• 품질경영학회지
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• 제20권2호
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• pp.44-54
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• 1992

In conventional fault-tree analysis, the failure probabilities of components of a system are treated as exact values in estimating the failure probability of the top event. For the plant layout and systems of the products, however, it is often difficult to evaluate the failure probabilities of components from past occurences, because the environments of the systems change. Furthermore, it might be necessary to consider possible failure of components of the systems even if they have never failed before. In the paper, instead of the probability of failure, we propose the possibility of failure, viz, a fuzzy set defined in probability space. Thus, in this paper based on a fuzzy fault-tree model, the maximum possibility of system failure is determined from the possibility of failure of each component within the system according to the extension principle.

• Nuclear Engineering and Technology
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• 제29권1호
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• pp.25-34
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• 1997

This study presents a new approach for expert opinion elicitation process to assess an uncertainty inherent in accident management. The need to work with rare event and limited data in accident management leads analysis to use expert opinions extensively. Unlike the conventional approach using point-valued probabilities, the study proposes the concept of fuzzy probability to represent expert opinion. The use of fuzzy probability has an advantage over the conventional approach when an expert's judgment is used under limited dat3 and imprecise knowledge. The study demonstrates a method of combining and propagating fuzzy probabilities. finally, the proposed methodology is applied to the evaluation of the probability of a bottom head failure for the flooded case in the Peach Bottom BWR nuclear power plant.

• 한국항행학회논문지
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• 제9권1호
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• pp.34-40
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• 2005

본 논문에서는 퍼지-PI 제어 알고리즘을 이용하는 무인 항공기용 터보제트 엔진 제어기를 제안한다. 터보제트 엔진의 가감속시 서지와 flame-out 현상을 방지하기 위해 연료 유량 제어 입력을 퍼지-PI 제어기로 생성한다. 가속도 오차의 로그함수를 사용하여 퍼지 추론 규칙을 만듦으로써 추종오차를 줄이도록 하였다. 제안된 제어기의 성능확인을 위한 컴퓨터 시뮬레이션은 선형 엔진 모델에 적용하였으며 엔진 출력이 기준 가감속 명령에 잘 추종함을 보였다.

• Nuclear Engineering and Technology
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• 제53권12호
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• pp.4014-4021
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• 2021

If safety injection systems (SISs) do not work in the event of a loss-of-coolant accident (LOCA), the accident can progress to a severe accident in which the reactor core is exposed and the reactor vessel fails. Therefore, it is considered that a technology that provides recoverable maximum time for SIS actuation is necessary to prevent this progression. In this study, the corresponding time was defined as the golden time. To achieve the objective of accurately predicting the golden time, the prediction was performed using the deep fuzzy neural network (DFNN) with rule-dropout. The DFNN with rule-dropout has an architecture in which many of the fuzzy neural networks (FNNs) are connected and is a method in which the fuzzy rule numbers, which are directly related to the number of nodes in the FNN that affect inference performance, are properly adjusted by a genetic algorithm. The golden time prediction performance of the DFNN model with rule-dropout was better than that of the support vector regression model. By using the prediction result through the proposed DFNN with rule-dropout, it is expected to prevent the aggravation of the accidents by providing the maximum remaining time for SIS recovery, which failed in the LOCA situation.

• Journal of applied mathematics & informatics
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• 제8권2호
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• pp.505-518
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• 2001

The huge and complicated plants such as nuclear power stations are likely to cause the operators to make mistakes due to a variety of inexplicable reasons and symptoms in case of emergency. That’s why the prevention system assisting the operators is being developed for. First of all. I suggest an improved fuzzy diagnosis. Secondly, I want to demonstrate that a classification system of nuclear plant’s accident investigating the causes of accidents foresees possible problems, and maintains the reliability of the diagnostic reports in spite of improper working in part. In the event of emergency in a nuclear plant, a lot of operational steps enable the operators to find out what caused the problems based on an emergent operating plan. Our system is able to classify their types within twenty to thirty seconds. As so, we expect the system to put down the accidents right after the rapid detection of the damage control-method concerned.

• Journal of Electrical Engineering and information Science
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• 제2권4호
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• pp.59-65
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• 1997

In this paper, a general fuzzy syntactic method for recognition of flaw patterns and for the measurement of flaw characteristic parameters for a non-destructive inspections signal, called eddy-current, is presented. Solutions are given to the subtasks of primitive pattern selection, signal to symbol transformation, pattern grammar formulation, and event-synchronous flaw pattern extraction based on the grammars. Fuzzy attribute grammars are used as the model for the pattern grammar because of their descriptive power in the face of uncertain constraints caused by nose or distortion in the signal waveform, due to their ability to handle syntactic as well as semantic information. This approach has been implemented and the performance of eh resultant system has been evaluated using a library of law patterns obtained from steam generator tubes in nuclear power plants by an eddy current-based non-destructive inspection method.

• 대한산업공학회지
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• 제39권6호
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• pp.577-586
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• 2013

In a shipyard, it is hard to predict block movement due to the uncertainty caused during the long period of shipbuilding operations. For this reason, block movement is rarely scheduled, while main operations such as assembly, outfitting and painting are scheduled properly. Nonetheless, the high operating costs of block movement compel task managers to attempt its management. To resolve this dilemma, this paper proposes a new block movement analysis framework consisting of the following operations: understanding the entire process, log clustering to obtain manageable processes, discovering the process model and detecting exceptional processes. The proposed framework applies fuzzy mining and trace clustering among the process mining technologies to find main process and define process models easily. We also propose additional methodologies including adjustment of the semantic expression level for process instances to obtain an interpretable process model, definition of each cluster's process model, detection of exceptional processes, and others. The effectiveness of the proposed framework was verified in a case study using real-world event logs generated from the Block Process Monitoring System (BPMS).

• 한국전자통신학회논문지
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• 제10권4호
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• pp.499-505
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• 2015

TIA(: Trend Impact Analysis)는 발생될 가능성이 있는 미래의 예기치 못한 사건들을 식별하고 분석하기 위한 고급 예측 도구에 속한다. 적응적인 뉴로-퍼지 추론 시스템은 인공신경망의 일종으로 신경망과 퍼지 로직 원리를 모두 통합하고 보편적 추정되는 것으로 간주한다. 본 논문에서는 적응적인 뉴로-퍼지 추론 시스템을 사용하여 예기치 못한 사건에 관한 심각성의 정도를 추론하고 이를 시간의 함수로서 도입하여 예기치 못한 사건들의 출현 확률에 관해 보다 타당한 추정치를 얻는데 있다. 이러한 접근방식에 대한 배후 개념은 예기치 못한 사건이 갑자기 출현되는 것이 아니라 관련 사건이 가지고 있는 속성 값에 대한 건드림 혹은 변화가 기존 속성 값의 한계를 벗어나 마치 새로운 사건인 것처럼 등장할 수 있음을 전제로 하고 있다. ANFIS 접근 방식은 이러한 사건을 식별해서 예기치 못한 사건의 심각성의 정도를 추론하는데 매우 적절한 방식이라 할 수 있다. 속성들의 변화 값들은 확률적인 동적 모델 및 Monte-Carlo 방법을 사용하여 얻을 수 있다. 제안된 모델에 관한 타당성은 강 유역의 예상치 못한 가뭄에 따른 충격 추세 곡선을 기존 연구 결과와의 비교를 통해 나타낸다.

• 한국지능시스템학회:학술대회논문집
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• 한국지능시스템학회 2008년도 춘계학술대회 학술발표회 논문집
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• pp.385-391
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• 2008

Robotics has emerged as an important field for the future. It is our vision that robots in future will be able to transcend these precincts and work side by side humans for the greater good of mankind. We developed a face robot for this purpose. However, Life like robots demands a certain level of intelligence. Some scientists have proposed an event based learning approach, in which the robot can be taken as a small child and through learning from surrounding entities develops its own personality. In fact some scientists have proposed an entire new personality of the robot itself in which robot can have its own internal states, intentions, beliefs, desires and feelings. Our approach should not only be to develop a robot personality model but also to understand human behavior and incorporate it into the robot model. Human's personality is very complex and rests on many factors like its physical surrounding, its social surrounding, and internal states and beliefs etc. This paper discusses the development of this platform to evaluate this and develop a standard by a society based approach including the cultural paradigm. For this purpose the fuzzy control theory is used. Since the fuzzy theory is very near human analytical thinking it provides a very good platform to develop such a model.

• International Journal of Automotive Technology
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• 제7권6호
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• pp.729-739
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• 2006

This paper describes an active fault-tolerant control system for an induction motor drive that propels an Electrical Vehicle(EV) or a Hybrid one(HEV). The proposed system adaptively reorganizes itself in the event of sensor loss or sensor recovery to sustain the best control performance given the complement of remaining sensors. Moreover, the developed system takes into account the controller transition smoothness in terms of speed and torque transients. In this paper which is the sequel of (Diallo et al., 2004), we propose to introduce more advanced and intelligent control techniques to improve the global performance of the fault-tolerant drive for automotive applications(e.g. EVs or HEVs). In fact, two control techniques are chosen to illustrate the consistency of the proposed approach: sliding mode for encoder-based control; and fuzzy logics for sensorless control. Moreover, the system control reorganization is now managed by a fuzzy decision system to improve the transitions smoothness. Simulations tests, in terms of speed and torque responses, have been carried out on a 4-kW induction motor drive to evaluate the consistency and the performance of the proposed fault-tolerant control approach.