• Journal of Korea Water Resources Association
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• v.40 no.3
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• pp.237-249
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• 2007

• Structural Engineering and Mechanics
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• v.28 no.4
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• pp.357-372
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• 2008

This study investigates effect of uncertainty in natural vibration period on the seismic demand. It is shown that since this uncertainty affects the acceleration and displacement responses differently, two ratios, one relating peak acceleration responses and the other relating the peak displacement responses, are not equal and both must be employed in evaluating and defining the critical seismic demand. The evaluation of the ratios is carried out using more than 200 strong ground motion records. The results suggest that the uncertainty in the natural vibration period impacts significantly the statistics of the ratios relating the peak responses. By using the statistics of the ratios, a procedure and sets of empirical equations are developed for estimating the probability consistent seismic demand for both linear and nonlinear systems.

• Journal of the Korean Society for Nondestructive Testing
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• v.29 no.3
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• pp.230-234
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• 2009

As concrete ages, the surrounding environment is expected to have growing influences on the concrete. As all the impacts of the environment cannot be considered in the strength-estimating model of a nondestructive concrete test, the increase in concrete age leads to growing uncertainty in the strength-estimating model. Therefore, the variation of the model error increases. It is necessary to include those impacts in the probability model of concrete strength attained from the nondestructive tests so as to build a more accurate reliability model for structural performance evaluation. This paper reviews and categorizes the existing strength-estimating statistical models of nondestructive concrete test, and suggests a new form of the strength-estimating statistical models to properly reflect the model uncertainty due to aging of the concrete. This new form of the statistical models will lay foundation for more accurate structural performance evaluation.

• Journal of Korea Water Resources Association
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• v.51 no.6
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• pp.523-533
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• 2018

The objective of this study is to analyze uncertainties of ensemble-based streamflow prediction method for model parameters and input data. ESP (Ensemble Streamflow Prediction) and BAYES-ESP (Bayesian-ESP) based on ABCD rainfall-runoff model were selected as streamflow prediction method. GLUE (Generalized Likelihood Uncertainty Estimation) was applied for the analysis of parameter uncertainty. The analysis of input uncertainty was performed according to the duration of meteorological scenarios for ESP. The result showed that parameter uncertainty was much more significant than input uncertainty for the ensemble-based streamflow prediction. It also indicated that the duration of observed meteorological data was appropriate to using more than 20 years. And the BAYES-ESP was effective to reduce uncertainty of ESP method. It is concluded that this analysis is meaningful for elaborating characteristics of ESP method and error factors of ensemble-based streamflow prediction method.

• Environmental Engineering Research
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• v.18 no.3
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• pp.123-128
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• 2013

The purposes of this study were to determine the efficiency of using a badge-type diffusive sampler to measure formaldehyde concentrations indoors, and to evaluate the uncertainty associated with the use of data from a diffusive sampler. A diffusive sampler using 2,4-dinitrophenylhydrazine (DNPH) reagent was found to be a suitable tool for measuring the formaldehyde concentration in an indoor environment. The agreement between results of the diffusive sampler and DNPH cartridge were good, showing a correlation coefficient of 0.996. The sampling rate for the diffusive sampler was calculated to be 1.428 L $hr^{-1}$, with a standard deviation of 0.084 L $hr^{-1}$. It was found through analysis that the uncertainty associated with the sampling rate and the mass of the formaldehyde transported into the diffusive sampler by diffusion was the dominant contributor to the total.

• Journal of Korean Society on Water Environment
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• v.36 no.5
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• pp.373-384
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• 2020

Hydrological models are based on a combination of parameters that describe the hydrological characteristics and processes within a watershed. For this reason, the model performance and accuracy are highly dependent on the parameters. However, model uncertainties caused by parameters with stochastic characteristics need to be considered. As a follow-up to the study conducted by Choi et al (2020), who developed a relatively simple semi-distributed hydrological model, we propose a tool to estimate the posterior distribution of model parameters using the Metropolis-Hastings algorithm, a type of Markov-Chain Monte Carlo technique, and analyze the uncertainty of model parameters and simulated stream flow. In addition, the uncertainty caused by the parameters of each version is investigated using the lumped and semi-distributed versions of the applied model to the Hapcheon Dam watershed. The results suggest that the uncertainty of the semi-distributed model parameters was relatively higher than that of the lumped model parameters because the spatial variability of input data such as geomorphological and hydrometeorological parameters was inherent to the posterior distribution of the semi-distributed model parameters. Meanwhile, no significant difference existed between the two models in terms of uncertainty of the simulation outputs. The statistical goodness of fit of the simulated stream flows against the observed stream flows showed satisfactory reliability in both the semi-distributed and the lumped models, but the seasonality of the stream flow was reproduced relatively better by the distributed model.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

• Smart Structures and Systems
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• v.22 no.4
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• pp.399-411
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• 2018

The difficulty in modeling complex nonlinear structures lies in the presence of significant sources of uncertainties mainly attributed to sudden changes in the structure's behavior caused by regular aging factors or extreme events. Quantifying these uncertainties and accurately representing them within the complex mathematical framework of Structural Health Monitoring (SHM) are significantly essential for system identification and damage detection purposes. This study highlights the importance of uncertainty quantification in SHM frameworks, and presents a comparative analysis between intrusive and non-intrusive techniques in quantifying uncertainties for SHM purposes through two different variations of the Kalman Filter (KF) method, the Ensemble Kalman filter (EnKF) and the Polynomial Chaos Kalman Filter (PCKF). The comparative analysis is based on a numerical example that consists of a four degrees-of-freedom (DOF) system, comprising Bouc-Wen hysteretic behavior and subjected to El-Centro earthquake excitation. The comparison is based on the ability of each technique to quantify the different sources of uncertainty for SHM purposes and to accurately approximate the system state and parameters when compared to the true state with the least computational burden. While the results show that both filters are able to locate the damage in space and time and to accurately estimate the system responses and unknown parameters, the computational cost of PCKF is shown to be less than that of EnKF for a similar level of numerical accuracy.

• Journal of Forest and Environmental Science
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• v.33 no.4
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• pp.295-304
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• 2017

This study compared and analyzed the construction of a land use change matrix for the Intergovernmental Panel on Climate Change's (IPCC) land use, land use change, and forestry area (LULUCF). We used National Forest Inventory (NFI) permanent sample plots (with a sample intensity of 4 km) and permanent sample plots with 500 m sampling intensity. The land use change matrix was formed using the point sampling method, Level-2 Land Cover Maps, and forest aerial photographs (3rd and 4th series). The land use change matrix using the land cover map indicated that the annual change in area was the highest for forests and cropland; the cropland area decreased over time. We evaluated the uncertainty of the land use change matrix. Our results indicated that the forest land use, which had the most sampling, had the lowest uncertainty, while the grassland and wetlands had the highest uncertainty and the least sampling. The uncertainty was higher for the 4 km sampling intensity than for the 500 m sampling intensity, which indicates the importance of selecting the appropriate sample size when constructing a national land use change matrix.

• 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.