标量网络分析仪误差校正分析及其应用

为了消除标量网络分析仪在测量过程中的系统误差．提高测量精度,采用误差分析及校正模型．用数值分析的方法去除系统误差,降低随机误差．理论计算和实际测量结果都表明,测量精度明显提高．该误差分析理论及误差测量方法在标量网络分析仪中得到了较好的应用．提高了仪器的性能．     

天体赤纬测定中的度盘精确定位与测微器精度研究

研究了天体赤纬测定中度盘定位的理论处理方法和测微器测量系统的误差及其修正方法。提出一种五点线性最小二乘的刻线定位方法，分析了影响测微器系统精度的原因，给出了这种误差的一种有效的测定和修正方法，经过系统误差修正后，大大提高了度盘的定位精度，可使通过对单一刻线的测量精度达到０．０４３″。     

非球面在线检测的系统误差分离与修正

为了解决非球面在线检测的系统误差问题,针对系统误差产生的机理、误差的数学模型、分离方法以及补偿方法进行了研究.提出一种将空间误差投影到不同平面上进行分析从而解决测量系统误差的新方法并建立了各系统误差的数学模型.根据最小二乘法的基本思想,建立了基于标准球面的系统误差分离数学模型,得到了各参数的最小二乘估计值,并利用误差修正模型进行了校正.利用标准球面进行测量实验,验证了该方法的有效性和精确性.实验结果表明所提出的解决测量系统误差的思路可行,最终可使测量系统精度达到1μm数量级,从而满足精磨阶段在线检测的需要.     

系统误差的消除或减弱

<正>测量过程中产生的一些误差,假如在重复性条件下的值是恒定不变的,或者是遵循一定规律变化的,就称为系统误差。在大多数情况下,系统误差可通过技术途径来消除或使之减弱。一、在测量之前,应尽可能预见到一切可能产生系统误差的来源,并设法消除,或使其影响减弱到可接受的程度     

浅谈消除系统误差的方法

首先介绍了系统误差的定义及分类,包括定值系统误差和变值系统误差,其中变值系统误差又分为线性系统误差和周期系统误差;然后分别介绍了不同种类的系统误差对测量结果的影响以及发现方法,最后阐述了消除和减少各种系统误差的方法。     

光弹调制式反射差分光谱仪的理论分析

反射差分光谱仪是一种测量灵敏度和精度较高的研究表面／界面的新型分析仪器，但微弱的反射差分信号易受到各种噪声的干扰．作者利用Jones表示法，对光弹调制式反射差分光谱仪构建了包含器件自身缺陷和安装误差的数学模型，通过确立误差源与测量结果的联系，分析出各误差源对测量结果的影响，特别是起偏器、光弹调制器和样品的安装误差以及位相调制误差，这些系统误差经过标定可得到补偿．     

高空光测系统误差的恒星法修正

在高空目标光学测量中,由于设备和环境条件的影响,数据存在一定的系统误差,系统误差修正是获取高精度位置参数的关键.针对高空光测系统误差修正问题,首先分析了80 km 以上目标光测数据的系统误差来源,建立了高空目标光测数据的系统误差模型,提出了基于恒星的修正方法,对大气折射误差和设备轴系误差进行联合修正.为验证方法的有效性,将该方法应用于实际高空光学测量,实验结果表明修正效果明显,可使系统误差修正精度提高到2″.     

21位双通道旋转变压器解码及测试数据分析

设计了一种基于CPLD的21位分辨率双通道旋转变压器的解码及精度测量方案.通过CPLD对AD2S82A解码输出的两路并行16位数据进行读取、纠错、编码、输出,最后输出21位角度信号,这样整个测角系统就成为了主系统的一个外设.实验中搭建测试平台,用自准直仪和多面棱镜对该测角系统精度进行测量.结果表明,测角误差均方根值达到20″,经系统误差修正后整个系统测角误差均方根值达到9.46″.最后本文对测角系统的系统误差产生原因进行了分析.     

从“随机过程”的角度认识测量误差及其分类

测量误差作为“随机过程”具有统计特性,通过自相关函数、功率谱密度等特性参量对“随机过程”中各因素引起的误差分量特点进行分析,并得出结论:经典误差理论中系统误差和随机误差之分应该即是对此“随机过程”的实用分类,其实质是“自相关性”的两种极限情况,系统误差是自相关系数等于1的完全正相关,随机误差是自相关系数等于0的完全不相关。     

多体运动理论与齿轮测量中心的几何误差建模

本文阐述了多体系统的拓扑结构和低序体阵列,其特征矩阵的基本概念和构建方法,分析了多体系统误差运动的基本规律,结合891E型齿轮测量中心研究了根据特征矩阵得到其空间几何误差的算法,为齿轮测量中心精度分析和几何误差提供了一种理想的建模方法.     

一种基于EMD的系统误差分离方法

现有的测量误差分离方法大多需要预先确定误差的变化特征和基函数,而基函数的选取没有明确的标准.该文提出了一种采用自适应分解的经验模态法对周期较大的系统误差进行提取和自相关分析与经验模态相结合的分解法对周期较小的系统误差进行提取的方法.通过实际仿真结果表明,自相关分析与经验模态分解法不需要确定误差特征就能够成功分离系统误差与随机误差,达到了预期效果.     

标准量插入神经网络实时误差修正技术研究

误差修正技术是提高测量仪器精度的重要途径，本详细介绍了具有时变系统误差与随机误差的非稳定动态测量过程实时误差预测修正方法，即：用标准量插入法实时分离误差，采取神经网络建模法对分离出的误差进行实时建模，跟踪误差的变化，对其进行预报修正，通过自行研制的动态测角仪对这种误差修正技术进行了验证，使该测角仪的精度提高近10倍。     

Effect of the measurement errors on two one-sided Shewhart control charts for monitoring the ratio of two normal variables

Monitoring the ratio between two random normal variables plays an important role in many industrial manufacturing processes. In this paper, we suggest designing two one-sided Shewhart control charts monitoring this ratio. The numerical results show that the one-sided charts have more advantages compared with the two-sided Shewhart chart proposed previously in the literature. Moreover, we investigate the effect of measurement error on the performance of these control charts where the measurement error is supposed to follow a linear covariate error model. The change of model parameters from an in-control condition to an out-of-control is presented without using a strict assumption about the independence of the shift size from measurement errors. A valuable finding from this study is that taking multiple measurements per item is not an effective way to reduce the negative effect of measurement error on the Shewhart charts' performance.     

混凝土桥梁损伤识别的理论与试验研究

针对目前桥梁损伤识别理论受到测量误差、环境随机因素与模型误差等影响识别精度低的缺点,利用数理统计与随机分析的基本理论,提出进行混凝土桥梁裂缝等损伤识别的新理论方法;该方法首先寻找监测响应量的主要影响因素作为原因量,其次建立原因量与响应量的统计模型,最后利用预测模型进行判别是否发生损伤;利用该方法可以有效地考虑环境随机因素、测量误差等不确定因数,避免结构分析的计算模型误差,因而可以有效地提高进行损伤识别的精度;两根钢筋混凝土小梁模型试验证实了本理论的正确性。     

Elimination of systematic mass measurement errors in liquid chromatography-mass spectrometry based proteomics using regression models and a priori partial knowledge of the sample content

The high mass measurement accuracy and precision available with recently developed mass spectrometers is increasingly used in proteomics analyses to confidently identify tryptic peptides from complex mixtures of proteins, as well as post-translational modifications and peptides from nonannotated proteins. To take full advantage of high mass measurement accuracy instruments, it is necessary to limit systematic mass measurement errors. It is well known that errors in m/z measurements can be affected by experimental parameters that include, for example, outdated calibration coefficients, ion intensity, and temperature changes during the measurement. Traditionally, these variations have been corrected through the use of internal calibrants (well-characterized standards introduced with the sample being analyzed). In this paper, we describe an alternative approach where the calibration is provided through the use of a priori knowledge of the sample being analyzed. Such an approach has previously been demonstrated based on the dependence of systematic error on m/z alone. To incorporate additional explanatory variables, we employed multidimensional, nonparametric regression models, which were evaluated using several commercially available instruments. The applied approach is shown to remove any noticeable biases from the overall mass measurement errors and decreases the overall standard deviation of the mass measurement error distribution by 1.2-2-fold, depending on instrument type. Subsequent reduction of the random errors based on multiple measurements over consecutive spectra further improves accuracy and results in an overall decrease of the standard deviation by 1.8-3.7-fold. This new procedure will decrease the false discovery rates for peptide identifications using high-accuracy mass measurements.     

Assessment of Digital Image Correlation Measurement Accuracy in the Ultimate Error Regime: Main Results of a Collaborative Benchmark

We report on the main results of a collaborative work devoted to the study of the uncertainties associated with Digital image correlation techniques (DIC). More specifically, the dependence of displacement measurement uncertainties with both image characteristics and DIC parameters is emphasised. A previous work [Bornert et al. (2009) Assessment of digital image correlation measurement errors: methodology and results. Exp. Mech. 49, 353–370] dedicated to situations with spatially fluctuating displacement fields demonstrated the existence of an ‘ultimate error’ regime, insensitive to the mismatch between the shape function and the real displacement field. The present work is focused on this ultimate error. To ensure that there is no mismatch error, synthetic images of in‐plane rigid body translation have been analysed. Several DIC softwares developed by or in use in the French community have been used to explore the effects of a large number of settings. The discrepancies between DIC evaluated displacements and prescribed ones have been statistically analysed in terms of random errors and systematic bias, in correlation with the fractional part τ of the displacement component expressed in pixels. Main results are as follows: (i) bias amplitude is almost always insensitive to subset size, (ii) standard deviation of random error increases with noise level and decreases with subset size and (iii) DIC formulations can be split up into two main families regarding bias sensitivity to noise. For the first one, bias amplitude increases with noise while it remains nearly constant for the second one. In addition, for the first family, a strong dependence of random error with τ is observed for noisy images.     

A simulation technique for the evaluation of random error effectsin time-domain measurement systems

While many papers deal with time-domain network analyzer calibration procedures for the correction of systematic errors, little work has been published about the treatment of random errors. This paper is focused on the evaluation of random error effects in time-domain measurement systems. As a first step, an experimental identification of the measurement system random errors is achieved. Random errors addressed are jitter, vertical noise, and fast time drifts. Based on this identification, mathematical models are developed to simulate random errors. At a second step, time-domain measurements are simulated with these random errors. These simulations are used to predict measurement system repeatability and dynamic range. Then, as an application example, simulations of the measurement of the complex propagation coefficient and S parameters of a lossy mismatched microstrip line are achieved. By comparison with real measurements, it is shown that random error effects can be accurately predicted by Monte Carlo simulations     

Performance characteristics of a composite multivariate quality control system.

We present the results of an evaluation of the performance characteristics of a composite multivariate quality control (CMQC) system that incorporates quality control rules for univariate, multivariate, and correlation conditions. The CMQC system evaluated is designed to help analysts detect unacceptable trends and systematic error in one or more variables, unacceptable random error in one or more variables, and unacceptable changes in the correlation structure of any pair of variables. It is also designed to be tolerant of missing data, to allow analysts to reject as few as one or as many as all variables in a run, and to provide analysts with control statistics and graphics that logically relate to sources of analytical error. We show that the various components of the CMQC system have adequate statistical power to detect systematic errors, random errors, and correlation changes under the conditions likely to be encountered with multivariate analytical measurement systems: (1) a single variable with increased systematic or random error; (2) all variables or a subgroup of variables affected by a common problem that increases systematic or random error; and (3) missing data for one or more variables in a run. We also show that the power of the multivariate component of the CMQC system to detect systematic and random errors is higher than the power of an alternative multivariate test criterion.