This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, incl...This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture, big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.展开更多
The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functi...The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.展开更多
We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functi...We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.展开更多
In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four ...In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four properties mentioned in Zuo and Serfling(2000)is proposed.Then aclassification rule based on the depth function family is proposed.The classification parameter b couldbe modified according to the type-Ⅰ error α,and the estimator of b has the consistency and achievesthe convergence rate n^(-1/2).With the help of the proper selection for depth family parameter c,theapproach for discriminant analysis could minimize the type-Ⅱ error β.A simulation study and a realdata example compare the performance of the different discriminant methods.展开更多
The 53.667 MHz continuous-wave heavy ion RFQ has been designed and manufactured for the SSC-LINAC project.This four-rod RFQ accelerates ions with maximum mass to charge ratio of 7 from 3.728 keV/u to 143 keV/u.Measure...The 53.667 MHz continuous-wave heavy ion RFQ has been designed and manufactured for the SSC-LINAC project.This four-rod RFQ accelerates ions with maximum mass to charge ratio of 7 from 3.728 keV/u to 143 keV/u.Measurements have been carried out to check the RF performance of the cavity and the quality of the electric field.The S11 of the power coupler is adjusted to better than-44 dB,and the Q0 of the cavity is 6440.The quality of the electric field is evaluated by the perturbation method.The measurement procedure and data analysis will be discussed in detail.The error due to gravity of the perturbation bead has been corrected by averaging the fields in different quadrants.As a result,the unflatness of the electric field is±2.5%,and the dipole field component distributes from 0%to 20%in different longitudinal positions,which indicates the asymmetry of the quadrupole field.The unflatness of the quadrupole field distribution represents a good agreement with the simulation results.High power RF test and beam commissioning of the RFQ are on schedule in early 2014.展开更多
文摘This paper introduces the operational characteristics of the era of big data and the current era of big data challenges, and exhaustive research and design of big data analytics platform based on cloud computing, including big data analytics platform architecture system, big data analytics platform software architecture, big data analytics platform network architecture big data analysis platform unified program features and so on. The paper also analyzes the cloud computing platform for big data analysis program unified competitive advantage and development of business telecom operators play a certain role in the future.
文摘The fixed-point algorithm and infomax algorithm are two of the most popular algorithms in independent component analysis(ICA).However,it is hard to take both stability and speed into consideration in processing functional magnetic resonance imaging(fMRI)data.In this paper,an optimization model for ICA is presented and an improved fixed-point algorithm based on the model is proposed.In the new algorithms a small step size is added to increase the stability.In order to accelerate the convergence,an improvement on Newton method is made,which makes cubic convergence for the new algorithm.Applying the algorithm and two other algorithms to invivo fMRI data,the results show that the new algorithm separates independent components stably,which has faster convergence speed and less computation than the other two algorithms.The algorithm has obvious advantage in processing fMRI signal with huge data.
基金supported by National Natural Science Foundation of China (Grant No. 11271080)
文摘We propose a new functional single index model, which called dynamic single-index model for functional data, or DSIM, to efficiently perform non-linear and dynamic relationships between functional predictor and functional response. The proposed model naturally allows for some curvature not captured by the ordinary functional linear model. By using the proposed two-step estimating algorithm, we develop the estimates for both the link function and the regression coefficient function, and then provide predictions of new response trajectories. Besides the asymptotic properties for the estimates of the unknown functions, we also establish the consistency of the predictions of new response trajectories under mild conditions. Finally, we show through extensive simulation studies and a real data example that the proposed DSIM can highly outperform existed functional regression methods in most settings.
基金supported by the Natural Science Foundation of China under Grant Nos.10901020,10726013 and 10771017
文摘In the past two decades,many statistical depth functions seemed as powerful exploratoryand inferential tools for multivariate data analysis have been presented.In this paper,a new depthfunction family that meets four properties mentioned in Zuo and Serfling(2000)is proposed.Then aclassification rule based on the depth function family is proposed.The classification parameter b couldbe modified according to the type-Ⅰ error α,and the estimator of b has the consistency and achievesthe convergence rate n^(-1/2).With the help of the proper selection for depth family parameter c,theapproach for discriminant analysis could minimize the type-Ⅱ error β.A simulation study and a realdata example compare the performance of the different discriminant methods.
基金supported by the National Natural Science Foundation of China(Grant Nos.11079001 and 10905003)the Linear Accelerator Center of PKU-IMPCAS
文摘The 53.667 MHz continuous-wave heavy ion RFQ has been designed and manufactured for the SSC-LINAC project.This four-rod RFQ accelerates ions with maximum mass to charge ratio of 7 from 3.728 keV/u to 143 keV/u.Measurements have been carried out to check the RF performance of the cavity and the quality of the electric field.The S11 of the power coupler is adjusted to better than-44 dB,and the Q0 of the cavity is 6440.The quality of the electric field is evaluated by the perturbation method.The measurement procedure and data analysis will be discussed in detail.The error due to gravity of the perturbation bead has been corrected by averaging the fields in different quadrants.As a result,the unflatness of the electric field is±2.5%,and the dipole field component distributes from 0%to 20%in different longitudinal positions,which indicates the asymmetry of the quadrupole field.The unflatness of the quadrupole field distribution represents a good agreement with the simulation results.High power RF test and beam commissioning of the RFQ are on schedule in early 2014.
