• Analytical Science and Technology
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• v.18 no.6
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• pp.475-482
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• 2005

The expression of uncertainty applied to the chemical analysis is highly recommended with increasing demands upon the systematic quality assurance and control(QA/QC) with ISO 17025. For the quantification of quality source, 7 major common sources of uncertainty, normally contributing to the quality of the chemical analysis, were selected from QA/QC literatures of chemical analysis. They were classified into repeatability, drift, uncertainty in standards, linearity of calibration, homogeneity, stability of sample, and matrix effect. And, the quantification of the sources by means of measurement uncertainty was proposed as a prerequisite steps for QA/QC. Examples applied to the quantification procedures of modelling, combination and expression of standard uncertainty for the 7 major common sources were presented as a reference guide for QA/QC in chemical analysis.

• Journal of the Society of Naval Architects of Korea
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• v.31 no.1
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• pp.71-83
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• 1994

Recently, an interest in the uncertainty analysis on measurement and prediction has been growing. An uncertainty analysis method is applied to the P.O.W test where error sources, estimated errors, their propagation route and their sensitivities to the uncertainty items are clearly illustrated. The uncertainty range for the results obtained from the HMRI Propeller Open Water test is within ${\pm}1%$ which is assumed to be lower than an usual measurement error range of ${\pm}1%$. It has been noticed that the uncertainty analysis can be used quite usefully for detecting dominant error-sources and hence improving the experimental measurement accuracy.

• Journal of the Korean Society of Tobacco Science
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• v.27 no.2
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• pp.226-234
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• 2005

The uncertainty of measurement in nitrate from burley leaf tobacco by continuous-flow analysis method was evaluated following internationally accepted guidelines. The sources of uncertainty associated with the analysis of nitrate were weight of standard and sample, purity of standard, dilution of standard solution, calibration curve, water content, etc. The calculation of uncertainty based on the GUM(Guide to the Expression of Uncertainty in Measurement) and EURACHEM/CITAC Guide. An expanded uncertainty was obtained by multiplying the combined standard uncertainty with a coverage factor (k) calculated from the effective degree of freedom. The concentration of nitrate from burley leaf tobacco was $2.09\%$ and the expanded uncertainty by multiplying by the coverage factor(k, 2.20) was $0.13\%\;at\;a\;95\%$ confidence level.

• Nuclear Engineering and Technology
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• v.35 no.1
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• pp.64-79
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• 2003

In the risk and reliability analysis of complex technological systems, the primary concern of formal uncertainty analysis is to understand why uncertainties arise, and to evaluate how they impact the results of the analysis. In recent times, many of the uncertainty analyses have focused on parameters of the risk and reliability analysis models, whose values are uncertain in an aleatory or an epistemic way. As the field of parametric uncertainty analysis matures, however, more attention is being paid to the explicit treatment of uncertainties that are addressed in the predictive model itself as well as the accuracy of the predictive model. The essential steps for evaluating impacts of these model uncertainties in the presence of parameter uncertainties are to determine rigorously various sources of uncertainties to be addressed in an underlying model itself and in turn model parameters, based on our state-of-knowledge and relevant evidence. Answering clearly the question of how to characterize and treat explicitly the forgoing different sources of uncertainty is particularly important for practical aspects such as risk and reliability optimization of systems as well as more transparent risk information and decision-making under various uncertainties. The main purpose of this paper is to provide practical guidance for quantitatively treating various model uncertainties that would often be encountered in the risk and reliability modeling process of complex technological systems.

• International journal of steel structures
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• v.18 no.5
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• pp.1607-1616
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• 2018

Quantitatively modeling and propagating all sources of uncertainty stand at the core of seismic fragility assessment of structures. This paper investigates the effects of various sources of uncertainty on seismic responses and seismic fragility estimates of single-layer reticulated domes. Sensitivity analyses are performed to examine the sensitivity of typical seismic responses to uncertainties in structural modeling parameters, and the results suggest that the variability in structural damping, yielding strength, steel ultimate strain, dead load and snow load has significant effects on the seismic responses, and these five parameters should be taken as random variables in the seismic fragility assessment. Based on this, fragility estimates and fragility curves incorporating different levels of uncertainty are obtained on the basis of the results of incremental dynamic analyses on the corresponding set of 40 sample models generated by Latin Hypercube Sampling method. The comparisons of these fragility curves illustrate that, the inclusion of only ground motion uncertainty is inappropriate and inadequate, and the appropriate way is incorporating the variability in the five identified structural modeling parameters as well into the seismic fragility assessment of single-layer reticulated domes.

• Analytical Science and Technology
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• v.19 no.6
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• pp.490-497
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• 2006

The sources of uncertainty in the analysis of liquified natural gas (LNG) process are evaluated. The uncertainty sources evaluated are the repeatability of measurement, non-linearity of GC, the uncertainty of standard gas used for calibration, difference of gas sampling and deviation after GC calibration and major revealed sources are the non-linearity of GC, the uncertainty of standard gas and the deviation after GC calibration. The determined values and uncertainties of methane and ethane as the major components are $90.0%mol/mol{\pm}1.9%$ (relative and 95% level of confidence) and $6.26%mol/mol{\pm}0.08$ (relative and 95% level of confidence), respectively. The contribution of uncertainty varies depending on the source of uncertainty and gas component. In the case of methane, non-linearity of GC, the uncertainty of standard gas and deviation after GC calibration contribute 0.28%, 0.25% and 0.24% of relative expanded uncertainty, respectively.

• Korean Journal of Food Science and Technology
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• v.36 no.5
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• pp.701-710
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• 2004

Uncertainty was quantified to evaluate calcium determination result in infant formula with AAS (Atomic Absorption Spectrometry) and ICP-AES (Inductively Coupled Plasma-Atomic Emission Spectrometry). Uncertainty sources in measurand, such as sample weight, final volume of sample, sample dilution and the instrumental result were identified and used as parameters for combined standard uncertainty based on the GUM (Guide to the expression of uncertainty in measurement) and Draft EURACHEM/CITAC Guide. Uncertainty components of each sources in measurand were identified as resolution, reproducibility and stability of chemical balance, standard material purity, standard material molecular weight, standard solution concentration, standard solution dilution factor, sample dilution factor, calibration curve, recovery, instrumental precision, reproducibility, and stability, Each uncertainty components were evaluated by uncertainty types and included to calculate combined uncertainty. The kinds of uncertainty sources and components in the analytical method by AAS and ICP-AES were same except sample dilution factor for AAS. The analytical results and combined standard uncertainties of calcium content were estimated within the certification range $(367{\pm}20\;mg/100g)$ of CRM (Certified Reference Material) and were not significantly different between method by AAS followed by ashing and method by ICP-AES followed by acid digestion as $359.52{\pm}23.61\;mg/100g\;and\;354.75{\pm}16.16\;mg/100g$, respectively. Identifying uncertainty sources related with precision, repeatability, stability, and maintaining proper instrumental conditions as well as personal proficiency was needed to reduce analytical error.

• Korean Journal of Optics and Photonics
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• v.20 no.2
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• pp.110-117
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• 2009

This work introduces the uncertainty evaluation formulation on color measurement of light sources and display devices, such as CIE 1931 (x, y) chromaticity, CIE 1960 (u, v) chromaticity, correlated color temperature, and distribution temperature. All the mentioned quantities are reduced from spectral data in the visible range, for which uncertainties are strongly correlated between different wavelengths. Using matrix algebra we have formulated the uncertainty propagation from the SI- traceable spectral irradiance standard to the individual color related measurement quantities taking the correlation between wavelengths into account. As a result, we have demonstrated uncertainty evaluation examples of 3 types of light sources: CIE illuminant A, LED white light, and LCD white light. This method can be applied to any other quantities based on spectral measurement such as solar irradiance, material color measurement, etc.

• Korean Journal of Clinical Pharmacy
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• v.26 no.1
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• pp.70-76
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• 2016

Background: The use of the extracts from evening primrose seeds as a health functional food has been gradually increased. Therefore, the monitoring and screening process has been considerably required for its quality control. Objective: This study aimed to estimate the measurement uncertainty associated with determination of penta-o-galloyl ${\beta}$-D-glucose (PGG) in extracts from evening primrose seeds by high-performance liquid chromatography. Methods: The sources of measurement uncertainty was expressed in accordance with mathematical/statistical theories of GUM & EURACHEM Guide. The expanded uncertainty was calculated by using the relative standard uncertainty between analytical result and sources of uncertainty in measurement (sample weight, final volume, extraction volume, standard solution, matrix and instrument etc). Results: In the results of 95% confidence interval, the uncertainty in measurement was $10,253.34{\pm}1,844.50{\mu}g/kg$ (k = 2.0). Conclusion: In this study, it showed that the value of uncertainty in measurement for determination of PGG in extracts from evening primrose seeds by HPLC has about 18.0% influence on PGG contents of the analytical results. The results would be very useful for the monitoring and screening of evening primrose seeds marketed in Korea for its quality control as dietary supplement.

• Journal of the Korea Institute of Military Science and Technology
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• v.10 no.1
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• pp.94-100
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• 2007

MVRS's measuring principles are based on the Doppler principle. It measures the velocities near the muzzle using the doppler signal output from the antenna and then predicts the velocity of the bullet leaving the muzzle by performing the regression analysis on previous measured velocities. There are a number of error sources when calculating the muzzle velocity. Antenna has long term frequency stability error and the doppler signal from the antenna has noise. These two error sources influence the accuracy of estimated velocities from the doppler signal. Estimated velocity errors result in the random error of data statistics. And when performing a regression analysis these random error components are transferred to the fitting error component. This study also analyzed the error components according to the hardware limitations of MVRS-700 and the signal processing method, and presented the calculated uncertainty of muzzle velocity.