An Outline of Supplement 1 to the Guide to the Expression of Uncertainty in Measurement on Numerical Methods for the Propagation of Distributions

Supplement 1 to the Guide to the Expression of Uncertainty in Measurement (GUM), concerned with numerical methods for the propagation of distributions, embodies a generalization of the uncertainty framework of the GUM. This paper presents a number of aspects of that supplement.__________Published in Izmeritel’naya Tekhnika, No. 4, pp. 17– 24, April, 2005.     

基于蒙特卡罗法的冲击力溯源系统不确定度评定

利用蒙特卡罗方法对多变量的冲击力溯源系统进行不确定性量化评定。该方法首先建立冲击力溯源系统各不确定变量的表征模型;再根据抽样空间相邻迭代解的误差限,建立样本量的适应度函数;最后利用适应度函数使冲击力测量模型的输入变量最佳逼近总体分布。为了验证该方法的有效性,利用该方法对冲击力溯源系统进行不确定度评定,评定结果表明,在95%的置信水平上,冲击力溯源系统的相对扩展不确定度优于0.818%,其不确定性主要来源于落锤上表面加速度分布不均匀和横向偏摆。     

基于蒙特卡洛法的铝合金抗拉强度测量不确定度评定

以铝合金抗拉强度为例,介绍了采用蒙特卡洛法(MCM)评定测量不确定度的步骤和方法。结果表明:采用蒙特卡洛法评定测量不确定度,无需考虑测量模型是否线性,避免了泰勒展开、求偏导数等复杂的数学推导过程,也无需将测量模型简化,可以很方便地求出标准不确定度以及给定包含概率下的扩展不确定度。     

分布传播及其在不确定度中的应用

论述分布传播,研究了分布传播分析,讨论了不确定度的分布传播,最后用实例说明了它在不确定度中的应用.     

Monte Carlo方法评定测量不确定度中模拟样本数M的确定

对<测量不确定度表示指南>补充文件1(GUM Sup.1)中确定M的方法进行了研究,在此基础上给出了确定M的自适应方法,克服了GUM Sup.1中确定M方法的不足,使M的确定更加科学实用.     

应用蒙特卡罗方法计算带置信水平的不确定度

本文介绍了一种采用蒙特卡罗统计模拟的方法来求解带置信水平的不确定度评定问题，这种方法求得的结果可使合成不确定度与置信水平精确关联，特别是高置信水平的不确定度。文中通过2个实例阐述了这种方法的运行过程并分析了结果。作者认为．这种方法可以作为有关不确定度评定标准的补充。     

测量不确定度评定的验证研究

测量不确定度有多种评定模型,采用不同的评定模型可能得到不同的结果。为了保证求得的不确定度数值有足够的精度,必须对不同模型计算出的测量不确定度进行有效的验证。文中首次研究了测量不确定度评定模型的验证,该方法首先通过埃奇沃思级数展开来表示出测量数据的分布函数,然后由蒙特卡罗模拟法产生大量符合此分布函数的测量数据的模拟值,计算出该模拟值的标准差作为不确定度评定的验证值,从而实现对各种不确定度评定模型的验证。最后通过多个宴例分析说明了该方法的右效件．     

基于蒙特卡罗方法的电力系统可靠性评估

介绍了蒙特卡罗方法的原理及其在电力系统可靠性评估中的应用。从进行电力系统充裕度与安全度评估所采用的数学模型、加快评估速度的各种改进算法等几个方面阐述了蒙特卡罗方法在评估电力系统可靠性中的应用现状,最后指出了今后的研究方向。     

应用Monte Carlo算法研究大分子蠕动阻力的影响因素

采用一种基于高分子Monte Carlo模拟算法分析分子蠕动阻力的软件,借助于二维空间中的8配位点键长涨落的格子链模型,对不同温度、分子量、共混小分子时的大分子蠕动行为进行了模拟分析,考察了温度、分子长、共混小分子等因素对蠕动阻力的影响,得到了随着温度增加、分子长减小和共混浓度增加,大分子蠕动阻力减小的结果,这与传统经验理论相符合。这些结果为选择大分子量添加剂黄原胶作为抗结块剂,以及选择β-环糊抑制玻璃化转变等提供了理论依据。     

晶粒生长的蒙特卡罗模拟研究进展

概述了经典的晶粒生长蒙特卡罗(MC)模型并指出了其存在的缺陷,总结了近几年国内外研究者对经典MC模型的改进算法,综述了MC方法模拟晶粒生长的应用研究,指出了目前研究中存在的问题,并展望了今后的主要研究方向.     

Quantitative Analysis of Dynamic Fault Trees Based on the Coupling of Structure Functions and Monte Carlo Simulation

This paper focuses on the quantitative analysis of Dynamic Fault Trees (DFTs) by means of Monte Carlo simulation. In a previous article, we defined an algebraic framework allowing to determine the structure function of DFTs. We exploit this structure function and the minimal cut sequences that it allows to determine, to know the failure mode configuration of the system, which is an input of Monte Carlo simulation. We show that the results obtained are in good accordance with theoretical results and that some results, such as importance measures and sensitivity indexes, are not provided by common quantitative analysis and yet interesting. We finally illustrate our approach on a DFT example from the literature. Copyright © 2014 John Wiley & Sons, Ltd.     

基于微扰蒙特卡罗的薄层状组织光学参数重构技术

针对层状组织体的光学参数（吸收系数和散射系数）快速重构问题展开讨论.发展了基于漫反射测量的适合于胃、宫颈等内脏薄层状组织光学参数重构的微扰蒙特卡罗模拟.数值模拟结果显示,该方法能够有效地利用漫反射光强重构临床应用范围内的光学参数,对单层模拟体的吸收系数和散射系数的重构误差均不大于±3%,对双层模拟体的重构误差小于10%,且两参数的重构之间几乎没有互扰.使用固体模型对上述算法进行的实验显示,重构出的散射参数与真值的误差小于±10%.进行一组光学参数重构耗时不超过1 min.结果表明,该方法可快速、准确地重构光学参数,为内脏器官近红外早期癌症诊断的临床应用提供保障.     

碳纳米管形成机理的Monte Carlo模拟

采用蒙特卡罗方法研究了碳纳米管生长初期"碳帽"的形成过程,并且考察了碳原子的添加速率对其形成过程的影响.模拟在立方元胞中进行,先随机放置一个金属团簇和一些碳原子形成共熔体,然后在距离共熔体表面0.6 nm处释放碳原子.模拟结果表明,碳原子首先吸附在金属团簇的表面,然后扩散到团簇中.当碳原子在团簇中的浓度达到饱和后,在镍团簇的另一侧析出,形成"碳帽"结构.碳原子的添加速率太快,不利于形成碳帽,甚至形不成"碳帽".     

蒙特卡洛仿真在工程项目进度管理中的应用

对于工序时间具有随机性的复杂工程项目,在Excel中运用蒙特卡洛仿真获得项目的完成时间分布并找出关键工序。在一新产品研制项目的仿真模型中,运用Excel中的“公式”表示项目完成时间的算法,用蒙特卡洛法处理工序时间的不确定性。仿真运行后,获得了项目完成时间的频数图和相关统计量,通过灵敏度分析确定各工序对项目完成时间变动的影响程度。     

基于蒙特卡罗模拟模型的投资项目风险分析

运用蒙特卡罗模拟模型和程序,结合实际工程项目,分析评估了项目的主要风险因素,借助EXCEL软件对项目风险进行了模拟和测试,给出了项目风险模拟的结果.由结果可以看出,项目总体上有较高的抗风险能力,并且能够达到预期的投资收益目标.同时,项目风险评估中的蒙特卡罗模拟方法占用的资源少、操作性强,对于项目风险评估是有用的.     

Fast Monte Carlo reliability evaluation using support vector machine

This paper deals with the feasibility of using support vector machine (SVM) to build empirical models for use in reliability evaluation. The approach takes advantage of the speed of SVM in the numerous model calculations typically required to perform a Monte Carlo reliability evaluation. The main idea is to develop an estimation algorithm, by training a model on a restricted data set, and replace system performance evaluation by a simpler calculation, which provides reasonably accurate model outputs. The proposed approach is illustrated by several examples. Excellent system reliability results are obtained by training a SVM with a small amount of information.     

借助Monte Carlo模拟制备组成均匀的聚丙烯腈/衣康酸

采用Monte Carlo方法模拟了丙烯腈（AN）与衣康酸（IA）共聚反应过程中任意转化率时的单体浓度、共聚组成和序列分布等参数,确定了单体IA分批投料的量,使其与丙烯腈单体组成比不变,以保证先后所生成的共聚物组成一致。比较了一次投料法和基于Monte Carlo模拟的分批投料法反应各阶段所得产物的组成和性能的差别。结...     

Uncertainty Evaluation and Validation of a Comparison Methodology to Perform In-house Calibration of Platinum Resistance Thermometers using a Monte Carlo Method

The uncertainty required by laboratories and industry for temperature measurements based on the practical use of platinum resistance thermometers (PRTs) can commonly be achieved by calibration using temperature reference conditions and comparison methodologies (TCM) instead of the more accurate primary fixed-point (ITS-90) method. TCM is suitable for establishing internal traceability chains, such as connecting reference standards to transfer and working standards. The data resulting from the calibration method can be treated in a similar way to that prescribed for the ITS-90 interpolation procedure, to determine the calibration coefficients. When applying this approach, two major tasks are performed: (i) the evaluation of the uncertainty associated with the estimate of temperature (a requirement shared by the ITS-90 method), based on knowledge of the uncertainties associated with the temperature fixed points and the measured electrical resistances, and (ii) the validation of this practical comparison considering that the reference data are obtained using the ITS-90 method. The conventional approach, using the GUM uncertainty framework, requires approximations with unavoidable loss of accuracy and might not provide adequate uncertainty evaluation for the methods mentioned, because the conditions for its valid use, such as the near-linearity of the mathematical model relating temperature to electrical resistance, and the near-normality of the measurand (temperature), might not apply. Moreover, there can be some difficulty in applying the GUM uncertainty framework relating to the formation of sensitivity coefficients through partial derivatives for a model that, as here, is somewhat complicated and not readily expressible in an explicit form. Alternatively, uncertainty evaluation can be carried out by a Monte Carlo method (MCM), a numerical implementation of the propagation of distributions that is free from such conditions and straightforward to apply. In this paper, (a) the use of MCM to evaluate uncertainties relating to the ITS-90 interpolation procedure, and (b) a validation procedure to perform in-house calibration of PRTs by comparison are discussed. An example illustrating (a) and (b) is presented.