手持式激光测距仪"测距标准偏差综合评定"的扩展不确定度

在确定的函数关系(数学模型)中,因变量(输出量)随自变量(输入量)的变化规律完全由数学逻辑所确定,自变量到因变量的传递系数(灵敏系数)也可由因变量的标准差和自变量的标准差之比所决定,而与自变量的来历(测量的或者给定的)没有关系[1].本文根据这一基本原理,采用模拟自变量随机变化的方法,成功计算了手持式激光测距仪的测距固定偏差和比例偏差系数的测量不确定度,从而计算出测距标准差综合评定的扩展不确定度,同时解决了复杂的数学模型中多个输入量到输出量的灵敏系数和输出量的不确定度的计算问题.     

基于遗传算法优化BP神经网络的可食用油墨粘度的预测

目的 通过研究遗传算法优化BP神经网络建立自变量与因变量之间的关系,从而对可食用油墨的粘度进行预测和模拟.方法 在前期关于可食用油墨的研究基础上,以醋酸浓度、壳聚糖用量、酒精用量、研磨速度为自变量,以配制得到的油墨粘度作为因变量,利用正交实验设计实验,运用BP神经网络结合遗传算法对可食用油墨的粘度进行预测和模拟.结果 以正交实验设计得到30组实验数据,利用Matlab 2018a软件中GAOT遗传算法工具箱,经过38次迭代训练,得到收敛精度为10-4的神经网络,粘度的预测值与对应的真实值相对误差介于0.05％～3.7％,拟合度R2值为0.8672,表明该神经网络对可食用油墨的粘度具有较好的预测能力和较高的预测精度.结论 遗传算法优化BP神经网络可以用来预测和模拟可食用油墨的粘度,可以将神经网络拓展到可食用油墨其他性能的评价体系中,从而对可食用油墨的生产和应用提供指导性的建议.     

截尾数据下一类半参数回归模型的估计

讨论了一类半参数回归模型y =x′β+g(t′α) +e .假定y被随机变量T右侧截尾 ,T与y独立 ,T～G。在G已知和未知两种情况下 ,构造了α、β和g(·) 的强相合估计     

甘肃张家川地区肝炎发病时间序列模型

目前国家有关肝炎发病的时间序列分析研究较少,应用SPSS软件对张家川地区2001年8月至2006年8月肝炎每月发病人数统计资料做时间序列分析观测到该时间序列是一组平稳即白噪声序列,无季节周期性.进行ARIMA模型拟合得到ARIMA(1,0,1)模型,数学表达式Y_t=0.824Y_(t-1)+a_t-0.086a_(t-1),且进行预测值比较,得到较好效果。     

运用Excel求建千斤顶校准方程

校准方程是计算液压千斤顶(以下简称"千斤顶")内插误差的依据,并关系到桩基工程和结构工程的力值控制和测试。JJG621-2012《液压千斤顶》检定规程规定,千斤顶的检定应给出被检千斤顶校准方程,即以力值为自变量、以千斤顶指示器示值为函数的拟合方程。拟合方程常常是根据变量的多组实验数据,进行数理统计和复杂的理论计算获得变量的函数关系的近似表达式。笔者根据相关资料和经验,谈谈求建千斤顶校准方程的方法。     

基于误差分析的需求不确定性研究

以某家用电器公司的洗衣机的销售为例,选择其下游的实际零售量作为自变量,公司的需求量为因变量,采用广义差分方法拟合简单计量经济模型,将其需求不确定性中因衔接因素的影响部分显现了出来.在对模型的模拟误差进行分析的基础上,得出公司的衔接不确定性主要来源于需求预测不准和短缺博弈、数量折扣策略及促销时间和方式等方面.并指出可以采用改善预测、历史数据分配法、滚动的总量折扣策略和淡季促销等措施来降低需求不确定性.     

结构方程模型及其在实证分析中的应用

一些抽象概念,如信任、动机、满意度,很难直接测量,而需采用多个指标来进行间接测量.传统的统计方法如回归分析难以有效测量多指标、多变量之间的关系,结构方程模型(SEM)的应用则能解决这个问题.结构方程模型包括两个部分:测量方程与结构方程.结构方程模型具有多个优点,如同时处理多个因变量、允许自变量与因变量均含有测量误差、衡量整个模型的拟合优度等.最后给出了一个采用结构方程模型来分析网上信任问题的实例.     

铑铁电阻温度计简单分度特性研究

收集18支英国Tinsley和10支中国云南制作的低温标准铑铁电阻温度计在0.5 K至27.1 K温区的分度数据,以2.1768K为参考温度点,定义电阻比值为W(T)=R(T)/R(2.1768K),再用最小二乘法以切比雪夫多项式拟合计算这些分度数据.采取参考函数加偏差修正的方式,用2.1768 K、13.8033 K和24.5561 K做单点、两点和三点简单分度,求出简单分度表对常规多点拟合分度表的偏差.选取W(24.5561 K)值相近的温度计做配对计算,可以进一步降低简单分度的最大内插偏差.其中有二对云南制温度计在1 K至25 K温区用两点简单分度算法获得了最大内插偏差小于0.5 mK的良好结果.     

无石棉胶乳抄取纸的均匀配方设计及性能研究——密度

无石棉胶乳抄取纸制备过程中,有效可行的配方设计方案是密封材料性能的重要保证.设计和拟合了U12(124×3×42)均匀设计表,确定7个自变量以及在自变量范围内的12个水平的配方试验方案,对制备得到的抄取纸进行了密度性能测试,并用多元线性回归方法考察了密度与配方组分的关系.结果表明,有机纤维,特别是芳纶浆粕对拉伸强度的影响很大,芳纶浆粕含量的增加有助于密度的增大;填料对密度的作用较小;在其他组分固定时,过分增加白炭黑含量,反而使密度有所降低.同时利用得到的回归方程对试样的配方进行了性能预测,且预测值与实测值比较接近.     

血管切片的三维重建

本文讨论血管的三维重建问题.我们通过研究,证明了以下的定理.定理设C(i)是中轴线和平面Z=i的交点,那么存在以C(i)为中点且端点P1(i),P2(i)在ω(i)上的线段,并且在P1(i),P2(i)处ω(i)的切线相互平行.根据定理,我们找到利用求截面图象边界曲线的平行切线方法找到中轴线和100个截面的交点及管道的直径59.1238pixel.并用这100个交点的数据拟合出中轴线的方程:x(t)=-0.207806-0.610303t+0.206455t2-0.0144935t3+0.000517774t4-8.394241977754047×10-6t5+6.133353112035975×10-8t6-1.6673218267444805×10-10t7y(t)=158.211+1.86595t-0.266798t2+0.0141407t3-0.000325412t4+3.043275597680807×10-6t5-9.899171274615063×10-9t6z(t)=t然后我们用中轴线的方程重建了三维血管,并求出了重建血管在40个平面上的截面ω′(i)(30≤i≤69),并与原始截面ω(i)(30≤i≤69)进行比较,截面平均符合率高达96.8024%.     

自适应蒙特卡洛法评定全站仪测距不确定度

全站仪测距精度的校准需要在标准基线场上进行,由于野外环境不可控和气象条件波动剧烈,因此判断全站仪的测量结果的可靠程度具有重要意义。为了解决全站仪测距不确定度评定模型的非线性和输入量强相关等问题,本文首先采用了自适应蒙特卡洛法进行不确定度评定,然后与GUM的不确定度评定结果进行对比,当测距距离为1 176 m时,自适应蒙特卡洛法评定的不确定度结果为2.2 mm,GUM为2.6 mm,结果显示两种不确定度评定方法的测量结果均在合理预期之内,且自适应蒙特卡洛法评定的不确定度置信区间更窄。自适应蒙特卡洛法结合了大量数据样本和自适应优化仿真次数的优势,不仅对全站仪测距过程中的各项误差源引入的不确定度分量评估更为全面,而且在保证了全站仪测距不确定度评定结果准确的同时,相比于蒙特卡洛法节约了70%的样本数量。     

Uncertainty Propagation for Platinum Resistance Thermometers Calibrated According to ITS-90

A new supplement to the GUM outlines uncertainty calculations using matrix algebra for models with more than one output quantity. This technique is applied to the problem of uncertainty propagation for platinum resistance thermometers (PRTs). PRTs are calibrated at specified sets of defining fixed points dependent on the desired temperature range. The problem of uncertainty propagation from the fixed-point calibration results plus the resistance of the PRT in use as input quantities to the coefficients of the deviation function as intermediate results and the temperature as a sole output quantity is discussed. A general solution in matrix form for any temperature range of the ITS-90 defined by PRTs is highlighted. The presented method allows for an easy consideration of the input quantity correlations, which differ with the circumstances of the accomplishment of the fixed-point calibrations and the resistance measurement of the thermometer in use. An example calculation for a specific temperature range based on a simplified model for the input quantity correlations demonstrates this benefit.     

Uncertainty Estimation of Some Sorption Isotherms Used for the Moisture Conditioning of Grains

Sorption isotherms relate the equilibrium condition between the moisture content of a solid material and the relative humidity of ambient where such material is placed. Isotherms are useful in many fields where the water vapor of ambient affects the properties of materials. In particular, the information given by the sorption isotherms is useful in the moisture conditioning of solid materials, which are used for the calibration of moisture meters for grains, cereals and wood, among others. There are many isotherm models (almost one for each material). However, most of them do not estimate the uncertainty or, in some cases, the estimation is incomplete. On the other hand, the most known method for uncertainty evaluation is given by the Guide to the Expression of Uncertainty in Measurement (GUM). However, this guide has some restrictions to be satisfactorily used; for example, the model of measurand must be linear and have a known probability distribution function for the model inputs and similar uncertainty values. So often, the sorption isotherm models used for grains are highly nonlinear. Therefore, the GUM could not provide reliable results. To overcome this, an alternative method is the use of Monte Carlo simulation method, which is suggested for nonlinear models in the GUM supplement. In this paper, the uncertainty estimation was done with the GUM and Monte Carlo methods applied to some sorption isotherms, which are used for grains and cereals. The results with both methods showed some discrepancies, which are due mainly to the nonlinearity of models.     

Assessing Differences Between Results Determined According to the Guide to the Expression of Uncertainty in Measurement

In some metrology applications multiple results of measurement for a common measurand are obtained and it is necessary to determine whether the results agree with each other. A result of measurement based on the Guide to the Expression of Uncertainty in Measurement (GUM) consists of a measured value together with its associated standard uncertainty. In the GUM, the measured value is regarded as the expected value and the standard uncertainty is regarded as the standard deviation, both known values, of a state-of-knowledge probability distribution. A state-of-knowledge distribution represented by a result need not be completely known. Then how can one assess the differences between the results based on the GUM? Metrologists have for many years used the Birge chisquare test as ‘a rule of thumb’ to assess the differences between two or more measured values for the same measurand by pretending that the standard uncertainties were the standard deviations of the presumed sampling probability distributions from random variation of the measured values. We point out that this is misuse of the standard uncertainties; the Birge test and the concept of statistical consistency motivated by it do not apply to the results of measurement based on the GUM. In 2008, the International Vocabulary of Metrology, third edition (VIM3) introduced the concept of metrological compatibility. We propose that the concept of metrological compatibility be used to assess the differences between results based on the GUM for the same measurand. A test of the metrological compatibility of two results of measurement does not conflict with a pairwise Birge test of the statistical consistency of the corresponding measured values.     

生活饮用水3M Petrifilm^TM菌落总数测试片法的不确定度评定

对3M PetrifilmTM菌落总数测试片法检测生活饮用水中菌落总数的测量结果进行了不确定度评定,评述了引起测量结果不确定度的主要因素,对样品稀释及方法精密度因素进行了详细评价,并对单一样品重复测量10次及30个样品每个样品平行测量2次的两种情况,分别进行了重复性标准偏差评价。结果显示：单一样品重复测量时,测量结果的不确定度主要由样品稀释和重复性标准偏差引起,扩展不确定度U95为20CFU/mL,k=2;多个样品每个样品平行测量2次时,测量结果的不确定度主要由重复性标准偏差引起,在95%的置信度时,扩展不确定度U（lgxj）为0.093,k=2.75。     

Calibration of Heat Stress Monitor and its Measurement Uncertainty

Wet-bulb globe temperature (WBGT) equation is a heat stress index that gives information for the workers in the industrial areas. WBGT equation is described in ISO Standard 7243 (ISO 7243 in Hot environments—estimation of the heat stress on working man, based on the WBGT index, ISO, Geneva, 1982). WBGT is the result of the combined quantitative effects of the natural wet-bulb temperature, dry-bulb temperature, and air temperature. WBGT is a calculated parameter. WBGT uses input estimates, and heat stress monitor measures these quantities. In this study, the calibration method of a heat stress monitor is described, and the model function for measurement uncertainty is given. Sensitivity coefficients were derived according to GUM. Two-pressure humidity generators were used to generate a controlled environment. Heat stress monitor was calibrated inside of the generator. Two-pressure humidity generator, which is located in Turkish Standard Institution, was used as the reference device. This device is traceable to national standards. Two-pressure humidity generator includes reference temperature Pt-100 sensors. The reference sensor was sheltered with a wet wick for the calibration of natural wet-bulb thermometer. The reference sensor was centred into a black globe that has got 150 mm diameter for the calibration of the black globe thermometer.     

基于MATLAB的MCM对GUM不确定度框架验证

测量不确定度评定领域中主要采用的方法是GUM,我国于2012年12月发布了新版JJF1059.1-2012《测量不确定度评定与表示》,并于2013年6月3日起实施。规范提出当JJF1059.1-2012不适用时,可考虑采用JJF1059.2-2012《用蒙特卡洛法评定测量不确定度》进行不确定度评定。同时规范强调GUM的评定结果可以用蒙特卡洛法（MCM）进行验证,验证评定结果一致时仍然可采用GUM进行不确定度评定。文中利用MATLAB数学工具,以实例的方式说明MCM对GUM不确定度框架如何验证并得出相关结论。     

一等标准铂电阻温度计在（0～419.527）℃温区检定的不确定度评定

介绍了一等标准铂电阻温度计的在锌凝固点检定的不确定度的评定方法,包括Ａ类标准不确定度及Ｂ类标准不确定度的确定,标准不确定度的合成及有效自由度的计算,并按误差传播公式计算出了在各温度点的扩展不确定度及包含因子。     

Technical Aspects of Guidelines for the Evaluation of Key Comparison Data

Some of the technical aspects of guidelines for key comparison data evaluation prepared by BIPM Director's Advisory Group on Uncertainties are considered. These guidelines relate to key comparisons based on the measurement of a travelling standard having good short-term stability and stability during transport, in cases where the institutes' measurements are realised independently. They include two procedures for forming a key comparison reference value (KCRV), and the associated uncertainty, and the consequent degrees of equivalence (including the associated uncertainties), in accordance with the Mutual Recognition Arrangement. The basis of the procedures is (a) the representation of the information provided by the participating institutes as probability density functions (pdf), and (b) the estimator (model) used as the KCRV. The calculation of the KCRV and the associated uncertainty and the degrees of equivalence is then undertaken in accordance with the law of propagation of uncertainty, as described in the Guide to the Expression of Uncertainty in Measurement (GUM), or the propagation of distributions, a generalisation of the law of propagation of uncertainty, covered in a supplemental guide to the GUM. Attention is paid to the choice of model, relating it to the conditions that apply to the key comparison. The first procedure is intended for cases where for each institute a Gaussian distribution is assigned to the measurand of which the institute's measurement is an estimate. The weighted mean is used as the model in this case. A consistency test is included to determine whether the model is consistent with the data. If the test is satisfied, the weighted mean is accepted as the KCRV. The second procedure is used in circumstances where (a) not all the pdf's assigned are Gaussian or (b) where the first procedure had previously been applied, the consistency test was not satisfied and there was no opportunity to correct all institutes' data regarded as discrepant. The model in this case is chosen to be a more robust estimator such as the median or another estimator considered appropriate for the particular comparison.     

0.005级压力式水深测量仪器检定装置的测量不确定度评定

通过GUM法和自适应蒙特卡洛法分别对0.005级压力式水深测量仪器检定装置进行测量不确定度评定,并对GUM法进行了验证。结果表明,压力测量模型比较复杂,2种方法均适合用来评定其测量不确定度,并且在标准不确定度有效位数取1位时,GUM法在各个检测点均通过了验证。同时,合理的数值容差取值非常重要,实际测试中,测量结果位数的选取应考虑仪器的测量精度和使用要求,还要考虑标准仪器的标准不确定度的有效位数。