• 한국데이터정보과학회:학술대회논문집
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• 한국데이터정보과학회 2005년도 춘계학술대회
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• pp.185-192
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• 2005

There are many data mining techniques such as association rule, decision tree, neural network analysis, clustering, genetic algorithm, bayesian network, memory-based reasoning, etc. We analyze 2003 Gyeongnam social indicator survey data using k-means clustering technique for environmental information. Clustering is the process of grouping the data into clusters so that objects within a cluster have high similarity in comparison to one another. In this paper, we used k-means clustering of several clustering techniques. The k-means clustering is classified as a partitional clustering method. We can apply k-means clustering outputs to environmental preservation and environmental improvement.

• Smart Structures and Systems
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• 제11권1호
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• pp.103-122
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• 2013

It is essential to update the model with reflecting observation or inspection data for reliability estimation of existing structures. Authors proposed updated reliability analysis by using Particle Filter. We discuss how to apply the proposed method through numerical examples on reinforced concrete structures after verification of the method with hypothetical linear Gaussian problem. Reinforced concrete structures in a marine environment deteriorate with time due to chloride-induced corrosion of reinforcing bars. In the case of existing structures, it is essential to monitor the current condition such as chloride-induced corrosion and to reflect it to rational maintenance with consideration of the uncertainty. In this context, updated reliability estimation of a structure provides useful information for the rational decision. Accuracy estimation is also one of the important issues when Monte Carlo approach such as Particle Filter is adopted. Especially Particle Filter approach has a problem known as degeneracy. Effective sample size is introduced to predict the covariance of variance of limit state exceeding probabilities calculated by Particle Filter. Its validity is shown by the numerical experiments.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2007년도 가을 학술발표논문집 Vol.34 No.2 (C)
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• pp.357-361
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• 2007

지능형 에이전트가 홈네트워크 환경 속에서 사용자에게 적절한 서비스를 제공하기 위해서는 에이전트가 속한 환경에 대한 모델이 필요하다. 온톨로지는 이러한 환경 모델을 표현하기 위한 유용한 도구로 복잡한 도메인의 조직적 구조 표현에 있어서 뛰어난 성능을 보여준다. 하지만 전통적 온톨로지는 크리스프 로직에 기반하기 때문에 현실세계의 불확실성을 표현하기에는 적합하지 않다. 본 논문에서는 온톨로지의 이러한 한계점을 보완하고, 불확실한 환경 속에서 지능형 홈 에이전트가 적절한 의사결정을 내릴 수 있도록 하는 베이지안 네트워크기반 온톨로지 추론 방법을 제안한다. 제안하는 방법에서는 온톨로지의 클래스 객체를 베이지안 네트워크의 노드로 나타내고, 객체 속성(object property)을 아크로 표현함으로써, 확률적 추론이 가능한 온톨로지를 제공한다. 몇 가지 시나리오와 설계 복잡도 분석을 통해서 제안하는 방법의 유용성을 평가한다.

• 한국컴퓨터정보학회논문지
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• 제10권2호
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• pp.215-221
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• 2005

최근에는 단백질 시퀀스, 소매점 거래 데이터, 웹 로그 등과 같은 상업적이거나 과학적인 데이터의 폭발적인 증가를 볼 수 있다. 이런 데이터들은 순서적인 면을 가지고 있는 시퀀스 데이터들이다. 본 논문에서는 이런 시퀀스 데이터들을 분류하는 문제를 다룬다. 분류 기법 으로는 의사결정 나무나 베이지안 분류기, K-NN방법 등 석러 종류가 있는데, 본 연구에서는 또-U방법을 이용하여 시퀀스들을 분류한다. 또한, 시퀀스들간의 유사도를 구하기 위한 새로운 계산 방법과 효율적인 계산 방법도 제안한다.

• 한국IT서비스학회지
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• 제11권sup호
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• pp.139-152
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• 2012

In the medical web forum, people share medical experience and information as patients and patents' families. Some people search medical information written in non-expert language and some people offer words of comport to who are suffering from diseases. Medical web forums play a role of the informative support and the emotional support. We propose the automatic classification model of articles in the medical web forum into the information support and emotional support. We extract text features of articles in web forum using text mining techniques from the perspective of linguistics and then perform supervised learning to classify texts into the information support and the emotional support types. We adopt the Support Vector Machine (SVM), Naive-Bayesian, decision tree for automatic classification. We apply the proposed model to the HealthBoards forum, which is also one of the largest and most dynamic medical web forum.

• 한국정보과학회:학술대회논문집
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• 한국정보과학회 2004년도 봄 학술발표논문집 Vol.31 No.1 (B)
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• pp.262-264
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• 2004

퍼지 클러스터링 방법은 일반적인 클러스터링 방법과는 달리 하나의 샘플이 다수의 집단에 속할 수 있으며 그 속하는 정도를 표현하여 보다 유연한 클러스터 분할의 분석을 가능하게 한다. 유전자 발현 데이터는 노이즈가 많고 공통된 기능을 가진 유전자들의 집단이 존재하기 때문에 퍼지 클러스터링을 사용하면 더욱 효율적으로 분석할 수 있다. 이러한 퍼지 클러스터링 방법에 있어서 중요한 것은 얼마나 분할이 정확하게 이루어졌으며 실제 데이터가 가지고 있는 분할과 결과가 얼마나 유사한가이다. 본 논문에서는 효과적인 유전자 클러스터의 평가를 위하여 베이지안 검증 방법을 제시하고, 결정트리로 생성된 규칙에 의하여 각 데이터의 특성에 따라 유연하게 검증하는 방법을 제안한다. 다양한 유전자 발현 데이터를 퍼지 c-means 알고리즘을 이용하여 클러스터링하고 제안하는 방법으로 검증한 결과, 그 유용성을 확인할 수 있었다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제12권3호
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• pp.193-197
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• 2012

This paper focuses on identifying which appliance is currently operating by analyzing electrical load signature for home energy monitoring system. The identification framework is comprised of three steps. Firstly, specific appliance features, or signatures, were chosen, which are DC (Duty Cycle), SO (Slope of On-state), VO (Variance of On-state), and ZC (Zero Crossing) by reviewing observations of appliances from 13 houses for 3 days. Five appliances of electrical rice cooker, kimchi-refrigerator, PC, refrigerator, and TV were chosen for the identification with high penetration rate and total operation-time in Korea. Secondly, K-NN and Naive Bayesian classifiers, which are commonly used in many applications, are employed to estimate from which appliance the signatures are obtained. Lastly, one of candidates is selected as final identification result by majority voting. The proposed identification frame showed identification success rate of 94.23%.

• 환경영향평가
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• 제15권6호
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• pp.373-383
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• 2006

The spatial mapping of risk is very useful data in planning for disaster preparedness. This research presents a methodology for making the landslide life risk map in the Boeun area which had considerable landslide damage following heavy rain in August, 1998. We have developed a three-stage procedure in spatial data analysis not only to estimate the probability of the occurrence of the natural hazardous events but also to evaluate the uncertainty of the estimators of that probability. The three-stage procedure consists of: (i)construction of a hazard prediction map of "future" hazardous events; (ii) validation of prediction results and estimation of the probability of occurrence for each predicted hazard level; and (iii) generation of risk maps with the introduction of human life factors representing assumed or established vulnerability levels by combining the prediction map in the first stage and the estimated probabilities in the second stage with human life data. The significance of the landslide susceptibility map was evaluated by computing a prediction rate curve. It is used that the Bayesian prediction model and the case study results (the landslide susceptibility map and prediction rate curve) can be prepared for prevention of future landslide life risk map. Data from the Bayesian model-based landslide susceptibility map and prediction ratio curves were used together with human rife data to draft future landslide life risk maps. Results reveal that individual pixels had low risks, but the total risk death toll was estimated at 3.14 people. In particular, the dangerous areas involving an estimated 1/100 people were shown to have the highest risk among all research-target areas. Three people were killed in this area when landslides occurred in 1998. Thus, this risk map can deliver factual damage situation prediction to policy decision-makers, and subsequently can be used as useful data in preventing disasters. In particular, drafting of maps on landslide risk in various steps will enable one to forecast the occurrence of disasters.

• 정보과학회 논문지
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• 제42권7호
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• pp.889-900
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• 2015

최근 복잡해진 시스템의 실행 환경에서 발생하는 문제들을 해결하기 위해 자가적응형 시스템의 중요성이 대두되고 있다. 그러나 시스템 설계 시점에 구축된 모델과 실행 환경 사이의 불확실성이 시스템을 알 수 없는 상황으로 이끌 수 있기 때문에 이를 다루기 위한 연구가 중요한 이슈로 떠오르고 있다. 본 논문은 불확실한 상황에서 적응 시점을 결정하고 모델에 실시간 환경을 반영하기 위한 방법을 제안한다. 적합한 적응 시점을 위해 시스템의 이전 상태들과 현재상태를 비교하여 베이지안 서프라이즈를 계산하고, 설계된 모델에 실시간 환경을 수행된 적응 정책의 결과를 모델에 반영한다. 제안 방법론을 네비게이션 시스템에 적용하여 제안 사항의 유효성을 확인하였다.

• International Journal of Naval Architecture and Ocean Engineering
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• 제11권1호
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• pp.22-32
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• 2019

On offshore platforms, oil and gas leaks are apt to be the initial events of major accidents that may result in significant loss of life and property damage. To prevent accidents induced by leakage, it is vital to perform a case-specific and accurate risk assessment. This paper presents an integrated method of Ddynamic Qquantitative Rrisk Aassessment (DQRA)-using the Decision Making Trial and Evaluation Laboratory (DEMATEL)-Bayesian Network (BN)-for evaluation of the system vulnerabilities and prediction of the occurrence probabilities of accidents induced by leakage. In the method, three-level indicators are established to identify factors, events, and subsystems that may lead to leakage, fire, and explosion. The critical indicators that directly influence the evolution of risk are identified using DEMATEL. Then, a sequential model is developed to describe the escalation of initial events using an Event Tree (ET), which is converted into a BN to calculate the posterior probabilities of indicators. Using the newly introduced accident precursor data, the failure probabilities of safety barriers and basic factors, and the occurrence probabilities of different consequences can be updated using the BN. The proposed method overcomes the limitations of traditional methods that cannot effectively utilize the operational data of platforms. This work shows trends of accident risks over time and provides useful information for risk control of floating marine platforms.