• Title/Summary/Keyword: Model Inference

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

  • Jeong, Dong-Won;Jing, Yixin;Baik, Dook-Kwon
    • Journal of KIISE:Databases
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    • v.35 no.1
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    • pp.29-43
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    • 2008
  • Although RDF ontologies might be expressed in XML tree model, existing methods for protection of XML documents are not suitable for securing RDF ontologies. The graph style and inference feature of RDF demands a new security model development. Driven by this goal, this paper proposes a new query-oriented model for the RDF ontology access control. The proposed model rewrites a user query using a three-layered ontology view. The proposal resolves the problem that the existing approaches should generate inference models depending on inference rules. Accessible ontology concepts and instances which a user can visit are defined as ontology views, and the inference view defined for controling an inference query enables a controlled inference capability for the user. This paper defines the three-layered view and describes algorithms for query rewriting according to the views. An implemented prototype with its system architecture is shown. Finally, the experiment and comparative evaluation result of the proposal and the previous approach is described.

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

  • Kim, Kwang-Baek
    • Journal of the Korea Society of Computer and Information
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    • v.14 no.8
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    • pp.25-31
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    • 2009
  • Widely used color information recognition methods based on the RGB color model with static fuzzy inference rules have limitations due to the model itself-the detachment of human vision and applicability of limited environment. In this paper, we propose a method that is based on HSI model with new inference process that resembles human vision recognition process. Also, a user can add, delete, update the inference rules in this system. In our method, we design membership intervals with sine, cosine function in H channel and with functions in trigonometric style in S and I channel. The membership degree is computed via interval merging process. Then, the inference rules are applied to the result in order to infer the color information. Our method is proven to be more intuitive and efficient compared with RGB model in experiment.

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

  • 김형근;최갑석
    • The Journal of Korean Institute of Communications and Information Sciences
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    • v.17 no.5
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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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    • v.17 no.4
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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
    • Proceedings of the Korea Water Resources Association Conference
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    • 2015.05a
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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.
    • Korean Journal of Construction Engineering and Management
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    • v.8 no.4
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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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    • v.17 no.2
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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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A Study on the Risk Assessment for Urban Railway Systems Using an Adaptive Neuro-Fuzzy Inference System(ANFIS) (적응형 뉴로-퍼지(ANFIS)를 이용한 도시철도 시스템 위험도 평가 연구)

  • Tak, Kil Hun;Koo, Jeong Seo
    • Journal of the Korean Society of Safety
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    • v.37 no.1
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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.

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

  • Mo, Eun-Jong;Jie, Min-Seok;Kim, Chin-Su;Lee, Kang-Woong
    • Journal of Institute of Control, Robotics and Systems
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    • v.14 no.1
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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.

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

  • Kim, Moon Kwon;La, Hyun Jung;Kim, Soo Dong
    • Journal of KIISE
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    • v.42 no.12
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    • pp.1590-1599
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    • 2015
  • As a variety of personal medical devices appear, it is possible to acquire a large number of diverse medical contexts from the devices. There have been efforts to analyze the medical contexts via software applications. In this paper, we propose a generic model of medical analytics schemes that are used by medical experts, identify inference methods for realizing each medical analytics scheme, and present guidelines for applying the inference methods to the medical analytics schemes. Additionally, we develop a PoC inference system and analyze real medical contexts to diagnose relevant diseases so that we can validate the feasibility and effectiveness of the proposed medical analytics schemes and guidelines of applying inference methods.