In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step ...In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step for the dynamic response simulation of rock mass in a high in-situ stress field.In this paper,stress initialization methods,including their principles and operating procedures for reproducing steady in-situ stress state in LS-DYNA,are first introduced.Then the most popular four methods,i.e.,explicit dynamic relaxation(DR)method,implicit-explicit sequence method,Dynain file method and quasi-static method,are exemplified through a case analysis by using the RHT and plastic hardening rock material models to simulate rock blasting under in-situ stress condition.Based on the simulations,it is concluded that the stress initialization results obtained by implicit-explicit sequence method and dynain file method are closely related to the rock material model,and the explicit DR method has an obvious advantage in solution time when compared to other methods.Besides that,it is recommended to adopt two separate analyses for the whole numerical simulation of rock mass under the combined action of in-situ stress and dynamic disturbance.展开更多
Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to sel...Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.展开更多
基金Project(41630642)supported by the Key Project of National Natural Science Foundation of ChinaProject(51974360)supported by the National Natural Science Foundation of ChinaProject(2018JJ3656)supported by the Natural Science Foundation of Hunan Province,China。
文摘In the context of deep rock engineering,the in-situ stress state is of major importance as it plays an important role in rock dynamic response behavior.Thus,stress initialization becomes crucial and is the first step for the dynamic response simulation of rock mass in a high in-situ stress field.In this paper,stress initialization methods,including their principles and operating procedures for reproducing steady in-situ stress state in LS-DYNA,are first introduced.Then the most popular four methods,i.e.,explicit dynamic relaxation(DR)method,implicit-explicit sequence method,Dynain file method and quasi-static method,are exemplified through a case analysis by using the RHT and plastic hardening rock material models to simulate rock blasting under in-situ stress condition.Based on the simulations,it is concluded that the stress initialization results obtained by implicit-explicit sequence method and dynain file method are closely related to the rock material model,and the explicit DR method has an obvious advantage in solution time when compared to other methods.Besides that,it is recommended to adopt two separate analyses for the whole numerical simulation of rock mass under the combined action of in-situ stress and dynamic disturbance.
基金Supported by the National Natural Science Foundation of China (60503020, 60503033, 60703086)the Natural Science Foundation of Jiangsu Province (BK2006094)+1 种基金the Opening Foundation of Jiangsu Key Labo-ratory of Computer Information Processing Technology in Soochow University ( KJS0714)the Research Foundation of Nanjing University of Posts and Telecommunications (NY207052, NY207082)
文摘Though K-means is very popular for general clustering, its performance, which generally converges to numerous local minima, depends highly on initial cluster centers. In this paper a novel initialization scheme to select initial cluster centers for K-means clustering is proposed. This algorithm is based on reverse nearest neighbor （RNN） search which retrieves all points in a given data set whose nearest neighbor is a given query point. The initial cluster centers computed using this methodology are found to be very close to the desired cluster centers for iterative clustering algorithms. This procedure is applicable to clustering algorithms for continuous data. The application of the proposed algorithm to K-means clustering algorithm is demonstrated. An experiment is carried out on several popular datasets and the results show the advantages of the proposed method.
