Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow ...Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.展开更多
Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July...Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.展开更多
Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data ma...Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data mainly to their numerical value and not to its relative behaviour. The disadvantage of using the default clustering?dendrograms of R is demonstrated. Instead, a script is provided that uses correlation as distance function, which better reveals biologically meaningful information. This optimized script was used to detect heterotic groups in Vitamaize hybrids (purple maize with high nutraceutical value). A field trial with different genetic combinations was performed through an agricultural phenomics approach (holistic evaluation of the phenotype). The grain yield data and other phenotypic variables were represented through heatmap figures. In the data set of Mexican tropical maize germplasm, at least three heterotic groups were detected, in contrast to only two heterotic groups reported earlier in temperate yellow maize from USA and Europe. This optimized script for heatmap correlation bicluster can also be used to better represent metabolomic fingerprints and transcriptomic data sets.展开更多
We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of interest.Likelihood-based inference is established to estima...We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of interest.Likelihood-based inference is established to estimate the regression coefficients,upon which bootstrap-based method is used to test the significance of covariates of interest.Simulation studies show the effectiveness of the method in terms of type-I error control,power performance in moderate sample size and robustness with respect to model mis-specification.We illustrate the application of the proposed method to some real data concerning health measurements.展开更多
In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme l...In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.展开更多
文摘Prediction of reservoir fracture is the key to explore fracture-type reservoir. When a shear-wave propagates in anisotropic media containing fracture,it splits into two polarized shear waves: fast shear wave and slow shear wave. The polarization and time delay of the fast and slow shear wave can be used to predict the azimuth and density of fracture. The current identification method of fracture azimuth and fracture density is cross-correlation method. It is assumed that fast and slow shear waves were symmetrical wavelets after completely separating,and use the most similar characteristics of wavelets to identify fracture azimuth and density,but in the experiment the identification is poor in accuracy. Pearson correlation coefficient method is one of the methods for separating the fast wave and slow wave. This method is faster in calculating speed and better in noise immunity and resolution compared with the traditional cross-correlation method. Pearson correlation coefficient method is a non-linear problem,particle swarm optimization( PSO) is a good nonlinear global optimization method which converges fast and is easy to implement. In this study,PSO is combined with the Pearson correlation coefficient method to achieve identifying fracture property and improve the computational efficiency.
文摘Based on the analysis of monitoring data on six pollution indexes of SO2, NO2, CO, O3, PM10 and PM2.5 from 53 monitoring points in 7 cities, including Beijing, Tianjin, Shijiazhuang, etc., from April 8 of 2014 to July 23 of 2014, this article adopted Pearson correlation coefficient method to determine the relevance among each pollutant of these cities with the help of SPSS. The results showed that such three leading indexes as SO2, PM10 and PM2.5 had strong correlation in Beijing, Tianjin and main cities of Hebei. Finally, some suggestions and preventive measures for the cooperative governance of air pollution in Beijing-Tianjin-Hebei Region were put forward, hoping this can help them.
文摘Heatmap cluster figures are often used to represent data sets in the?omic sciences. The default option of the frequently used R heatmap function is to cluster data according to Euclidean distance, which groups data mainly to their numerical value and not to its relative behaviour. The disadvantage of using the default clustering?dendrograms of R is demonstrated. Instead, a script is provided that uses correlation as distance function, which better reveals biologically meaningful information. This optimized script was used to detect heterotic groups in Vitamaize hybrids (purple maize with high nutraceutical value). A field trial with different genetic combinations was performed through an agricultural phenomics approach (holistic evaluation of the phenotype). The grain yield data and other phenotypic variables were represented through heatmap figures. In the data set of Mexican tropical maize germplasm, at least three heterotic groups were detected, in contrast to only two heterotic groups reported earlier in temperate yellow maize from USA and Europe. This optimized script for heatmap correlation bicluster can also be used to better represent metabolomic fingerprints and transcriptomic data sets.
文摘We propose two simple regression models of Pearson correlation coefficient of two normal responses or binary responses to assess the effect of covariates of interest.Likelihood-based inference is established to estimate the regression coefficients,upon which bootstrap-based method is used to test the significance of covariates of interest.Simulation studies show the effectiveness of the method in terms of type-I error control,power performance in moderate sample size and robustness with respect to model mis-specification.We illustrate the application of the proposed method to some real data concerning health measurements.
文摘In view of the difficulty in predicting the cost data of power transmission and transformation projects at present,a method based on Pearson correlation coefficient-improved particle swarm optimization(IPSO)-extreme learning machine(ELM)is proposed.In this paper,the Pearson correlation coefficient is used to screen out the main influencing factors as the input-independent variables of the ELM algorithm and IPSO based on a ladder-structure coding method is used to optimize the number of hidden-layer nodes,input weights and bias values of the ELM.Therefore,the prediction model for the cost data of power transmission and transformation projects based on the Pearson correlation coefficient-IPSO-ELM algorithm is constructed.Through the analysis of calculation examples,it is proved that the prediction accuracy of the proposed method is higher than that of other algorithms,which verifies the effectiveness of the model.
