• 한국산업정보학회:학술대회논문집
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• 한국산업정보학회 2000년도 추계공동학술대회논문집
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• pp.375-389
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• 2000

Fuzzy logic ignores some information in the reasoning process. Neural networks are powerful tools for the pattern processing, but, not appropriate for the logical reasoning. To model human knowledge, besides pattern processing capability, the logical reasoning capability is equally important. Another new neural network called neural logic network is able to do the logical reasoning. Because the fuzzy inference is a fuzzy logical reasoning, we construct fuzzy inference network based on the neural logic network, extending the existing rule- inference. network. And the traditional propagation rule is modified. For the search strategies to find out the belief value of a conclusion in the fuzzy inference network, we conduct a simulation to evaluate the search costs for searching sequentially and searching by means of search priorities.

• Transactions on Control, Automation and Systems Engineering
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• 제3권1호
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• pp.58-65
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• 2001

This paper proposes a self tuning fuzzy inference method by the genetic algorithm in the fuzzy-sliding mode control for a robot. Using this method, the number of inference rules and the shape of membership functions are optimized without an expert in robotics. The fuzzy outputs of the consequent part are updated by the gradient descent method. And, it is guaranteed that he selected solution become the global optimal solution by optimizing the Akaikes information criterion expressing the quality of the inference rules. The trajectory tracking simulation and experiment of the polishing robot show that the optimal fuzzy inference rules are automatically selected by the genetic algorithm and the proposed fuzzy-sliding mode controller provides reliable tracking performance during the polishing process.

• 지능정보연구
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• 제9권1호
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• pp.91-107
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• 2003

하이퍼링크 기반 추론은 웹의 하이퍼텍스트 기능을 이용함으로써 접근성, 멀티미디어 기능,빠른 응답 시간, 서버의 안정성, 사용 및 업그레이드의 용이성, 플랫폼 독립성 등을 갖는 의료 전문가시스템을 구현할 수 있도록 해 준다. 전문가의 규칙에 따라 서로 하이퍼링크된 HTML문서들은 웹 서버에 적재된 후 추론 기능을 제공하게 되는데, 이러한 HTML문서들은 자체 개발한 WeBIS (Web-based Inference System)라는 GUI 기반 의사결정 그래프 편집 도구에 의해 자동으로 관리된다. 그럼에도 불구하고, 의료분야 전문가시스템이 다루는 규칙베이스의 크기가 큰 경우에 지식공학자가 이들 규칙들을 수작업으로 입력, 관리하는 것이 매우 어렵게 된다. 따라서, 본 연구에서는 고혈압 관리를 위 한 의사결정 그래프 자동 생성 시스템을 개발하였다. 이러한 일련의 과정을 통하여 본 연구에서는 하이퍼링크 기반 추론 기법을 이용하여 웹 기반 의료 전문가 시스템을 개발하는 방법론을 제시하였고, 그 응용으로써 빠른 응답속도와 안정성을 보이는 웹기반 고혈압 관리 시스템을 구현하였다.

• 한국정보처리학회논문지
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• 제4권12호
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• pp.3010-3022
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• 1997

1970년대 후반에 제안된 전문가 시스템은 인공지능의 한 분야로서, 인간의 사고방식을 모방함으로써 다양한 분야에서 야기되는 문제들을 해결해준다. 대부분의 전문가 시스템은 추론엔진과 지식베이스등과 같은 많은 요소들로 구성 된다. 특히 전문가 시스템의 성능은 추론엔진의 효율성에 의해 좌우된다. 이러한 추론 엔진은 지식 베이스가 구축될 때, 가능한 한 적은 제약성을 가져야 함은 물론, 다양한 추론 방법을 제공해야 한다는 특징을 갖고 있어야 한다. 이 논문에서는 지식 영역과 추론 방식에 대한 범용성을제공하는 추론 엔진을 설계 및 구현하였다. 이를 위해 추론 방식은 사용자에 의해 전향추론과 후향추론 및 직첩추론이 선택적으로 수행된다. 또한 목표 영역에서의 지식 획득을 위한 쉬운 표준화와 모듈화를 가능케하는 생성 규칙을 사용하였을 뿐만 아니라 확장된 CBM을 통해 지식 베이스를 구축하였다. 아울러, Rete 패턴 매칭과 ECBM을 이용한 추론 엔진간의 성능분석을 수행하였다.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제15권4호
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• pp.217-231
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• 2015

A novel fuzzy learning framework that employs fuzzy inference to solve the problem of Multiple Instance Learning (MIL) is presented. The framework introduces a new class of fuzzy inference systems called Multiple Instance Mamdani Fuzzy Inference Systems (MI-Mamdani). In multiple instance problems, the training data is ambiguously labeled. Instances are grouped into bags, labels of bags are known but not those of individual instances. MIL deals with learning a classifier at the bag level. Over the years, many solutions to this problem have been proposed. However, no MIL formulation employing fuzzy inference exists in the literature. Fuzzy logic is powerful at modeling knowledge uncertainty and measurements imprecision. It is one of the best frameworks to model vagueness. However, in addition to uncertainty and imprecision, there is a third vagueness concept that fuzzy logic does not address quiet well, yet. This vagueness concept is due to the ambiguity that arises when the data have multiple forms of expression, this is the case for multiple instance problems. In this paper, we introduce multiple instance fuzzy logic that enables fuzzy reasoning with bags of instances. Accordingly, a MI-Mamdani that extends the standard Mamdani inference system to compute with multiple instances is introduced. The proposed framework is tested and validated using a synthetic dataset suitable for MIL problems. Additionally, we apply the proposed multiple instance inference to fuse the output of multiple discrimination algorithms for the purpose of landmine detection using Ground Penetrating Radar.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2003년도 ISIS 2003
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• pp.447-450
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• 2003

In this research, we propose an automatic knowledge acquisition and composite knowledge expression mechanism based on machine learning and relational database. Most of traditional approaches to develop a knowledge base and inference engine of expert systems were based on IF-THEN rules, AND-OR graph, Semantic networks, and Frame separately. However, there are some limitations such as automatic knowledge acquisition, complicate knowledge expression, expansibility of knowledge base, speed of inference, and hierarchies among rules. To overcome these limitations, many of researchers tried to develop an automatic knowledge acquisition, composite knowledge expression, and fast inference method. As a result, the adaptability of the expert systems was improved rapidly. Nonetheless, they didn't suggest a hybrid and generalized solution to support the entire process of development of expert systems. Our proposed mechanism has five advantages empirically. First, it could extract the specific domain knowledge from incomplete database based on machine learning algorithm. Second, this mechanism could reduce the number of rules efficiently according to the rule extraction mechanism used in machine learning. Third, our proposed mechanism could expand the knowledge base unlimitedly by using relational database. Fourth, the backward inference engine developed in this study, could manipulate the knowledge base stored in relational database rapidly. Therefore, the speed of inference is faster than traditional text -oriented inference mechanism. Fifth, our composite knowledge expression mechanism could reflect the traditional knowledge expression method such as IF-THEN rules, AND-OR graph, and Relationship matrix simultaneously. To validate the inference ability of our system, a real data set was adopted from a clinical diagnosis classifying the dermatology disease.

• 전자공학회논문지SC
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• 제39권1호
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• pp.1-6
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• 2002

본 논문에서는 제한된 센서를 이용한 에어콘의 온도제어용 실내 ·외 온도 추론 시스템을 제안하였다. 제안한 온도추론 시스템은 자연계의 면역 시스템의 네트워크 이론을 이용한 실내온도 추론과정과 실외온도 추론과정으로 구성되어 있으며, 실시간 온도추론이 가능하도록 설계하였다. 면역기법을 이용한 온도 추론 시스템은 과거의 정보를 효과적으로 이용함으로써 주어진 입력 데이터뿐만 아니라 학습되지 않는 데이터에 대해서도 온도 추론능력이 우수하다.

• Smart Structures and Systems
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• 제14권2호
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• pp.129-144
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• 2014

In this paper, a fuzzy inference system and an analytical hierarchy process-based online evaluation technique is developed to monitor the condition of the 32-km Donghai Bridge in Shanghai. The system has 478 sensors distributed along eight segments selected from the whole bridge. An online evaluation subsystem is realized, which uses raw data and extracted features or indices to give a set of hierarchically organized condition evaluations. The thresholds of each index were set to an initial value obtained from a structure damage and performance evolution analysis of the bridge. After one year of baseline monitoring, the initial threshold system was updated from the collected data. The results show that the techniques described are valid and reliable. The online method fulfills long-term infrastructure health monitoring requirements for the Donghai Bridge.

• 시스템엔지니어링학술지
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• 제18권1호
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• pp.84-89
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• 2022

For system maintenance optimization, it is necessary to establish a state information system by CBM+ including CBM and RCM, and sensor selection for CBM+ application requires system process for function model analysis at the early design stage. The study investigated the contents of CBM and CBM+, analyzed the function analysis tasks and procedures of the system, and thus presented a D-FMEA based sensor selection inference methodology at the early stage of design for CBM+ application, and established it as a D-FMEA based sensor selection inference process. The D-FMEA-based sensor inference methodology and procedure in the early design stage were presented for diesel engine sub assembly.

• Nuclear Engineering and Technology
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• 제22권2호
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• pp.128-138
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• 1990

본 논문에서는 원자로냉각재펌프 고장진단 전문가시스템 (ESRCP)에 대해 기술하였다. 이 시스템의 목적은 RCP의 고장진단과 함께 발전소 운전원에게 적절한 운전 조작 및 비상조치 사항 등을 알려주는데 있다. 진단을 위한 일차적 증상은 RCP 영역에 관련된 경보들이다. 경보처리는 Rule-based Deduction 또는 Priority Factor Operation에 의한다. 고장진단은 Rule-based Deduction이나 Bayesian Inference에 의해 수행된다. 각종 Sensor들의 측정값들은 정확한 원인을 진단하기 위해 필요로 하다 증상들이 부족하거나 불착실성을 나타낼 때는 Bayesian Inference로 고장을 진단한다.