机械式温湿度计湿度测量结果不确定度评定

主要是根据JJG205-2005《机械式温湿度计检定规程》和JJF1033-2008《计量标准考核规范》，对机械式温湿度计湿度的测量结果不确定度进行评定。     

机械式温湿度表测量不确定度评定

本文根据JJG205-2005《机械式温湿度计检定规程》对机械式温湿度计进行不确定度评定;本文分为湿度测量不确定度评定和温度测量不确定度评定两部分。     

检定机械式温湿度计的不确定度评定

以机械式温湿度计测量结果为研究对象,论述了机械式温湿度计温湿度测量结果的误差来源及其测量不确定度的分析。     

机械式温湿度计示值误差的测量不确定度评定

本文主要阐述了机械式温湿度计示值误差测量结果的不确定度的来源和评定方法,适用于机械式温湿度计测量结果的不确定度的评定。     

温湿度示值误差不确定度评定

按照JJG 205-2005《机械式温湿度计检定规程》的要求,对机械式温湿度计的检定方法进行分析,并从精密露点仪示值误差、被检温湿度计分辨、温湿度检定箱均匀性、波动性等方面引入的不确定分量进行了分析,结合JJF1059.1-2012《测量不确定度评定与表示》的要求,给出了机械式温湿度计示值误差测量结果的不确定度评定。     

机械式温湿度计相对湿度检定的不确定度评定

一、概述本文按照JJG205-2005《机械式温湿度计》检定规程的要求,使用实验室配置的精密露点仪标准装置、标准温湿度检定箱,由温湿度检定箱提供稳定的温湿度环境,将被检温湿度计和露点仪标准器放入箱内,由露点仪测量的相对湿度值作为标准值,被检温湿度计的湿度示值为测量值。     

机械式温湿度计示值误差的测量不确定度评定实例

对用直接比较法在恒温恒湿试验箱中检定的机械式温湿度计温度及湿度示值误差的测量不确定度进行分析,对每个不确定度分量从最不利的因素考虑,得出示值误差的测量不确定度.在检定条件相同的情况下可直接使用此结果.     

双金属温湿度计湿度示值误差的测量不确定度评定

介绍了双金属温湿度计湿度部分校准的测量原理,分析了其示值误差的测量不确定度的来源,建立相应数学模型,逐一评定了各不确定度分量并进行标准不确定度的合成工作。最后给出了不确定度结果的报告样式。     

冷镜式露点仪计量标准检定或校准结果的测量不确定度评定

本文以冷镜式露点仪作为标准器具,在温湿度检定箱中检定机械式温湿度计,在检定点为(20℃时):40%RH、60%RH、80%RH,对冷镜式露点仪计量标准检定/校准结果进行测量不确定度评定。冷镜式露点仪就是先测量露点温度T_d和环境温度T,然后通过温度与饱和水蒸气压的方程或者查。     

温湿度计湿度测量结果的不确定度评定

本文详细介绍了温湿度计湿度测量结果的不确定度评定方法。     

Challenge problems: uncertainty in system response given uncertain parameters

The risk assessment community has begun to make a clear distinction between aleatory and epistemic uncertainty in theory and in practice. Aleatory uncertainty is also referred to in the literature as variability, irreducible uncertainty, inherent uncertainty, and stochastic uncertainty. Epistemic uncertainty is also termed reducible uncertainty, subjective uncertainty, and state-of-knowledge uncertainty. Methods to efficiently represent, aggregate, and propagate different types of uncertainty through computational models are clearly of vital importance. The most widely known and developed methods are available within the mathematics of probability theory, whether frequentist or subjectivist. Newer mathematical approaches, which extend or otherwise depart from probability theory, are also available, and are sometimes referred to as generalized information theory (GIT). For example, possibility theory, fuzzy set theory, and evidence theory are three components of GIT. To try to develop a better understanding of the relative advantages and disadvantages of traditional and newer methods and encourage a dialog between the risk assessment, reliability engineering, and GIT communities, a workshop was held. To focus discussion and debate at the workshop, a set of prototype problems, generally referred to as challenge problems, was constructed. The challenge problems concentrate on the representation, aggregation, and propagation of epistemic uncertainty and mixtures of epistemic and aleatory uncertainty through two simple model systems. This paper describes the challenge problems and gives numerical values for the different input parameters so that results from different investigators can be directly compared.     

100ml容量瓶容量测量结果的不确定度评定

主要叙述了在100ml容量瓶的检定过程中,对其容量测量结果的不确定度的评定     

广义不确定性系统理论的外延综论

在概述广义不确定性系统内涵基础上，讨论了广义不确定性系统的外延类别及其相关理论的基本研究框架和基本原理，为深入研究广义不确定性系统理论奠定了基础。     

考虑不确定性的多学科设计优化方法研究综述

目的研究多学科不确定性设计优化中多学科设计优化方法、不确定性建模与传递、不确定性设计优化的相关理论。方法通过研究并分析国内外相关文献,总结归纳考虑不确定性的多学科设计优化中的耦合系统解耦方法、参数和代理模型不确定性的建模方法,以及高效的不确定性传递和设计优化方法。结论系统探讨了在面对复杂多变的外界环境时,多学科设计优化对不确定性量化与传递的需求,提出多学科设计优化不仅要考虑确定性的系统,而且需要考虑由于外界环境变化导致的系统响应的不确定性。针对现有的多学科不确定性设计优化方法的理论研究,提出提高计算效率的关键在于将传统的三层嵌套循环计算框架解耦成单层循环。研究结果表明,考虑不确定性的多学科设计优化将成为复杂多学科系统设计的有力支撑,能显著提高系统的可靠性和稳健性,提高使用寿命,同时能够加快产品的更新换代设计。     

A probabilistic approach for representation of interval uncertainty

In this paper, we propose a probabilistic approach to represent interval data for input variables in reliability and uncertainty analysis problems, using flexible families of continuous Johnson distributions. Such a probabilistic representation of interval data facilitates a unified framework for handling aleatory and epistemic uncertainty. For fitting probability distributions, methods such as moment matching are commonly used in the literature. However, unlike point data where single estimates for the moments of data can be calculated, moments of interval data can only be computed in terms of upper and lower bounds. Finding bounds on the moments of interval data has been generally considered an NP-hard problem because it includes a search among the combinations of multiple values of the variables, including interval endpoints. In this paper, we present efficient algorithms based on continuous optimization to find the bounds on second and higher moments of interval data. With numerical examples, we show that the proposed bounding algorithms are scalable in polynomial time with respect to increasing number of intervals. Using the bounds on moments computed using the proposed approach, we fit a family of Johnson distributions to interval data. Furthermore, using an optimization approach based on percentiles, we find the bounding envelopes of the family of distributions, termed as a Johnson p-box. The idea of bounding envelopes for the family of Johnson distributions is analogous to the notion of empirical p-box in the literature. Several sets of interval data with different numbers of intervals and type of overlap are presented to demonstrate the proposed methods. As against the computationally expensive nested analysis that is typically required in the presence of interval variables, the proposed probabilistic representation enables inexpensive optimization-based strategies to estimate bounds on an output quantity of interest.     

检定立式金属罐容量测量不确定度的研究

本文结合立式金属罐的容量测量实例，详述了标准不确定度的计算及扩展不确定度的获得过程。     

测量不确定度评定方法在电学计量中的应用研究

介绍了测量不确定度评定理论,论述了在对电学仪器进行计量校正所采用的理论,并分析了各种不确定度的特点和适用性,为电学仪器的计量提供参考并指出发展方向.     

Error and uncertainty in modeling and simulation

This article develops a general framework for identifying error and uncertainty in computational simulations that deal with the numerical solution of a set of partial differential equations (PDEs). A comprehensive, new view of the general phases of modeling and simulation is proposed, consisting of the following phases: conceptual modeling of the physical system, mathematical modeling of the conceptual model, discretization and algorithm selection for the mathematical model, computer programming of the discrete model, numerical solution of the computer program model, and representation of the numerical solution. Our view incorporates the modeling and simulation phases that are recognized in the systems engineering and operations research communities, but it adds phases that are specific to the numerical solution of PDEs. In each of these phases, general sources of uncertainty, both aleatory and epistemic, and error are identified. Our general framework is applicable to any numerical discretization procedure for solving ODEs or PDEs. To demonstrate this framework, we describe a system-level example: the flight of an unguided, rocket-boosted, aircraft-launched missile. This example is discussed in detail at each of the six phases of modeling and simulation. Two alternative models of the flight dynamics are considered, along with aleatory uncertainty of the initial mass of the missile and epistemic uncertainty in the thrust of the rocket motor. We also investigate the interaction of modeling uncertainties and numerical integration error in the solution of the ordinary differential equations for the flight dynamics.     

Three Types of Mathematical Uncertainty Models

Mathematical models have been constructed for three types of uncertainty (interval, stochastic, and Bayesian), and the application of these models is discussed for describing measurements in the presence of unmonitored fluctuations leading to ambiguities in the results. __________ Translated from Izmeritel'naya Tekhnika, No. 9, pp. 39–44, September, 2005.     

基于不确定度的精密测量结果的处理

从正确处理精密测量结果的角度出发，通过精密测量实例，分析了采用测量不确定度处理精密测量结果的方法，讨论了采用测量不确定度处理精密测量结果的意义。