• Title/Summary/Keyword: Baysian technique

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On-line identification of the toxicological substance in the water system using Baysian technique (베이지언 기법을 이용한 수계 내의 독성물질 판단)

  • Jung, Ha-Kyu;Jung, Jong-Hyuk;Lee, Hyun-Wook;Kwon, Won-Tae;Kim, Sang-Gil;Jeon, Sook-Lye
    • Proceedings of the KSME Conference
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    • 2007.05b
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    • pp.3122-3127
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    • 2007
  • Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and monitor the water pollution. The monitoring system, however, could determine whether the examined water was safe or not. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is given to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.

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On-line identification of the toxicological substance in the water system using Baysian technique (베이지언 기법을 이용한 수계 내의 독성물질 판단)

  • Jung, Ha Kyu;Jung, Jong Hyuk;Lee, Hyun Wook;Kwon, Won Tae;Kim, Sang Gil;Jeon, Sook Lye
    • Journal of Korean Society of Water and Wastewater
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    • v.22 no.1
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    • pp.73-78
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    • 2008
  • Water resource can be examined using biological sensors. Algae has been one of the biological sensors used to evaluate and to monitor the water pollution. The monitoring system, however, has not been used to determine what kind of the toxicological substance is in the water. It needs additional expensive chemical test to figure out the cause of the water pollution. In this study, an endeavor is made to identify the toxicant in the water using the shape of the chlorophyll fluorescence induction curve(FIC) from algae using monitoring system. Fundamental curves are obtained from the experiments with specified amount of toxicant. Baysian method is utilized to determine the unknown toxicant in the water by comparing it with the fundamental curves. The results shows that the proposed method works fairly well.

Bayesian Reliability Estimation for the Multi-Processor Systems with Multiport Memory Interconnection Networks Structure (다중포트 기억 상호연결 네트워크 구조를 하는 다중프로세서 시스템의 베이지안 신뢰도 추정)

  • 조옥래
    • Journal of the Korea Society of Computer and Information
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    • v.4 no.1
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    • pp.68-75
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    • 1999
  • In this paper, we propose a Baysian method estimating system reliability which is more effective and precise than conventional methods using prior information. This technique estimates system reliabilities that an entire system and multiprocessing system is normally working in multiprocessor system and multiple port connected memory architecture. The reason is why internetwork with multiprocessor system is mainly connected as multiple bus structure, crossbar switching structure and multiport connected memory structure.

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Back-analysis Technique in Tunnelling Using Extended Bayesian Method md Relative Convergence Measurement (확장 Baysian 방법과 상대변위를 이용한 터널 역해석 기법)

  • Choi Min-Kwang;Cho Kook-Hwan;Lee Geun-Ha;Choi Chung-Sik
    • Journal of the Korean Geotechnical Society
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    • v.21 no.3
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    • pp.99-108
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    • 2005
  • One of the most important and difficult tasks in designing underground structure is the estimation of engineering properties of the ground. The main purpose of this study is to propose a new back-analysis technique in tunnelling to estimate geotechnical parameters around a tunnel. In this study, the Extended Bayesian Method, which appropriately combines objective information with subjective one, is adopted to optimize engineering parameters. By using only relative convergence data measured during tunnelling as input values in back-analysis, inevitable errors in absolute convergence estimation are excluded and 3-dimensional numerical analysis is applied to consider a trend of relative convergence occurrence. Finally, 3-dimensional back-analysis technique using relative convergence is proposed and evaluated using a hypothetical site.

A Case Study of Back-analysis Technique in Tunnelling Using Extended Bayesian Method and Relative Convergence Measurement (확장 Baysian 방법과 상대변위를 이용한 터널 역해석 기법의 적용사례연구)

  • Lee In-Mo;Choi Min-Kwang;Cho Kook-Hwan;Lee Geun-Ha;Choi Chung-Sik
    • Journal of the Korean Geotechnical Society
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    • v.21 no.3
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    • pp.109-118
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    • 2005
  • It is a very difficult task to estimate engineering properties of the ground when designing underground structures, especially in tunnelling. Therefore, a feed-back system to combine the data measured in construction field with priorly estimated information at the design stage is necessary. In this paper, 3-dimensional back-analysis in tunnelling, to which only relative convergence is applied as input values, is carried out to estimate the optimum geotechnical parameters. For this purpose, the Extended Bayesian Method (EBM), which appropriately combines the objective information with the subjective one, is applied to optimize engineering parameters and 3-dimensional numerical analysis is carried out to predict a trend of relative convergence occurrence. The data measured from two tunnelling sites are used to verify the applicability of the proposed back-analysis technique. from the results of analysis, the proposed back-analysis technique is verified.

On-line Signature Verification using Segment Matching and LDA Method (구간분할 매칭방법과 선형판별분석기법을 융합한 온라인 서명 검증)

  • Lee, Dae-Jong;Go, Hyoun-Joo;Chun, Myung-Geun
    • Journal of KIISE:Software and Applications
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    • v.34 no.12
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    • pp.1065-1074
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    • 2007
  • Among various methods to compare reference signatures with an input signature, the segment-to-segment matching method has more advantages than global and point-to-point methods. However, the segment-to-segment matching method has the problem of having lower recognition rate according to the variation of partitioning points. To resolve this drawback, this paper proposes a signature verification method by considering linear discriminant analysis as well as segment-to-segment matching method. For the final decision step, we adopt statistical based Bayesian classifier technique to effectively combine two individual systems. Under the various experiments, the proposed method shows better performance than segment-to-segment based matching method.