Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Ext...Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.展开更多
For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great signific...For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great significance for 5G mm-Wave OTA testing.Among all test cases,beam peak search is the most time-consuming,taking up the majority of the overall test time.Therefore,the objective of this work is to determine a suitable beam peak search grid for 5G mm-Wave UEs with satisfactory accuracy and efficiency.Through radiation property investigation of 5G mm-Wave commercial UEs,more reasonable reference array configuration(4×2)and reference deployment scenario(composite beam)are proposed for beam peak search grid analysis.The effect of different grid configurations on beam peak search precision are characterized quantitatively.The determination of associated measurement uncertainty(MU)term along with quantitative analysis approach are proposed based on statistical analysis.Finally,the recommended minimum number of beam peak search grid points is 182 based on the proposed 4×2 array under composite beam scenario.Compared with currently-required 1106 points in 3GPP/CTIA specifications,over 80%reduction can be achieved without increasing the MU limit.The feasibility of the proposed MU analysis as well as the recommended grids is demonstrated through measurements.展开更多
Stress field plays a key role in geodynamics. In this study, an algorithm to determine the stress tensor and its confidence range from focal mechanism data by using grid search method was proposed. The experiment uses...Stress field plays a key role in geodynamics. In this study, an algorithm to determine the stress tensor and its confidence range from focal mechanism data by using grid search method was proposed. The experiment uses artificial focal mechanism data which were generated by extensional, compression and strike-slip stress regime and different level of noise, shows that the precision of the estimated stress tensor based on this algorithm is greatly improved compared with traditional algorithms. This algorithm has three advantages：（1） The global optimal solution of the stress tensor is determined by fine grid search of 1o×1o×1o×0.01 and local minimum value is avoided; （2） precision of focal mechanism data can be considered, i.e., different weight of the focal mechanism data contributes differently to the process of determining stress tensor; （3） the confidence range of the determined stress tensor can be obtained by using F-test. We apply this algorithm in the boundary zone of China, Vietnam and Laos, and obtain the stress field with SSE-NNW compressive stress direction and NEE-SWW extensional stress direction. The stress ratio is 0.6, which shows that the eigen values of the stress tensor are nearly in arithmetic sequence. The stress field in this region is consistent with the left-lateral strike slip of the Dienbien-Lauangphrabang arc fault. The result will be helpful in studying the geological dynamic process in this region.展开更多
The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth...The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.展开更多
Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads ...Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.展开更多
Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-...Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.展开更多
With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, ...With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.展开更多
Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed sea...Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.展开更多
Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources ...Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.展开更多
This article introduces a group search optimization (GSO) based tuning model for modelling and managing Smart Micro-Grids connected system. In existing systems, typically tuned PID controllers are engaged to point out...This article introduces a group search optimization (GSO) based tuning model for modelling and managing Smart Micro-Grids connected system. In existing systems, typically tuned PID controllers are engaged to point out the load frequency control (LFC) problems through different tuning techniques. Though, inappropriately tuned PID controller may reveal pitiable dynamical reply and also incorrect option of integral gain may even undermine the complete system. This research is used to explain about an optimized energy management system through Group Search Optimization (GSO) for building incorporation in smart micro-grids (MGs) with zero grid-impact. The essential for this technique is to develop the MG effectiveness, when the complete PI controller requires to be tuned. Consequently, we proposed that the proposed GSO based algorithm with appropriate explanation or member representation, derivation of fitness function, producer process, scrounger process, and ranger process. An entire and adaptable design of MATLAB/SIMULINK also proposed. The related solutions and practical test verifications are given. This paper verified that the proposed method was effective in Micro-Grid (MG) applications. The comparison results demonstrate the advantage of the proposed technique and confirm its potential to solve the problem.展开更多
基金the University of Transport Technology under grant number DTTD2022-12.
文摘Determination of Shear Bond strength(SBS)at interlayer of double-layer asphalt concrete is crucial in flexible pavement structures.The study used three Machine Learning(ML)models,including K-Nearest Neighbors(KNN),Extra Trees(ET),and Light Gradient Boosting Machine(LGBM),to predict SBS based on easily determinable input parameters.Also,the Grid Search technique was employed for hyper-parameter tuning of the ML models,and cross-validation and learning curve analysis were used for training the models.The models were built on a database of 240 experimental results and three input variables:temperature,normal pressure,and tack coat rate.Model validation was performed using three statistical criteria:the coefficient of determination(R2),the Root Mean Square Error(RMSE),and the mean absolute error(MAE).Additionally,SHAP analysis was also used to validate the importance of the input variables in the prediction of the SBS.Results show that these models accurately predict SBS,with LGBM providing outstanding performance.SHAP(Shapley Additive explanation)analysis for LGBM indicates that temperature is the most influential factor on SBS.Consequently,the proposed ML models can quickly and accurately predict SBS between two layers of asphalt concrete,serving practical applications in flexible pavement structure design.
基金supported by the Beijing Natural Science Foundation under Grant L253002.
文摘For 5G millimeter wave(mm-Wave)user equipments(UEs),all test cases must be evaluated in Over-The-Air(OTA)manner.Test time increases dramatically compared to Sub-6 GHz.Therefore,test time reduction is of great significance for 5G mm-Wave OTA testing.Among all test cases,beam peak search is the most time-consuming,taking up the majority of the overall test time.Therefore,the objective of this work is to determine a suitable beam peak search grid for 5G mm-Wave UEs with satisfactory accuracy and efficiency.Through radiation property investigation of 5G mm-Wave commercial UEs,more reasonable reference array configuration(4×2)and reference deployment scenario(composite beam)are proposed for beam peak search grid analysis.The effect of different grid configurations on beam peak search precision are characterized quantitatively.The determination of associated measurement uncertainty(MU)term along with quantitative analysis approach are proposed based on statistical analysis.Finally,the recommended minimum number of beam peak search grid points is 182 based on the proposed 4×2 array under composite beam scenario.Compared with currently-required 1106 points in 3GPP/CTIA specifications,over 80%reduction can be achieved without increasing the MU limit.The feasibility of the proposed MU analysis as well as the recommended grids is demonstrated through measurements.
基金supported by Natural Science Foundation of China(No.41674055)the Earthquake Science and Technology Spark Plan in Hebei Province(DZ20140101002)
文摘Stress field plays a key role in geodynamics. In this study, an algorithm to determine the stress tensor and its confidence range from focal mechanism data by using grid search method was proposed. The experiment uses artificial focal mechanism data which were generated by extensional, compression and strike-slip stress regime and different level of noise, shows that the precision of the estimated stress tensor based on this algorithm is greatly improved compared with traditional algorithms. This algorithm has three advantages：（1） The global optimal solution of the stress tensor is determined by fine grid search of 1o×1o×1o×0.01 and local minimum value is avoided; （2） precision of focal mechanism data can be considered, i.e., different weight of the focal mechanism data contributes differently to the process of determining stress tensor; （3） the confidence range of the determined stress tensor can be obtained by using F-test. We apply this algorithm in the boundary zone of China, Vietnam and Laos, and obtain the stress field with SSE-NNW compressive stress direction and NEE-SWW extensional stress direction. The stress ratio is 0.6, which shows that the eigen values of the stress tensor are nearly in arithmetic sequence. The stress field in this region is consistent with the left-lateral strike slip of the Dienbien-Lauangphrabang arc fault. The result will be helpful in studying the geological dynamic process in this region.
基金Project supported by the National Natural Science Foundation of China(Grant No.11002086)the Shanghai Leading Academic Discipline Project(Grant No.J50103)
文摘The core of smoothed particle hydrodynamics （SPH） is the nearest neighbor search subroutine. In this paper, a nearest neighbor search algorithm which is based on multiple background grids and support variable smooth length is introduced. Through tested on lid driven cavity flow, it is clear that this method can provide high accuracy. Analysis and experiments have been made on its parallelism, and the results show that this method has better parallelism and with adding processors its accuracy become higher, thus it achieves that efficiency grows in pace with accuracy.
文摘Diagnosing the cardiovascular disease is one of the biggest medical difficulties in recent years.Coronary cardiovascular(CHD)is a kind of heart and blood vascular disease.Predicting this sort of cardiac illness leads to more precise decisions for cardiac disorders.Implementing Grid Search Optimization(GSO)machine training models is therefore a useful way to forecast the sickness as soon as possible.The state-of-the-art work is the tuning of the hyperparameter together with the selection of the feature by utilizing the model search to minimize the false-negative rate.Three models with a cross-validation approach do the required task.Feature Selection based on the use of statistical and correlation matrices for multivariate analysis.For Random Search and Grid Search models,extensive comparison findings are produced utilizing retrieval,F1 score,and precision measurements.The models are evaluated using the metrics and kappa statistics that illustrate the three models’comparability.The study effort focuses on optimizing function selection,tweaking hyperparameters to improve model accuracy and the prediction of heart disease by examining Framingham datasets using random forestry classification.Tuning the hyperparameter in the model of grid search thus decreases the erroneous rate achieves global optimization.
基金supported by the Natural Science Foundation of Henan Province(No.222300420596)China Railway Science and Technology Innovation Program Funded Project(CZ02-Special-03)Science and Technology Innovation Project funded by China Railway Tunnel Group(Tunnel Research 2021-03)。
文摘Acoustic emission(AE)source localization is a fundamental element of rock fracture damage imaging.To improve the efficiency and accuracy of AE source localization,this paper proposes a joint method comprising a three-dimensional(3D)AE source localization simplex method and grid search scanning.Using the concept of the geometry of simplexes,tetrahedral iterations were first conducted to narrow down the suspected source region.This is followed by a process of meshing the region and node searching to scan for optimal solutions,until the source location is determined.The resulting algorithm was tested using the artificial excitation source localization and uniaxial compression tests,after which the localization results were compared with the simplex and exhaustive methods.The results revealed that the localization obtained using the proposed method is more stable and can be effectively avoided compared with the simplex localization method.Furthermore,compared with the global scanning method,the proposed method is more efficient,with an average time of 10%–20%of the global scanning localization algorithm.Thus,the proposed algorithm is of great significance for laboratory research focused on locating rupture damages sustained by large-sized rock masses or test blocks.
文摘With the rapid increase of educational resources, how to search for necessary educational resource quickly is one of most important issues. Educational resources have the characters of distribution and heterogeneity, which are the same as the characters of Grid resources. Therefore, the technology of Grid resources search was adopted to implement the educational resources search. Motivated by the insufficiency of currently resources search methods based on metadata, a method of extracting semantic relations between words constituting metadata is proposed. We mainly focus on acquiring synonymy, hyponymy, hypernymy and parataxis relations. In our schema, we extract texts related to metadata that will be expanded from text spatial through text extraction templates. Next, metadata will be obtained through metadata extraction templates. Finally, we compute semantic similarity to eliminate false relations and construct a semantic expansion knowledge base. The proposed method in this paper has been applied on the education grid.
基金This work is supported by the National Natural Science Foundation of China under grant U1836110,U1836208,U1536206,61602253,61672294by the National Key R&D Program of China under grant 2018YFB1003205+5 种基金by China Postdoctoral Science Foundation(2017M610574)by the Jiangsu Basic Research Programs-Natural Science Foundation under grant numbers BK20181407by the Priority Academic Program Development of Jiangsu Higher Education Institutions(PAPD)fundby the Major Program of the National Social Science Fund of China(17ZDA092)Qing Lan Projectby the Collaborative Innovation Center of Atmospheric Environment and Equipment Technology(CICAEET)fund,China.
文摘Nowadays,cloud computing is used more and more widely,more and more people prefer to using cloud server to store data.So,how to encrypt the data efficiently is an important problem.The search efficiency of existed search schemes decreases as the index increases.For solving this problem,we build the two-level index.Simultaneously,for improving the semantic information,the central word expansion is combined.The purpose of privacy-preserving content-aware search by using the two-level index(CKESS)is that the first matching is performed by using the extended central words,then calculate the similarity between the trapdoor and the secondary index,finally return the results in turn.Through experiments and analysis,it is proved that our proposed schemes can resist multiple threat models and the schemes are secure and efficient.
文摘Coronary artery disease(CAD)is one of themost authentic cardiovascular afflictions because it is an uncommonly overwhelming heart issue.The breakdown of coronary cardiovascular disease is one of the principal sources of death all over theworld.Cardiovascular deterioration is a challenge,especially in youthful and rural countries where there is an absence of humantrained professionals.Since heart diseases happen without apparent signs,high-level detection is desirable.This paper proposed a robust and tuned random forest model using the randomized grid search technique to predictCAD.The proposed framework increases the ability of CADpredictions by tracking down risk pointers and learning the confusing joint efforts between them.Nowadays,the healthcare industry has a lot of data but needs to gain more knowledge.Our proposed framework is used for extracting knowledge from data stores and using that knowledge to help doctors accurately and effectively diagnose heart disease(HD).We evaluated the proposed framework over two public databases,Cleveland and Framingham datasets.The datasets were preprocessed by using a cleaning technique,a normalization technique,and an outlier detection technique.Secondly,the principal component analysis(PCA)algorithm was utilized to lessen the feature dimensionality of the two datasets.Finally,we used a hyperparameter tuning technique,randomized grid search,to tune a random forest(RF)machine learning(ML)model.The randomized grid search selected the best parameters and got the ideal CAD analysis.The proposed framework was evaluated and compared with traditional classifiers.Our proposed framework’s accuracy,sensitivity,precision,specificity,and f1-score were 100%.The evaluation of the proposed framework showed that it is an unrivaled perceptive outcome with tuning as opposed to other ongoing existing frameworks.
文摘This article introduces a group search optimization (GSO) based tuning model for modelling and managing Smart Micro-Grids connected system. In existing systems, typically tuned PID controllers are engaged to point out the load frequency control (LFC) problems through different tuning techniques. Though, inappropriately tuned PID controller may reveal pitiable dynamical reply and also incorrect option of integral gain may even undermine the complete system. This research is used to explain about an optimized energy management system through Group Search Optimization (GSO) for building incorporation in smart micro-grids (MGs) with zero grid-impact. The essential for this technique is to develop the MG effectiveness, when the complete PI controller requires to be tuned. Consequently, we proposed that the proposed GSO based algorithm with appropriate explanation or member representation, derivation of fitness function, producer process, scrounger process, and ranger process. An entire and adaptable design of MATLAB/SIMULINK also proposed. The related solutions and practical test verifications are given. This paper verified that the proposed method was effective in Micro-Grid (MG) applications. The comparison results demonstrate the advantage of the proposed technique and confirm its potential to solve the problem.
