• Journal of KIISE:Software and Applications
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• v.28 no.9
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• pp.681-687
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• 2001

In order to raise a class discrimination power by combining multiple classifiers under the Bayesian decision theory, the upper bound of a Bayes error rate bounded by the conditional entropy of a class variable and decision variables obtained from training data samples should be minimized. Wang and Wong proposed a tree dependence first-order approximation scheme of a high order probability distribution composed of the class and multiple feature pattern variables for minimizing the upper bound of the Bayes error rate. This paper presents an extended high order product approximation scheme dealing with higher order dependency more than the first-order tree dependence, based on the minimization of the upper bound of the Bayes error rate. Multiple recognizers for unconstrained handwritten numerals from CENPARMI were combined by the proposed approximation scheme using the Bayesian formalism, and the high recognition rates were obtained by them.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.2B
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• pp.155-162
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• 2009

Univariate frequency analyses are widely used in practical hydrologic design. However, a storm event is usually characterized by amount, intensity, and duration of the storm. To fully understand these characteristics and to use them appropriately in hydrologic design, a multivariate statistical approach is necessary. This study applied a Gumbel mixed model to a bivariate storm frequency analysis using hourly rainfall data collected for 46 years at the Seoul rainfall gauge station in Korea. This study estimated bivariate return periods of a storm such as joint return periods and conditional return periods based on the estimation of joint cumulative distribution functions of storm characteristics. These information on statistical behaviors of a storm can be of great usefulness in the analysis and assessment of the risk associated with hydrologic design problems.

• Journal of Korea Water Resources Association
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• v.45 no.5
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• pp.505-516
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• 2012

The study applies a hydrologic simulation model, HEC-1 developed by Hydrologic Engineering Center to Daecheong dam watershed for modeling hourly inflows of Daecheong dam. Although the HEC-1 model provides an automatic optimization technique for some of the parameters, the built-in optimization model is not sufficient in estimating reliable parameters. In particular, the optimization model often fails to estimate the parameters when a large number of parameters exist. In this regard, a main objective of this study is to develop Bayesian Markov Chain Monte Carlo simulation based HEC-1 model (BHEC-1). The Clark IUH method for transformation of precipitation excess to runoff and the soil conservation service runoff curve method for abstractions were used in Bayesian Monte Carlo simulation. Simulations of runoff at the Daecheong station in the HEC-1 model under Bayesian optimization scheme allow the posterior probability distributions of the hydrograph thus providing uncertainties in rainfall-runoff process. The proposed model showed a powerful performance in terms of estimating model parameters and deriving full uncertainties so that the model can be applied to various hydrologic problems such as frequency curve derivation, dam risk analysis and climate change study.

• Journal of Korea Water Resources Association
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• v.49 no.4
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• pp.275-282
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• 2016

The objective of this study is to propose a new method to determine the drought event and the design drought severity. In order to define a drought event from precipitation data, theory of run was applied with the cumulative rainfall deficit. When we have a large amount of rainfall over the threshold level, in this study, we compare with the previous cumulative rainfall deficit to determine whether the drought is relieved or not. The recurrence characteristics of the drought severity on the specific duration was analyzed by the conditional bivariate copula function and confidence intervals were estimated to quantify uncertainties. The methodology was applied to Seoul station with the historical dataset (1909~2015). It was observed that the past droughts considered as extreme hydrological events had from 10 to 50 years of return period. On the other hand, the current on-going drought event started from 2013 showed the significantly higher return period. It is expected that the result of this study may be utilized as the reliable criteria based on the concept of return period for the drought contingency plan.

• KDI Journal of Economic Policy
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• v.13 no.4
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• pp.117-140
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• 1991

The purpose of this paper is primarily to introduce a nonparametric statistical tool developed by Baek and Brock to detect a unidirectional causal ordering between two economic variables and apply it to interesting macroeconomic relationships among money, production and prices. It can be applied to any other causal structure, for instance, defense spending and economic performance, stock market index and market interest rates etc. A key building block of the test for nonlinear Granger causality used in this paper is the correlation. The main emphasis is put on nonlinear causal structure rather than a linear one because the conventional F-test provides high power against the linear causal relationship. Based on asymptotic normality of our test statistic, the nonlinear causality test is finally derived. Size of the test is reported for some parameters. When it is applied to a money, production and prices model, some evidences of nonlinear causality are found by the corrected size of the test. For instance, nonlinear causal relationships between production and prices are demonstrated in both directions, however, these results were ignored by the conventional F-test. A similar results between money and prices are obtained at high lag variables.

• Journal of Korea Water Resources Association
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• v.40 no.4
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• pp.299-311
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• 2007

Climatic abnormal phenomena involving El Nino and La Nina have been frequently reported in recent decades. The interannual climate variability represented by El Nino-Southern Oscillation (ENSO) is sometimes investigated to account for the climatic abnormal phenomena around the world. Although many hydroclimatologists have studied the impact of ENSO on regional precipitation and streamflow, however, there are still many difficulties in finding the dominant causal relationship between them. This relationship is very useful in making hydrological forecasting models for water resources management. In this study, the seasonal relationships between ENSO and hydrologic variables were investigated in Korea. As an ENSO indicator, Southern Oscillation Index (SOI) was used. Monthly precipitation, monthly mean temperature, and monthly dam inflow data were used after being transformed to the standardized normal index. Seasonal relationships between ENSO and hydrologic variables were investigated based on the exceedance probability and distribution of hydrologic variables conditioned on the ENSO episode. The results from the analysis of this study showed that the warm ENSO episode affects increases in precipitation and temperature, and the cold ENSO episode is related with decreases in precipitation and temperature in Korea. However, in some regions, the local relationships do not correspond with the general seasonal relationship.