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
To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)mo...To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.展开更多
Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for mode...Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data.First,the concepts of Pearson and Kendall correlation coefficients are presented,and the copula theory is briefly introduced.Thereafter,a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula.Second,these two methods are compared in computational efficiency,applicability,and capability of fitting data.Finally,four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method.The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters,but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way.It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data.The Gaussian copula using the Kendall method is superior to that using the Pearson method,which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters.There exists a strong negative correlation between the two parameters underlying load-displacement curves.Neglecting such correlation will not capture the scatter in the measured load-displacement curves.These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering.展开更多
Va R(在险价值 )方法被广泛地应用在金融市场风险管理中 .传统的计算 Va R方法——历史模拟法 ,Risk Metrics方法和 Monte Carlo模拟法 ,都不能对市场风险分布的“厚尾”现象给出较满意的分布和计算方法 .本文把 Pearson 分布应用到 Va ...Va R(在险价值 )方法被广泛地应用在金融市场风险管理中 .传统的计算 Va R方法——历史模拟法 ,Risk Metrics方法和 Monte Carlo模拟法 ,都不能对市场风险分布的“厚尾”现象给出较满意的分布和计算方法 .本文把 Pearson 分布应用到 Va R模型的计算中 ,得到了很好的效果 .展开更多
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.
基金supported in part by the National Key Research and Development Program of China(No.2018YFB1500800)the National Natural Science Foundation of China(No.51807134)the State Key Laboratory of Reliability and Intelligence of Electrical Equipment,Hebei University of Technology(No.EERI_KF20200014)。
文摘To improve energy efficiency and protect the environment,the integrated energy system(IES)becomes a significant direction of energy structure adjustment.This paper innovatively proposes a wavelet neural network(WNN)model optimized by the improved particle swarm optimization(IPSO)and chaos optimization algorithm(COA)for short-term load prediction of IES.The proposed model overcomes the disadvantages of the slow convergence and the tendency to fall into the local optimum in traditional WNN models.First,the Pearson correlation coefficient is employed to select the key influencing factors of load prediction.Then,the traditional particle swarm optimization(PSO)is improved by the dynamic particle inertia weight.To jump out of the local optimum,the COA is employed to search for individual optimal particles in IPSO.In the iteration,the parameters of WNN are continually optimized by IPSO-COA.Meanwhile,the feedback link is added to the proposed model,where the output error is adopted to modify the prediction results.Finally,the proposed model is employed for load prediction.The experimental simulation verifies that the proposed model significantly improves the prediction accuracy and operation efficiency compared with the artificial neural network(ANN),WNN,and PSO-WNN.
基金supported by the National Basic Research Program of China ("973" Program) (Grant No. 2011CB013506)the National Natural Science Foundation of China (Grant Nos. 51028901 and 50839004)
文摘Determining the joint probability distribution of correlated non-normal geotechnical parameters based on incomplete statistical data is a challenging problem.This paper proposes a Gaussian copula-based method for modelling the joint probability distribution of bivariate uncertain data.First,the concepts of Pearson and Kendall correlation coefficients are presented,and the copula theory is briefly introduced.Thereafter,a Pearson method and a Kendall method are developed to determine the copula parameter underlying Gaussian copula.Second,these two methods are compared in computational efficiency,applicability,and capability of fitting data.Finally,four load-test datasets of load-displacement curves of piles are used to illustrate the proposed method.The results indicate that the proposed Gaussian copula-based method can not only characterize the correlation between geotechnical parameters,but also construct the joint probability distribution function of correlated non-normal geotechnical parameters in a more general way.It can serve as a general tool to construct the joint probability distribution of correlated geotechnical parameters based on incomplete data.The Gaussian copula using the Kendall method is superior to that using the Pearson method,which should be recommended for modelling and simulating the joint probability distribution of correlated geotechnical parameters.There exists a strong negative correlation between the two parameters underlying load-displacement curves.Neglecting such correlation will not capture the scatter in the measured load-displacement curves.These results substantially extend the application of the copula theory to multivariate simulation in geotechnical engineering.
