• 한국지능시스템학회논문지
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• 제7권1호
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• pp.45-53
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• 1997

This paper presents hybrid expert system for diagnosis of electric power transformer faults. The expert system diagnose and detect faults in oil-filled power transformers based on dissolved gas analysis. As the preprocessing stage, fuzzy information theory is used to manage the uncertainty in transformer fault diagnosis using dissolved gas analysis. The Kohonen neural network takes the interim results by applying fuzzy informations theory as inputs, and performs the transformer fault diagnosis. The Proposed system tested gas records of power transformers from Korea Electric Power Corporation to verify the diagnosis performance of transformer faults.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2002년도 합동 추계학술대회 논문집 정보 및 제어부문
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• pp.597-600
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• 2002

This paper has been studied expert system using fuzzy theory for fault diagnosis of HDD test systems by detecting systems fault and presenting the way of repair using test history and rule base built via interview from exports. The rules of fault diagnosis of HDD test systems are classified into 2 types, fuzzy and crisp, and these have been serialized to decide whether fault diagnosis be done or not by fuzzy rules and to present the way of repair by crisp rules. And then this paper has designed expert system using fuzzy theory for fault diagnosis.

• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 ITC-CSCC -3
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• pp.1893-1896
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• 2002

Remediation Selection Expert System (RSES) is a rule-based expert system which is used far the selection of remediation techniques fur petroleum contaminated sites. In this paper, we describe a fuzzy logic-based sub-system: Site Characterization Sub-System (SCSS). It is an enhancement of the RSES, which is used to analyze the hydraulic properties of contaminated sites. This paper focuses on an explanation on how to apply fuzzy set theory for identification of soil types and hydraulic properties of a contaminated site. To illustrate application of fuzzy set theory to the problem, two sample cases are presented in detail.

• 한국항해항만학회:학술대회논문집
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• 한국항해항만학회 2011년도 추계학술대회
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• pp.34-36
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• 2011

In recent years, shipping at the sea has been rapidly grown in marine nations and vessel's collisions are increasing as well. The collision avoidance is one of issues maritime safety. To reduce vessels' collisions, the fuzzy inference system is one of popular algorithms for collision avoidance. In this paper we aim to implement Matlab. Fuzzy logic toolbox software for collision avoidance algorithm. For this we used an original Matlab fuzzy logic toolbox and customized the toolbox for the collision avoidance algorithm.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2005년도 심포지엄 논문집 정보 및 제어부문
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• pp.24-26
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• 2005

At present, KS-1000 which is one of a commercial measurement instrument for motor fault diagnosis has been used in industrial field. The measurement system of KS-1000 is composed of three part : harmonic acquisition, signal processing by KS-1000 algorithm, diagnosis for motor fault. First of all, voltage signal taken from harmonic sensor is analysed for frequency by KS-1000 algorithm. Then, based on the result values of analysis skilled expert makes a judgment about whether motor system is the abnormality or degradation state. But the expert system such a motor fault diagnosis is very difficult to bring the expectable results by mathematical modeling due to the complexity of judgment process. In this reason, we propose an automation system using fuzzy model based on genetic algorithm(GA) that builded a qualitative model of a system without priori knowledge about a system provided numerical input output data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권2호
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• pp.284-310
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• 2024

Identifying clinical pathways for disease diagnosis and treatment process recommendations are seriously decision-intensive tasks for health care practitioners. It requires them to rely on their expertise and experience to analyze various categories of health parameters from a health record to arrive at a decision in order to provide an accurate diagnosis and treatment recommendations to the end user (patient). Technological adaptation in the area of medical diagnosis using AI is dispensable; using expert systems to assist health care practitioners in decision-making is becoming increasingly popular. Our work architects a novel knowledge-based recommender system model, an expert system that can bring adaptability and transparency in usage, provide in-depth analysis of a patient's medical record, and prescribe diagnostic results and treatment process recommendations to them. The proposed system uses a set of parallel discrete fuzzy rule-based classifier systems, with each of them providing recommended sub-outcomes of discrete medical conditions. A novel knowledge-based combiner unit extracts significant relationships between the sub-outcomes of discrete fuzzy rule-based classifier systems to provide holistic outcomes and solutions for clinical decision support. The work establishes a model to address disease diagnosis and treatment recommendations for primary lung disease issues. In this paper, we provide some samples to demonstrate the usage of the system, and the results from the system show excellent correlation with expert assessments.

• 한국경영과학회지
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• 제20권1호
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• pp.159-177
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• 1995

The objective of this paper is to propose a knowledge-based fuzzy post adjustment so that unstructured problems can be solved more realistically by expert systems. Major part of this mechanism forcuses on fuzzily assessing the influence of various external factors and accordingly improving the solutions of unstructured problem being concerned. For this purpose, three kinds of knowledge are used : user knowledge, expert knowledge, and machine knowledge. User knowledge is required for evaluating the external factors as well as operating the expert systems. Machine knowledge is automatically derived from historical instances of a target problem domain by using machine learning techniques, and used as a major knowledge source for inference. Expert knowledge is incorporate dinto fuzzy membership functions for external factors which seem to significantly affect the target problems. We applied this mechanism to a prototyoe expert system whose major objective is to provide expert guidance for stock market timing such as sell, buty, or wait. Experiments showed that our proposed mechanism can improve the solution quality of expert systems operating in turbulent decision-making environments.

• Industrial Engineering and Management Systems
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• 제16권1호
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• pp.129-140
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• 2017

This research was conducted in order to solve the problem of selecting an operational strategy for projects in project-based organizations by designing a fuzzy expert system. In the current research, we first determined the contributing parameters in operational strategy of project-based organizations based on existing research literature and experts' opinion. Next, we divided them into two groups of model inputs and outputs and the rules governing them were determined by referring to research literature and educational instances. In order to integrate rules, the revised Ternary Grid (revised TG) and expert opinions were applied according to a hybrid algorithm. The Ultimate rules were provided in Fuzzy Inference System format (FIS). In this FIS, proper manufacturing decisions are recommended to the user based on selected competitive priority and also project properties. This paper is the first study in which rules and relations governing the parameters contributing operational strategy in project-based organizations are acquired in a guided integrated process and in the shape of an expert system. Using the decision support system presented in this research, managers of project-based organizations can easily become informed of proper manufacturing decisions in proportion with selected competitive priority and project properties; and also be ensured that theoretical background and past experiences are considered.

• 대한전기학회논문지:전력기술부문A
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• 제48권6호
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• pp.665-670
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• 1999

This paper introduces a fuzzy expert systems for fault diagnosis, where the causal relationships between faults and protective devices are defined as fuzzy relations. The uncertainties existing in the fault diagnosis are figured out using the possibility theory and the possibility measure is associated with the fuzzy relation to evaluate the possibilities of faults. Besides, the knowledge base in the expert system is described and explained. In this way, multiple-fault can be handled easily and simultaneously together with single faults.

• 제어로봇시스템학회논문지
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• 제3권5호
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• pp.435-442
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• 1997

The roof crane system is used for transporting a variable load to a target position. The goal of crane control system is transporting the load to a goal position as quick as possible without rope oscillation. The crane is generally operated by an expert operator, but recently an automatic control system with high speed and rapid tansportation is required. In this paper, we developed a simple fuzzy controller which has been introduced expert's knowledge base for anti-swing and rapid tranportation to goal position. In particular, we proposed the synthesis reasoning method which synthesizes on the basis of expert knowledge of the angle control input and position control input which are inferenced parallel and simultaneously. And we confirmed that the performance of the developed controller is effective as a result of applying it to crane simulator and also verified whether the proposed synthesis rules have been applied correctly using clustering algorithm from the measured data.