基于正弦激励的压电加速度计模型参数辨识

针对压电加速度计常规校准无法完全满足实际机械动态量测量要求的问题,采用基于加速度计模型参数校准的方法。参数未知的线性二阶微分方程用来表示加速度计动态特性,利用绝对法振动校准加速度计频率响应数据,采用最小二乘算法确定了未知的参数的值,同时利用蒙特卡罗法确定了参数值的不确定度。最后对加速度计进行了瞬态冲击加速度校准,计算辨识所得模型在相同冲击激励下的预测输出。结果表明:瞬态冲击加速度校准与计算辨识模型结果相差不超过1%。     

Excel在基于最小二乘法PCR仪样本示值误差不确定度评定中的应用

目的：探讨Excel在基于最小二乘法PCR仪样本示值误差不确定度评定中的应用。方法：依据JJF-2015校准规范中PCR仪样本示值误差的校准以及最小二乘法在不确定评定中的应用,推导基于最小二乘法的PCR仪样本示值误差不确定度评定。结果：根据PCR仪样本示值误差不确定度评定设计Excel,包括常量输入、变量输入、中间计算值设置及计算、结果输出,采用实际案例对其应用进行验证。结论：设计的Excel可简单快速地进行PCR仪样本示值误差的不确定度评定。     

最小二乘法回归分析在计量标准量值溯源中的应用

文章介绍了某天然气计量站临界流文丘里喷嘴标准装置和涡轮标准装置的实流校准及结果,探讨了校准数据的最小二乘法回归使用和量值溯源不确定度评定方法,并通过与ISO 9300中的标准数据进行比较、重新校准、计量比对的方式进行了验证。     

针对JJF 1305-2011校准规范中几点问题的探讨

针对JJF 1305-2011《线位移传感器校准规范》中一元线性回归参数公式存在的问题,本文基于最小二乘法从方差角度给出了其正确表达式,并对回归系数的无偏性加以论证。同时,从考量线位移传感器重要指标出发,利用不确定度传递率,计算线性度的不确定度,并讨论其回归系数及其重复性等因素引起的不确定度分量。     

Y145A羊毛细度气流仪校准结果不确定度的评定

本文通过对线性最小二乘法校准不确定度的评定 ,并通过数学计算方法的转换 ,导出Y14 5A羊毛细度气流仪校准结果—羊毛纤维直径 (di)的不确定度评定模型。     

基于非等间距GM(1,1)的校准周期预测方法研究

为科学合理地确定计量器具的校准周期,提出了基于非等间距GM(1,1)的计量器具校准周期预测方法.该方法以灰色系统理论为依据,首先将已知的非等间距序列进行累加生成处理,对生成序列建立白化微分方程,通过最小二乘法求待辩识参数,进而得出非等间距GM(1,1);并通过Matlab7.5.5实现了非等间距GM(1,1)模型的快速...     

加速度计校准的贝叶斯不确定度评估

主要论述了贝叶斯统计用于加速度计校准结果的分析.首先介绍了对于线性测量模型,GUM、GUM S1以及基于贝叶斯统计分析测量不确定度的过程,说明三种方法分析的不同之处.然后结合实际工作中振动与冲击校准加速度计的数据,利用不同先验分布的贝叶斯统计和GUM系列方法进行了分析并对结果进行了比较.针对冲击加速度国际关键比对的部分数据建立了贝叶斯独立和层次两种不同的数据统计模型,在此基础之上结合马尔科夫链蒙特卡罗法(MCMC)对比对参考值和相应不确定度的计算,并且与通用方法的计算结果进行了比较.通过不同方法得到结果的一致性与差异性说明了贝叶斯统计用于不确定度评估的优缺点.     

Y145A羊毛细度气流仪校准结果不确定度的评定

本文通过对线性最小二乘法校准不确定度的评定，并通过数学计算方法的转换，导出Y145A羊毛细度气流仪校准结果一羊毛纤维直径(di)的不确定度评定模型。     

Excel VBA自定义函数辅助最小二乘法校准不确定度的评定

介绍了基于Excel VBA编程语言开发自定义函数用于线性最小二乘法校准不确定度评定,实践证明,通过自定义函数的方法来评定不确定度,方便快捷.     

校准曲线基于数学规划的通用求解方法

以最小二乘法为基础．讨论了计量测试工作中建立校准曲线时的回归分析问题。结合一个算例,介绍了回归分析的通用模型和基于数学规划的通用求解方法。     

标准数字时钟同步偏差测量不确定度评定

测量不确定度及其评定不仅适用于计量领域中的检定、校准、检测,也可应用于社会经济、科学研究、工程建设、生活环境等一切与测量有关的其它领域。标准数字时钟在医院、学校、机场、工厂等需要提供高性能时间的场所具有广泛应用,因此标准数字时钟的测量不确定分析也很有实际应用意义。本文简单介绍了测量不确定度的历史起源及基本概念,着重阐述了标准数字时钟同步偏差的测量不确定度的评定步骤和计算方法,为从事计量检定工作的人员进行测量不确定度评定时提供参考。     

应变传感器计量特性的校准方法

为了合理评价应变传感器（应变计）的计量特性,讨论了应变计的相关国家标准和技术规范中计量特性的不同之处,并以振弦式应变计为例,归纳出应变计的计量特性参数。对应变计校准现状进行分析,设计了一种基于激光干涉法的高精度校准装置,标距范围可以达到500mm,并提出了系列计量特性的高精度计算方法。通过试验对应变计的计量特性进行测试,并评估了综合误差的校准不确定度U=0.10%（包含因子k=2）。     

A fully Bayesian multivariate approach to before-after safety evaluation

This paper presents a fully Bayesian multivariate approach to before-after safety evaluation. Although empirical Bayes (EB) methods have been widely accepted as statistically defensible safety evaluation tools in observational before-after studies for more than a decade, EB has some limitations such that it requires a development and calibration of reliable safety performance functions (SPFs) and the uncertainty in the EB safety effectiveness estimates may be underestimated when a fairly large reference group is not available. This is because uncertainty (standard errors) of the estimated regression coefficients and dispersion parameter in SPFs is not reflected in the final safety effectiveness estimate of EB.Fully Bayesian (FB) methodologies in safety evaluation are emerging as the state-of-the-art methods that have a potential to overcome the limitations of EB in that uncertainty in regression parameters in the FB approach is propagated throughout the model and carries through to the final safety effectiveness estimate. Nonetheless, there have not yet been many applications of fully Bayesian methods in before-after studies. Part of reasons is the lack of documentation for a step-by-step FB implementation procedure for practitioners as well as an increased complexity in computation. As opposed to the EB methods of which steps are well-documented in the literature for practitioners, the steps for implementing before-after FB evaluations have not yet been clearly established, especially in more general settings such as a before-after study with a comparison group/comparison groups. The objectives of this paper are two-fold: (1) to develop a fully Bayesian multivariate approach jointly modeling crash counts of different types or severity levels for a before-after evaluation with a comparison group/comparison groups and (2) to establish a step-by-step procedure for implementing the FB methods for a before-after evaluation with a comparison group/comparison groups.The fully Bayesian multivariate approach introduced in this paper has additional advantages over the corresponding univariate approaches (whether classical or Bayesian) in that the multivariate approach can recover the underlying correlation structure of the multivariate crash counts and can also lead to a more precise safety effectiveness estimate by taking into account correlations among different crash severities or types for estimation of the expected number of crashes. The new method is illustrated with the multivariate crash count data obtained from expressways in Korea for 13 years to assess the safety effectiveness of decreasing the posted speed limit.     

正弦波拟合参数的不确定度评定

介绍了用4参数正弦波最小二乘拟合法拟合参数不确定度分析和评价过程。讨论了主要的不确定度来源,包括信号源误差、采集序列谐波失真、采集序列噪声及非谐波失真、采集序列中信号周期抖动、软件拟合运算误差的影响等,给出了减小不确定度的主要措施。结合一个幅度拟合实例给出了评价结果。     

Bayesian Additive Regression Tree Calibration of Complex High-Dimensional Computer Models

Complex natural phenomena are increasingly investigated by the use of a complex computer simulator. To leverage the advantages of simulators, observational data need to be incorporated in a probabilistic framework so that uncertainties can be quantified. A popular framework for such experiments is the statistical computer model calibration experiment. A limitation often encountered in current statistical approaches for such experiments is the difficulty in modeling high-dimensional observational datasets and simulator outputs as well as high-dimensional inputs. As the complexity of simulators seems to only grow, this challenge will continue unabated. In this article, we develop a Bayesian statistical calibration approach that is ideally suited for such challenging calibration problems. Our approach leverages recent ideas from Bayesian additive regression Tree models to construct a random basis representation of the simulator outputs and observational data. The approach can flexibly handle high-dimensional datasets, high-dimensional simulator inputs, and calibration parameters while quantifying important sources of uncertainty in the resulting inference. We demonstrate our methodology on a CO₂ emissions rate calibration problem, and on a complex simulator of subterranean radionuclide dispersion, which simulates the spatial–temporal diffusion of radionuclides released during nuclear bomb tests at the Nevada Test Site. Supplementary computer code and datasets are available online.     

Logistic 组稀疏回归模型的 Bayes 建模及变分推断

在工程应用中,如数据挖掘、成本预测以及风险预测等,Logistic 回归是一类十分重要的预测方法．当前,大部分 Logistic 回归方法都是基于优化准则而设计,这类回归方法具有参数调试过程繁琐、模型解释性差、估计子没有置信区间等缺点．本文从 Bayes 概率角度研究 Logistic 组稀疏性回归的建模与推断问题．具体来说,首先利用高斯-方差混合公式提出 Logistic 组稀疏回归的 Bayes 概率模型;其次,通过变分 Bayes 方法设计出一个高效的推断算法．在模拟数据上的实验结果表明,本文所提出的方法具有较好的预测性能．     

Measurement Uncertainty of Dew-Point Temperature in a Two-Pressure Humidity Generator

This article describes the measurement uncertainty evaluation of the dew-point temperature when using a two-pressure humidity generator as a reference standard. The estimation of the dew-point temperature involves the solution of a non-linear equation for which iterative solution techniques, such as the Newton?CRaphson method, are required. Previous studies have already been carried out using the GUM method and the Monte Carlo method but have not discussed the impact of the approximate numerical method used to provide the temperature estimation. One of the aims of this article is to take this approximation into account. Following the guidelines presented in the GUM Supplement 1, two alternative approaches can be developed: the forward measurement uncertainty propagation by the Monte Carlo method when using the Newton?CRaphson numerical procedure; and the inverse measurement uncertainty propagation by Bayesian inference, based on prior available information regarding the usual dispersion of values obtained by the calibration process. The measurement uncertainties obtained using these two methods can be compared with previous results. Other relevant issues concerning this research are the broad application to measurements that require hygrometric conditions obtained from two-pressure humidity generators and, also, the ability to provide a solution that can be applied to similar iterative models. The research also studied the factors influencing both the use of the Monte Carlo method (such as the seed value and the convergence parameter) and the inverse uncertainty propagation using Bayesian inference (such as the pre-assigned tolerance, prior estimate, and standard deviation) in terms of their accuracy and adequacy.     

基于分位数函数和Bootstrap分布的不确定度评定研究

最大信息熵方法是基于概率分布评定测量不确度的主要方法之一。其所依赖的高阶矩需要较大样本的测量数据,而校准/检测实验室的测量一般为小样本,故用最大熵方法评定小样本测量不确定度缺乏一定的可靠性。提出了基于分位数函数和概率权重矩作为约束条件的最大信息熵不确定度评定法,把矩的计算从高次降为一次,并结合遗传算法求解概率分布,用Bootstrap分布估计扩展不确定度和包含区间,解决了由分位数区间估计分布不对称所致的复杂计算问题。     

Evaluating Uncertainties in Interpolations Between Calibration Data for Thermocouples

Two methods for evaluating thermocouple calibration uncertainty over the temperature range of the calibration are presented, when the thermocouple is calibrated at only a few temperatures. The evaluation of the uncertainty at fixed-point temperatures is well established, but it is often not clear how the uncertainty arising from interpolation between fixed points can be determined. We present a conventional method, based on that described in the “Guide to the expression of uncertainty in measurement” (GUM), and a numerically-based Monte Carlo method, for quantifying the calibration uncertainty arising from the use of an interpolating polynomial defined by calibration data. The two methods are compared and found to be in excellent agreement, but the Monte Carlo method is, in general, more flexible, e.g., when measurements are described by non-normal distributions.     

航天器检漏线性拟合参数的不确定度评定分析

航天器检漏中需要进行线性参数拟合及其不确定度的评定。采用最小二乘法建立线性方程的斜率公式,推导斜率的A类及B类不确定度的计算公式,对其进行合理简化及影响分析。最后,实例说明该方法的具体应用。