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Journal articles on the topic 'Evaluation of Measurement Uncertainty'

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Author: Grafiati

Published: 14 March 2023

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1

Wan, Xiao Xia, Xin Guo Huang, and Zhen Liu. "Uncertainty Evaluation of Spectral Color Measurement." Advanced Materials Research 174 (December 2010): 36–39. http://dx.doi.org/10.4028/www.scientific.net/amr.174.36.

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Uncertainty evaluation of spectral color measurement is the best method of evaluation of color measurement result’s quality. Firstly type A and type B uncertainty of spectral reflectance are analyzed based on different uncertainty's sources, secondly uncertainty of chromaticity parameters are calculated based on spectral reflectance’s uncertainty. Lastly practicability of uncertainty evaluation of spectral color measurement is proved by experiments.

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Białek, Agnieszka, Sarah Douglas, Joel Kuusk, Ilmar Ansko, Viktor Vabson, Riho Vendt, and Tânia Casal. "Example of Monte Carlo Method Uncertainty Evaluation for Above-Water Ocean Colour Radiometry." Remote Sensing 12, no. 5 (February 29, 2020): 780. http://dx.doi.org/10.3390/rs12050780.

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We describe a method to evaluate an uncertainly budget for the in situ Ocean Colour Radiometric measurements. A Monte Carlo approach is chosen to propagate the measurement uncertainty inputs through the measurements model. The measurement model is designed to address instrument characteristics and uncertainty associated with them. We present the results for a particular example when the radiometers were fully characterised and then use the same data to show a case when such characterisation is missing. This, depending on the measurement and the wavelength, can increase the uncertainty value significantly; for example, the downwelling irradiance at 442.5 nm with fully characterised instruments can reach uncertainty values of 1%, but for the instruments without such characterisation, that value could increase to almost 7%. The uncertainty values presented in this paper are not final, as some of the environmental contributors were not fully evaluated. The main conclusion of this work are the significance of thoughtful instrument characterisation and correction for the most significant uncertainty contributions in order to achieve a lower measurements uncertainty value.

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Cheng, Yinbao, Zhongyu Wang, Xiaohuai Chen, Yaru Li, Hongyang Li, Hongli Li, and Hanbin Wang. "Evaluation and Optimization of Task-oriented Measurement Uncertainty for Coordinate Measuring Machines Based on Geometrical Product Specifications." Applied Sciences 9, no. 1 (December 20, 2018): 6. http://dx.doi.org/10.3390/app9010006.

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Measuring instruments are intended to be intelligent, precise, multi-functional and developing multidirectionally, scientific, and reasonable; the reliable evaluation of measurement uncertainty of precision instruments is also becoming more and more difficult, and the evaluation of the Coordinate Measuring Machines (CMM) measurement uncertainty is among the typical problems. Based on Geometric Product Specification (GPS), this paper has systematically studied the CMM uncertainty for evaluating the size and geometrical errors oriented toward measurement tasks, and thus has realized the rapid and reliable evaluation of the CMM uncertainty for task-oriented measurement. For overestimation of the CMM uncertainty for task-oriented measurements in the initial evaluation, a systematic optimization solution has been proposed. Finally, the feasibility and validity of the evaluation model and the optimization method have been verified by three different types of measurement examples of diameter, flatness and perpendicularity. It is typical and representative to systematically solve the problem of the CMM uncertainty for evaluating the measurement tasks targeted at dimensions and geometric errors, and the research contents can be effectively applied to solve the uncertainty evaluation problems of other precision instruments, which are of great practical significance not only for promoting the combination of modern uncertainty theory and practical applications but also for improving the application values of precision measurement instruments.

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KOIKE, Masayoshi. "Expression of Uncertainty in Measuremnet. Evaluation Methods of Uncertainty in Measurement." Journal of the Japan Society for Precision Engineering 65, no. 7 (1999): 941–44. http://dx.doi.org/10.2493/jjspe.65.941.

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Bernstein, Johannes, and Albert Weckenmann. "Measurement uncertainty evaluation of optical multi-sensor-measurements." Measurement 45, no. 10 (December 2012): 2309–20. http://dx.doi.org/10.1016/j.measurement.2011.10.032.

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Schiering, Nadine, and Olaf Schnelle-Werner. "Uncertainty evaluation in industrial pressure measurement." Journal of Sensors and Sensor Systems 8, no. 2 (July 30, 2019): 251–59. http://dx.doi.org/10.5194/jsss-8-251-2019.

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Abstract. In the process and manufacturing industry, pressure is one of the variables that most often need to be recorded and monitored. Whether in standard applications or under special operating conditions, each application requires pressure gauges that are perfect for their needs. In Germany, pressure gauges are calibrated by accredited calibration laboratories, according to guideline DKD-R 6-1 (DKD-R 6-1, 2014). This calibration guideline establishes minimum requirements for the calibration procedure and the measurement uncertainty calculation when calibrating pressure gauges. In addition to the uncertainty contributions due to the calibration, the uncertainty contributions due to the specific application, like extreme temperatures, high pressure in containers, extreme height differences, shocks, aggressive media or problematic physical product properties, should be taken into account. This paper presents the approach in which the measurement uncertainty can be calculated in industrial pressure measurements. Furthermore, the individual uncertainty contributions and their identification or origin are discussed. Finally, an example of a measurement uncertainty budget is shown as an important tool in the measurement uncertainty calculation.

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Stefopoulos, Georgios, Stylianos Rigas, Panagiotis Tsirikoglou, and Anestis I. Kalfas. "Evaluation of pressure and species concentration measurement using uncertainty propagation." E3S Web of Conferences 345 (2022): 02008. http://dx.doi.org/10.1051/e3sconf/202234502008.

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This paper presents a probabilistic uncertainity evaluation method as described in the Guide to the Expression of Uncertainty in Measurements (GUM) and its application to probe measurements on pressure and fuel concentration. All sources of unceratinties are expressed as probability distributions. Consequently, the overall standard uncertainty of the quantity can be calculated using the Gaussian error propagation formula. The result of the uncertainty evaluation yields the most probable value of the measurand and describes its distribution in terms of rectangular (standard uncertainty) or gaussian (“expanded” uncertainty) distribution. A pitot-static probe and a fuel-concentration stem probe are used in order to demonstrate the principle of the probabilistic uncertainty evaluation method. The uncertainty induced by the pressure and concentration data acquisition system as well as the calibration of the fuel-concentration probe are included in the analysis. The overall “expanded” uncertainties for the measured and calculated values are presented as a function of different inlet fuel flows. In addition to this, the individual sources of uncertainty to the overall standard uncertainty are presented and discussed. Moreover, the transformation of standard uncertainty to “expanded” uncertainty will provide the deviation of the measurement in a 95% or 99% normal distributed interval instead of a 67% rectangular distributed interval.

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Dieng, A., and A. Veres. "Radiotherapy dose measurement uncertainty evaluation." Physica Medica 29 (June 2013): e45. http://dx.doi.org/10.1016/j.ejmp.2013.08.137.

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9

Kristiansen, Jesper. "The Guide to Expression of Uncertainty in Measurement Approach for Estimating Uncertainty." Clinical Chemistry 49, no. 11 (November 1, 2003): 1822–29. http://dx.doi.org/10.1373/clinchem.2003.021469.

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Abstract Background: The aim of the Guide to Expression of Uncertainty in Measurement (GUM) is to harmonize the different practices for estimating and reporting uncertainty of measurement. Although there are clear advantages in having a common approach for evaluating uncertainty, application of the GUM approach to chemistry measurements is not straightforward. In the above commentary, Krouwer suggests that the GUM approach should not be applied to diagnostic assays, because (a) the quality of diagnostic assays is to low, and (b) the GUM uncertainty intervals are too narrow to predict the outliers that occasionally trouble these methods. Methods: Some of the examples presented by Krouwer are reviewed. Sodium measurements are modeled mathematically to illustrate the GUM approach to uncertainty. A standardized uncertainty evaluation process is presented. Results: Modeling of sodium measurements demonstrates how the GUM uncertainty interval reflects the treatment of a bias: The width of the uncertainty interval varied depending on whether a correction for a calibrator lot bias was applied, but in both cases it was consistent with the distribution of measurement results. Expanding the uncertainty interval to include outliers runs counter to the definition of uncertainty. Used appropriately, the GUM uncertainty can be helpful in detecting outliers. In standardizing the uncertainty evaluation, the importance of the analytical imprecision and traceability was emphasized. It is problematic that manufacturers of commercial assays rarely inform about the uncertainty of the values assigned to the calibrators. As demonstrated by an example, external quality-assurance data may be used to estimate this uncertainty. Conclusions: The GUM uncertainty should be applied to measurements in laboratory medicine because it may actually support the forces that drive the work on improving the quality of measurement procedures. However, it is important that the GUM approach is made more manageable by standardizing the uncertainty evaluation procedure as much as possible. It is essential to focus on the traceability and uncertainty of calibrators and reagents supplied by manufacturers of assays. Information about uncertainty is necessary in the evaluation of the uncertainty associated with manufacturers’ measurement procedures, and in general it may force manufacturers to increase their efforts in improving the metrologic and analytical quality of their products.

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Vulevic, Branislav, Cedomir Belic, and Luka Perazic. "Measurement uncertainty in broadband radiofrequency radiation level measurements." Nuclear Technology and Radiation Protection 29, no. 1 (2014): 53–57. http://dx.doi.org/10.2298/ntrp1401053v.

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For the evaluation of measurement uncertainty in the measurement of broadband radio frequency radiation, in this paper we propose a new approach based on the experience of the authors of the paper with measurements of radiofrequency electric field levels conducted in residential areas of Belgrade and over 35 municipalities in Serbia. The main objective of the paper is to present practical solutions in the evaluation of broadband measurement uncertainty for the in-situ RF radiation levels.

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de Oliveira, Elcio. "Critical Metrological Evaluation of Fuel Analyses by Measurement Uncertainty." Metrology and Measurement Systems 18, no. 2 (January 1, 2011): 235–48. http://dx.doi.org/10.2478/v10178-011-0006-4.

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Critical Metrological Evaluation of Fuel Analyses by Measurement UncertaintyIt is now widely recognized that the evaluation of the uncertainty associated with a result is an essential part of any quantitative analysis. One way to use the estimation of measurement uncertainty as a metrological critical evaluation tool is the identification of sources of uncertainty on the analytical result, knowing the weak steps, in order to improve the method, when it is necessary. In this work, this methodology is applied to fuel analyses and the results show that the relevant sources of uncertainty are: beyond the repeatability, the resolution of the volumetric glassware and the blank in the analytical curve that are little studied.

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Jiang, Wei, and Qi Zhang. "Study on Random-Fuzzy Variables Method for ADC Measurement Uncertainty Evaluation." Applied Mechanics and Materials 568-570 (June 2014): 76–81. http://dx.doi.org/10.4028/www.scientific.net/amm.568-570.76.

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The random-fuzzy variables (RFVs) method based on the theory of evidence is studied, for the need of ADC uncertainty evaluation and the limitations of existing approaches. The connotation of RFVs adopted for expression of measurement result together with its associated uncertainty is discussed, and the RFVs mathematics for uncertainty propagation is analyzed. RFVs can naturally separate the contributions to the measurement uncertainty of the systematic and random effects. Taking power measurements as an example, RFVs method is applied to the presentation and propagation of the measurement uncertainty of ADC, and the results are compared with those obtained by GUM, which shows the RFVs method can be effectively employed in evaluating uncertainty of ADC, and is capable of providing the interval of confidence for all possible levels of confidence, within which the measurement result is supposed to lie.

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Mihaljević, Morana, Damir Markučič, Biserka Runje, and Zdenka Keran. "Measurement uncertainty evaluation of ultrasonic wall thickness measurement." Measurement 137 (April 2019): 179–88. http://dx.doi.org/10.1016/j.measurement.2019.01.027.

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Bui, Duc Ky, Ngoc Quynh Nguyen, Duc Thang Duong, Ngoc Thiem Le, Quang Tuan Ho, Thanh Ha Tran, Anh Duong Bui Thi, Huu Quyet Nguyen, and Van Trieu Duong. "Evaluating uncertainty of some radiation measurand using Monte Carlo method." Nuclear Science and Technology 9, no. 4 (September 3, 2021): 34–40. http://dx.doi.org/10.53747/jnst.v9i4.135.

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Evaluating measurement uncertainty of a physical quantity is a mandatory requirement for laboratories within the recognition ISO/IEC 17025 certification to access reliability of measured results. In this work, the uncertainty of ionizing radiation measurements such as air-kerma, personal dose equivalent Hp(d) was evaluated based on GUM method and Monte Carlo method. An uncertainty propagation software has been developed for evaluation of the measurement uncertainty more convenient.

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Jakubiec, Władysław, Wojciech Płowucha, and Paweł Rosner. "Evaluation of measurement uncertainty in mechanical engineering. Uncertainty budget." Mechanik, no. 12 (December 2016): 1802–5. http://dx.doi.org/10.17814/mechanik.2016.12.567.

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Zakharov, I. P., O. A. Botsiura, I. Yu Tsybina, and О. О. Zakharov. "MEASUREMENT UNCERTAINTY EVALUATION AT MICROMETER CALIBRATION." Ukrainian Metrological Journal, no. 3A (November 30, 2020): 196–201. http://dx.doi.org/10.24027/2306-7039.3a.2020.220313.

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Menin, Boris. "Plank’s Constant: Evaluation of Measurement Uncertainty." New Horizons in Mathematical Physics 2, no. 2 (June 8, 2018): 21–28. http://dx.doi.org/10.22606/nhmp.2018.22001.

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18

Désenfant, Michèle, and Marc Priel. "Road map for measurement uncertainty evaluation." Measurement 39, no. 9 (November 2006): 841–48. http://dx.doi.org/10.1016/j.measurement.2006.04.008.

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19

Ciccarelli, Matteo, and Kirstin Hubrich. "Forecast uncertainty: sources, measurement and evaluation." Journal of Applied Econometrics 25, no. 4 (May 4, 2010): 509–13. http://dx.doi.org/10.1002/jae.1179.

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20

Delker, Collin. "Evaluating Risk in an Abnormal World: How Arbitrary Probability Distributions Affect False Accept and Reject Evaluation." NCSL International measure 13, no. 2 (June 2021): 45–55. http://dx.doi.org/10.51843/measure.13.2.6.

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Risk mitigation strategies commonly use the test uncertainty ratio (TUR) and end-of-period reliability (EOPR) to ensure a measurement is adequate for making acceptance decisions. Unfortunately, the common guidance of maintaining a TUR of at least 4:1 was developed to simplify the underlying calculus in an era predating modern computing and assumes that the probability distributions describing the product and the measurement uncertainty are unbiased and normally distributed. The Guide to the Expression of Uncertainty in Measurement and its supplements describe several situations where uncertainty in the measurement will not follow a normal distribution. Despite the evidence of non-normal behavior in measurements and products, risk evaluations typically assume normality in both distributions. While evaluating the probability of false accept (PFA) and the probability of false reject (PFR) is more challenging when the probability distributions are non-normal, the calculus is straightforward using either numerical integration or Monte Carlo techniques. This work considers several case studies of risk evaluation, including both global and specific risk, when the product or the test measurement uncertainty do not follow normal distributions. Neglecting non-normal behavior might greatly affect PFA and PFR by either over- or underestimating the probabilities depending on the parameters of the distributions.

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Yadav, Sanjay, Jiro Matsuda, and Lalith Prasantha Liyanawadu Chitarage. "Studies on Uncertainty Evaluation in Straightness Measurement." Journal of Robotics and Mechatronics 13, no. 6 (December 20, 2001): 643–50. http://dx.doi.org/10.20965/jrm.2001.p0643.

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The evaluation of uncertainty associated with measurements of geometrical forms is a subject of considerable interest these days in machine design. In the present study, a straightness measuring machine that was developed at National Research Laboratory of Metrology (NRLM), Japan is investigated to evaluate various uncertainty components associated with measurements over a length of 500mm of a datum cylinder. Investigations on the heating effect of the measuring machine due to heat generated by an electric motor, the effect of the stability and shape of the probe head, the effect of bending of the carriage bed and the effect of angular movement of the carriage i.e., yawing and pitching, are carried out, with consideration of the magnitudes of the uncertainty components. The studies have broken the 200mm limit in straightness measurement range imposed by the non-availability of good reference standards beyond this range and would lead to manufacturing of reliable, accurate and precise commercial straightness measuring machine.

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22

Didenko, Valeriy Ivanovich. "Improved evaluation of uncertainty for indirect measurement." ACTA IMEKO 6, no. 2 (July 21, 2017): 89. http://dx.doi.org/10.21014/acta_imeko.v6i2.320.

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<p class="Abstract"><span lang="EN-US">The paper gives formulae for uncertainty evaluation of an indirect measurement based on direct measurements made by different types of measuring devices. The first type of the measuring devices has the specifications of a total error (e.g. digital instruments), while the second type has the specifications of offset, gain and linearity errors (e.g. analog to digital converters). The choice of a device range and the configuration of measuring circuits for decreasing uncertainty are considered. The conversion of the specifications for the first type to the specifications for the second type is discussed.</span></p>

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Du, Wen Yue, Jian Guo Yu, and Shi Yu. "Uncertainty Evaluations on the Measurement of “Carbon” in the Research on Karst Carbon Sink Effect - A Case for Dissolved Inorganic Carbon." Applied Mechanics and Materials 303-306 (February 2013): 703–13. http://dx.doi.org/10.4028/www.scientific.net/amm.303-306.703.

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In order to set up an evaluation method for the uncertainty in determining dissolved inorganic carbon (DIC) in karst river basin by Non-dispersive Infrared (NDIR) absorption detection technique, the continuous propagation model of uncertainty is used to evaluating the uncertainties from DIC measurement in two typical karst groundwater samples. The main steps are as follows: to fit the calibration curve by means of double-error regression firstly; and then to quantify each uncertainty component in the evaluation process; lastly to obtain the synthetic uncertainty model for DIC determination results. Calculations through experimental results show that: (1) the main sources of measurement uncertainty derive from the sub-uncertainties of calibration solutions, calibration curve fitting and measurements process; (2) the lower the DIC content in groundwater samples, the greater the relative uncertainty of measurement results, and the sub-uncertainty from the fitting of calibration curve gives one major contribution to the total uncertainty.

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Giercuszkiewicz-Bajtlik, Mieczysława, and Barbara Gworek. "Experimental methods of evaluating measurement uncertainty resulting from sample collection and preparation for analysis in chemical laboratories / Niepewność pomiaru wynikająca z poboru i przygotowania próbek do badań w laboratorium chemicznym oszacowana metodą doświadczalną." Ochrona Srodowiska i Zasobów Naturalnych 25, no. 3 (September 1, 2014): 21–25. http://dx.doi.org/10.2478/oszn-2014-0016.

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Abstract A measurement result should be presented with its measurement uncertainty determined according to the evaluation method. The paper presents an experimental method used to evaluate measurement uncertainty resulting from the collection and preparation of samples for analysis in chemical labs. Evaluation of measurement uncertainty of the content of particular elements in actual samples has been conducted with the method of precision determination using ROBUST ANOVA analysis of variance. Analyses of soil samples were made in the Laboratory of the Institute of Environmental Protection - National Research Institute. The experimental method of evaluating measurement uncertainty allows a fast and relatively simple evaluation of all sources of uncertainty resulting from sample collection and preparation for analysis in chemical labs

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Khamkhanova, D. N., M. T. Khadykov, V. I. Mosorov, and K. K. Bakhrunov. "Evaluation of the measurement uncertainty during the tensile tests of high-strength bolts." iPolytech Journal 26, no. 4 (December 29, 2022): 601–11. http://dx.doi.org/10.21285/1814-3520-2022-4-601-611.

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A developed methodology for evaluating measurement uncertainty during the tensile tests of metals and alloys is presented. In this connection, the various sources of uncertainty are considered. The object of studies included high-strength bolts made of 40Kh “Selekt” steel, which were subjected to tensile tests according to the GOST 1497-85 State Standard using a UMM-100 universal tensile testing machine. The basic sources of uncertainty in the measurement of relative elongation and reduction were shown to include repeated measurements of relative elongation; errors of measuring the finite length by a vernier calliper and marking the initial length of a calculated section, as well as the measurement error of the tensile testing machine. These also include operator reading error, repeated relative reduction measurements, as well as the error of measuring the sample diameter by a micrometer before and after the tests. During the measurements, temperature deviation was demonstrated to constitute an additional source of uncertainty when the ambient temperature is different from the standard temperature value ((20±5)°C). Assumptions underlying laws describing the distribution of input values were assigned. Tensile tests are shown to be characterised by two components of uncertainty evaluated as types A and B. A mathematical model constructed for measuring relative elongation and relative reduction during tensile tests is presented. The standard uncertainties of input values are evaluated based on the assumed laws of their distribution. A correlation between the final length of the calculated section, the diameter of the sample following the test, and the applied force, is revealed. Expressions for the calculation of sensitivity coefficients, which characterize variations in the output value (relative elongation) depending on variations of input values, were obtained. The total and extended measurement uncertainties were evaluated. Based on the performed studies, a procedure for evaluating measurement uncertainty when carrying out tensile tests of high-strength bolts was described. The evaluation of measurement uncertainty during the product testing was shown to be a rather labourconsuming work. In this regard, the authors propose the development of procedures for evaluating the measurement uncertainty during the tests with their inclusion into regulatory documents for control methods.

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Zhao, Feng Xia, Qing Hai Wang, Lin Na Zhang, and Peng Zheng. "Uncertainty Evaluation of SEM-Based Nanoroughness Measurement." Advanced Materials Research 472-475 (February 2012): 1319–22. http://dx.doi.org/10.4028/www.scientific.net/amr.472-475.1319.

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In metrology, a measurement process without uncertainty evaluation is incomplete. The same applies to nanoroughness metrology. In this paper, Scanning Electron Microscopy (SEM） measurement uncertainty of nanoroughness is studied based on ISO Geometric Product Specification and Verification (GPS). The uncertainty propagation model of SEM-based nanoroughness measurement is presented. The evaluation model of SEM-based standard measurement uncertainty is conducted. As a case study, the line edge roughness uncertainty produced by the JSM-6700F SEM is evaluated.

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Płowucha, Wojciech, Władysław Jakubiec, and Paweł Rosner. "Evaluation of measurement uncertainty – Monte Carlo method." Mechanik 90, no. 12 (December 11, 2017): 1152–54. http://dx.doi.org/10.17814/mechanik.2017.12.195.

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Advantages of Monte Carlo method are presented and compared with A and B type method of measurement uncertainty evaluation. Problem of uncertainty determination, in case of two or more dominant components, is discussed. Results of experiment to evaluate impact of probing strategy on measurement uncertainty of roundness deviation are presented. Issue of ‘systematic error’ in evaluation of coordinate measurement uncertainty is analyzed.

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Chuangui, Yang, Liu Xingbao, Yue Xiaobin, Mi Liang, Wang Junwen, Xia Yangqiu, Yu Hailian, and Chen Heng. "Uncertainty analysis and evaluation of measurement of the positioning repeatability for industrial robots." Industrial Robot: An International Journal 45, no. 4 (June 18, 2018): 492–504. http://dx.doi.org/10.1108/ir-06-2017-0109.

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PurposeThis paper aims to solve the nonlinear problem in the uncertainty evaluation of the measurement of the positioning repeatability (RP) of industrial robots and provide guidance to restrict the uncertainty of measurement of RP (uRP).Design/methodology/approachFirstly, some uncertain sources existing in the measurement procedure of RP are identified. Secondly, the probability distribution function (PDF) of every source is established on the basis of its measurements. Some spatial combined normal distributions are adopted. Then, a method, based on Monte Carlo method (MCM) and established measurement model, is developed for the estimation ofuRP. Thirdly, some tests are developed for the identification and validation of the selected PDFs of uncertain sources. Afterwards, the proposed method is applied for the evaluation and validation of theuRP. Finally, influence analyses of some key factors are proposed for the quantification of their relative contributions touRP.FindingsResults show that the proposed method can reasonably and objectively estimate theuRPof the selected industrial robot, and changes of the industrial robots’ position and the laser trackers measurement are correlated. Additionally, theuRPof the selected industrial robot can be restricted by using the results of its key factors onuRP.Originality/valueThis paper proposes the spatial combined normal distribution to model the uncertainty of the repeatability of the laser tracker and industrial robot. Meanwhile, the proposed method and influence analyses can be used in estimating and restricting theuRPand thus useful in determining whether the RP of a tested industrial robot meets its requirements.

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Zhang, Xiao Zhang, Ai Dong Ge, Jian Wei Ma, and Yu Jie Bai. "Evaluation of Measurement Uncertainty from Imperfect Data." Applied Mechanics and Materials 457-458 (October 2013): 815–20. http://dx.doi.org/10.4028/www.scientific.net/amm.457-458.815.

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The uncertainty evaluation process of imperfect experimental data is presented in this paper. In the process, data neither in steady state nor under normal distribution compared with the conventional assumptions are considered. Results of the evaluation show that the uncertainty is asymmetry to the mean of the data while symmetry in conventional way. Furthermore, three ways to deal with the uncertainty propagation are discussed, and the probability propagation is simulated by Monte Carlo method.

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Saito, Ichiro, and Hideo Onuki. "Evaluation of measurement uncertainty for cryogenic radiometer." JOURNAL OF THE ILLUMINATING ENGINEERING INSTITUTE OF JAPAN 80, Appendix (1996): 288. http://dx.doi.org/10.2150/jieij1980.80.appendix_288.

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Vlahinic, Sasa, Dubravko Frankovic, Marijana Zivic Durovic, and Nino Stojkovic. "Measurement Uncertainty Evaluation of Transmission Line Parameters." IEEE Transactions on Instrumentation and Measurement 70 (2021): 1–7. http://dx.doi.org/10.1109/tim.2021.3070600.

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Cheng, Guo, Siyuan Liu, and Ke Zhang. "Evaluation of planar inclination error measurement uncertainty." International Journal of Wireless and Mobile Computing 19, no. 1 (2020): 48. http://dx.doi.org/10.1504/ijwmc.2020.10031479.

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Zhang, Ke, Guo Cheng, and Siyuan Liu. "Evaluation of planar inclination error measurement uncertainty." International Journal of Wireless and Mobile Computing 19, no. 1 (2020): 48. http://dx.doi.org/10.1504/ijwmc.2020.109263.

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Kim, Jong Jip, and Su Yeon Kang. "Uncertainty evaluation in electrochemical noise resistance measurement." Corrosion Science and Technology 12, no. 5 (October 31, 2013): 220–26. http://dx.doi.org/10.14773/cst.2013.12.5.220.

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Wang, X. M., J. L. Xiong, and J. Z. Xie. "Evaluation of Measurement Uncertainty Based on Monte Carlo Method." MATEC Web of Conferences 206 (2018): 04004. http://dx.doi.org/10.1051/matecconf/201820604004.

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The developments of scientific technology are inseparable from various measurement methods. Measurement uncertainty is an indication that evaluates the credibility of the measurement results directly, and it affects the development of technology and economy indirectly. Monte Carlo method (MCM) is an effective method to evaluate the measurement uncertainty, because it can evaluate the measurement uncertainty in the complex model and environment. The application range of MCM is large than the traditional method that recommended in the “Guide to the Uncertainty in Measurement (GUM)”. Based on the study of Monte Carlo method, this paper establishes a model for MCM to evaluate the uncertainty of measurement. Finally, as an example, we use the MCM to evaluate the measurement uncertainty of the six-and-a-half digital multimeter (DMM), which verifies the validity of MCM for evaluating the measurement uncertainty.

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Chen, Xiao Huai, Z. Y. Cheng, and Ye Tai Fei. "Research on Measurement Uncertainty Evaluation Methods Based on Bayesian Principle." Key Engineering Materials 381-382 (June 2008): 583–86. http://dx.doi.org/10.4028/www.scientific.net/kem.381-382.583.

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In current application of measurement uncertainty evaluation, dynamic uncertainty evaluation simply uses the static uncertainty methods. To change the situation, a new evaluation method of measurement uncertainty is investigated based on Bayesian principle in this paper. Bayesian evaluation method uses conjugate normal-inverted gamma distribution as the distribution function in uncertainty evaluation, which can be employed to evaluate both static and dynamic measurement uncertainty. The evaluation method put forward in this paper can achieve higher evaluation accuracy than the conventional methods, particularly in processing dynamic data with small samples. It has been proved in theory and by computer simulation.

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Kovačević, Aleksandar, Dragoljub Brkić, and Predrag Osmokrović. "Evaluation of measurement uncertainty using mixed distribution for conducted emission measurements." Measurement 44, no. 4 (May 2011): 692–701. http://dx.doi.org/10.1016/j.measurement.2010.12.006.

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Jakubiec, Władysław, Wojciech Płowucha, and Paweł Rosner. "Evaluation of measurement uncertainty in mechanical engineering. Discussion of uncertainty budget." Mechanik, no. 12 (December 2016): 1806–8. http://dx.doi.org/10.17814/mechanik.2016.12.568.

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IMAI, Hidetaka. "Expression of Uncertainty in Measurement. From Error/Accuracy to Uncertainty Evaluation." Journal of the Japan Society for Precision Engineering 65, no. 7 (1999): 937–40. http://dx.doi.org/10.2493/jjspe.65.937.

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García-Balboa, José, Antonio Ruiz-Armenteros, José Rodríguez-Avi, Juan Reinoso-Gordo, and Juan Robledillo-Román. "A Field Procedure for the Assessment of the Centring Uncertainty of Geodetic and Surveying Instruments." Sensors 18, no. 10 (September 20, 2018): 3187. http://dx.doi.org/10.3390/s18103187.

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The uncertainty evaluation of survey measurements is a daily and essential task in any surveying work. The result of a measurement is, in fact, only complete when accompanied by a statement of its uncertainty. Miscentring, or centring error, is one of the sources of uncertainty in every basic survey measurement which may have a great effect on horizontal angle measurement for short distances. In the literature, different terms and values are considered to refer to this source of uncertainty. Standard ISO 17123 provides different procedures for assessing the measurement uncertainty of geodetic and surveying instruments, with the aim of checking their suitability for the intending and immediate task in field conditions. ISO 17123 is aware of the importance of uncertainty in the instrument centring, but it does not propose any standardised procedure for its assessment. In this study, we propose such a procedure following a Type A evaluation (through the statistical analysis of series of observations), avoiding using values from Type B evaluations (from manufacturer’s specifications, handbooks, personal experiences, etc.) that could be unsuitable for the conditions of the task. Uncertainty can be individualised for a particular instrument (which includes the plummet type), ground mark, operator, and other factors on which the results could be dependent. The testing methodology includes a configuration of the test field, measurements, and calculation, following the structure of each part of the standard ISO 17123. An experimental application is included with two different total stations, which also includes a statistical analysis of the results.

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Mueller, Tobias, Meike Huber, and Robert Schmitt. "Modelling complex measurement processes for measurement uncertainty determination." International Journal of Quality & Reliability Management 37, no. 3 (January 1, 2020): 494–516. http://dx.doi.org/10.1108/ijqrm-07-2019-0232.

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Purpose Measurement uncertainty is present in all measurement processes in the field of production engineering. However, this uncertainty should be minimized to avoid erroneous decisions. Present methods to determine the measurement uncertainty are either only applicable to certain processes and do not lead to valid results in general or require a high effort in their application. To optimize the costs and benefits of the measurement uncertainty determination, a method has to be developed which is valid in general and easy to apply. The paper aims to discuss these issues. Design/methodology/approach This paper presents a new technique for determining the measurement uncertainty of complex measurement processes. The approximation capability of artificial neural networks with one hidden layer is proven for continuous functions and represents the basis for a method for determining a measurement model for continuous measurement values. Findings As this method does not require any previous knowledge or expertise, it is easy to apply to any measurement process with a continuous output. Using the model equation for the measurement values obtained by the neural network, the measurement uncertainty can be derived using common methods, like the Guide to the expression of uncertainty in measurement. Moreover, a method for evaluating the model performance is presented. By comparing measured values with the output of the neural network, a range in which the model is valid can be established. Combining the evaluation process with the modelling itself, the model can be improved with no further effort. Originality/value The developed method simplifies the design of neural networks in general and the modelling for the determination of measurement uncertainty in particular.

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Shen, Han Lin, and Hai Yan Luo. "Evaluation of Uncertainty of Hydrogen's Consistency in Transformer Oil by Gas Chromatography." Advanced Materials Research 936 (June 2014): 1567–70. http://dx.doi.org/10.4028/www.scientific.net/amr.936.1567.

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According to the Evaluation and Expression of Uncertainty in Measurements, the author establishes the mathematical model for measuring the purity of hydrogen in transformer oil by means of Gas Chromatography. Meanwhile the author analyses resources of uncertainty and deduces formulas of uncertainty measurement. By calculating uncertainties of the components, the author obtains the synthesis uncertainty.

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Kušnerová, Milena, Jan Valíček, Marta Harničárová, Jan Kmec, Michal Řepka, Roman Danel, Anton Panda, and Zuzana Palková. "The Combined Relative Uncertainty of Measurement Results by Prototype Semi-Automated Calorimetric Chamber." Measurement Science Review 19, no. 2 (April 1, 2019): 53–60. http://dx.doi.org/10.2478/msr-2019-0009.

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Abstract The paper presents an evaluation of the combined relative uncertainty of the result of direct step temperature measurements aimed at evaluation of the indirect measurements of the specific thermal capacity of the heat insulating concrete by means of a pair of resistive cable thermometers fitted with Pt100 temperature sensors and integrated into a computer-controlled calorimetric chamber. In particular, it is a proposal of evaluation of the overall relative uncertainty of the measurement of partial temperatures measured in equidistant time steps, in a relatively wider time interval. In practice, the uncertainty of the result of step temperature measurements is most often declared only by the instrument uncertainty specified by the manufacturer. The exact evaluation of the result of the measurements of thermal and temperature material parameters measured by the calorimetric comparison method is required by the fact that the investigated samples are made of newly designed non-tabulated building materials and that the measurements are made by a prototype device.

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Li, Jin Hai, Xing Long Wang, Suo Sheng Cao, and Zhan Biao Song. "Evaluation of Uncertainty Measurement with Intelligent Contact Type Interferometer." Applied Mechanics and Materials 333-335 (July 2013): 348–52. http://dx.doi.org/10.4028/www.scientific.net/amm.333-335.348.

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An evaluation is conducted to the uncertainty measurement with contact type interferometer after intellectualized improvement. CMOS sensing photographing technology and computer technology are applied to the improvement of contact type interferometer. A mathematical modeling is established by taking pixel number as reading unit and gauge block length comparative measurement method as means. The purpose is to analyze the source of measurement uncertainty, estimate the standard uncertainty of input, evaluate and set forth the expanded uncertainty. The result of evaluation is superior to the level of measurement uncertainty before improvement.

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Xin, Tian, Jun Gang Zhou, Shi Qing Su, Zhong Chun Xu, and Ya Liu. "Evaluation of Measurement Uncertainty for Ball Pressure Test Based on Reading Microscope." Applied Mechanics and Materials 742 (March 2015): 61–64. http://dx.doi.org/10.4028/www.scientific.net/amm.742.61.

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Ball pressure test is the heat resistance test of the electrical products material, whether it meets the requirements of material performance to eliminate security risks. Using reading microscope for ball pressure test measurement and its measurement uncertainty was analyzed in this paper, including measurement uncertainty sources, established mathematical model, uncertainty example and measurement uncertainty result. The measurement uncertainty was analyzed to ensure the accuracy of ball pressure test measurement result.

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Płowucha, Wojciech. "Evaluation of coordinate measurement uncertainty of parallelism of axes by means of sensitivity analysis method." Mechanik 91, no. 12 (December 10, 2018): 1136–39. http://dx.doi.org/10.17814/mechanik.2018.12.203.

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The examples of measurement of three variants of the parallelism of the connecting-rod center axes the theoretical basis of a new method for estimating the uncertainty of coordinate measurements. This is a continuation of the article “Estimation of coordinate measurement uncertainty – theoretical fundamentals” (Mechanik 11, 2018). In the first variant, this tolerance field is in the form of a cylinder, in the other two it has the form of a pair of planes. In the examples presented, uncertainty budgets contain six or nine input factors. In all cases, when the axes of the measured holes are parallel to one of the machine axes, only two input factors affect the uncertainty of the measurement of parallelism deviation.

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Choukulkar, Aditya, W. Alan Brewer, Scott P. Sandberg, Ann Weickmann, Timothy A. Bonin, R. Michael Hardesty, Julie K. Lundquist, et al. "Evaluation of single and multiple Doppler lidar techniques to measure complex flow during the XPIA field campaign." Atmospheric Measurement Techniques 10, no. 1 (January 23, 2017): 247–64. http://dx.doi.org/10.5194/amt-10-247-2017.

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Abstract. Accurate three-dimensional information of wind flow fields can be an important tool in not only visualizing complex flow but also understanding the underlying physical processes and improving flow modeling. However, a thorough analysis of the measurement uncertainties is required to properly interpret results. The XPIA (eXperimental Planetary boundary layer Instrumentation Assessment) field campaign conducted at the Boulder Atmospheric Observatory (BAO) in Erie, CO, from 2 March to 31 May 2015 brought together a large suite of in situ and remote sensing measurement platforms to evaluate complex flow measurement strategies. In this paper, measurement uncertainties for different single and multi-Doppler strategies using simple scan geometries (conical, vertical plane and staring) are investigated. The tradeoffs (such as time–space resolution vs. spatial coverage) among the different measurement techniques are evaluated using co-located measurements made near the BAO tower. Sensitivity of the single-/multi-Doppler measurement uncertainties to averaging period are investigated using the sonic anemometers installed on the BAO tower as the standard reference. Finally, the radiometer measurements are used to partition the measurement periods as a function of atmospheric stability to determine their effect on measurement uncertainty. It was found that with an increase in spatial coverage and measurement complexity, the uncertainty in the wind measurement also increased. For multi-Doppler techniques, the increase in uncertainty for temporally uncoordinated measurements is possibly due to requiring additional assumptions of stationarity along with horizontal homogeneity and less representative line-of-sight velocity statistics. It was also found that wind speed measurement uncertainty was lower during stable conditions compared to unstable conditions.

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Gao, Zhi Qing, Nai Qi Shen, Xu Li, and Zong Yun Shu. "Evaluation of Measurement Uncertainty for Water Content Tests in Soil Samples." Advanced Materials Research 718-720 (July 2013): 984–88. http://dx.doi.org/10.4028/www.scientific.net/amr.718-720.984.

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Measurement uncertainty in the water content test for soil samples is an index for evaluating the reliability of the experiment. It affects the accuracy of some parameters such as porosity and saturation, which are derived from water content. According to the requirements of the standard JJF 1059-1999 Uncertainty Evaluation and Denotation of the Measurement Results, the uncertainty for the measurement results of the water content in soil samples was evaluated. Concerning the characteristics of the water content determination in soil samples, a pail of soil sample was taken as specimen due to its relative homogeneity. The factors affecting the measurement accuracy were discussed. The result shows that the expanded uncertainty of measurement results of the water content in soil samples was 1.6% under the proposed testing conditions.

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Figueiredo Junior, Heber, Roger Matsumoto Moreira, and Pedro Bastos Costa. "Discontinuity measurement uncertainty evaluation using the Feeler PIG." International Journal of Metrology and Quality Engineering 13 (2022): 15. http://dx.doi.org/10.1051/ijmqe/2022017.

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The Feeler PIG is equipment that uses a geometric sensor to carry out the internal inspection of piping of different sizes. Several experiments on laboratory benches were carried out to evaluate the measuring accuracy of the Feeler PIG, considering a rotating metallic disk with discontinuities machined in its body and a single sensor for the detection of defects. The present work aims to study the measurement uncertainties with the Feeler PIG, through a laboratory experiment, comparing the results found by the device measurements, in a pipe with a nominal diameter of 6” in PVC, with synthetic discontinuities made in a calibrated 3D printer in a laboratory. The tests were based on the operational use of the PIG, in which it moves inside the pipe under different speed conditions and with an arrangement of synthetic discontinuities with 5 different geometries. The methodology used was the same performed in a calibration laboratory, in which the sum of type A and B uncertainties are multiplied by the coverage factor (k), which proved capable of reaching expanded uncertainties of the order of ± 3.1% to 5.0% using a confidence level of 95.45%.

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Chen, Lei, Shuang Li, Yucen Zhong, and Zhenyao Shen. "Improvement of model evaluation by incorporating prediction and measurement uncertainty." Hydrology and Earth System Sciences 22, no. 8 (August 3, 2018): 4145–54. http://dx.doi.org/10.5194/hess-22-4145-2018.

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Abstract. Numerous studies have been conducted to assess uncertainty in hydrological and non-point source pollution predictions, but few studies have considered both prediction and measurement uncertainty in the model evaluation process. In this study, the cumulative distribution function approach (CDFA) and the Monte Carlo approach (MCA) were developed as two new approaches for model evaluation within an uncertainty condition. For the CDFA, a new distance between the cumulative distribution functions of the predicted data and the measured data was established in the model evaluation process, whereas the MCA was proposed to address conditions with dispersed data points. These new approaches were then applied in combination with the Soil and Water Assessment Tool in the Three Gorges Region, China. Based on the results, these two new approaches provided more accurate goodness-of-fit indicators for model evaluation compared to traditional methods. The model performance worsened when the error range became larger, and the choice of probability density functions (PDFs) affected model performance, especially for non-point source (NPS) predictions. The case study showed that if the measured error is small and if the distribution can be specified, the CDFA and MCA could be extended to other model evaluations within an uncertainty framework and even be used to calibrate and validate hydrological and NPS pollution (H/NPS) models.

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