• The Korean Journal of Financial Management
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• v.2 no.1
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• pp.21-34
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• 1986

The purpose of this study is to analyse the risk and uncertainty involved in the capital budgeting which is executed in long periods and requires massive capital expenditure. Under risk and uncertainty conditions, the estimates in the capital budgeting are random variables rather than known constants. Two approaches have emerged in performing economic analysis that explicitly incorporate risk and uncertainty conditions in the analysis. One approach is to develop a descriptive model which describes the economic performance of an individual investment alternative. But no recomendation would be forthcoming from the model. Rather, the decision-maker would be furnished descriptive information concerning each alternative; the final choice among the alternatives would required a separate action. The second approach is to develop a normative model which includes an objective function to be maximized or minimized. The output from the model prescribes the course of action to be taken. Owing to the fact that the normative approach considers the fitness of criteria for decision-making its reasonableness looks better. But it is almost imposible that we correctly and easily derive the individuals' utility function. So within we recognize the limits of the descriptive methods, it is more practicle to analyse the investment alternatives by sensitivity analysis.

• Journal of Korea Water Resources Association
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• v.38 no.12 s.161
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• pp.1051-1060
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• 2005

The present study analyzes hydrologic utilization of optimal radar-derived rainfall by using semi-distributed TOPMODEL and evaluates the impacts of radar rainfall and model parametric uncertainty on a hydrologic model. Monte Carlo technique is used to produce the flow ensembles. The simulated flows from the corrected radar rainfalls with real-time bias adjustment scheme are well agreed to observed flows during 22-26 July 2003. It is shown that radar-derived rainfall is useful for simulating streamflow on a basin scale. These results are diagnose with which radar-rainfall Input and parametric uncertainty influence the character of the flow simulation uncertainty. The main conclusions for this uncertainty analysis are that the radar input uncertainty is less influent than the parametric one, and combined uncertainty with radar and Parametric input can be included the highest uncertainty on a streamflow simulation.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.26 no.6
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• pp.1547-1555
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• 2015

We study the problem of nonignorable nonresponse in a two-way contingency table and there may be one or two missing categories. We describe a nonignorable nonresponse model for the analysis of two-way categorical table. One approach to analyze these data is to construct several tables (one complete and the others incomplete). There are nonidentifiable parameters in incomplete tables. We describe a hierarchical Bayesian model to analyze two-way categorical data. We use a nonignorable nonresponse model with Bayesian uncertainty analysis by placing priors in nonidentifiable parameters instead of a sensitivity analysis for nonidentifiable parameters. To reduce the effects of nonidentifiable parameters, we project the parameters to a lower dimensional space and we allow the reduced set of parameters to share a common distribution. We use the griddy Gibbs sampler to fit our models and compute DIC and BPP for model diagnostics. We illustrate our method using data from NHANES III data to obtain the finite population proportions.

• Journal of Radiation Protection and Research
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• v.16 no.2
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• pp.67-74
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• 1991

Parameter uncertainty and sensitivity of KFOOD model for calculating the ingestion dose via terrestrial food-chain pathway was analyzed with using Monte-Carlo approach. For the rice ingestion pathway, estimated values from KFOOD code were very conservative. Most sensitive input parameters in model were deposition velocities and soil-to-plant transfer coefficient of radionuclides.

• Journal of the Korean Society for Precision Engineering
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• v.17 no.1
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• pp.68-75
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• 2000

In this paper, the robust controller design is performed for the speed control of the underwater vehicle diesel engine. Nonlinear model equations are acquired through the mathematical modeling using mean torque production model technique. It is very difficult to design the robust controller because those are high nonlinear and not expressed in terms of the matched uncertainty Therefore those are converted into the separable model into the linear nominal system and the nonlinear uncertainty term.

• 유체기계공업학회:학술대회논문집
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• 2002.12a
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• pp.378-386
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• 2002

Multi-path ultrasonic Sow measurement system uncertainty is determined by assigning an expected error of each component of flow measurement and then defining the total flow measurement uncertainty as square root of the sum of squared values of the individual error. Sources of uncertainty for flow measurement are geometry, transit time and velocity profile integration uncertainty. A theoretical uncertainty model for multi-path ultrasonic transit time flowmeter configured with parallel 5 chords, is derived from and calculated by dry calibration method.

• Journal of Korean Society for Quality Management
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• v.45 no.3
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• pp.533-547
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• 2017

Purpose: This study proposes the main sources of uncertainty and uncertainty analysis of a measurement system of insensitive munitions tests. Methods: We established the mathematical model for calculating measurement uncertainty of insensitive munitions tests, conducted experiments for calculating uncertainties of dynamic sensitivity and overshoot value, and estimated the distributions of uncertainty factors. Results: The measurement uncertainty calculation methods are presented, which include experimental data processing methods for calculating uncertainties of dynamic sensitivity and overshoot value. Conclusion: The measurement of explosion pressure in insensitive munitions tests is an important issue to the reporting test results and classifying reaction types. The more efforts to ensure the reliability of the insensitive munitions tests results are required.

• Journal of Environmental Science International
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• v.20 no.3
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• pp.321-327
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• 2011

Bootstrap methods is the computer-based resampling method that estimates the standard errors and confidence intervals of summary statistics using the plug-in principle for assessing the accuracy or uncertainty of statistical estimates, and the BCa method among the Bootstrap methods is known much superior to other Bootstrap methods in respect of the standards of statistical validation. Therefore this study suggests the method of the representation and treatment of uncertainty in flood risk assessment and water resources planning from the construction and application of rainfall frequency analysis model considersing the uncertainty based on the nonparametric BCa method among the Bootstrap methods for the assessement of the estimation of probability rainfall and the effect of uncertainty considering the uncertainty of the parameter estimation of probability in the rainfall frequency analysis that is the most fundamental in flood risk assessement and water resources planning.

• Journal of Information Technology Applications and Management
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• v.24 no.4
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• pp.13-24
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• 2017

This study aims to analyze which subsidiary configuration strategy is more effective under uncertainty especially technology base multinational corporations (henceforth MNCs). In previous studies real option theory scholars argue that high breadth subsidiary configuration is most effective strategy because that provides flexibility to MNCs global network. In this study I want unveil more various types of uncertainty such as technology and learning uncertainty which are more important for technology base firm and further more examine the effect of MNCs subsidiary configuration on firm R&D performance each uncertainty case. Empirical study is performed by negative binominal model based on Japanese 108 multinational corporations. The result shows that under technology uncertainty, high breadth subsidiary configuration is better for firm R&D performance but under learning uncertainty high depth subsidiary configuration is better. Thus, the effects of MNCs subsidiary configuration on firm value can differ by types of uncertainty.