Evaluation of measurement uncertainty using mixed distribution for conducted emission measurements

Kovačević, Aleksandar; Brkić, Dragoljub M.; Osmokrović, P.

doi:10.1016/j.measurement.2010.12.006

Cited by 12 publications

(10 citation statements)

References 5 publications

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“…Furthermore, two ways to evaluate the standard uncertainties are usable according to the nature of the sources of uncertainty; a type A assessment of the standard uncertainty is carried out by a statistical analysis of series of observations and a Type B assessment of standard uncertainty which is based on scientific judgment, using all available information about the possible variability of the input quantity such as the manufacturer's specifications, data provided by calibration certificates, other certificates, experimental data, etc [15,16].…”

Section: Eurachem/citacmentioning

confidence: 99%

Evaluation of measurement uncertainty of dissolution tests by the ISO-GUM approach and Monte-Carlo simulation

et al. 2021

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Measurement uncertainty, no doubt, is an important element in examining and interpreting the results obtained in chemical analyses, particularly in the pharmaceutical field. Obviously, this is required by several guides and standards; however, few documents are interested in estimating the measurement uncertainty associated with dissolution test results. In this paper, the Guide to the expression of Uncertainty in Measurement (GUM) and its Supplement 1 "Monte-Carlo Simulation" (MCS) were used, side by side, to estimate the measurement uncertainty of two dissolution test methods, each using a different instrumental technique, namely UV-Visible Spectrophotometry and High Performance Liquid Chromatography (HPLC). A comparative study of both approaches was successfully conducted then with the aim of not only examining the compatibility between the results of the two methods, but also the presentation and discussion of the advantages and limitations of the applicability of each approach in drug analyses. The results obtained with the ISO-GUM and the Monte-Carlo simulation, for the two cases of analysis, revealed comparability between the estimates of uncertainty. Indeed, the standard uncertainties obtained by the two approaches are very close (uGUM Repaglinide (RG) = 1.1885% & uMCM Repaglinide (RG) = 1.1854% and uGUM Irbesartan (IB) = 1.4028 % & uMCM Irbesartan (IB) = 1.3071%). On the other hand, it has been found that the ISO-GUM approach slightly overestimates the expanded uncertainty because of the used value k = 2 of the coverage factor. Moreover, we have found that the conditions of applicability of the analytical approach and its numerical complement are not always obvious, in particular, when the model of calculation of the measurand is complicated and the instruments of measurements used are complex.

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Section: Eurachem/citacmentioning

confidence: 99%

Evaluation of measurement uncertainty of dissolution tests by the ISO-GUM approach and Monte-Carlo simulation

et al. 2021

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“…It is well known how different parameters can affect electrical components, affecting not only their nominal value, but also increasing their degradation over time and consequently reducing their estimated lifetime [1][2][3][4]. Furthermore, frequency has a strong impact on electrical components and parasitic components can give rise to problems that were not considered in the design phase [5][6][7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

“…The need to define the measurement uncertainty comes from the fact that every component which constitutes any kind of system shows a nominal value which is not fixed, but rather changes according to different probability distribution functions (PDFs) [11][12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

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A Methodology to Analyze and Evaluate the Uncertainty Propagation due to Temperature and Frequency and Design Optimization for EMC Testing Instrumentation

Bosi

Sánchez²,

Pajares³

et al. 2021

Electricity

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This paper presents a study and proposes a new methodology to analyze, evaluate and reduce the overall uncertainty of instrumentations for EMC measurements. For the scope of this work, the front end of a commercial EMI receiver is chosen and variations due to tolerances, temperature and frequency response of the system are evaluated. This paper illustrates in detail how to treat each block composing the model by analyzing each discrete component, and how to evaluate their influence on the measurand. Since a model can have hundreds or even thousands of parameters, the probability distribution functions (PDFs) of some variable might be unknown. So, a method that allows to obtain in a fast and easy way the uncertainty of the measurement despite having so many variables, to then being able to evaluate the influence of each component on the measurand, is necessary for a correct design. In this way, it will be possible to indicate which discrete components have the most influence on the measurand and thus set the maximum tolerances allowed and being able to design a cost-effective solution. Furthermore, this works presents a methodology which can easily be extended and applied to estimate and compute the uncertainty for electromagnetic interferences, energy storage systems (ESS), energy production, electric machines, electric transports and power plants in general.

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“…It is also used in mechanical and dimensional measurements, such as: gear measurement instruments (Kost et al, 2015), dynamic coordinate measurements (Garcia et al, 2013) and Brinell hardness testing (Leyi et al, 2011). In the field of physics and electricity, applications are found for nonlinear physical laws (Vujisić et al, 2011), for passive electrical circuits (Stanković et al, 2011) and for conducted emission measurement (Kovačević et al, 2011). In the field of chemistry, this method was used for the estimation of plutonium (Heasler et al, 2006), in the determination of Pb content in herbs (Lam et al, 2010) and in the measurement of nitrogen content in liquid fuel (Theodorou et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Measurement uncertainty of plane-strain fracture toughness KIC testing by the Monte Carlo Method

et al. 2018

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The reliable determination of materials' mechanical properties is a fundamental factor for their application in engineering, and the estimation of the measurement uncertainty in testing laboratories has a direct impact on the interpretation of the results. Recent literature demonstrates that one of the most widely used methodologies for uncertainty estimation, the Guide to the Expression of Uncertainty in Measurement (GUM), has limitations, especially in cases where the mathematical model has a high degree of non-linearity. Furthermore, it makes approximations for the final probability distribution. In these cases, it is recommended that the measurement uncertainty is determined by the Monte Carlo Method (MCM), which considers the propagation of the distribution rather than the propagation of uncertainties. Thus, given the limitations of the GUM method and the importance of estimating the measurement uncertainty of mechanical tests, this work aims to implement the measurement uncertainty estimation for the plane-strain fracture toughness (K IC) test of metallic materials through the Monte Carlo Method. The results of the work confirm the importance of estimating the measurement uncertainty of fracture toughness tests.

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Evaluation of measurement uncertainty using mixed distribution for conducted emission measurements

Cited by 12 publications

References 5 publications

Evaluation of measurement uncertainty of dissolution tests by the ISO-GUM approach and Monte-Carlo simulation

Evaluation of measurement uncertainty of dissolution tests by the ISO-GUM approach and Monte-Carlo simulation

A Methodology to Analyze and Evaluate the Uncertainty Propagation due to Temperature and Frequency and Design Optimization for EMC Testing Instrumentation

Measurement uncertainty of plane-strain fracture toughness KIC testing by the Monte Carlo Method

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