• Title/Summary/Keyword: Bayesian Inference

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Inference of System Resource States Using Bayesian Network for Self-Optimizing and Self-Healing Component-based Middleware (컴포넌트 기반 미들웨어 자기최적화와 자가치료을 위한 베이지안 네트워크를 사용한 시스템 자원 상태 추론)

  • Choi Bo-Yoon;Kim Kyung-Joong;Cho Sung-Bae
    • Proceedings of the Korean Information Science Society Conference
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    • 2005.11a
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    • pp.829-831
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    • 2005
  • 최근 컴포넌트 기반 미들웨어의 최적화에 대한 연구가 활발히 이루어지고 있다. CPU점유율이 높은 어플리케이션의 동시 실행은 시스템에 부하를 주기 때문에, 시스템 성능을 약화시키고 실행중인 어플리케이션에 영향을 준다. 컴포넌트 기반 미들웨어는 여러 개의 재사용 가능한 컴포넌트를 조합하여 어플리케이션을 구성하기 때문에 동적으로 재구성이 가능하다. 본 논문은 컴포넌트 기반 미들웨어가 시스템 상황에 대한 정보를 받아들여 시스템의 상황을 스스로 판단하고 자가치료 또는 시스템의 성능을 최적화시키는 컴포넌트를 선택하는 방법을 제안한다. 상황판단을 위해 유연한 추론이 가능하고, 데이터로부터 자동학습이 가능한 베이지안 네트워크를 사용하였다. 두 시간 가량의 데이터를 리눅스 사용자로부터 획득하여 실험한 결과, 테스트 데이터에 대해 $76.5\%$의 성능을 보였다.

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Enhancing Security Gaps in Smart Grid Communication

  • Lee, Sang-Hyun;Jeong, Heon;Moon, Kyung-Il
    • International Journal of Advanced Culture Technology
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    • v.2 no.2
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    • pp.7-10
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    • 2014
  • In order to develop smart grid communications infrastructure, a high level of interconnectivity and reliability among its nodes is required. Sensors, advanced metering devices, electrical appliances, and monitoring devices, just to mention a few, will be highly interconnected allowing for the seamless flow of data. Reliability and security in this flow of data between nodes is crucial due to the low latency and cyber-attacks resilience requirements of the Smart Grid. In particular, Artificial Intelligence techniques such as Fuzzy Logic, Bayesian Inference, Neural Networks, and other methods can be employed to enhance the security gaps in conventional IDSs. A distributed FPGA-based network with adaptive and cooperative capabilities can be used to study several security and communication aspects of the smart grid infrastructure both from the attackers and defensive point of view. In this paper, the vital issue of security in the smart grid is discussed, along with a possible approach to achieve this by employing FPGA based Radial Basis Function (RBF) network intrusion.

18S Ribosomal DNA Sequences Provide Insight into the Phylogeny of Patellogastropod Limpets (Mollusca: Gastropoda)

  • Yoon, Sook Hee;Kim, Won
    • Molecules and Cells
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    • v.23 no.1
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    • pp.64-71
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    • 2007
  • To investigate the phylogeny of Patellogastropoda, the complete 18S rDNA sequences of nine patellogastropod limpets Cymbula canescens (Gmelin, 1791), Helcion dunkeri (Krauss, 1848), Patella rustica Linnaeus, 1758, Cellana toreuma (Reeve, 1855), Cellana nigrolineata (Reeve, 1854), Nacella magellanica Gmelin, 1791, Nipponacmea concinna (Lischke, 1870), Niveotectura pallida (Gould, 1859), and Lottia dorsuosa Gould, 1859 were determined. These sequences were then analyzed along with the published 18S rDNA sequences of 35 gastropods, one bivalve, and one chiton species. Phylogenetic trees were constructed by maximum parsimony, maximum likelihood, and Bayesian inference. The results of our 18S rDNA sequence analysis strongly support the monophyly of Patellogastropoda and the existence of three subgroups. Of these, two subgroups, the Patelloidea and Acmaeoidea, are closely related, with branching patterns that can be summarized as [(Cymbula + Helcion) + Patella] and [(Nipponacmea + Lottia) + Niveotectura]. The remaining subgroup, Nacelloidea, emerges as basal and paraphyletic, while its genus Cellana is monophyletic. Our analysis also indicates that the Patellogastropoda have a sister relationship with the order Cocculiniformia within the Gastropoda.

Robust Bayesian Inference in Finite Population Sampling under Balanced Loss Function

  • Kim, Eunyoung;Kim, Dal Ho
    • Communications for Statistical Applications and Methods
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    • v.21 no.3
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    • pp.261-274
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    • 2014
  • In this paper we develop Bayes and empirical Bayes estimators of the finite population mean with the assumption of posterior linearity rather than normality of the superpopulation under the balanced loss function. We compare the performance of the optimal Bayes estimator with ones of the classical sample mean and the usual Bayes estimator under the squared error loss with respect to the posterior expected losses, risks and Bayes risks when the underlying distribution is normal as well as when they are binomial and Poisson.

User Emotion Extraction Engine($E^3$) based on Fuzzy Inference and Bayesian Networks in Smart Phone Environment (스마트폰 환경에서 퍼지 추론과 베이지안 네트워크에 기반한 사용자 감성 추출 엔진)

  • Lee, Seong-Ho;Bang, Jae-Hun;Lee, Sung-Young
    • Proceedings of the Korean Information Science Society Conference
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    • 2011.06a
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    • pp.100-103
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    • 2011
  • 최근 스마트폰의 보급이 일반화되면서 보다 개인화된(Personalized) 서비스를 제공하려는 시도가 다각도로 이루어지고 있다. 이러한 시도 중 하나가 사용자의 감성을 인식하여 보다 효과적인 서비스를 제공하려는 것이다. 본 논문에서는 스마트폰으로부터 얻어낸 위치인식정보와 사용자 정보를 베이지안 네트워크를 활용하여 상황정보를 도출한다. 이 상황정보와 사용자의 선호도 정보를 퍼지 추론을 이용하여 얻은 결과 값을 수정된 Valence-Arousal 모델에 매핑하여 사용자의 감성정보를 추출하는 감성 추출 엔진을 제안한다. 유용성 평가를 위해 현재 상용 중인 스마트폰에 제안하는 감성 추출 엔진을 이용, 사용자 감성을 인식하고 적절한 서비스를 추천하는 애플리케이션을 구현하였다.

A Method of Ontology Inference based on Bayesian Probability for Decision Making of Intelligent Home Agents (지능형 홈 에이전트의 의사결정을 위한 베이지안 확률기반 온톨로지 추론 방법)

  • Lim, Sung-Soo;Cho, Sung-Bae
    • Proceedings of the Korean Information Science Society Conference
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    • 2007.10c
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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.

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Comparison Of Interval Estimation For Relative Risk Ratio With Rare Events

  • Kim, Yong Dai;Park, Jin-Kyung
    • Communications for Statistical Applications and Methods
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    • v.11 no.1
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    • pp.181-187
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    • 2004
  • One of objectives in epidemiologic studies is to detect the amount of change caused by a specific risk factor. Risk ratio is one of the most useful measurements in epidemiology. When we perform the inference for this measurement with rare events, the standard approach based on the normal approximation may fail, in particular when there are no disease cases observed. In this paper, we discuss and evaluate several existing methods for constructing a confidence interval of risk ratio through simulation when the disease of interest is a rare event. The results in this paper provide guidance with how to construct interval estimates for risk difference and risk ratio when there are no disease cases observed.

Visual Object Tracking based on Real-time Particle Filters

  • Lee, Dong- Hun;Jo, Yong-Gun;Kang, Hoon
    • 제어로봇시스템학회:학술대회논문집
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    • 2005.06a
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    • pp.1524-1529
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    • 2005
  • Particle filter is a kind of conditional density propagation model. Its similar characteristics to both selection and mutation operator of evolutionary strategy (ES) due to its Bayesian inference rule structure, shows better performance than any other tracking algorithms. When a new object is entering the region of interest, particle filter sets which have been swarming around the existing objects have to move and track the new one instantaneously. Moreover, there is another problem that it could not track multiple objects well if they were moving away from each other after having been overlapped. To resolve reinitialization problem, we use competitive-AVQ algorithm of neural network. And we regard interfarme difference (IFD) of background images as potential field and give priority to the particles according to this IFD to track multiple objects independently. In this paper, we showed that the possibility of real-time object tracking as intelligent interfaces by simulating the deformable contour particle filters.

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Bayesian inference in finite population sampling under measurement error model

  • Goo, You Mee;Kim, Dal Ho
    • Journal of the Korean Data and Information Science Society
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    • v.23 no.6
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    • pp.1241-1247
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    • 2012
  • The paper considers empirical Bayes (EB) and hierarchical Bayes (HB) predictors of the finite population mean under a linear regression model with measurement errors We discuss how to calculate the mean squared prediction errors of the EB predictors using jackknife methods and the posterior standard deviations of the HB predictors based on the Markov Chain Monte Carlo methods. A simulation study is provided to illustrate the results of the preceding sections and compare the performances of the proposed procedures.

Probabilistic real-time updating for geotechnical properties evaluation

  • Ng, Iok-Tong;Yuen, Ka-Veng;Dong, Le
    • Structural Engineering and Mechanics
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    • v.54 no.2
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    • pp.363-378
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    • 2015
  • Estimation of geotechnical properties is an essential but challenging task since they are major components governing the safety and reliability of the entire structural system. However, due to time and budget constraints, reliable geotechnical properties estimation using traditional site characterization approach is difficult. In view of this, an alternative efficient and cost effective approach to address the overall uncertainty is necessary to facilitate an economical, safe and reliable geotechnical design. In this paper a probabilistic approach is proposed for real-time updating by incorporating new geotechnical information from the underlying project site. The updated model obtained from the proposed method is advantageous because it incorporates information from both existing database and the site of concern. An application using real data from a site in Hong Kong will be presented to demonstrate the proposed method.