基于组合权重的系统评价模型

提出了基于组合权重的系统评价新模型 ( CWSE) ,即 :直接根据评价指标样本数据集 ,用基于加速遗传算法的投影寻踪方法确定各评价指标的分类权重 ,用基于加速遗传算法的层次分析法确定各评价指标的排序权重 ,用加速遗传算法对各评价指标的分类权重和排序权重进行综合得到组合权重 ,然后以这些组合权重与各评价对象相应评价指标的标准化值进行加权平均 ,得到系统评价的综合指标值 ,据此可对各评价对象进行分类排序 .用 CW SE模型评价中国 30个区域 1995年开发度的结果表明 ,根据开发度的强弱可把这些区域分成 3个强开发区域、6个较强开发区域、10个中等开发区域和 11个弱开发区域 ;CWSE模型简便、通用 ,计算结果较为客观和稳定 ,为系统工程理论和实践提供了新的研究方法 .     

Exact results for the asymmetric simple exclusion process with a blockage

We present new results for the current as a function of transmission rate in the one-dimensional totally asymmetric simple exclusion process (TASEP) with a blockage that lowers the jump rate at one site from one tor<1. Exact finitevolume results serve to bound the allowed values for the current in the infinite system. This proves the existence of a nonequilibrium phase transition, corresponding to an immiscibility gap in the allowed values of the asymptotic densities which the infinite system can have in a stationary state. A series expansion inr, derived from the finite systems, is proven to be asymptotic for all sufficiently large systems. Padé approximants based on this series, which make specific assumptions about the nature of the singularity atr=1, match numerical data for the infinite system to 1 part in 10⁴.     

气体放电击穿过程的物理和数值研究

本文对低气压（１０＾－２Ｐａ）热阴极气体放电的击穿过程给出了物理描述和相应的双流体数学型，并发展了一种选择和调整未知初始条件的有效算法，可以通过伴随试射法得到对初始条件十分敏感的非线性两点边值常微分方程组的数值解，从而给出这类气体放电中击穿过程的定量描述。     

改进的部分子演化模型及其对EMC效应和核Drell－Yan过程的解释

用解析方法证明了部分子演化模型可为双重Ｘ重新标度模型提供物理基础，进而利用修正的Ａｌｔｅｒａｌｌｉ－Ｐａｒｉｓｉ方程描述小ｘ区域的核遮蔽和反遮蔽效应，以改进部分子演化模型，使我们不必引入核遮蔽因子便可统一地描述ＥＭＣ效应、核遮蔽和反遮蔽效应．最后，利用改进的部分子演化模型相当好地解释了核Ｄｒｅｌｌ－Ｙａｎ过程的实验数据．     

On a kind of integrals of empirical processes concerning insurance risk

This paper proves the strong consistence and the central limit theorems for empirical right tail deviations.     

FRACTIONAL DIFFUSION EQUATIONS WITH INTERNAL DEGREES OF FREEDOM

We present a generalization of the linear one-dimensional diffusion equation by com-bining the fractional derivatives and the internal degrees of freedom. The solutions areconstructed from those of the scalar fractional diffusion equation. We analyze the in-terpolation between the standard diffusion and wave equations defined by the fractionalderivatives. Our main result is that we can define a diffusion process depending on theinternal degrees of freedom associated to the system.     

Technically optimal inspection policy with arithmetical adaption

A process is considered whose quality deteriorates accordingto a constant failure intensity . As in practice it can be difficultto estimate the true value of the purpose of this paper isto present a strategy which can be applied without knowing .In order to maximize the number of conforming items per timeunit perfect inspections and renewals are performed. The lengthof the inspection interval is described by an arithmetical sequenceand changes by the time depending on perceived assignable causes.Optimal adaptive control plans provide nearly the same performanceas in the case when is known.     

Functional Quantization and Small Ball Probabilities for Gaussian Processes

Quantization consists in studying the L ^r -error induced by the approximation of a random vector X by a vector (quantized version) taking a finite number n of values. We investigate this problem for Gaussian random vectors in an infinite dimensional Banach space and in particular, for Gaussian processes. A precise link proved by Fehringer⁽⁴⁾ and Dereich et al. ⁽³⁾ relates lower and upper bounds for small ball probabilities with upper and lower bounds for the quantization error, respectively. We establish a complete relationship by showing that the same holds for the direction from the quantization error to small ball probabilities. This allows us to compute the exact rate of convergence to zero of the minimal L ^r -quantization error from logarithmic small ball asymptotics and vice versa.     

Nonparametric Estimation of Regression Functions in Point Process Models

We prove that the empirical L ²-risk minimizing estimator over some general type of sieve classes is universally, strongly consistent for the regression function in a class of point process models of Poissonian type (random sampling processes). The universal consistency result needs weak assumptions on the underlying distributions and regression functions. It applies in particular to neural net classes and to radial basis function nets. For the estimation of the intensity functions of a Poisson process a similar technique yields consistency of the sieved maximum likelihood estimator for some general sieve classes. This revised version was published online in August 2006 with corrections to the Cover Date.     

Nonlinear Filtering for Diffusions in Random Environments

Suppose that the signal X to be estimated is a diffusion process in a random medium W and the signal is correlated with the observation noise. We study the historical filtering problem concerned with estimating the signal path up until the current time based upon the back observations. Using Dirichlet form theory, we introduce a filtering model for general rough signal X ^W and establish a multiple Wiener integrals representation for the unnormalized pathspace filtering process. Then, we construct a precise nonlinear filtering model for the process X itself and give the corresponding Wiener chaos decomposition.