• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.834-839
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• 2005

When a great variety of services become available to user through the broadband convergence network in the ubiquitous home environment, an intelligent agent is required to deal with the complexity of services and perceive intension of a user. Different from the old-fashioned command-based user interface for selecting services, conversation enables flexible and rich interactions between human and agents, but diverse expressions of the user's background and context make conversation hard to implement by using either user-initiative or system-initiative methods. To deal with the ambiguity of diverse expressions between user and agents, we have to apply hierarchial bayesian networks for the mixed initiative conversation. Missing information from user's query is analyzed by hierarchial bayesian networks to inference the user's intension so that can be collected through the agent's query. We have implemented this approach in ubiquitous home environment by implementing simulation program.

• Journal of Digital Convergence
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• v.15 no.4
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• pp.461-468
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• 2017

The current available recommendation systems for mobile games have a couple of problems. First, there is no knowing whether they make a pattern recommendation for games that actual users prefer or for games that they are simply interested in. It is also impossible to know the subjective preference of users in a direct manner. An AHP(Analytic Hierarchy Process)-based recommendation system for mobile games was thus developed to reflect the subjective preference of users directly, but it had its own problem since the degree of preference could vary among users in spite of the same scale for their preferable items. In an effort to solve those problems, this study implemented a recommendation system for mobile games by applying triangular fuzzy numbers of the Fuzzy-AHP technique and the independence of evaluation items in the Bayesian Network. The findings show that the proposed recommendation system recorded the highest accuracy of recommendation results and the highest level of user satisfaction.

• Journal of Digital Convergence
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• v.11 no.3
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• pp.345-352
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• 2013

The demand and production for mobile music increases as the number of smart phone users increase. Thus, the standard of selection of a user's preferred music has gotten more diverse and complicated as the range of popular music has gotten wider. Research to find intelligent techniques to ingeniously recommend music on user preferences under mobile environment is actively being conducted. However, existing music recommendation systems do not consider and reflect users' preferences due to recommendations simply employing users' listening log. This paper suggests a personalized music-recommending system that well reflects users' preferences. Using AHP, it is possible to identify the musical preferences of every user. The user feedback based on the Bayesian network was applied to reflect continuous user's preference. The experiment was carried out among 12 participants (four groups with three persons for each group), resulting in a 87.5% satisfaction level.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.10
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• pp.995-999
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• 2010

Every year, traffic accidents and traffic congestion have been rapidly increasing, Although the roadway design and signal system have been improved to relieve traffic accidents, traffic casualties and property damage do not decrease. This paper develops a real-time traffic accident detection and analysis system (RTADAS): In the proposed system, we aim to precisely detect traffic accidents at different design and flow of intersections, However, because the data collected from intersections have uncertainty and complicated causal dependency between them, we construct probability-based networks for correct accident detection.

• Proceedings of the Korean Information Science Society Conference
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• 2006.06a
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• pp.211-213
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• 2006

실시간으로 변화하는 컴퓨터 통신 환경에서 멀티미디어 응용 프로그램은 QoS를 만족하기 위해 안정적으로 튜닝 되고 재구성되는 것이 필요하다. 그러나 안정적으로 QoS를 보장하는 것은 응용 프로그램의 자원 예약이나 실시간 보장과 같은 메카니즘을 제공하지 않은 일반적인 목적의 시스템 상에서 수행될 때 많은 어려움을 가지게 된다. 특히, 예측 불가능한 개방형 환경에서 최우선 자원 할당에 의해 발생되는 자원의 유효성에 대응하기 위해 QoS 적응은 수행되어야 한다. 그러나 적응을 언제, 어떻게 조정해야 하고 폭 넓은 범위에서 응용 프로그램에 어떻게 적용시킬지를 알기 위해 일반적인 알고리즘을 제시해야할 필요가 있다. 이러한 목적을 위해, 본 논문에서는 멀티미디어 어플리케이션의 파라미터를 모델링하고, 파라미터간의 관계를 정량적으로 얻기 위해 계층적 QoS 프로빙 알고리즘을 적용한다. 이것을 기반으로 설계된 베이지안 네트워크를 이용하여 불확실한 정보를 확률값으로 처리함으로써 적응 행동을 예측하도록 한다. 마지막으로 실제 실험을 통해 제안된 미들웨어의 유용성을 확인한다.

• Proceedings of the Korea Water Resources Association Conference
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• 2019.05a
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• pp.312-312
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• 2019

지구온난화로 인하여 기상학적 변동성 증가 및 수질, 수자원, 생태계 등의 다양한 영역에 영향을 야기하고 있으며, 이를 통한 피해가 전 세계적으로 증가하고 있는 추세이다. 이에 본 연구에서는 최근 다양한 분야에서 수문학적 빈도에 영향을 미친다고 알려진 AO(Arctic Oscillation), NAO(North Atlantic Oscillation), ENSO(El $Ni{\tilde{n}}o$-Southern Oscillation), PDO(Pacific Decadal Oscillation), MJO(Madden-Julian Oscillation)등의 외부인자중 SST, MJO를 활용하여 계절단위의 수문량 정도에서 기상학적 변량과 관측유역 강수량의 관계를 정립하고 발생 가능한 24시간 지속시간 극치강수량을 모의하였다. 이를 위하여 Bayesian 통계기법을 이용한 비정상성 빈도해석모형을 근간으로 외부 기상인자에 의한 계절강수량 예측모형인 계층적 베이지안 네트워크(Hierarchical Bayesian Network, HBN)를 구축한 후 산정된 결과를 입력 자료로 하여 직접적으로 일단위 이하의 극치강수량을 상세화 시킬 수 있는 베타 모델(four parameter beta, 4PB)을 연계한 계층적 베이지안 네트워크 베타모델(Hierarchical Bayesian Network-4beta Model, HBN4BM)을 개발하여 기상변동성을 고려한 상세화 모형을 개발하였다. 여름강수량 산정 결과 한강 유역의 경우 2016년은 관측값 573.85mm, 모의 값 567.15mm를 나타내어 약 1.2%의 오차를 나타냈으며, 2017년 및 2018년은 4.5%, 6.8%의 오차에서 모의가 이루어졌다. 금강의 경우 2016년은 다른 연도에 비하여 35.2%라는 큰 오차를 보였지만 불확실성 구간에서 모의가 이루어 졌으며, 2017년 및 2018년은 0.3%, 2.1%의 작은 오차가 발생하였다. 24시간 모의 결과는 최소 0.7%에서 최대 27.1%의 오차를 나타냈으며, 평균적으로 16.4%의 오차 결과가 모의되어 모형의 신뢰성을 확인하였다.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.11 no.2
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• pp.169-174
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• 2018

In general, the methods of the analysis of variance(ANOVA) for the continuous data and the chi-square test for the discrete data are used for statistical analysis of the effect and the association. In multidimensional data, analysis of hierarchical structure is required and statistical linear model is adopted. The structure of the linear model requires the normality of the data. A multidimensional categorical data analysis methods are used for causal relations, interactions, and correlation analysis. In this paper, Bayesian network model using probability distribution is proposed to reduce analysis procedure and analyze interactions and causal relationships in categorical data analysis.

• Journal of KIISE:Software and Applications
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• v.34 no.6
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• pp.549-561
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• 2007

Vision-based scene understanding is to infer and interpret the context of a scene based on the evidences by analyzing the images. A probabilistic approach using Bayesian networks is actively researched, which is favorable for modeling and inferencing cause-and-effects. However, it is difficult to gather meaningful evidences sufficiently and design the model by human because the real situations are dynamic and uncertain. In this paper, we propose a learning method of Bayesian network that reduces the computational complexity and enhances the accuracy by searching an efficient BN structure in spite of insufficient evidences and training data. This method represents the domain knowledge as ontology and builds an efficient hierarchical BN structure under constraint rules that come from the ontology. To evaluate the proposed method, we have collected 90 images in nine types of circumstances. The result of experiments indicates that the proposed method shows good performance in the uncertain environment in spite of few evidences and it takes less time to learn.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.23 no.3
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• pp.333-340
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• 2019

A combat system performs a given mission enduring various threats. It is important to analyze the reliability of combat systems in order to increase their ability to perform a given mission. Most of studies considered no threat or on threat and didn't analyze all the dependent relationships among the components. In this paper, we analyze the loss probability of the function of the combat system and use it to analyze the reliability. The proposed method is divided into two layers, A lower layer and a upper layer. In lower layer, the failure probability of each components is derived by using FTA to consider various threats. In the upper layer, The loss probability of function is analyzed using the failure probability of the component derived from lower layer and BBN in order to consider the dependent relationships among the components. Using the proposed method, it is possible to analyze considering various threats and the dependency between components.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.17 no.4
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• pp.59-73
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• 2016

The demand for subsurface transport is increasing. The users and the operators of road tunnels are exposed to risks with different causes. One main cause, however, is the traffic situation in the event of accidents. The importance of a Quantified Risk Assessment is increasing to quantify the safety of road tunnels and to balance the requirements (capacity, reliability, availability, maintainability and safety) of various stakeholders. Although there are classical methods for risk assessments, such as ETA and FTA. These methods are used for relatively simple cases because it could not relevantly reflect the diversity and relationship of the parameters. Therefore, a quantitative risk assessment based on Bayesian Probabilistic Networks considering interdependence between the parameters of a complex underground system as a double deck tunnel is provided.