• The Journal of Korean Institute of Communications and Information Sciences
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• v.35 no.6B
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• pp.888-894
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• 2010

For the indoor positioning, wireless fingerprinting is most favorable because fingerprinting is most accurate among the technique for wireless network based indoor positioning which does not require any special equipments dedicated for positioning. The deployment of a fingerprinting method consists of off-line phase and on-line phase and more efficient and accurate methods have been studied. This paper proposes a bayesian algorithm for wireless fingerprinting and indoor location determination using fuzzy clustering with bayesian learning as a statistical learning theory.

• Computational Structural Engineering
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• v.11 no.4
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• pp.219-227
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• 1998

실제 구조물에 있어 확률, 통계 및 이론으로 구해진 랜덤성을 갖는 객관적 불확실성뿐만 아니라 설계자의 경험이나 공학적 판단에 의해 주관적으로 평가되는 인간오차나 시공중의 과오 또는 구조설계에 미치는 사회적, 정치적 및 경제적 요청 등의 퍼지성을 갖는 주관적 불확실성이 존재하기 때문에 현실적으로 랜덤성과 퍼지성을 동시에 고려한 실뢰성평가 즉, 안전성평가에 대한 퍼지이론의 도입이 필수 불가결하다. 따라서 본 연구에서는 기존 구조물의 객관적·주관적 불확실성을 동시에 고려한 신뢰성해석방법으로 베이즈의 의사결정이론에 퍼지이론을 병합한 퍼지-베이즈 신뢰성해석 알고리즘을 개발하여 건축구조물의 신뢰성평가 및 안전성평가에 적용하여 분석하였다.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2011.04a
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• pp.328-331
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• 2011

준정적 횡하중을 재하 받는 철근콘크리트 보-기둥 접합부의 전단강도에 대한 주요 영향요인을 Bayesian parameter estimation의 신뢰성 이론 접목을 통해 검토하였다. 이와 같은 연구 scope의 수행을 위해 철근콘크리트 보-기둥의 실험 database가 구축되었다. 실험 database는 일정한 criteria을 적용하여 구축되었으며, 포함된 시편들은 최종적으로 접합부 내의 전단파괴가 지배하는 경우들이다. 포함된 시편들의 상세는 ACI (American Concrete Institute) 352R-02를 기준으로 평가되어졌다. 보-기둥 접합부의 전단강도에 영향 요인을 편중되지 않게 평가하고자, Bayesian parameter estimation의 신뢰성 이론을 적용하였다. Bayesian parameter estimation의 적용을 통해 전단강도에 영향이 적은 변수 (not informative parameter)를 순차적으로 제거 (stepwise removal process)함으로 주요 영향요인의 우선 순위를 확인할 수 있었다. 검토된 8개의 변수들 중에서, 횡하중을 재하 받는 철근콘크리트 보-기둥의 전단강도는 주로 콘크리트 압축강도, in-plane geometry, 종방향 보의 주철근 그리고 접합부 내의 구속철근 순으로 영향을 줌을 알 수 있었다.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.36 no.3B
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• pp.248-253
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• 2011

Collision avoidance algorithm of vessels have been studied to avoid collision and grounding of a vessel due to human error. In this paper, We propose a collision avoidance algorithm using bayesian estimation theory for safety sailing and reduced risk of collision accident. We calculate collision risk for efficient collision avoidance using bayesian algorithm and determined the safest and most effective collision risk is predicted by using re-planned with re-evaluated collision risk in the future(t=t'). Others ship position is assumed to be informed from AIS. Experimental results show that we estimate the safest and most effective collision risk.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.14 no.6
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• pp.47-54
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• 2014

Based on the indiscernible relation of rough set, the inevitability of superposition and inconsistency of data makes the reduction of attributes very important in information system. Rough set has difficulty in the difference of attribute reduction between consistent and inconsistent information system. In this paper, we propose the new uncertainty measure and attribute reduction algorithm by Bayesian posterior probability for correlation analysis between condition and decision attributes. We compare the proposed method and the conditional information entropy to address the uncertainty of inconsistent information system. As the result, our method has more accuracy than conditional information entropy in dealing with uncertainty via mutual information of condition and decision attributes of information system.

• Journal of Korea Water Resources Association
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• v.31 no.4
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• pp.407-413
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• 1998

This study reviewed the recent studies for the climate change impact on water resource systems and applied one of the techniques to a real reservoir system - the Skagit hydropower system in U.S.A. The technique assumed that the climate change results in ±5% change in monthly average and/or standard deviation of the observed inflows for the Skagit system. For each case of the altered average and standard deviation, an optimal operating policy was derived using s SDP(Stochastic Dynamic Programming) model and compared with the operating policy for the non-climate change case. The results showed that the oparating policy of the Skagit system is more sensitive to the change in the streamflow average than that in the streamflow standard deviation. The derived operating policies were also simulated using the synthetic streamflow scenarios and their average annual gains were compared as a performance index. To choose the best operating policy among the derived policies, a Bayesian decision strategy was also presented with an example. Keywords : climate change, reservoir operating policy, stochastic dynamic programming, Bayesian decision theory.

• Proceedings of the Acoustical Society of Korea Conference
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• 1997.06a
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• pp.13-16
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• 1997

본 논문에서는 Bayesian 결정 이론을 이용한 기존의 Radial Basis Function Networks 이되는 출력층에서 선형 조합되는 것과는 다른 형태의 방법을 제안하고자 한다. 제안하고자 하는 방법은 은닉층의 출력값과 가중치와의 곱해진 값이 출력층의 입력으로 들어오는데 이들 입력신호를 경쟁을 통하여 가장 큰 값만을 출력신호 인정하는 방법이다. 이런 경우에 파라미터 갱신을 할 때도 모든 가중치를 다 갱신하는 것이 아니라 출력되는 은닉층에 연결된 가중치만을 갱신하게된다. 이렇게 할 경우 계산량 감소뿐만 아니라 학습시간을 단축할 수 있다는 장점이 있다. 그리고 제안한 방법을 이용할 경우 비선형 분류문제에서도 우수한 성능결과를 확인 할 수 있었으며 기존의 RBFN rhk Wiener Filter와 성능을 비교하였다.

• Journal of Internet Computing and Services
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• v.13 no.5
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• pp.21-31
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• 2012

Threat Evaluation(TE) which has air intelligence attained by identifying friend or foe evaluates the target's threat degree, so it provides information to Weapon Assignment(WA) step. Most of TE data are passed by sensor measured values, but existing techniques(fuzzy, bayesian network, and so on) have many weaknesses that erroneous linkages and missing data may fall into confusion in decision making. Therefore we need to efficient Threat Evaluation system that can refine various sensor data's linkages and calculate reliable threat values under unpredictable war situations. In this paper, we suggest new threat evaluation system based on information fusion JDL model, and it is principle that combine fuzzy which is favorable to refine ambiguous relationships with bayesian network useful to inference battled situation having insufficient evidence and to use learning algorithm. Finally, the system's performance by getting threat evaluation on an air defense scenario is presented.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.49 no.1
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• pp.53-58
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• 2012

With a game-theoretic view, p-persistence slotted ALOHA is structured as a non-cooperative game, in which a Nash equilibrium is sought to provide a value for the probability of attempting to deliver a packet. An expression of Nash equilibrium necessarily includes the number of active outer stations, which is hardly available in many practical applications. In this paper, we thus propose a Bayesian scheme of predicting the number of active outer stations prior to deciding whether to attempt to deliver a packet or not. Despite only requiring the minimal information that an outer station is genetically able to acquire by itself, the Bayesian scheme demonstrates the competitive predicting performance against a method which depends on heavy information.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.505-514
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• 2000

A Markov Chain Monte Carlo method with data augmentation is developed to compute the features of the posterior distribution. For each observed failure epoch, we introduced mixture failure model of Rayleigh and Erlang(2) pattern. This data augmentation approach facilitates specification of the transitional measure in the Markov Chain. Gibbs steps are proposed to perform the Bayesian inference of such models. For model determination, we explored sum of relative error criterion that selects the best model. A numerical example with simulated data set is given.