• 한국지구과학회지
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• 제31권5호
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• pp.437-447
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• 2010

Spatial estimation of environmental variables has been regarded as an important preliminary procedure for decision-making. A minimum variance criterion, which has often been adopted in traditional kriging algorithms, does not always guarantee the optimal estimates for subsequent decision-making processes. In this paper, a geostatistical framework is illustrated that consists of uncertainty modeling via stochastic simulation and risk modeling based on loss functions for the selection of optimal estimates. Loss functions that quantify the impact of choosing any estimate different from the unknown true value are linked to geostatistical simulation. A hybrid loss function is especially presented to account for the different impact of over- and underestimation of different land-use types. The loss function-specific estimates that minimize the expected loss are chosen as optimal estimates. The applicability of the geostatistical framework is demonstrated and discussed through a case study of copper mapping.

• Journal of the Korean Data and Information Science Society
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• 제16권4호
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• pp.1079-1086
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• 2005

Most discussions of single imputation methods and the EM algorithm concern point estimation of population quantities with missing values. A second concern is how to get standard errors of the point estimates obtained from the filled-in data by single imputation methods and EM algorithm. Now we focus on how to estimate standard errors with incorporating the additional uncertainty due to nonresponse. There are some approaches to account for the additional uncertainty. The general two possible approaches are considered. One is the jackknife method of resampling methods. The other is multiple imputation(MI). These two approaches are reviewed and compared through simulation studies.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2001년도 ICCAS
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• pp.39.3-39
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• 2001

Since Kalman filter and wavelet transform techniques are both suitable for a nonstationary process, wavelet-Kalman filter was proposed and applied to various industrial fields. However, the wavelet-Kalman filter subjected to model uncertainty with nonstationary process has not been considered. Thus, the robust wavelet-Kalman filter method is proposed in this paper. The proposed method can prevent the degradation of filter performance when parameter uncertainty exists in both the state and measurement matrices and preserve the merits of the standard Kalman filter in the sense that it produces optimal estimates. A simple example shows that the proposed approach outperforms the standard Kalman filter and the nominal wavelet-Kalman filter.

• 제어로봇시스템학회논문지
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• 제6권11호
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• pp.963-967
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• 2000

This paper deals with a sliding mode control with uncertainty adaptation for the robust stabilization of input-delay systems with unknown uncertainties. A sliding surface including a state predictor is employed to compensate for the effect of the input delay. The proposed method does not need a priori knowledge of upper bounds on the norm of uncertainties, but estimates those upper bounds by adaptation laws based on the sliding surface. Then, a robust control law with the uncertainty adaptation is derived to ensure the existence of the sliding mode. A numerical example is given to illustrate the design procedure.

• Fisheries and Aquatic Sciences
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• 제21권8호
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• pp.24.1-24.9
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• 2018

The Korean bottom trawl survey has been deployed on a regular basis for about the last decade as part of groundfish stock assessments. The regularity indicates that they sample groundfish once per grid cell whose sides are half of one latitude and that of one longitude, respectively, and whose inside is furthermore divided into nine nested grids. Unless they have a special reason (e.g., running into a rocky bottom), their sample location is at the center grid of the nine nested grids. Given data collected by the survey, we intended to show how to appropriately estimate not only the survey index of a fish stock but also its uncertainty. For the regularity reason, we applied the systematic sampling theory for the above purposes and compared its results with a reference, which was based on the simple random sampling. When using the survey data about 11 fish stocks, collected by the spring and fall surveys in 2014, the survey indices of those stocks estimated under the systematic sampling were overall more precise than those under the simple random sampling. In estimates of the survey indices in number, the standard errors of those estimates under the systematic sampling were reduced from those under the simple random sampling by 0.23~27.44%, while in estimates of the survey indices in weight, they decreased by 0.04~31.97%. In bias of the estimates, the systematic sampling was the same as the simple random sampling. Our paper is first in formally showing how to apply the systematic sampling theory to the actual data collected by the Korean bottom trawl surveys.

• 한국임상수의학회지
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• 제32권4호
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• pp.319-323
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• 2015

The performance of diagnostic test accuracy is usually summarized by a variety of statistics such as sensitivity, specificity, predictive value, likelihood ratio, and kappa. These indices are most commonly presented when evaluations of competing diagnostic tests are reported, and it is of utmost importance to compare the accuracies of diagnostic tests to decide on the best available test for certain medical disorder. However, it is important to emphasize that specific point values of these indices are merely estimates. If parameter estimates are reported without a measure of uncertainty (precision), knowledgeable readers cannot know the range within which the true values of the indices are likely to lie. Therefore, when evaluations of diagnostic accuracy are reported the precision of estimates should be stated in parallel. To reflect the precision of any estimate of a diagnostic performance characteristic or of the difference between performance characteristics, the computation of confidential interval (CI), an indicator of precision, is widely used in medical literatures in that CIs are more informative to interpret test results than the simple point estimates. The majority of peer-reviewed journals usually require CIs to be specified for descriptive estimates, whereas domestic veterinary journals seem less vigilant on this issues. This paper describes how to calculate the indices and associated CIs using practical examples when assessing diagnostic test performance.

• 한국환경과학회지
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• 제20권12호
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• pp.1541-1551
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• 2011

This study applied the Bayesian method for the quantification of the parameter uncertainty of spatial linear mixed model in the estimation of the spatial distribution of probability rainfall. In the application of Bayesian method, the prior sensitivity analysis was implemented by using the priors normally selected in the existing studies which applied the Bayesian method for the puppose of assessing the influence which the selection of the priors of model parameters had on posteriors. As a result, the posteriors of parameters were differently estimated which priors were selected, and then in the case of the prior combination, F-S-E, the sizes of uncertainty intervals were minimum and the modes, means and medians of the posteriors were similar to the estimates using the existing classical methods. From the comparitive analysis between Bayesian and plug-in spatial predictions, we could find that the uncertainty of plug-in prediction could be slightly underestimated than that of Bayesian prediction.

• 한국건설관리학회논문집
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• 제8권5호
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• pp.191-200
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• 2007

This paper introduces a tool for predicting potential cost overrun during project execution and for quantifying the uncertainty on the expected project cost, which is occasionally changed by the unknown effects resulted from project's complications and unforeseen environments. The model proposed in this stuff is useful in diagnosing cost performance as a project progresses and in monitoring the changes of the uncertainty as indicators for a warning signal. This model is intended for the use by project managers who forecast the change of the uncertainty and its magnitude. The paper presents a mathematical approach for modifying the costs of incomplete work packages and project cost, and quantifying reduced uncertainties at a consistent confidence level as actual cost information of an ongoing project is obtained. Furthermore, this approach addresses the effects of actual informed data of completed work packages on the re-estimates of incomplete work packages and describes the impacts on the variation of the uncertainty for the expected project cost incorporating Multivariate Probabilistic Analysis (MPA) and Bayes' Theorem. For the illustration purpose, the Introduced model has employed an example construction project. The results are analyzed to demonstrate the use of the model and illustrate its capabilities.

• Journal of the Korean Statistical Society
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• 제32권1호
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• pp.1-10
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• 2003

We consider obtaining graphical summaries of uncertainty in estimates of parameters in nonlinear models. A nonlinear constrained optimization algorithm is developed for likelihood based confidence intervals for the functions of parameters in the model The results are applied to the problem of finding significance levels in nonlinear models.

• Architectural research
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• 제10권1호
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• pp.41-48
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• 2008

Construction projects have frequently exceeded their schedule despite reliable estimates at the start of a project. This problem was attributed to unpredictable causes at the beginning and to shortage of proper tools to accurately predict project completion date. To supplement this difficulty, project managers need a comprehensive system that can be employed to monitor the progress of an ongoing project and to evaluate potential delay for achieving the goal on time. This paper proposed a progressive-based expert system for quantitative assessments of project delay at the early stages of the execution. Furthermore, the system is used to inspect the change of the uncertainty on completion date and its magnitude. The proposed expert system is helpful for furnishing project managers a warning signal as a project is going behind schedule and for tracking the changed uncertainty at a desired confidence level. The main objectives of this paper are to offer a new system to overcome the difficulties of conventional forecasting tools and to apply a construction project into the system to illustrate its effectiveness. This paper focuses on construction phase of project development and is intended for the use by project managers.