With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has be...With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has been the subject of numerous scientific publications.On January 1,2018,we conducted literature data collection and processing on flood research and categorized the retrieved paper records into Whole SCI Dataset(WS)and High-Citation SCI Dataset(HCS).These data sets can serve as basic data for bibliometric analysis to identify the status of global flood research during 1990-2017.Our study shows that while the Chinese Academy of Sciences was the most productive institution during this period,the United States was the most productive country.Besides,our keyword analysis reveals the potential popular issues and future trends of flood research.展开更多
A rough set probabilistic data association(RS-PDA)algorithm is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking appl...A rough set probabilistic data association(RS-PDA)algorithm is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking application.In this new algorithm,the measurements lying in the intersection of two or more validation regions are allocated to the corresponding targets through rough set theory,and the multi-target tracking problem is transformed into a single target tracking after the classification of measurements lying in the intersection region.Several typical multi-target tracking applications are given.The simulation results show that the algorithm can not only reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.展开更多
孪生支持向量机(twin support vector machine,TSVM)能有效地处理交叉或异或等类型的数据.然而,当处理集值数据时,TSVM通常利用集值对象的均值、中值等统计信息.不同于TSVM,提出能直接处理集值数据的孪生支持函数机(twin support functi...孪生支持向量机(twin support vector machine,TSVM)能有效地处理交叉或异或等类型的数据.然而,当处理集值数据时,TSVM通常利用集值对象的均值、中值等统计信息.不同于TSVM,提出能直接处理集值数据的孪生支持函数机(twin support function machine,TSFM).依据集值对象定义的支持函数,TSFM在巴拿赫空间取得非平行的超平面.为了抑制集值数据中的离群点,TSFM采用了弹球损失函数并引入了集值对象的权重.考虑到TSFM是无穷维空间的优化问题,测度采用狄拉克测度的线性组合的形式,这构建有限维空间的优化模型.为了有效地求解优化模型,利用采样策略将模型转化成二次规划(quadratic programming,QP)问题并推导出二次规划问题的对偶形式,这为判断哪些采样点是支持向量提供了理论基础.为了分类集值数据,定义集值对象到巴拿赫空间的超平面的距离并由此得出判别规则.也考虑支持函数的核化以便取得数据的非线性特征,这使得提出的模型可用于不定核函数.实验结果表明,TSFM能获取交叉类型的集值数据的内在结构,并且在离群点或集值对象包含少量高维事例的情况下取得了良好的分类性能.展开更多
基金Supported by National Natural Science Foundation of China(60675039)National High Technology Research and Development Program of China(863 Program)(2006AA04Z217)Hundred Talents Program of Chinese Academy of Sciences
基金National Key Research and Development Program of China(2016YFE0122600)。
文摘With an increasing number of scientific achievements published,it is particularly important to conduct literature-based knowledge discovery and data mining.Flood,as one of the most destructive natural disasters,has been the subject of numerous scientific publications.On January 1,2018,we conducted literature data collection and processing on flood research and categorized the retrieved paper records into Whole SCI Dataset(WS)and High-Citation SCI Dataset(HCS).These data sets can serve as basic data for bibliometric analysis to identify the status of global flood research during 1990-2017.Our study shows that while the Chinese Academy of Sciences was the most productive institution during this period,the United States was the most productive country.Besides,our keyword analysis reveals the potential popular issues and future trends of flood research.
文摘A rough set probabilistic data association(RS-PDA)algorithm is proposed for reducing the complexity and time consumption of data association and enhancing the accuracy of tracking results in multi-target tracking application.In this new algorithm,the measurements lying in the intersection of two or more validation regions are allocated to the corresponding targets through rough set theory,and the multi-target tracking problem is transformed into a single target tracking after the classification of measurements lying in the intersection region.Several typical multi-target tracking applications are given.The simulation results show that the algorithm can not only reduce the complexity and time consumption but also enhance the accuracy and stability of the tracking results.
文摘孪生支持向量机(twin support vector machine,TSVM)能有效地处理交叉或异或等类型的数据.然而,当处理集值数据时,TSVM通常利用集值对象的均值、中值等统计信息.不同于TSVM,提出能直接处理集值数据的孪生支持函数机(twin support function machine,TSFM).依据集值对象定义的支持函数,TSFM在巴拿赫空间取得非平行的超平面.为了抑制集值数据中的离群点,TSFM采用了弹球损失函数并引入了集值对象的权重.考虑到TSFM是无穷维空间的优化问题,测度采用狄拉克测度的线性组合的形式,这构建有限维空间的优化模型.为了有效地求解优化模型,利用采样策略将模型转化成二次规划(quadratic programming,QP)问题并推导出二次规划问题的对偶形式,这为判断哪些采样点是支持向量提供了理论基础.为了分类集值数据,定义集值对象到巴拿赫空间的超平面的距离并由此得出判别规则.也考虑支持函数的核化以便取得数据的非线性特征,这使得提出的模型可用于不定核函数.实验结果表明,TSFM能获取交叉类型的集值数据的内在结构,并且在离群点或集值对象包含少量高维事例的情况下取得了良好的分类性能.
