离轴非球面轮廓测量导轨直线度误差补偿模型

由于研磨阶段非球面的面形误差将由几十微米收敛到几个微米,因此采用高重复精度的离散测量技术是决定误差收敛效率、影响加工进程的关键。在新一代数控光学加工中心(FSGJ Ⅱ)上,设计了双测头对非球面进行面形定量检测的轮廓测量机构。通过对测头运动导轨在x、z方向的直线精度的分析,建立了导轨直线度误差补偿模型,以较低的成本实现了较高的测量精度。     

牛顿第二定律实验中的误差分析

本文是通过对加速度的实验值和理论值进行比较和分析，给出误差的合理范围。     

热传导型半导体瞬态问题特征变网格有限元法及其分析

热传导型半导体器件的瞬时状态由四个方程的非线性偏微分方程组的初边值问题所决定，其中电子位势方程是椭圆型的，电子和空穴浓度方程是对流扩散型的，温度方程为热传导型的。本文提出解这类问题的特征变网格有限元法，并进行了理论分析，在一定条件下，得到了某种意义下的最佳L^2误差估计结果。     

A Nonconforming Arbitrary Quadrilateral Finite Element Method for Approximating Maxwell's Equations

The main aim of this paper is to provide convergence analysis of Quasi-Wilson nonconforming finite element to Maxwell's equations under arbitrary quadrilateral meshes.The error estimates are derived,which are the same as those for conforming elements under conventional regular meshes.     

抽样调查中最佳测量次数的确定

现场抽样调查中，由于测量误差的存在，使得所测变量实测值的方差增大，通过增加每个体的测量次数可以控制测量误差，但这样每个体调查费用增大。本文对测量信度Ｒ，每个体测量次数ｍ与相应所需的样本含量ｎｍ、调查费用Ｔｎ的关系进行了探讨，并介绍了如何根据Ｒ，及每个体测量费用占其总费用构成比Ｃ，确定最佳测量次数ｍ值，以达到最佳控制调查费用的目的，这对我们在大型现场调查中进行经济效益分析具有重大的理论指导意义。     

一种基于数字水印的错误检测和定位技术

为了增强视频通信系统在有误码信道的适应能力 ,提出了一种基于数字水印技术的错误检测方法 ,使得解码器可以对误码块进行有效的补偿。其原理为编码器在量化后离散余弦变换 (DCT)系数中嵌入易碎的数字水印 ,解码器可以通过提取水印从而确认错误以及错误发生的首宏块位置。仿真结果证明 :在信道误码率为 5× 10 - 4时 ,与常用的基于语法的方法相比 ,本方法的错误检测率是其的 1.32～ 1.82倍 ,错误正确定位率是其的 2 .95～ 5 .32倍 ,而且 PSNR只有微弱降低 ,同时算法复杂度很低     

PADE’型逼近的收敛性

文章给出了Ｐａｄｅ型逼近积分形式的误差公式，并用该公式证明了Ｐａｄｅ型逼近的两个收敛定理．     

一种新的$1/f$过程小波模型

给出了一种新的二进小波1/f过程模型,从理论上证明了一类谱指数为H的近似1/f过程可通过一簇平稳随机过程产生.由于所提方法利用了1/f过程小波系数的相关性,因而有效地减少了合成1/f过程的谱误差.数值实验结果表明,新模型很好地改进了已有模型.     

Discrete duality finite volume schemes for Leray−Lions−type elliptic problems on general 2D meshes

Discrete duality finite volume schemes on general meshes, introduced by Hermeline and Domelevo and Omnès for the Laplace equation, are proposed for nonlinear diffusion problems in 2D with nonhomogeneous Dirichlet boundary condition. This approach allows the discretization of non linear fluxes in such a way that the discrete operator inherits the key properties of the continuous one. Furthermore, it is well adapted to very general meshes including the case of nonconformal locally refined meshes. We show that the approximate solution exists and is unique, which is not obvious since the scheme is nonlinear. We prove that, for general W^?1,p′(Ω) source term and W^1‐(1/p),p(?Ω) boundary data, the approximate solution and its discrete gradient converge strongly towards the exact solution and its gradient, respectively, in appropriate Lebesgue spaces. Finally, error estimates are given in the case where the solution is assumed to be in W^2,p(Ω). Numerical examples are given, including those on locally refined meshes. © 2006 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007     

Estimating tail probabilities of heavy tailed distributions with asymptotically zero relative error

Efficient estimation of tail probabilities involving heavy tailed random variables is amongst the most challenging problems in Monte-Carlo simulation. In the last few years, applied probabilists have achieved considerable success in developing efficient algorithms for some such simple but fundamental tail probabilities. Usually, unbiased importance sampling estimators of such tail probabilities are developed and it is proved that these estimators are asymptotically efficient or even possess the desirable bounded relative error property. In this paper, as an illustration, we consider a simple tail probability involving geometric sums of heavy tailed random variables. This is useful in estimating the probability of large delays in M/G/1 queues. In this setting we develop an unbiased estimator whose relative error decreases to zero asymptotically. The key idea is to decompose the probability of interest into a known dominant component and an unknown small component. Simulation then focuses on estimating the latter ‘residual’ probability. Here we show that the existing conditioning methods or importance sampling methods are not effective in estimating the residual probability while an appropriate combination of the two estimates it with bounded relative error. As a further illustration of the proposed ideas, we apply them to develop an estimator for the probability of large delays in stochastic activity networks that has an asymptotically zero relative error.