• Tunnel and Underground Space
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• v.15 no.2 s.55
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• pp.129-136
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• 2005

This paper describes the implementation of rock mass rating evaluation based on genetic algorithm(GA) and conditional simulation technique to estimate RMR in the area without sufficient borehole data RMR were estimated by GA and conditional simulation technique with reflecting distribution feature and spatial correlation. And RMR determined by GA were compared with the results from kriging. Through the analysis of the results from 30 simulations, the uncertainty of estimation could be quantified.

• The Transactions of the Korea Information Processing Society
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• v.3 no.6
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• pp.1636-1645
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• 1996

In this paper we proposed a new scheme for conditional two dimensional (2-D)moment-based classification of patterns on the basis of Gibbs random fields which are will suited for representing spatial continuity that is the characteristic of the most images. This implementation contains two parts: feature extraction and pattern classification. First of all, we extract feature vector which consists of conditional 2-D moments on the basis of estimated Gibbs parameter. Note that the extracted feature vectors are invariant under translation, rotation, size of patterns the corresponding template pattern. In order to evaluate the performance of the proposed scheme, classification experiments with training document sets of characters have been carried out on 486 66Mhz PC. Experiments reveal that the proposed scheme has high classification rate over 94%.

• Proceedings of the Korea Water Resources Association Conference
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• 2016.05a
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• pp.98-98
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• 2016

본 연구에서는 유입량의 불확실성을 고려하여 미래 저수량을 확률론적으로 예측하였다. 월별 유입량을 표본으로 한 확률밀도함수를 핵밀도함수(kernel function)를 이용하여 추정하고, 추정된 확률분포로 월별 유입량을 모의 발생하였다. 모의 발생된 유입량을 통해 연속적인 조건부 확률을 산정하였고, 이의 누적확률분포(F(x))는 해당 저수량에 도달하지 못할 확률, 즉 실패확률을 의미하므로 1-F(x)로 해당 저수량 이상을 확보할 수 있는 확률을 산정하였다. 보령댐을 대상으로 분석한 결과 2016년 2월 말 저수량 27.8 백만$m^3$ 기준으로 3월부터 6월까지 정상용수공급환원 기준 저수량을 만족할 확률이 각각 2.3%, 12.5%, 24.2%, 33.5%로 나타났다. 지역적 가뭄에 대응하기 위해 하천유지용수 감량, 용수 대체공급, 자율 급수조정 및 금강-보령댐 도수로를 이용한 용수공급으로 20.6만$m^3/day$의 용수가 비축될 경우, 정상용수공급환원 기준 저수량을 만족할 확률이 10.2%, 40.3%, 73.8%, 78.7%로 용수비축의 효과가 크게 나타나는 것을 확인하였다. 저수량의 확률론적 예측을 통해 미래 저수량의 확률적 발생가능성을 추정할 수 있으며, 가뭄이 발생할 경우, 가뭄 대응효과를 정량적으로 나타낼 수 있어 가뭄 위험 상황 전달 및 용수공급조정 의사결정 시 활용할 수 있을 것으로 판단된다.

• Journal of the Korea Society for Simulation
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• v.33 no.1
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• pp.19-26
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• 2024

The phase-type, PH, distribution is defined as the time to absorption into a terminal state in a continuous-time Markov chain. As the PH distribution includes family of exponential distributions, it has been widely used in stochastic models. Since the PH distribution is represented and generated by an initial probability vector and a generator matrix which is called the Markovian representation, we need to find a vector and a matrix that are consistent with given set of moments if we want simulate a PH distribution. In this paper, we propose an approach to simulate a PH distribution based on distribution function which can be obtained directly from moments. For the simulation of PH distribution of order 2, closed-form formula and streamlined procedures are given based on the Jordan decomposition and the minimal Laplace transform which is computationally more efficient than the moment matching methods for the Markovian representation. Our approach can be used more effectively than the Markovian representation in generating higher order PH distribution in queueing network simulation.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09b
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• pp.53-56
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• 2003

유사도 혹은 자료간의 거리 개념은 많은 기계학습 알고리즘에서 사용되고 있는 중요한 측정개념이다 하지만 입력되는 자료의 속성들중 순서가 정의되지 않은 범주형 속성이 포함되어 있는 경우, 자료간의 유사도나 거리 측정에 어려움이 따른다. 비거리 기반의 알고리즘들의 경우-C4.5, CART-거리의 측정없이 작동할 수 있지만, 거리기반의 알고리즘들의 경우 범주형 속성의 거리 정보 결여로 효과적으로 적용될 수 없는 문제점을 갖고 있다. 본 논문에서는 이러한 범주형 자료들간 거리 측정을 자료 집합의 특성을 충분히 고려한 방법을 제안한다. 이를 위해 자료 집합의 선험적인 정보를 필요로 한다. 이런 선험적 정보인 조건부 확률을 기반으로한 거리 측정방법을 제시하고 오류 피드백을 통해서 속성 간 거리 측정을 최적화 하려고 노력한다. 주어진 자료 집합에 대해 서로 다른 두 범주형 값이 목적 속성에 대해서 유사한 분포를 보인다면 이들 값들은 비교적 가까운 거리로 결정한다 이렇게 결정된 거리를 기반으로 학습 단계를 진행하며 이때 발생한 오류들에 대해 피드백 작업을 진행한다. UCI Machine Learning Repository의 자료들을 이용한 실험 결과를 통해 제안한 거리 측정 방법의 우수한 성능을 확인하였다.

• Journal of Korea Water Resources Association
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• v.33 no.4
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• pp.437-446
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• 2000

THe drought analysis is performed by applications of truncation level method and conditional probability concept for hydrologic time series in Han river basin. The distributed trend of conditional probability is determined using kriging method for the time series. This study uses daily flowrate, monthly rainfall, and daily high temperature data sets. The daily flowrate data of 12 years(1986~1997) is used for the analysis. Also, the 14 years' data sets(1986~1999) for monthly rainfall and daily high temperature obtained from the National Weather Service of Korea are used in this study. In the cases of flowrate and rainfall data sets, the estimated value corresponding to the truncation level is decreased as the truncation level is increased but in the high temperature data, the value is increased as the truncation level is increased. The conditional probability varies according to the observations and sites. However, the distributed trend of drought is similar over the basin. As a result, the possibility of the drought is high in the middle and lower parts of Han river basin and thus it is recommended the distributed trend of drought be considered when the plan or measures for drought are established.

• Journal of the Korean earth science society
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• v.32 no.3
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• pp.249-264
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• 2011

A multi-Gaussian kriging approach extended to space-time domain is presented for uncertainty modeling as well as time-series mapping of environmental variables. Within a multi-Gaussian framework, normal score transformed environmental variables are first decomposed into deterministic trend and stochastic residual components. After local temporal trend models are constructed, the parameters of the models are estimated and interpolated in space. Space-time correlation structures of stationary residual components are quantified using a product-sum space-time variogram model. The ccdf is modeled at all grid locations using this space-time variogram model and space-time kriging. Finally, e-type estimates and conditional variances are computed from the ccdf models for spatial mapping and uncertainty analysis, respectively. The proposed approach is illustrated through a case of time-series Particulate Matter 10 ($PM_{10}$) concentration mapping in Incheon Metropolitan city using monthly $PM_{10}$ concentrations at 13 stations for 3 years. It is shown that the proposed approach would generate reliable time-series $PM_{10}$ concentration maps with less mean bias and better prediction capability, compared to conventional spatial-only ordinary kriging. It is also demonstrated that the conditional variances and the probability exceeding a certain thresholding value would be useful information sources for interpretation.

• Journal of Korea Water Resources Association
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• v.48 no.1
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• pp.37-44
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• 2015

A Bayesian nonstationary probability rainfall estimation model using the Grid method is developed. A hierarchical Bayesian framework is consisted with prior and hyper-prior distributions associated with parameters of the Gumbel distribution which is selected for rainfall extreme data. In this study, the Grid method is adopted instead of the Matropolis Hastings algorithm for random number generation since it has advantage that it can provide a thorough sampling of parameter space. This method is good for situations where the best-fit parameter values are not easily inferred a priori, and where there is a high probability of false minima. The developed model was applied to estimated target year probability rainfall using hourly rainfall data of Seoul station from 1973 to 2012. Results demonstrated that the target year estimate using nonstationary assumption is about 5~8% larger than the estimate using stationary assumption.

• The Korean Journal of Applied Statistics
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• v.11 no.2
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• pp.247-255
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• 1998

In this paper, a new subset selection problem in the Poisson model is considered under the normal predictors. It turns out that the subset model has bigger valiance than that of the Poisson model with random predictors and this has been used to derive new subset selection method similar to Mallows'$C_p$.

• Journal of the Korean Data and Information Science Society
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• v.23 no.1
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• pp.99-111
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• 2012

The log-density ratio of the conditional densities of the predictors given the response variable provides useful information for variable selection in the logistic regression model. In this paper, we consider the predictors that are needed and how they should be included in the model. If the conditional distributions are skewed, the distributions can be considered as gamma distributions. Under this assumption, linear and log terms are generally included in the model. The log-odds graph is a very useful graphical tool in this study. A graphical study is presented which shows that if the conditional distributions of x|y for the two groups overlap significantly, we need both the linear and quadratic terms. On the contrary, if they are well separated, only the linear or log term is needed in the model.