• Journal of Korean Institute of Industrial Engineers
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• v.40 no.2
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• pp.151-162
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• 2014

This study proposes an optimization model to plan the patient distribution and medical resource allocation considering the diverse characteristics of disaster. For reflecting the particularity of disaster response, we configured a few scenarios such as availability of emergency surgery of non-major medical staff and the change in number of patients estimated reflecting the uncertainty, urgency and convergence of disaster. And we finally tested the effects of the scenarios' combination on the objective function defined as maximum number of survival patients. Our experimental results are expected to highlight the significance of the proposed model as well as the applicability of scenarios under disaster response.

• Journal of the Korean Geotechnical Society
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• v.28 no.11
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• pp.5-16
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• 2012

Since geologic data are often sampled at sparse locations, it is important not only to predict attribute values at unsampled locations, but also to assess the uncertainty attached to the prediction. In this paper, the assessment of the local uncertainty of prediction for the depth to weathered soil was performed by using the indicator kriging. A conditional cumulative distribution function (ccdf) was first modeled, and then E-type estimate was computed for the spatial distribution of the depth to the weathered soil. Also, optimal estimate of spatial distribution for the depth to weathered soil was determined by using ccdf and loss function. The design procedure and method considering the minimum expected loss presented in this paper can be used in the decision-making process for geotechnical engineering design.

• Journal of Korean Society of Forest Science
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• v.102 no.4
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• pp.477-483
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• 2013

The purpose of this study was to calculate uncertainty of emission factor collected data and to evaluate the applicability of Monte Carlo simulation technique. To estimate the distribution of emission factors (Such as Basic wood density, Biomass expansion factor, and Root-to-shoot ratio), four probability density functions (Normal, Lognormal, Gamma, and Weibull) were used. The two sample Kolmogorov-Smirnov test and cumulative density figure were used to compare the optimal probability density function. It was observed that the basic wood density showed the gamma distribution, the biomass expansion factor results the log-normal distribution, and root-shoot ratio showd the normal distribution for Pinus densiflora in the Gangwon region; the basic wood density was the normal distribution, the biomass expansion factor was the gamma distribution, and root-shoot ratio was the gamma distribution for Pinus densiflora in the central region, respectively. The uncertainty assessment of emission factor were upper 62.1%, lower -52.6% for Pinus densiflora in the Gangwon region and upper 43.9%, lower -34.5% for Pinus densiflora in the central region, respectively.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.33 no.10
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• pp.1163-1170
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• 2009

Reliability analysis is of great importance in the advanced product design, which is to evaluate reliability due to the associated uncertainties. There are three types of uncertainties: the first is the aleatory uncertainty which is related with inherent physical randomness that is completely described by a suitable probability model. The second is the epistemic uncertainty, which results from the lack of knowledge due to the insufficient data. These two uncertainties are encountered in the input variables such as dimensional tolerances, material properties and loading conditions. The third is the metamodel uncertainty which arises from the approximation of the response function. In this study, an integrated method for the reliability analysis is proposed that can address all these uncertainties in a single Bayesian framework. Markov Chain Monte Carlo (MCMC) method is employed to facilitate the simulation of the posterior distribution. Mathematical and engineering examples are used to demonstrate the proposed method.

• International Journal of Aeronautical and Space Sciences
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• v.8 no.2
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• pp.126-131
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• 2007

In this paper, Monte Carlo Simulation (MCS) method was used as an uncertainty assessment tool for air flow, net thrust measurement. Uuncertainty sources of the net thrust measurement were analyzed, and the probability distribution characteristics of each source were discussed. Detailed MCS methodology was described including the effect of the number of simulation. Compared to the conventional sensitivity coefficient method, the MCS method has advantage in the uncertainty assessment. The MCS is comparatively simple, convenient and accurate, especially for complex or nonlinear measurement modeling equations. The uncertainty assessment result by MCS was compared with that of the conventional sensitivity coefficient method, and each method gave different result. The uncertainties in the net thrust measurement by the MCS and the conventional sensitivity coefficient method were 0.906% and 1.209%, respectively. It was concluded that the first order Taylor expansion in the conventional sensitivity coefficient method and the nonlinearity of model equation caused the difference. It was noted that the uncertainty assessment method should be selected carefully according to the mathematical characteristics of the model equation of the measurement.

• Nuclear Engineering and Technology
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• v.54 no.6
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• pp.2084-2093
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• 2022

Probabilistic safety assessment is widely used to quantify the risks of nuclear power plants and their uncertainties. When the lognormal distribution describes the uncertainties of basic events, the uncertainty of the top event in a fault tree is approximated with the sum of lognormal random variables after minimal cutsets are obtained, and rare-event approximation is applied. As handling complicated analytic expressions for the sum of lognormal random variables is challenging, several approximation methods, especially Monte Carlo simulation, are widely used in practice for uncertainty analysis. In this study, a theoretical approach for analyzing the sum of lognormal random variables using an efficient numerical integration method is proposed for uncertainty analysis in probability safety assessments. The change of variables from correlated random variables with a complicated region of integration to independent random variables with a unit hypercube region of integration is applied to obtain an efficient numerical integration. The theoretical advantages of the proposed method over other approximation methods are shown through a benchmark problem. The proposed method provides an accurate and efficient approach to calculate the uncertainty of the top event in probabilistic safety assessment when the uncertainties of basic events are described with lognormal random variables.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.4
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• pp.39-52
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• 2022

This study examines whether accounting conservatism can support real earnings management by reducing accrual earnings management techniques. The net impact of conservative reporting on earnings management is also analyzed. It is assumed that moderating the role of conservative financial reporting during uncertainty can mitigate earnings management practices. For our analysis, 5354 firm-year observations for the period 2007-2020 of nonfinancial companies listed on the Pakistan Stock Exchange are applied. To measure conservatism in the non-financial sector of Pakistan, Khan and Watts' (2009) model is used to provide evidence that conservatism is a way to restrict earnings management during uncertainty. "Prospector" and "Defender" Business strategy is applied for measuring firm-level uncertainty. To measure accrual earnings management Modified Jones (1995) model and Dechow and Dichev (2002) approach and Kasznik (1999) model are applied, and for real earnings management Roychowdhury model is applied which follows three approaches to measure real earnings management i.e. cash flow manipulation, Overproduction, and discretionary expenses. The estimations support our hypothesis by providing statistically significant proof that conservative financial reporting in a developing economy like Pakistan may be used to overcome the net impact of earnings management during uncertainty. Our results provide critical and practical implications for investors, researchers, and standard setters.

• Proceedings of the Korea Water Resources Association Conference
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• 2005.05b
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• pp.324-328
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• 2005

This study is to figure out uncertainty relationship between input data and calibrated parameter on unsteady hydraulic routing model. The uncertainty would be present to model results as a variant water surface profile along the channel. Firstly, Manning's n is calibrated through the model with assumed uncertainty on input hydrograph. Then, spatially distributed n-values sets based on the calibrated n values are used to get water profile of each n-values set. The results show that ${\pm}0.002$ of error in Manning's n cause ${\pm}30cm$ of maximum water surface differences at the Sumjin river.

• Structural Engineering and Mechanics
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• v.49 no.1
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• pp.41-64
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• 2014

The main objective of this article is the exploitation of a stochastic hybrid mesh-free method based on stochastic generalized finite difference (SGFD), Newmark finite difference (NFD) methods and Monte Carlo simulation for thermoelastic wave propagation and coupled thermoelasticity analysis based on GN theory (without energy dissipation). A thick hollow cylinder with Gaussian uncertainty in mechanical properties is considered as an analyzed domain for the problem. The effects of uncertainty in mechanical properties with various coefficients of variations on thermo-elastic wave propagation are studied in details. Also, the time histories and distribution on thickness of cylinder of maximum, mean and variance values of temperature and radial displacement are studied for various coefficients of variations (COVs).

• Earthquakes and Structures
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• v.6 no.1
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• pp.19-43
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• 2014

A risk assessment framework for evaluating building structures is implemented in this study. This framework allows considering sources of uncertainty both on structural capacity and seismic demand. In particular randomness on seismic load, incident angle, material properties, floor mass and structural damping are considered; in addition the choice of fibre modelling versus plastic hinge model is also considered as a source of uncertainty. The main objective of this work is to study the contribution of these sources of uncertainty on the fragilities of steel and steel-reinforced concrete composite 3D building structures. The fragility curves are expressed in the form of a two-parameter lognormal distribution where vertical statistics in conjunction with metaheuristic optimization are implemented for calculating the two parameters.