• Journal of Soil and Groundwater Environment
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• v.18 no.5
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• pp.15-25
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• 2013

In this study, two kinds of soil-based proficiency testing materials (PTMs), NICE-012L and NICE-012R were prepared and certified for Benzen, Toluene, Etylbenzene and Xylene with evaluation of uncertainties. In order to analyse BTEX (Benzen Toluene Etylbenzene Xylene) for the candidate materials, GC/MS was used after pretreatment according to methods of soil analysis by Ministry of Environment. For the homogeneity test among bottles in terms of candidate materials, ISO 13528 and IUPAC Protocol were used and according to the result, both candidate materials showed sufficient homogeneity. Also, the stability test over the candidate materials was accessed according to the ISO Guide 35 by classifying short-term and long-term stability and the result showed that both candidate materials showed decent stability. The reference values of the two candidate materials depending on BTEX components were derived from the average of the 11 samples that were used for verification of the samples' homogeneity. Uncertainty of measurement was combined by uchar that was caused by a characteristic value, $u_{bb}$ that was caused by between-bottle homogeneity, and $u_{stab}$ that was caused by stability, and then combined uncertainty ($u_{PTM}$) was multiplied to the coverage factor (k) derived from the effective degree of freedom from each factor that leads to expanded uncertainty (U) in about 95% of confidence level. The proficiency testing materials developed through this study were supplied to National Institute of Environmental Research (NIER) and utilized as an external proficiency testing materials for evaluating analysis capacity of soil agencies with specialty in terms of soil analysis approved by Minister of Environment.

• Nuclear Engineering and Technology
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• v.41 no.9
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• pp.1181-1190
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• 2009

Knowing more about the Local Power Density (LPD) at the hottest part of a nuclear reactor core can provide more important information than knowledge of the LPD at any other position. The LPD at the hottest part needs to be estimated accurately in order to prevent the fuel rod from melting in a nuclear reactor. Support Vector Machines (SVMs) have successfully been applied in classification and regression problems. Therefore, in this paper, the power peaking factor, which is defined as the highest LPD to the average power density in a reactor core, was estimated by SVMs which use numerous measured signals of the reactor coolant system. The SVM models were developed by using a training data set and validated by an independent test data set. The SVM models' uncertainty was analyzed by using 100 sampled training data sets and verification data sets. The prediction intervals were very small, which means that the predicted values were very accurate. The predicted values were then applied to the first fuel cycle of the Yonggwang Nuclear Power Plant Unit 3. The root mean squared error was approximately 0.15%, which is accurate enough for use in LPD monitoring and for core protection that uses LPD estimation.

• Journal of Korea Water Resources Association
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• v.40 no.1 s.174
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• pp.89-99
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• 2007

Uncertainty analysis is used to eliminate the climatic variables of input nodes and construct the model of an optimal type from COMBINE-GRNNM-GA(Type-1), which have been developed in this issue(2007). The input variable which has the lowest smoothing factor during the training performance, is eliminated from the original COMBINE-GRNNM-GA (Type-1). And, the modified COMBINE-GRNNM-GA(Type-1) is retrained to find the new and lowest smoothing factor of the each climatic variable. The input variable which has the lowest smoothing factor, implies the least useful climatic variable for the model output. Furthermore, The sensitive and insensitive climatic variables are chosen from the uncertainty analysis of the input nodes. The optimal COMBINE-GRNNM-GA(Type-1) is developed to estimate and calculate the PE which is missed or ungaged and the $ET_r$ which is not measured with the least cost and endeavor Finally, the PE and $ET_r$. maps can be constructed to give the reference data for drought and irrigation and drainage networks system analysis using the optimal COMBINE-GRNNM-GA(Type-1) in South Korea.

• The KSFM Journal of Fluid Machinery
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• v.17 no.4
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• pp.5-10
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• 2014

It is possible for the nation's largest flowmeter calibration system in K-water to calibrate flow rate up to $2,700m^3/h$ and diameter 800mm. However, the calibration and measurement capability of K-water's system is not satisfied in comparison with other developed countries. In this study, we find the dominant factors related to the uncertainty of weight and time measurement for gravimetric flowmeter calibration system. As a results of improving the system, the combined standard uncertainty has been improved $1.099{\times}10^{-3}$ to $2.332{\times}10^{-4}$. So calibration and measurement capability got 0.08 percent of the relative expanded uncertainty for maximum flow rate using the coverage factor(k=2).

• Journal of the Korean Data and Information Science Society
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• v.27 no.1
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• pp.9-18
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• 2016

In the Heckman-Meyers model, which is frequently referred by IAA, Swiss Solvency Test, EU Solvency II, the assumption of parameter distribution is key factor. While in theory Bayesian analysis somewhat reflects parameter uncertainty using prior distribution, it is often the case where both Heckman-Meyers and Bayesian are necessary to better manage the parameter uncertainty. Therefore, this paper proposes the use of Bayesian H-M CRM, a combination of Heckman-Meyers model and Bayesian, and analyzes its efficiency.

• Journal of Korea Water Resources Association
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• v.36 no.4
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• pp.661-671
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• 2003

The reliability model is developed for analyzing parameter uncertainty and estimating of flood inundation characteristics in a protected lowland. The approach is based on the concept of levee safety factor and the statistical analysis of model parameters affecting the variability of flood levels. Monte Carlo simulation is incorporated into the varied flow and unsteady flow analysis to quantify the impact of parameter uncertainty on the variability of flood levels. The model is applied to a main stem of the Nakdong River from Hyunpoong to Juckpogyo station. Simulation results show that the characteristics of channel overflow and return now are well simulated and the mass conservation was satisfied. The inundation depth and area are estimated by taking into consideration of the uncertainty of width and duration time of levee failure.

• Journal of the Korean Vacuum Society
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• v.14 no.4
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• pp.186-194
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• 2005

A Spinning Rotor Gauge was calibrated between $4.04\times10^{-3}$ Pa and $1.11\times10^{-2}$ Pa at the high vacuum standard by static expansion method. The results were analysed according to the document of 'Guide to the Expression of Uncertainty in Measurement' of ISO. The expanded uncertainty was $3.0035\times10^{-3}$ Pa at $7.5448\times10^{-3}$ Pa. $95\%$ confidence level, and coverage factor of k = 1.

• Smart Structures and Systems
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• v.29 no.2
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• pp.321-336
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• 2022

Model uncertainty is a key factor that could influence the accuracy and reliability of numerical model-based analysis. It is necessary to acquire an appropriate updating approach which could search and determine the realistic model parameter values from measurements. In this paper, the Bayesian model updating theory combined with the transitional Markov chain Monte Carlo (TMCMC) method and K-means cluster analysis is utilized in the updating of the structural model parameters. Kriging and polynomial chaos expansion (PCE) are employed to generate surrogate models to reduce the computational burden in TMCMC. The selected updating approaches are applied to three structural examples with different complexity, including a two-storey frame, a ten-storey frame, and the national stadium model. These models stand for the low-dimensional linear model, the high-dimensional linear model, and the nonlinear model, respectively. The performances of updating in these three models are assessed in terms of the prediction uncertainty, numerical efforts, and prior information. This study also investigates the updating scenarios using the analytical approach and surrogate models. The uncertainty quantification in the Bayesian approach is further discussed to verify the validity and accuracy of the surrogate models. Finally, the advantages and limitations of the surrogate model-based updating approaches are discussed for different structural complexity. The possibility of utilizing the boosting algorithm as an ensemble learning method for improving the surrogate models is also presented.

• Journal of The Korean Society of Agricultural Engineers
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• v.58 no.2
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• pp.91-99
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• 2016

To estimate design floods for hydraulic structures, statistical methods has been used in the analysis of rainfall data. However, due to the lack of rainfall data in some regions, it is difficult to apply the statistical methods for estimation of design rainfall. In addition, increased uncertainty of design rainfall arising from the limited rainfall data can become an important factor for determining the design floods. The main objective of this study was to assess the uncertainty of the future design floods under RCP (representative concentration pathways) scenarios using a bootstrap technique. The technique was used in this study to quantify the uncertainty in the estimation of the future design floods. The Yongdang watershed in South Korea, 2,873 ha in size, was selected as the study area. The study results showed that the standard errors of the basin of Yongdang reservoir were calculated as 2.0~6.9 % of probable rainfall. The standard errors of RCP4.5 scenario were higher than the standard errors of RCP8.5 scenario. As the results of estimation of design flood, the ranges of peak flows considered uncertainty were 2.3~7.1 %, and were different each duration and scenario. This study might be expected to be used as one of guidelines to consider when designing hydraulic structures.

• Management & Information Systems Review
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• v.34 no.3
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• pp.41-60
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• 2015

Under the environment uncertainty, inno-biz firms are likely to enter into new market with their manager's enthusiasm, know-how, and innovative idea. Therefore, the environment uncertainty would be a new opportunity for their managers with entrepreneurship. The inno-biz firms have limitation on resources that they can utilize, but the industrial infrastructure of the regions that they are located in can be a factor overcoming the limitation of utilizing resources. Using inno-biz firms, this study presents a research model with the effects of environment uncertainty, industrial infra structure, and entrepreneurship on competitive performance. This study examined the relationship showed by the research model, and found the significant relationship by using SEM. And this study found the mediating effect of environment uncertainty, industrial infrastructure, and entrepreneurship on innovative capability. Research results, implications and limitations of this study are provided in conclusion of this study.