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
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
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
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
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
文摘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.
基金funded by“The Pearl River Talent Recruitment Program”of Guangdong Province in 2019(Grant No.2019CX01G338)the Research Funding of Shantou University for New Faculty Member(Grant No.NTF19024-2019).
文摘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.
基金Funded by the National Natural Science Foundation of China(No.51275393)the Fundamental Research Funds for the Central Universities(No.xjj2017160)
文摘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.
基金funded by a science and technology project of State Grid Corporation of China“Comparative Analysis of Long-Term Measurement and Prediction of the Ground Synthetic Electric Field of±800 kV DC Transmission Line”(GYW11201907738)Paulo R.F.Rocha acknowledges the support and funding from the European Research Council(ERC)under the European Union’s Horizon 2020 Research and Innovation Program(Grant Agreement No.947897).
文摘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.
文摘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.
