$B$值独立随机元序列加权和的完全收敛性和强大数律

In this paper, we obtain theorems of complete convergence and strong laws of large numbers for weighted sums of sequences of independent random elements in a Banach space of type p （1 ≤ p ≤ 2）. The results improve and extend the corresponding results on real random variables obtained by [1] and [2].     

大梯度指数掺杂透射式GaAs光电阴极响应特性的理论分析

讨论了一种具有超快时间响应特性的新光电阴极, 即大梯度指数掺杂透射式GaAs 负电子亲和势 (NEA) 光电阴极, 模拟了它的量子效率、时间分辨和空间分辨能力等特性. 理论分析结果表明, 由于大梯度指数掺杂设计方式, 在吸收层内形成较大的内建电场, 因此光生电子在GaAsNEA阴极内的渡越时间大大缩短, 当GaAs吸收层厚度～0.9 μm时, 其响应时间达到～ 10 ps, 说明这种新NEA阴极具有远优于传统均匀掺杂NEA阴极的超快响应特性. 同时在整个光谱响应范围内, 量子效率达到约10%-20%, 空间分辨力显著高于以往的计算结果. 分析结果表明,在保证较高的量子效率条件下, 这种新阴极能够突破常规GaAsNEA阴极的时间分辨率极限, 提高GaAsNEA阴极本身的分辨力, 有望用于超快摄影、电子加速器和自由电子激光器的电子源等领域, 进一步扩展NEA光电阴极的应用范围.     

Full‐Order observer design for nonlinear complex large‐scale systems with unknown time‐varying delayed interactions

This article is concerned with the problem of state observer for complex large‐scale systems with unknown time‐varying delayed interactions. The class of large‐scale interconnected systems under consideration is subjected to interval time‐varying delays and nonlinear perturbations. By introducing a set of argumented Lyapunov–Krasovskii functionals and using a new bounding estimation technique, novel delay‐dependent conditions for existence of state observers with guaranteed exponential stability are derived in terms of linear matrix inequalities (LMIs). In our design approach, the set of full‐order Luenberger‐type state observers are systematically derived via the use of an efficient LMI‐based algorithm. Numerical examples are given to illustrate the effectiveness of the result. © 2014 Wiley Periodicals, Inc. Complexity 21: 123–133, 2015     

Ginzburg-Landou模型的占位时的大偏差

本文讨论了一个Ginzburg-Landou模型.通过占位时和经验密度之间的比较定理得到了这个模型的占位时的大偏差,给出了相应的速率函数具体表达式.     

On the M/G/1 queue with D-policy

This paper deals with the M/G/1 queue with D-policy, i.e., the server is turned off at the end of a busy period and turned on when the cumulative amount of work firstly exceeds some fixed value D. We first concentrate on the computation of the steady-state probabilities. The first moments and relationships among the busy period, the number of customers served and other performance measures are investigated. Some variants of the main model and the special case of the M/M/1 are also studied.     

Asymptotic Expansions for Large Deviation Probabilities of Noncentral Generalized Chi-Square Distributions

Asymptotic expansions for large deviation probabilities are used to approximate the cumulative distribution functions of noncentral generalized chi-square distributions, preferably in the far tails. The basic idea of how to deal with the tail probabilities consists in first rewriting these probabilities as large parameter values of the Laplace transform of a suitably defined function f_k; second making a series expansion of this function, and third applying a certain modification of Watson's lemma. The function f_k is deduced by applying a geometric representation formula for spherical measures to the multivariate domain of large deviations under consideration. At the so-called dominating point, the largest main curvature of the boundary of this domain tends to one as the large deviation parameter approaches infinity. Therefore, the dominating point degenerates asymptotically. For this reason the recent multivariate asymptotic expansion for large deviations in Breitung and Richter (1996, J. Multivariate Anal.58, 1–20) does not apply. Assuming a suitably parametrized expansion for the inverse g⁻¹ of the negative logarithm of the density-generating function, we derive a series expansion for the function f_k. Note that low-order coefficients from the expansion of g⁻¹ influence practically all coefficients in the expansion of the tail probabilities. As an application, classification probabilities when using the quadratic discriminant function are discussed.     

Double k-Class Estimators in Regression Models with Non-spherical Disturbances

In this paper, we consider a family of feasible generalised double k-class estimators in a linear regression model with non-spherical disturbances. We derive the large sample asymptotic distribution of the proposed family of estimators and compare its performance with the feasible generalized least squares and Stein-rule estimators using the mean squared error matrix and risk under quadratic loss criteria. A Monte-Carlo experiment investigates the finite sample behaviour of the proposed family of estimators.     

求无穷级数和以及多重积分极限的概率方法

文 [1 ]举例说明了概率思想在求无穷级数的和以及多重积分极限方面的应用 ,本文将对文 [1 ]中的例 1加以推广 ,拓展这一类题的解题思路 ,并对例 2用不同的概率方法加以证明 ,从而使数学分析与概率统计的有关知识联系起来 ,达到知识的融汇贯通 .     

马氏过程函数的强大数定律

本文主要研究了马氏过程函数以及马氏环境中马氏链函数的强大数定律．