Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,th...Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data.展开更多
Using the daily maximum temperature of the RegCM4 dynamical downscaling from four global climate models under the historical and RCP4.5 simulations,this study firstly identified the cluster high temperature event(CHTE...Using the daily maximum temperature of the RegCM4 dynamical downscaling from four global climate models under the historical and RCP4.5 simulations,this study firstly identified the cluster high temperature event(CHTE)occurring in China through a simplified objective method,and then projected its change during the 21st century in terms of the CHTE metrics including frequency,duration,extreme intensity,cumulative intensity,maximum influential area,average influential area,and comprehensive intensity.The ensemble projection indicates that all the CHTE metrics tend to increase toward the end of the 21st century on the national scale.Besides,the occurrence of CHTE shows a longer month span during the middle and the end of the 21st century(from April to October)compared to the present(from April to September),accompanied with the peaks of the frequency,duration,and cumulative intensity shifting from the present July ahead to June.Relative to 1986-2005,the projected slight,moderate,and extreme CHTEs increase by 55%,50%,and 50%(58%,43%,and 60%)during 2046-2065(2080-2099),respectively;the projected severe CHTE increases by 11%during 2046-2065 while decreases by 11% during 2080-2099.Spatially,the CHTE frequency,duration,and cumulative intensity are projected to increase in a widespread region.The largest increase appears in southern China for the frequency and in Xinjiang and Southeast China for the duration and cumulative intensity.We further divided China into five sub-regions to examine the regional features of CHTE changes.It is found that in addition to the increase of CHTEs in each single subregion,a pronounced enhancement is also projected for the occurrence of cross-regional CHTEs,particularly for that across more than two subregions.展开更多
In this article, clustered recurrent gap time is investigated. A marginal additive haz- ards model is proposed without specifying the association of the individuals within the same cluster. The relationship among the ...In this article, clustered recurrent gap time is investigated. A marginal additive haz- ards model is proposed without specifying the association of the individuals within the same cluster. The relationship among the gap times for the same individual is also left unspecified. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic proper- ties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease （CGD） is illustrated.展开更多
This paper presents the investigation of the clustering of the intruders in a vertically vibrated granular bed by means of event-driven simulations. The results indicate that the position of intruders in the vertical ...This paper presents the investigation of the clustering of the intruders in a vertically vibrated granular bed by means of event-driven simulations. The results indicate that the position of intruders in the vertical direction is not a key factor for their aggregation. Energy dissipation of the intruders and host particles are calculated in the process of intruder-host and host-host collisions. The relative energy dissipation of the intruders to that of the host particles is obtained. We find that clustering of the intruders happens when the relative energy dissipation is negative. The conclusion is verified when the restitution coefficient, density and diameter of the intruders are varied.展开更多
基金supported by the National Magnetic Confinement Fusion Science Program of China(Nos.2018YFE0301104 and 2018YFE0301100)National Natural Science Foundation of China(Nos.12075096 and 51821005)。
文摘Various types of plasma events emerge in specific parameter ranges and exhibit similar characteristics in diagnostic signals,which can be applied to identify these events.A semisupervised machine learning algorithm,the k-means clustering algorithm,is utilized to investigate and identify plasma events in the J-TEXT plasma.This method can cluster diverse plasma events with homogeneous features,and then these events can be identified if given few manually labeled examples based on physical understanding.A survey of clustered events reveals that the k-means algorithm can make plasma events(rotating tearing mode,sawtooth oscillations,and locked mode)gathering in Euclidean space composed of multi-dimensional diagnostic data,like soft x-ray emission intensity,edge toroidal rotation velocity,the Mirnov signal amplitude and so on.Based on the cluster analysis results,an approximate analytical model is proposed to rapidly identify plasma events in the J-TEXT plasma.The cluster analysis method is conducive to data markers of massive diagnostic data.
基金jointly supported by the National Key Research and Development Program of China(2018YFA0606301)the National Natural Science Foundation of China(41991285 and 42025502).
文摘Using the daily maximum temperature of the RegCM4 dynamical downscaling from four global climate models under the historical and RCP4.5 simulations,this study firstly identified the cluster high temperature event(CHTE)occurring in China through a simplified objective method,and then projected its change during the 21st century in terms of the CHTE metrics including frequency,duration,extreme intensity,cumulative intensity,maximum influential area,average influential area,and comprehensive intensity.The ensemble projection indicates that all the CHTE metrics tend to increase toward the end of the 21st century on the national scale.Besides,the occurrence of CHTE shows a longer month span during the middle and the end of the 21st century(from April to October)compared to the present(from April to September),accompanied with the peaks of the frequency,duration,and cumulative intensity shifting from the present July ahead to June.Relative to 1986-2005,the projected slight,moderate,and extreme CHTEs increase by 55%,50%,and 50%(58%,43%,and 60%)during 2046-2065(2080-2099),respectively;the projected severe CHTE increases by 11%during 2046-2065 while decreases by 11% during 2080-2099.Spatially,the CHTE frequency,duration,and cumulative intensity are projected to increase in a widespread region.The largest increase appears in southern China for the frequency and in Xinjiang and Southeast China for the duration and cumulative intensity.We further divided China into five sub-regions to examine the regional features of CHTE changes.It is found that in addition to the increase of CHTEs in each single subregion,a pronounced enhancement is also projected for the occurrence of cross-regional CHTEs,particularly for that across more than two subregions.
基金supported by the National Natural Science Foundation of China under Grant Nos.11501037,11771431,and 11690015
文摘In this article, clustered recurrent gap time is investigated. A marginal additive haz- ards model is proposed without specifying the association of the individuals within the same cluster. The relationship among the gap times for the same individual is also left unspecified. An estimating equation-based inference procedure is developed for the model parameters, and the asymptotic proper- ties of the resulting estimators are established. In addition, a lack-of-fit test is presented to assess the adequacy of the model. The finite sample behavior of the proposed estimators is evaluated through simulation studies, and an application to a clinic study on chronic granulomatous disease （CGD） is illustrated.
基金Project supported by the Science Fund for Creative Research Groups of the National Natural Science Foundation of China (GrantNo. 50921002)
文摘This paper presents the investigation of the clustering of the intruders in a vertically vibrated granular bed by means of event-driven simulations. The results indicate that the position of intruders in the vertical direction is not a key factor for their aggregation. Energy dissipation of the intruders and host particles are calculated in the process of intruder-host and host-host collisions. The relative energy dissipation of the intruders to that of the host particles is obtained. We find that clustering of the intruders happens when the relative energy dissipation is negative. The conclusion is verified when the restitution coefficient, density and diameter of the intruders are varied.
