为了解决传统水文水利计算工作量大和一些相关软件计算准确性低的问题,本文主要研究了如何基于开发工具软件Delph i7.0、SQL Server 2005、EXCEL来开发新疆玛纳斯河流域源流汇流计算软件的方法,并重点介绍了相关水文数据库和数据库管理...为了解决传统水文水利计算工作量大和一些相关软件计算准确性低的问题,本文主要研究了如何基于开发工具软件Delph i7.0、SQL Server 2005、EXCEL来开发新疆玛纳斯河流域源流汇流计算软件的方法,并重点介绍了相关水文数据库和数据库管理、水文要素频率计算、源流设计径流分析计算、源流径流模拟与预报等四个主要功能设计中所需要注意的问题和步骤,可为同类软件开发提供较好的实例参考。展开更多
Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data ...Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.展开更多
文摘为了解决传统水文水利计算工作量大和一些相关软件计算准确性低的问题,本文主要研究了如何基于开发工具软件Delph i7.0、SQL Server 2005、EXCEL来开发新疆玛纳斯河流域源流汇流计算软件的方法,并重点介绍了相关水文数据库和数据库管理、水文要素频率计算、源流设计径流分析计算、源流径流模拟与预报等四个主要功能设计中所需要注意的问题和步骤,可为同类软件开发提供较好的实例参考。
基金Project(RDF 11-02-03)supported by the Research Development Fund of XJTLU,China
文摘Information analysis of high dimensional data was carried out through similarity measure application. High dimensional data were considered as the a typical structure. Additionally, overlapped and non-overlapped data were introduced, and similarity measure analysis was also illustrated and compared with conventional similarity measure. As a result, overlapped data comparison was possible to present similarity with conventional similarity measure. Non-overlapped data similarity analysis provided the clue to solve the similarity of high dimensional data. Considering high dimensional data analysis was designed with consideration of neighborhoods information. Conservative and strict solutions were proposed. Proposed similarity measure was applied to express financial fraud among multi dimensional datasets. In illustrative example, financial fraud similarity with respect to age, gender, qualification and job was presented. And with the proposed similarity measure, high dimensional personal data were calculated to evaluate how similar to the financial fraud. Calculation results show that the actual fraud has rather high similarity measure compared to the average, from minimal 0.0609 to maximal 0.1667.
