De Giorgi猜想起源于Bernstain提出的一个著名的几何问题:在小于8维的全空间中,方程△u-u+u^3=0的单调解是否退化成一维方程的解,这就是所谓的解的一维对称性问题.Birindelli关于Heisenberg群上次Laplace方程解的一维对称性做了大量工作...De Giorgi猜想起源于Bernstain提出的一个著名的几何问题:在小于8维的全空间中,方程△u-u+u^3=0的单调解是否退化成一维方程的解,这就是所谓的解的一维对称性问题.Birindelli关于Heisenberg群上次Laplace方程解的一维对称性做了大量工作.利用Heisenberg型群的左平移不变性构造平移参数族,用平移的方法将欧氏空间半线性椭圆方程解的一维对称性结果推广到了Heisenberg型群上.展开更多
In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameter...In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters.To this end,a method of Handover Parameters Adjustment for Conflict Avoidance(HPACA)is proposed.Considering the movement of users,HPCAC can dynamically adjust handover range to optimize the mobility load balancing.The movement of users is an important factor of handover,which has a dramatic impact on system performance.The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput,call blocking ratio,load balancing index,radio link failure ratio,ping-pong handover ratio and call dropping ratio.展开更多
Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using ge...Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using generalized estimation equation techniques, we propose a new kind of regression models for parameterizing covariance structures. Using a novel Cholesky factor, the entries in this decomposition have moving average and log innovation interpretation and are modeled as the regression coefficients in both the mean and the linear functions of covariates. The resulting estimators for eovarianee are shown to be consistent and asymptotically normally distributed. Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.展开更多
基金supported by the National Natural Science Foundation of China under Grant No.61071118the National Basic Research Program of China(973 Program)under Grant No.2012CB316004+1 种基金Special Fund of Chongqing Key Laboratory(CSTC)Chongqing Municipal Education Commission’s Science and Technology Research Project under Grant No.KJ111506
文摘In order to achieve dynamical optimization of mobility load balancing,we analyze the conflict between mobility load balancing and mobility robustness optimization caused by the improper operation of handover parameters.To this end,a method of Handover Parameters Adjustment for Conflict Avoidance(HPACA)is proposed.Considering the movement of users,HPCAC can dynamically adjust handover range to optimize the mobility load balancing.The movement of users is an important factor of handover,which has a dramatic impact on system performance.The numerical evaluation results show the proposed approach outperforms the existing method in terms of throughput,call blocking ratio,load balancing index,radio link failure ratio,ping-pong handover ratio and call dropping ratio.
基金supported by National Natural Science Foundation of China(Grant Nos.11271347 and 11171321)
文摘Modeling the mean and covariance simultaneously is a common strategy to efficiently estimate the mean parameters when applying generalized estimating equation techniques to longitudinal data. In this article, using generalized estimation equation techniques, we propose a new kind of regression models for parameterizing covariance structures. Using a novel Cholesky factor, the entries in this decomposition have moving average and log innovation interpretation and are modeled as the regression coefficients in both the mean and the linear functions of covariates. The resulting estimators for eovarianee are shown to be consistent and asymptotically normally distributed. Simulation studies and a real data analysis show that the proposed approach yields highly efficient estimators for the parameters in the mean, and provides parsimonious estimation for the covariance structure.
