As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning...As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.展开更多
Aim Cluster analysis was conducted on data from 5,169 United States （U.S.） Arizona children, age＇s 5-59-months with the goal of delineating patterns of caries in the primary dentition of pre-school children without...Aim Cluster analysis was conducted on data from 5,169 United States （U.S.） Arizona children, age＇s 5-59-months with the goal of delineating patterns of caries in the primary dentition of pre-school children without a priori pattern definitions. Methodology Cluster analyses were conducted using all data for children ages 0-4 years in aggregate： 1） for all subjects, and 2） for subjects without crowned restored teeth. Each of these two sets of analyses consisted of 8 differently specified cluster analyses as a validation procedure. Results The caries patterns identified from the clustering analysis are： 1） smooth surfaces （other than the maxillary incisor）, 2） maxillary incisor, 3） occlusal surfaces of first molars, and 4） pit and fissure surfaces of second molars. Conclusion The cluster analysis findings were consistent with results produced by multidimensional scaling. These cross-validated patterns may represent resulting disease conditions from different risks or the timing of various risk factor exposures. As such, the patterns may be useful case definitions for caries risk factor investigations in children under 60 months of age.展开更多
The graphical representation method, Robust CoPlot, is a robust variant of the classical CoPlot method. CoPlot is an adaptation of multidimensional scaling (MDS), and is a practical tool for visual inspection and rich...The graphical representation method, Robust CoPlot, is a robust variant of the classical CoPlot method. CoPlot is an adaptation of multidimensional scaling (MDS), and is a practical tool for visual inspection and rich interpretation of multivariate data. CoPlot enables presentation of a multidimensional dataset in a two dimensions, in a manner that relations between both variables and observations to be analyzed together. It has also been used as a supplemental tool to cluster analysis, data envelopment analysis (DEA) and outlier detection methods in the literature. However, this method is very sensitive to outliers. When a multidimensional dataset contains outliers, this can lead to undesirable consequences such as the inaccurate representation of the variables. The motivation is to produce Robust CoPlot that is not unduly affected by outliers. In this study, we have presented a new MATLAB package RobCoP for generating robust graphical representation of a multidimensional dataset. This study serves a useful purpose for researchers studying the implementation of Robust CoPlot method by providing a description of the software package RobCoP;it also offers some limited information on the Robust CoPlot analysis itself. The package presented here has enough flexibility to allow a user to select an MDS type and vector correlation method to produce either classical or Robust CoPlot results.展开更多
Objective:To analysis the development trend and research focus of empowerment theory applied to Nursing in China.Methods:Literatures related to the objective were searched and collected from CNKI,WangFang,VIP and CBM,...Objective:To analysis the development trend and research focus of empowerment theory applied to Nursing in China.Methods:Literatures related to the objective were searched and collected from CNKI,WangFang,VIP and CBM,then Excel 2003 was used to setup the database and co-word matrix,SPSS 21.0 was utilized to make the visualized analysis by way of multivariate statistics analysis,cluster analysis and multidimensional scaling analysis.Results:Literatures with the number of 486 were selected out and 18 high frequency keywords were retrieved from 140 journals.Among the literatures,the first one was published in 2002,then a tremendous rising started since 2009,and reached the peak in 2017,mainly from the southern part of China,such as the province of Jiangsu,Guangdong,and Zhejiang.Regarding the content of the literatures,the research of intervention accounted for 61.32%,then the research of description came to the second at the ratio of 24.49%.What’s more,378(77.78%)were cited,154(31.69%)were funded.Conclusion:Nowadays,empowerment applied in the therapy of chronic disease is the focus and trend of the research of empowerment theory,and the psychological empowerment to nursing staff,as well as the constructed empowerment is going mature.In the future,more attention should be paid to the study and practice of empowerment theory,in order to vary the direction of research and enrich the theory.展开更多
基金This work was supported in part by the National Key Research and Development Program of China(2019YFB1600100)in part by the Foundation of Shaanxi Key Laboratory of Integrated and Intelligent Navigation under Grant SKLIIN-20190103.
文摘As the basis of location-based services(LBS),positioning is one of the most essential parts in intelligent transportation systems(ITS).Although global positioning system(GPS)has been widely used in vehicle positioning,it can not achieve lane level positioning accuracy.Motivated by the mature ranging technologies such as radar and ultra-wideband(UWB),several cooperative positioning(CP)methods have been proposed to enhance the accuracy and robustness of GPS.In this paper,we proposed a twostage CP algorithm that combines multidimensional scaling(MDS)and Procrustes analysis for vehicles with GPS information.Specifically,the optimized MDS based on the scaling by majorizing a complicated function(SMACOF)algorithm is first proposed to get the relative coordinates of vehicles which can tackle measurements of different error distributions,then Procrustes analysis is carried out to transform the relative coordinates of vehicles to their absolute coordinates based on GPS information.All the computations are performed at the mobile edge computing node(MECN)for the request of ultra-reliable and low latency communications(URLLC).Simulation results validate that the proposed algorithm can greatly improve the positioning accuracy and robustness for vehicles.
基金Support for this work was through NIH NIDCR NRSA #T32-DE07255
文摘Aim Cluster analysis was conducted on data from 5,169 United States （U.S.） Arizona children, age＇s 5-59-months with the goal of delineating patterns of caries in the primary dentition of pre-school children without a priori pattern definitions. Methodology Cluster analyses were conducted using all data for children ages 0-4 years in aggregate： 1） for all subjects, and 2） for subjects without crowned restored teeth. Each of these two sets of analyses consisted of 8 differently specified cluster analyses as a validation procedure. Results The caries patterns identified from the clustering analysis are： 1） smooth surfaces （other than the maxillary incisor）, 2） maxillary incisor, 3） occlusal surfaces of first molars, and 4） pit and fissure surfaces of second molars. Conclusion The cluster analysis findings were consistent with results produced by multidimensional scaling. These cross-validated patterns may represent resulting disease conditions from different risks or the timing of various risk factor exposures. As such, the patterns may be useful case definitions for caries risk factor investigations in children under 60 months of age.
文摘The graphical representation method, Robust CoPlot, is a robust variant of the classical CoPlot method. CoPlot is an adaptation of multidimensional scaling (MDS), and is a practical tool for visual inspection and rich interpretation of multivariate data. CoPlot enables presentation of a multidimensional dataset in a two dimensions, in a manner that relations between both variables and observations to be analyzed together. It has also been used as a supplemental tool to cluster analysis, data envelopment analysis (DEA) and outlier detection methods in the literature. However, this method is very sensitive to outliers. When a multidimensional dataset contains outliers, this can lead to undesirable consequences such as the inaccurate representation of the variables. The motivation is to produce Robust CoPlot that is not unduly affected by outliers. In this study, we have presented a new MATLAB package RobCoP for generating robust graphical representation of a multidimensional dataset. This study serves a useful purpose for researchers studying the implementation of Robust CoPlot method by providing a description of the software package RobCoP;it also offers some limited information on the Robust CoPlot analysis itself. The package presented here has enough flexibility to allow a user to select an MDS type and vector correlation method to produce either classical or Robust CoPlot results.
文摘Objective:To analysis the development trend and research focus of empowerment theory applied to Nursing in China.Methods:Literatures related to the objective were searched and collected from CNKI,WangFang,VIP and CBM,then Excel 2003 was used to setup the database and co-word matrix,SPSS 21.0 was utilized to make the visualized analysis by way of multivariate statistics analysis,cluster analysis and multidimensional scaling analysis.Results:Literatures with the number of 486 were selected out and 18 high frequency keywords were retrieved from 140 journals.Among the literatures,the first one was published in 2002,then a tremendous rising started since 2009,and reached the peak in 2017,mainly from the southern part of China,such as the province of Jiangsu,Guangdong,and Zhejiang.Regarding the content of the literatures,the research of intervention accounted for 61.32%,then the research of description came to the second at the ratio of 24.49%.What’s more,378(77.78%)were cited,154(31.69%)were funded.Conclusion:Nowadays,empowerment applied in the therapy of chronic disease is the focus and trend of the research of empowerment theory,and the psychological empowerment to nursing staff,as well as the constructed empowerment is going mature.In the future,more attention should be paid to the study and practice of empowerment theory,in order to vary the direction of research and enrich the theory.
