble algorithm Combined prediction Cloud model
Machine learning enabling prediction in mechanical performance of Ti6Al4V fabricated by large-scale laser powder bed fusion via a stacking model 认领 引用
12
作者 Changjun HAN Fubao YAN +4 位作者 Daolin YUAN Kai LI Yongqiang YANG Jiong ZHANG Di WANG 《Frontiers of Mechanical Engineering》 SCIE CSCD 2024年第4期29-43,共15页
Determining appropriate process parameters in large-scale laser powder bed fusion(LPBF)additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experime... Determining appropriate process parameters in large-scale laser powder bed fusion(LPBF)additive manufacturing pose formidable challenges that necessitate advanced approaches to minimize trial-and-error during experimentation.This work proposed a data-driven approach based on stacking ensemble learning to predict the mechanical properties of Ti6Al4V alloy fabricated by large-scale LPBF for the first time.This method can adapt to the complexity of large-scale LPBF data distribution and exhibits a more generalized predictive capability compared to base models.Specifically,the stacking model utilized artificial neural network(ANN),gradient boosting regressor,kernel ridge regression,and elastic net as base models,with the Lasso model serving as the meta-model.Bayesian optimization and cross-validation were utilized for model optimization and training based on a limited data set,resulting in higher predictive accuracy compared to traditional artificial neural network model.The statistical analysis of the ANN and stacking models indicates that the stacking model exhibits superior performance on the test set,with a coefficient of determination value of 0.944,mean absolute percentage error of 2.51%,and root mean squared error of 27.64,surpassing that of the ANN model.All statistical metrics demonstrate superiority over those obtained from the ANN model.These results confirm that by integrating the base models,the stacking model exhibits superior predictive stability compared to individual base models alone,thereby providing a reliable assessment approach for predicting the mechanical properties of metal parts fabricated by the LPBF process. 展开更多
关键词 machine learning laser powder bed fusion ensemble learning stacking algorithm additive manufacturing
暂未订购 下载PDF
已选0条
引用分析
参考文献
引证文献
 统计分析
检索结果
已选文献
上一页 1 下一页 到第 页
在线咨询 使用帮助 返回顶部 意见反馈