• Journal of the Korea Society of Computer and Information
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• v.9 no.2
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• pp.1-6
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• 2004

In this paper, we proposed the intelligent navigation agent model for successive electronic commerce management. For allowing intelligence, we used fuzzy conditional probability and trapezoidal. we proposed the model that can Process the vague keywords effectively. Through the this, we verified that we can get the more appropriate navigation result than any other crisp retrieval keywords condition. Our goal of study is make an intelligent automatic navigation agent model.

• Proceedings of the Korean Association of Geographic Inforamtion Studies Conference
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• 2002.03b
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• pp.49-59
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• 2002

이 연구에서는 강원도 삼척시를 대상으로 산불취약지역 분석모델을 개발하고, 개발된 분석모델을 기반으로 산불취약지역을 표출하였으며, 이를 위한 전산프로그램을 개발하였다. 산불취약지역 공간분석자료로는 NGIS 사업을 통해 구축된 1/25천 축척의 수치지형도, 수치임상도 그리고 과거 산불발화위치자료를 사용하였다. 산불발화위치에 대한 공간적 분포특성(지형, 임상, 접근성)을 기반으로 모델을 설정하였으며, 공간분석은 간단하면서도 일반인들이 이해하기 쉬운 Conditional probability, Likelihood ratio 방법을 사용하였다. 그리고 각각의 모델에 대한 검증(cross validation)을 실시하였다. 모델 검증방법으로는 과거 산불발화위치자료를 발생시기에 따라 두 개의 그룹으로 나누어 하나는 예측을 위한 자료로 사용하고, 다른 하나는 검증을 위한 자료로 사용하였다. 모델별 예측성능은 prediction rate curve를 비교·분석하여 판단하였다. 삼척시를 대상으로 한 예측성능에서 Likelihood ratio 모델이 Conditional probability 모델보다 더 낳은 결과를 보였다. 산불취약지역 분석기술로 작성된 상세 산불취약지역지도와 현재 산림청에서 예보하고 있는 전국단위의 산불발생위험지수와 함께 상호보완적으로 사용한다면 산불취약지역에 대한 산불감시인력 및 감시시설의 효율적인 배치를 통하여 일선 시군 또는 읍면 산불예방업무의 효율성이 한층 더 증대될 것으로 기대된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2010.05a
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• pp.1415-1419
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• 2010

하천의 제방은 홍수 시, 유수를 원활하게 소통시키고, 제내지를 보호하기 위해 하천을 따라 축조한 시설이다. 이러한 제방의 설계에는 제방의 재료에 대한 기준뿐만 아니라 수리/수문학적 요소, 지반공학적 요소, 구조적 요소들이 요구된다. 본 연구에서는 제방의 설계 요소 가운데 수리/수문학적 요소의 하나인 월류에 대한 기준을 검토하였다. 우리나라는 제방의 설계 시, 월류에 대한 안전성 확보를 위해 여유고를 설정하고 있는데, 여유고의 기준은 계획홍수량의 규모에 따라 결정되어 있다. 반면에 미국은 제방 월류에 대한 안전성 평가 지표로서 여유고와 위험도 개념의 조건부 비 초과 확률(conditional nonexceedance probability; CNP)을 사용하고 있다. 조건부 비 초과 확률이란, 주어진 홍수의 정도 아래에서 목표 수위(target stage)가 초과되지 않을 확률이다. 본 연구에서는 국내에서 기 수립된 실제 하천의 제방고와 조건부 비 초과 확률, 여유고가 고려된 미국의 기준을 적용하여 도출된 제방 소요 높이에 근거하여 국내 기준의 적절성을 검토하였다.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.89-102
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• 1994

A new method for approximating the average run length (ARL) of cumulative sum (CUSUM) chart is proposed. This method uses the conditional expectation for the test statistic before the stopping time and its asymptotic conditional density function. The values obtained by this method are compared with some other methods in normal and exponential case.

• Journal of the Korean Statistical Society
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• v.25 no.4
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• pp.489-497
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• 1996

Lower bounds for the full level probabilities are derived under order restrictions in weights. Discussions are made on typical isotonic cones such as linear order, simple tree order, and unimodal order cones. We also discuss applications of these results for constructing conditional likelihood ratio tests for ordered hypotheses in a contingency table. A real data set on torus mandibularis will be analyzed for illustrating the testing procedure.

• Journal of the Korean Statistical Society
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• v.9 no.2
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• pp.121-125
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• 1980

In this note, we establish a result which characterizes a partial ordering of probability distributions on a partially ordered space by that of conditional distributions. This result is then reduced to prove the conjecture made by Nevius, Proschan and Sethuraman (1977).

• The Journal of Korea Robotics Society
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• v.4 no.1
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• pp.56-61
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• 2009

A probability-based data generation is a typical context-generation method that is a not only simple and strong data generation method but also easy to update generation conditions. However, the probability-based context-generation method has been found its natural-born ambiguousness and confliction problems in generated context data. In order to compensate for the disadvantages of the probabilistic random data generation method, a situation propagation network is proposed in this paper. The situation propagating network is designed to update parameters of probability functions are included in probability-based data generation model. The proposed probability-based context-generation model generates two kinds of contexts: one is related to independent contexts, and the other is related to conditional contexts. The results of the proposed model are compared with the results of the probabilitybased model with respect to performance, reduction of ambiguity, and confliction.

• Genomics & Informatics
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• v.20 no.2
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• pp.17.1-17.11
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• 2022

Genetic associations have been quantified using a number of statistical measures. Entropy-based mutual information may be one of the more direct ways of estimating the association, in the sense that it does not depend on the parametrization. For this purpose, both the entropy and conditional entropy of the phenotype distribution should be obtained. Quantitative traits, however, do not usually allow an exact evaluation of entropy. The estimation of entropy needs a probability density function, which can be approximated by kernel density estimation. We have investigated the proper sequence of procedures for combining the kernel density estimation and entropy estimation with a probability density function in order to calculate mutual information. Genotypes and their interactions were constructed to set the conditions for conditional entropy. Extensive simulation data created using three types of generating functions were analyzed using two different kernels as well as two types of multifactor dimensionality reduction and another probability density approximation method called m-spacing. The statistical power in terms of correct detection rates was compared. Using kernels was found to be most useful when the trait distributions were more complex than simple normal or gamma distributions. A full-scale genomic dataset was explored to identify associations using the 2-h oral glucose tolerance test results and γ-glutamyl transpeptidase levels as phenotypes. Clearly distinguishable single-nucleotide polymorphisms (SNPs) and interacting SNP pairs associated with these phenotypes were found and listed with empirical p-values.

• Communications for Statistical Applications and Methods
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• v.18 no.6
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• pp.799-808
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• 2011

Let {${\Omega}$, $\mathcal{F}$, P} be a probability space and {$X_n{\mid}n{\geq}1$} be a sequence of random variables defined on it. A finite sequence of random variables {$X_i{\mid}1{\leq}i{\leq}n$} is a conditional associated given $\mathcal{F}$ if for any coordinate-wise nondecreasing functions f and g defined on $R^n$, $Cov^{\mathcal{F}}$ (f($X_1$, ${\ldots}$, $X_n$), g($X_1$, ${\ldots}$, $X_n$)) ${\geq}$ 0 a.s. whenever the conditional covariance exists. We obtain the H$\grave{a}$jek-R$\grave{e}$nyi-type inequality for conditional associated random variables. In addition, we establish the strong law of large numbers, the three series theorem, integrability of supremum, and a strong growth rate for $\mathcal{F}$-associated random variables.

• KIISE Transactions on Computing Practices
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• v.22 no.4
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• pp.184-188
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• 2016

The cleaning robot is popularly used as a home appliance. The state-of-the-art cleaning robot can clean more efficiently by using information gathered from its sensor, which is difficult for low-price cleaning robots due to limitation in this aspect. In this paper, we suggested a method for the moving pattern of cleaning robot based on grammatical evolution. Optimized program is generated by using moving pattern grammar, which is defined by Backus-Naur form. In addition, conditional probability is used between each of the grammar elements during the program creation process. The proposed method is evaluated by robot simulation in order to verify its performance and further compare it with existing algorithms. The experiment results showed that the proposed method is better than the compared algorithms.