• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.26 no.7
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• pp.781-786
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• 2016

All measurements are subject to uncertainty and a measurement result is complete only when it is accompanied by a statement of the associated uncertainty. By international agreement, this uncertainty has a probabilistic basis and reflects incomplete knowledge of the quantity value. The "Guide to the Expression of Uncertainty in Measurement", commonly known as the GUM, is the definitive document on this subject. The requirements for estimation of measurement uncertainty apply to all results provided by calibration laboratories and results produced by testing laboratories under the optional circumstances. In this paper, a procedure for estimation of measurement uncertainty from vibration testing is proposed on KS F 2868:2003 as an example. Both Type A and Type B evaluation of uncertainty are considered to calculate the combined standard uncertainty and expanded uncertainty.

• Journal of the Korea Safety Management & Science
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• v.9 no.1
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• pp.145-156
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• 2007

This paper is to propose model classification and evaluation of measurement uncertainty. In order to obtain type A and B uncertainty, variety of measurement mathematical models are illustrated by example. The four steps to evaluate expanded uncertainty are indicated as following; First, to get type A standard uncertainty, measurement mathematical models of single, double, multiple, design of experiment and serial autocorrelation are shown. Second, to solve type B standard uncertainty measurement mathematical models of empirical probability distributions and multivariate are presented. Third, type A and B combined uncertainty, considering sensitivity coefficient, linearity and correlation are discussed. Lastly, expanded uncertainty, considering degree of freedom for type A, B uncertainty and coverage factor are presented with uncertainty budget. SPC control chart to control expanded uncertainty is shown.

• Nuclear Engineering and Technology
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• v.53 no.8
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• pp.2523-2533
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• 2021

In the last decade, the best estimate plus uncertainty methodologies in nuclear technology and nuclear power plant design have become a trending topic in the nuclear field. Since BEPU was allowed for licensing purposes by the most important regulator bodies, different uncertainty assessment methods have become popular, overall non-parametric methods. While non-parametric tolerance regions can be well stated and used in uncertainty quantification for licensing purposes, the propagation of the uncertainty through different codes (multi-scale, multiphysics) in cascade needs a better depiction of uncertainty than the one provided by the tolerance regions or a probability distribution. An alternative method based on the parametric or distributional probability boxes is used to perform uncertainty quantification and propagation regarding statistic uncertainty from one code to another. This method is sample-size independent and allows well-defined tolerance intervals for uncertainty quantification, manageable for uncertainty propagation. This work characterizes the distributional p-boxes behavior on uncertainty quantification and uncertainty propagation through nested random sampling.

• Nuclear Engineering and Technology
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• v.53 no.3
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• pp.860-865
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• 2021

A data-based model, such as an AAKR model is widely used for monitoring the drifts of sensors in nuclear power plants. However, since a training dataset and a test dataset for a data-based model cannot be constructed with the data from all the possible states, the model uncertainty cannot be good enough to represent the uncertainty of estimations. In fact, the errors of estimation grow much bigger if the incoming data come from inexperienced states. To overcome this limitation of the model uncertainty, a new measure of uncertainty for a data-based model is developed and the predicted uncertainty is introduced. The predicted uncertainty is defined in every estimation according to the incoming data. In this paper, the AAKR model is used as a data-based model. The predicted uncertainty is similar in magnitude to the model uncertainty when the estimation is made for the incoming data from the experienced states but it goes bigger otherwise. The characteristics of the predicted model uncertainty are studied and the usefulness is demonstrated with the pressure signals measured in the flow-loop system. It is expected that the predicted uncertainty can quite reduce the false alarm by using the variable threshold instead of the fixed threshold.

• Proceedings of the KSR Conference
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• 2003.10c
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• pp.301-306
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• 2003

Recently, uncertainty of measurement became a major concern for the people working on the laboratory evaluation and accreditation. 'uncertainty of measurement is a parameter associated with the result of a measurement that characteristics the dispersion of the value that could reasonably be attributed to the measured.' This study analysed how to estimate uncertainty of measurement in mechanical characteristic exam for Plastic material. its uncertainty was estimated according to International Organization for Standardization(ISO), they were named to A type uncertainty, B type uncertainty, combined standard uncertainty, and expanded uncertainty. We obtained that the combined standard uncertainty was 0.96697 MPa and the expanded uncertainty was 2.291MPa.

• Journal of Korean Academy of Nursing
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• v.37 no.4
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• pp.594-602
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• 2007

Purpose: This study was conducted to investigate the correlation among uncertainty, mastery and appraisal of uncertainty in hospitalized children's mothers. Method: Self report questionnaires were used to measure the variables Variables were uncertainty, mastery and appraisal of uncertainty. In data analysis, the SPSSWIN 12.0 program was utilized for descriptive statistics, Pearson's correlation coefficients, and regression analysis. Result: Reliability of the instruments was cronbach's $alpha=.84{\sim}.94$. Mastery negatively correlated with uncertainty(r=-.444, p=.000) and danger appraisal of uncertainty(r=-.514, p=.000). In regression of danger appraisal of uncertainty, uncertainty and mastery were significant predictors explaining 39.9%. Conclusion: Mastery was a significant mediating factor between uncertainty and danger appraisal of uncertainty in hospitalized children's mothers. Therefore, nursing interventions which improve mastery must be developed for hospitalized children's mothers.

• Nuclear Engineering and Technology
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• v.54 no.3
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• pp.790-802
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• 2022

Large-scale reactor simulation often requires the use of Monte Carlo calculation techniques to estimate important reactor parameters. One drawback of these Monte Carlo calculation techniques is they inevitably result in some uncertainty in calculated quantities. The present study includes parametric uncertainty quantification (UQ) and sensitivity analysis (SA) on the Advanced Test Reactor Critical (ATRC) facility housed at Idaho National Laboratory (INL) and addresses some complications due to Monte Carlo uncertainty when performing these analyses. This approach for UQ/SA includes consideration of Monte Carlo code uncertainty in computed sensitivities, consideration of uncertainty from directly measured parameters and a comparison of results obtained from brute-force Monte Carlo UQ versus UQ obtained from a surrogate model. These methodologies are applied to the uncertainty and sensitivity of k_eff for two sets of uncertain parameters involving fuel plate geometry and fuel plate composition. Results indicate that the less computationally-expensive method for uncertainty quantification involving a linear surrogate model provides accurate estimations for k_eff uncertainty and the Monte Carlo uncertainty in calculated k_eff values can have a large effect on computed linear model parameters for parameters with low influence on k_eff.

• Journal of the Korean Operations Research and Management Science Society
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• v.26 no.3
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• pp.105-117
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• 2001

This paper models supply uncertainty in the dynamic Newsboy problem context. The system consists of one supplier and one retailer who places an order to the supplier every period to meet stochastic demand. Supply uncertainty is modeled as the uncertainty in quantities delivered by the supplier. That is, the supplier delivers exactly the amount ordered by the retailer with probability of $\beta$ and the amount minus K with probability of (1-$\beta$). We formulate the problem as a dynamic programming problem and prove that retailer’s optimal replenishment policy is a stationary base-stock policy. Through a numerical study, we found that the cost increase due to supply uncertainty is significant and that the costs increase more rapidly as supply uncertainty increases. We also identified the effects of various system parameters. One of the interesting results is that as retailer’s demand uncertainty, the other uncertainty in our model, increases, the cost increase due to supply uncertainty becomes less significant.

• International Journal of Precision Engineering and Manufacturing
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• v.3 no.3
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• pp.5-11
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• 2002

This paper describes the methods for calibration and evaluation of the relative expanded uncertainty of a multi-axis force/moment sensor. In order to use the sensor in the industry, it should be calibrated and its relative expanded uncertainty should be also evaluated. At present, the confidence of the sensor is shown with only interference error. However, it is not accurate, because the calibrated multi-axis force/moment sensor has an interference error as well as a reproducibility error of the sensor, etc. In this paper, the methods fur calibration and for evaluation of the relative expanded uncertainty of a multi-axis force/moment sensor are newly proposed. Also, a six-axis force/moment sensor is calibrated with the proposed calibration method and the relative expanded uncertainty is evaluated using the proposed uncertainty evaluation method and the calibration results. It is thought that the methods fur calibration and evaluation of the uncertainty can be usually used for calibration and evaluation of the uncertainty of the multi-axis force/moment sensor.

• Journal of Korean Academy of Nursing
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• v.48 no.4
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• pp.485-496
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• 2018

Purpose: This study aimed to test the mediating effect of uncertainty appraisal and coping style in the relation between uncertainty and anxiety in hospitalized pregnant women diagnosed with preterm labor. Methods: The participants were 105 pregnant women diagnosed with preterm labor in hospitals in Korea. Data were collected from July to October 2017. The measurements included the Uncertainty in Illness Scale, Uncertainty Appraisal Scale, Coping Style Scale, and State Anxiety Inventory. Data were analyzed using descriptive statistics, an independent t-test, correlation, and multiple regression following the Baron and Kenny method and Sobel test for mediation. Results: The mean score for anxiety was 2.29 out of 4.00 points and for uncertainty it was 2.46 out of 5.00 points. There were significant correlations among uncertainty, uncertainty danger appraisal, uncertainty opportunity appraisal, problem-focused coping, emotion-focused coping, and anxiety. Uncertainty danger appraisal (${\beta}=.64$, p<.001) had a complete mediating effect in the relation between uncertainty and anxiety (Z=4.54, p<.001). Uncertainty opportunity appraisal (${\beta}=-.45$, p<.001) had a complete mediating effect in the relation between uncertainty and anxiety (Z=3.28, p<.001). Emotion-focused coping (${\beta}=-.23$, p=.021) had a partial mediating effect in the relation between uncertainty and anxiety (Z=2.02, p=.044). Conclusion: Nursing intervention programs focusing on managing uncertainty appraisal and improving emotion-focused coping are highly recommended to decrease anxiety in hospitalized pregnant women diagnosed with preterm labor.