The paper analyzes the variation characteristics of energy fields of seismicity 2.1≤M L ≤4.5 in Jiangsu and its neighboring areas during the period between January 1970 and December 2007.It also analyzes the variati...The paper analyzes the variation characteristics of energy fields of seismicity 2.1≤M L ≤4.5 in Jiangsu and its neighboring areas during the period between January 1970 and December 2007.It also analyzes the variations of time "weight" coefficients of the major typical energy fields,using random function theory with seismic energy fields as a space-time random function field based on Empirical Orthogonal Function (EOF) expansion methods.The results show that the expansion accuracy of the first seven typical fields is 0.9244.The strength of seismic energy varies remarkably in different tectonic blocks in the study area.High value areas are in middle and southern Jiangsu,and the south Yellow Sea.The distribution of the typical fields also shows that it is an area that affects most significantly the seismic energy fields of the study region.The time "weight" coefficients of the first six typical fields vary with time,and the amplitude of the variations has strong temporal correlations with moderate-strong earthquakes in the region.展开更多
The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial charact...The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.展开更多
In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorith...In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorithm was proposed to analyze the statistical characteristics of the engine’s backward RCS.Firstly,the K-nearest neighbor method was used to calculate the dynamic window width of the K-KDE,and the Euclidean distance of each adjacent sample was used to judge the local density of the sample,and then the window width of the kernel function was adjusted by the distance between the sample point and the nearest neighbor to complete the KDE.Secondly,based on the K-KDE and the traditional KDE algorithm,the cumulative probability density function(CPDF)of four RCS random distribution sample points subject to fixed parameters was calculated.The results showed that the root mean square error of the K-KDE was reduced by 31.2%,38.8%,38.1%and 31.9%respectively compared with the KDE.Finally,the K-KDE combined with the second generation statistical analysis models were used to analyze the statistical characteristics of the engine backward RCS.展开更多
基金the Key Projects in the National S&T Pillar Program during the Eleventh "Five-year Plan" Period(2006BAC01B03-03-01),China Earthquake AdministrationYouth Fund of Earthquake Administration of Jiangsu Province(2009),China
文摘The paper analyzes the variation characteristics of energy fields of seismicity 2.1≤M L ≤4.5 in Jiangsu and its neighboring areas during the period between January 1970 and December 2007.It also analyzes the variations of time "weight" coefficients of the major typical energy fields,using random function theory with seismic energy fields as a space-time random function field based on Empirical Orthogonal Function (EOF) expansion methods.The results show that the expansion accuracy of the first seven typical fields is 0.9244.The strength of seismic energy varies remarkably in different tectonic blocks in the study area.High value areas are in middle and southern Jiangsu,and the south Yellow Sea.The distribution of the typical fields also shows that it is an area that affects most significantly the seismic energy fields of the study region.The time "weight" coefficients of the first six typical fields vary with time,and the amplitude of the variations has strong temporal correlations with moderate-strong earthquakes in the region.
基金Projects(LQ16E080012,LY14F030012)supported by the Zhejiang Provincial Natural Science Foundation,ChinaProject(61573317)supported by the National Natural Science Foundation of ChinaProject(2015001)supported by the Open Fund for a Key-Key Discipline of Zhejiang University of Technology,China
文摘The accurate estimation of road traffic states can provide decision making for travelers and traffic managers. In this work,an algorithm based on kernel-k nearest neighbor(KNN) matching of road traffic spatial characteristics is presented to estimate road traffic states. Firstly, the representative road traffic state data were extracted to establish the reference sequences of road traffic running characteristics(RSRTRC). Secondly, the spatial road traffic state data sequence was selected and the kernel function was constructed, with which the spatial road traffic data sequence could be mapped into a high dimensional feature space. Thirdly, the referenced and current spatial road traffic data sequences were extracted and the Euclidean distances in the feature space between them were obtained. Finally, the road traffic states were estimated from weighted averages of the selected k road traffic states, which corresponded to the nearest Euclidean distances. Several typical links in Beijing were adopted for case studies. The final results of the experiments show that the accuracy of this algorithm for estimating speed and volume is 95.27% and 91.32% respectively, which prove that this road traffic states estimation approach based on kernel-KNN matching of road traffic spatial characteristics is feasible and can achieve a high accuracy.
文摘In order to solve the problem of low accuracy of traditional fixed window width kernel density estimation(KDE)in radar cross section(RCS)statistical characteristics analysis,an improved Epanechnikov KDE(K-KDE)algorithm was proposed to analyze the statistical characteristics of the engine’s backward RCS.Firstly,the K-nearest neighbor method was used to calculate the dynamic window width of the K-KDE,and the Euclidean distance of each adjacent sample was used to judge the local density of the sample,and then the window width of the kernel function was adjusted by the distance between the sample point and the nearest neighbor to complete the KDE.Secondly,based on the K-KDE and the traditional KDE algorithm,the cumulative probability density function(CPDF)of four RCS random distribution sample points subject to fixed parameters was calculated.The results showed that the root mean square error of the K-KDE was reduced by 31.2%,38.8%,38.1%and 31.9%respectively compared with the KDE.Finally,the K-KDE combined with the second generation statistical analysis models were used to analyze the statistical characteristics of the engine backward RCS.
