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Benchmark Test Distributions for Expanded Uncertainty Evaluation Algorithms

IEEE Transactions on Instrumentation and Measurement, 2016
Expanded uncertainty estimation is normally required for mission-critical applications, e.g., those involving health and safety. It helps to get a distribution range of the required confidence level for the uncertainty evaluation of a system. There are a number of available techniques to estimate the expanded uncertainty. However, there is currently no
Arvind Rajan, Ye Chow Kuang, Melanie Po-Leen Ooi   +2 more
exaly   +3 more sources

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Calculation of the expanded uncertainty for large uncertainties using the lognormal distribution

Accreditation and Quality Assurance, 2020
For large uncertainties, calculating the expanded uncertainty using a normal distribution for the values of the measurand can lead to negative values for the lower limit of the expanded uncertainty and unrealistic large values for the upper limit, when the relative uncertainty is constant over wide concentration range.
Alex Williams
exaly   +2 more sources

Rounding the expanded uncertainty

Accreditation and Quality Assurance, 2019
When reporting expanded uncertainty, rounding the number of significant figures can affect the associated level of confidence. A simple numerical method is used to measure the level of confidence obtained after different rounding methods have been applied. This also offers some insight into the meaning of level of confidence.
B D Hall
exaly   +2 more sources

Performance comparison between expanded uncertainty evaluation algorithms

2015 IEEE International Instrumentation and Measurement Technology Conference (I2MTC) Proceedings, 2015
The use of normal approximation to estimate expanded uncertainty has been very widespread; yet this is one of the practices that is being criticized by various quarters for lack of rigor and potentially misleading. Monte Carlo method is probably the only method trusted to generate reliable expanded uncertainty.
Melanie Po-Leen Ooi, Arvind Rajan, Serge Demidenko   +2 more
exaly   +2 more sources

Moments and maximum entropy method for expanded uncertainty estimation in measurements

2017 IEEE International Instrumentation and Measurement Technology Conference (I2MTC), 2017
The normal approximation and Monte Carlo simulation methods are widely used in the metrology to evaluate the expanded uncertainty, whereby the latter method is known to be the most robust and reliable. In some cases, however, (e.g., when the probability distribution is not known a priori) different frameworks may be desired as an alternative to the ...
Arvind Rajan, Ye Chow Kuang, Melanie Po-Leen Ooi, Serge N. Demidenko   +3 more
openaire   +2 more sources

A computer program for a general case evaluation of the expanded uncertainty

Accreditation and Quality Assurance, 2003
The ISO Guide to the Expression of Uncertainty in Measurement provides a uniform method for the evaluation of combined standard uncertainty of a measurand whose expectation and standard deviation are stable over the measurement period. However, the method provided for the evaluation of the expanded uncertainty is not complete. Particularly, it does not
exaly   +2 more sources

Estimation of Expanded Uncertainty in Measurement When Implementing a Bayesian Approach

Measurement Techniques, 2018
Issues with the estimation of expanded uncertainty in the fi rst draft of the revised Guide to the Expression of Uncertainty in Measurement (GUM) based on the Bayesian approach are considered.
Igor Zakharov
exaly   +1 more source

A Method for Estimating the Resultant Expanded Uncertainty Value Based on Interval Arithmetic

Applied Sciences (Switzerland)
The article describes a method for determining the resultant expanded uncertainty value in the case of analyzing an uncertainty budget with many components.
Marian Kampik, Jerzy Roj, Łukasz Drozdz   +2 more
exaly   +2 more sources

The use of a Monte Carlo method for evaluating uncertainty and expanded uncertainty

Metrologia, 2006
The Guide to the Expression of Uncertainty in Measurement (GUM) is the internationally accepted master document for the evaluation of uncertainty. It contains a procedure that is suitable for many, but not all, uncertainty evaluation problems met in practice.
Maurice G Cox, Bernd R L Siebert
openaire   +1 more source