With the deepening informationization of Resources & Environment Remote Sensing geological survey conducted,some potential problems and deficiency are:(1) shortage of unified-planed running environment;(2) inconsi...With the deepening informationization of Resources & Environment Remote Sensing geological survey conducted,some potential problems and deficiency are:(1) shortage of unified-planed running environment;(2) inconsistent methods of data integration;and(3) disadvantages of different performing ways of data integration.This paper solves the above problems through overall planning and design,constructs unified running environment, consistent methods of data integration and system structure in order to advance the informationization展开更多
地学数据具有结构复杂、多尺度、数据量大的特点.将多源地学数据库集成在同一操作平台与数据库中进行分析使用,使用传统的空间数据储存方法有一定的难度.采用大型数据库系统Oracle 9 i Spatial构建地学空间数据库方法,提供了建立空间数...地学数据具有结构复杂、多尺度、数据量大的特点.将多源地学数据库集成在同一操作平台与数据库中进行分析使用,使用传统的空间数据储存方法有一定的难度.采用大型数据库系统Oracle 9 i Spatial构建地学空间数据库方法,提供了建立空间数据库的实例与应用程序访问空间数据库方法.实践表明,采用Oracle Spatial构建多源地学空间数据库具有空间数据格式统一、数据安全性高、数据存储量大的优点.展开更多
针对智能交通系统中行程时间估计的不确定性量化的难题,提出一种全局-局部不确定性感知行程时间估计方法(global and local uncertainty-aware travel time estimation,GLUTTE)。首先,通过多任务学习策略建模整体路线与各局部路段的行...针对智能交通系统中行程时间估计的不确定性量化的难题,提出一种全局-局部不确定性感知行程时间估计方法(global and local uncertainty-aware travel time estimation,GLUTTE)。首先,通过多任务学习策略建模整体路线与各局部路段的行程时间关系及其不确定性。其次,采用多粒度分位数回归方法,综合考虑全局和局部特征,提供准确的置信区间估计。实验结果表明,所提方法能够有效量化不确定性,同时保证准确性并提供可靠的置信区间,从而提升结果的可用性和可信度。展开更多
Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clu...Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.展开更多
文摘With the deepening informationization of Resources & Environment Remote Sensing geological survey conducted,some potential problems and deficiency are:(1) shortage of unified-planed running environment;(2) inconsistent methods of data integration;and(3) disadvantages of different performing ways of data integration.This paper solves the above problems through overall planning and design,constructs unified running environment, consistent methods of data integration and system structure in order to advance the informationization
文摘地学数据具有结构复杂、多尺度、数据量大的特点.将多源地学数据库集成在同一操作平台与数据库中进行分析使用,使用传统的空间数据储存方法有一定的难度.采用大型数据库系统Oracle 9 i Spatial构建地学空间数据库方法,提供了建立空间数据库的实例与应用程序访问空间数据库方法.实践表明,采用Oracle Spatial构建多源地学空间数据库具有空间数据格式统一、数据安全性高、数据存储量大的优点.
文摘针对智能交通系统中行程时间估计的不确定性量化的难题,提出一种全局-局部不确定性感知行程时间估计方法(global and local uncertainty-aware travel time estimation,GLUTTE)。首先,通过多任务学习策略建模整体路线与各局部路段的行程时间关系及其不确定性。其次,采用多粒度分位数回归方法,综合考虑全局和局部特征,提供准确的置信区间估计。实验结果表明,所提方法能够有效量化不确定性,同时保证准确性并提供可靠的置信区间,从而提升结果的可用性和可信度。
文摘Clustering is one of the unsupervised learning problems.It is a procedure which partitions data objects into groups.Many algorithms could not overcome the problems of morphology,overlapping and the large number of clusters at the same time.Many scientific communities have used the clustering algorithm from the perspective of density,which is one of the best methods in clustering.This study proposes a density-based spatial clustering of applications with noise(DBSCAN)algorithm based on the selected high-density areas by automatic fuzzy-DBSCAN(AFD)which works with the initialization of two parameters.AFD,by using fuzzy and DBSCAN features,is modeled by the selection of high-density areas and generates two parameters for merging and separating automatically.The two generated parameters provide a state of sub-cluster rules in the Cartesian coordinate system for the dataset.The model overcomes the problems of clustering such as morphology,overlapping,and the number of clusters in a dataset simultaneously.In the experiments,all algorithms are performed on eight data sets with 30 times of running.Three of them are related to overlapping real datasets and the rest are morphologic and synthetic datasets.It is demonstrated that the AFD algorithm outperforms other recently developed clustering algorithms.
