• 제목/요약/키워드: inference model

검색결과 1,158건 처리시간 0.028초

RDF 온톨로지 접근 제어를 위한 3 계층 온톨로지 뷰 보안 모델 (A Three-Layered Ontology View Security Model for Access Control of RDF Ontology)

  • 정동원;징이신;백두권
    • 한국정보과학회논문지:데이타베이스
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    • 제35권1호
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    • pp.29-43
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    • 2008
  • RDF 온톨로지는 XML 트리 모델을 이용하여 표현할 수 있다. 그러나 XML 문서를 보호하기 위해 개발된 XML 보안 모델을 RDF 온톨로지에 적용하는 방법은 부적합하다. RDF는 그래프 모델로서 추론 기능을 제공하므로 새로운 보안 모델의 개발이 요구된다. 이 논문에서는 RDF 온톨로지 접근 제어를 위한 새로운 질의 지향 모델을 제안한다. 제안 모델은 3 계층 온톨로지 뷰를 이용하여 사용자 질의를 재작성한다. 이를 통해 제안 모델은 추론 규칙에 따라 추론 모델을 생성하는 기존 접근 방법의 문제점을 해결한다. 사용자가 방문할 수 있는 접근 가능한 온톨로지 개념들과 인스턴스들을 각각 온톨로지 뷰로서 정의하며, 또한 추론 질의에 대한 제어를 위해 정의한 추론 뷰를 통해 사용자의 추론 기능을 제어할 수 있다. 이 논문에서는 3 계층 뷰를 정의하고 이에 따라 질의를 재작성하는 알고리즘에 대하여 기술한다. 시스템 구조와 구현된 프로토타입에 대하여 기술한다. 마지막으로. 제안 모델과 기존 접근 방법에 대한 실험 및 평가 결과에 대하여 기술한다.

개선된 퍼지 추론 기법을 이용한 칼라 분석 (Color Analysis with Enhanced Fuzzy Inference Method)

  • 김광백
    • 한국컴퓨터정보학회논문지
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    • 제14권8호
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    • pp.25-31
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    • 2009
  • RGB 모델을 통한 정적인 추론 규칙을 적용한 기존의 색채 정보 인식 방법은 RGB 모델이 가지는 인간 시각과의 괴리감과 특정한 환경에서만 적용할 수 있는 문제점이 있다. 본 논문에서는 HSI 모델을 적용하여 색채에 대한 인간 인식 과정과 유사한 형태의 추론 방식과, 사용자에 의해서 추론 규칙을 추가, 수정, 삭제 할 수 있는 방법을 제안한다. 본 논문에서는 각각의 H, S, I 소속 구간에 대하여 H는 Sine, Cosine 함수를 사용하여 소속 구간을 설계하며, S, I는 삼각형 타입의 소속 함수로 설계한다. 설계된 각각의 소속 구간에 대하여 소속 구간 병합을 적용하여 소속도를 계산하고, 계산된 결과들은 미리 제시된 추론 규칙에 적용하여 색채를 추론한다. 제안된 두가지 방법을 적용하여 실험한 결과, 기존의 방법보다 제안된 방법이 비교적 직관적이며 효율적인 형태로 결론을 도출할 수 있음을 확인하였다.

뉴로-퍼지 추론 시스템을 이용한 물체인식 (Object Recognition Using Neuro-Fuzzy Inference System)

  • 김형근;최갑석
    • 한국통신학회논문지
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    • 제17권5호
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    • pp.482-494
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    • 1992
  • In this paper, the neuro-fuzzy inferene system for the effective object recognition is studied. The proposed neuro-fuzzy inference system combines learning capability of neural network with inference process of fuzzy theory, and the system executes the fuzzy inference by neural network automatically. The proposed system consists of the antecedence neural network, the consequent neural network, and the fuzzy operational part, For dissolving the ambiguity of recognition due to input variance in the neuro-fuzzy inference system, the antecedence’s fuzzy proposition of the inference rules are automatically produced by error back propagation learining rule. Therefore, when the fuzzy inference is made, the shape of membership functions os adaptively modified according to the variation. The antecedence neural netwerk constructs a separated MNN(Model Classification Neural Network)and LNN(Line segment Classification Neural Networks)for dissolving the degradation of recognition rate. The antecedence neural network can overcome the limitation of boundary decisoion characteristics of nrural network due to the similarity of extracted features. The increased recognition rate is gained by the consequent neural network which is designed to learn inference rules for the effective system output.

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Recent advances in Bayesian inference of isolation-with-migration models

  • Chung, Yujin
    • Genomics & Informatics
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    • 제17권4호
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    • pp.37.1-37.8
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    • 2019
  • Isolation-with-migration (IM) models have become popular for explaining population divergence in the presence of migrations. Bayesian methods are commonly used to estimate IM models, but they are limited to small data analysis or simple model inference. Recently three methods, IMa3, MIST, and AIM, resolved these limitations. Here, we describe the major problems addressed by these three software and compare differences among their inference methods, despite their use of the same standard likelihood function.

Computation of daily solar radiation using adaptive neuro-fuzzy inference system in Illinois

  • Kim, Sungwon
    • 한국수자원학회:학술대회논문집
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    • 한국수자원학회 2015년도 학술발표회
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    • pp.479-482
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    • 2015
  • The objective of this study is to develop adaptive neuro-fuzzy inference system (ANFIS) model for estimating daily solar radiation using limited weather variables at Champaign and Springfield stations in Illinois. The best input combinations (one, two, and three inputs) can be identified using ANFIS model. From the performance evaluation and scatter diagrams of ANFIS model, ANFIS 3 (three input) model produces the best results for both stations. Results obtained indicate that ANFIS model can successfully be used for the estimation of daily global solar radiation at Champaign and Springfield stations in Illinois. These results testify the generation capability of ANFIS model and its ability to produce accurate estimates in Illinois.

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An Information-based Forecasting Model for Project Progress and Completion Using Bayesian Inference

  • Yoo, Wi-Sung;Hadipriono, Fabian C.
    • 한국건설관리학회논문집
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    • 제8권4호
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    • pp.203-213
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    • 2007
  • In the past, several construction projects have exceeded their schedule resulting in financial losses to the owners; at present there are very few methods available to accurately forecast the completion date of a project. These nay be because of unforeseen outcomes that cannot be accounted for earlier and because of deficiency of proper tools to forecast completion date of said project. To overcome these difficulties, project managers may need a tool to predict the completion date at the early stage of project development. Bayesian Inference introduced in this paper is one such tool that can be employed to forecast project progress at all construction stages. Using this inference, project managers can combine an initially planned project progress (growth curve) with reported information from ongoing projects during the development, and in addition, dynamically revise this initial plan and quantify the uncertainty of completion date. This study introduces a theoretical model and proposes a mathematically information-based framework to forecast a project completion date that corresponds with the actual progress data and to monitor the modified uncertainties using Bayesian Inference.

Two Properties of Ancillary Statistics

  • Lee, Yong-Goo
    • Journal of the Korean Statistical Society
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    • 제17권2호
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    • pp.93-100
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    • 1988
  • Two properties of ancillary statistics are considered. One is to find a role of ancillary statistics in the statistical inference by showing that the ancillary statistic can recover the lost information and to give a criteria for comparing the conditional inference with unconditional inference. The other is to find an ancillary statistic of translation model and its relationship with observed Fisher information.

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적응형 뉴로-퍼지(ANFIS)를 이용한 도시철도 시스템 위험도 평가 연구 (A Study on the Risk Assessment for Urban Railway Systems Using an Adaptive Neuro-Fuzzy Inference System(ANFIS))

  • 탁길훈;구정서
    • 한국안전학회지
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    • 제37권1호
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    • pp.78-87
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    • 2022
  • In the risk assessment of urban railway systems, a hazard log is created by identifying hazards from accident and failure data. Then, based on a risk matrix, evaluators analyze the frequency and severity of the occurrence of the hazards, conduct the risk assessment, and then establish safety measures for the risk factors prior to risk control. However, because subjective judgments based on the evaluators' experiences affect the risk assessment results, a more objective and automated risk assessment system must be established. In this study, we propose a risk assessment model in which an adaptive neuro-fuzzy inference system (ANFIS), which is combined in artificial neural networks (ANN) and fuzzy inference system (FIS), is applied to the risk assessment of urban railway systems. The newly proposed model is more objective and automated, alleviating the limitations of risk assessments that use a risk matrix. In addition, the reliability of the model was verified by comparing the risk assessment results and risk control priorities between the newly proposed ANFIS-based risk assessment model and the risk assessment using a risk matrix. Results of the comparison indicate that a high level of accuracy was demonstrated in the risk assessment results of the proposed model, and uncertainty and subjectivity were mitigated in the risk control priority.

퍼지 로직 시스템을 이용한 항공기 가스터빈 엔진 오류 검출에 대한 연구 (Fault Diagnosis in Gas Turbine Engine Using Fuzzy Inference Logic)

  • 모은종;지민석;김진수;이강웅
    • 제어로봇시스템학회논문지
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    • 제14권1호
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    • pp.49-53
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    • 2008
  • A fuzzy inference logic system is proposed for gas turbine engine fault isolation. The gas path measurements used for fault isolation are exhaust gas temperature, low and high rotor speed, and fuel flow. The fuzzy inference logic uses rules developed from a model of performance influence coefficients to isolate engine faults while accounting for uncertainty in gas path measurements. Inputs to the fuzzy inference logic system are measurement deviations of gas path parameters which are transferred directly from the ECM(Engine Control Monitoring) program and outputs are engine module faults. The proposed fuzzy inference logic system is tested using simulated data developed from the ECM trend plot reports and the results show that the proposed fuzzy inference logic system isolates module faults with high accuracy rate in the environment of high level of uncertainty.

다양한 의료 분석 방식을 지원하는 효과적 추론 기법 설계 및 적용 지침 (A Design of Effective Inference Methods and Their Application Guidelines for Supporting Various Medical Analytics Schemes)

  • 김문권;라현정;김수동
    • 정보과학회 논문지
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    • 제42권12호
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    • pp.1590-1599
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    • 2015
  • 다양한 개인 의료 장비들이 등장함에 따라 개인 의료 컨텍스트가 풍부하게 수집되고 있다. 이렇게 수집된 의료 컨텍스트를 분석함으로써 소프트웨어적으로 질병을 진단하기 위한 노력이 이어지고 있다. 본 논문에서는 의료 전문가들이 사용하는 의료 분석 기법을 정형화하고, 각 의료 기법을 실현화하기 위한 추론 기법을 식별하며, 추론기법의 적용 지침을 제시한다. 또한, 의료 기법을 제공하는 추론 시스템을 PoC 수준에서 개발하고, 실제 의료 컨텍스트를 분석하여 질병 진단 실험을 수행함으로써 제시하는 의료 분석 기법 및 추론 기법 적용 지침의 실효성과 그 효과를 검증한다.