• Title/Summary/Keyword: reliabiltity evaluation

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Evaluation of the Performance and Reliability of a Real-Time System Using Fuzzy-Random Variables (퍼지-랜덤 변수를 이용한 실시간 제어 시스템의 성능 및 신뢰도 평가기법 연구)

  • 민병조;이석주;김학배
    • Journal of Institute of Control, Robotics and Systems
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    • v.6 no.6
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    • pp.433-440
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    • 2000
  • To flexibly evaluate performance and reliability of a real-time system which is intrinsically characterized by stringent timing constraints to generate correct responses, we propose fuzzyrandom variables and build a discrete event model embedded with fuzzy-random variables. Also, we adapt fuzzy-variables to a path-space approach, which derives the upper and lower bounds of reliability by using a semi-Markov model that explicitly contains the deadline information. Consequently, we propose certain formulas of state automata properly transformed by fuzzy-random variables, and present numerical examples applying the formulas to RTP(Rapid Thermal Process) to show that a complex system can be properly evaluated based on this model by computer simulation.

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Evaluation of the Performance and Reliability of a Real-time Power System Described by a DES Model using Fuzzy-Random Variables (퍼지-랜덤 변수를 이용한 DES 모델링을 통한 실시간 전력 시스템의 성능 및 신뢰도 평가)

  • Min, Byeong-Jo;Lee, Seok-Ju;Kim, Hak-Bae
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.49 no.7
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    • pp.363-369
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    • 2000
  • To flexibly evaluate performance and reliability of an electric power system in the aspect of the real-time system which is intrinsically characterized by stringent timing constraints fails catastrophically if its control input is not updated by its digital controller computer within a certain time limit called the hard deadline, we propose fuzzy-random variables and build a discrete event model embedded with fuzzy-random variables. Also, we adapt fuzzy-variables to a path-space approach, which derives the upper and lower bounds of reliability by using a semi-Markov model that explicitly contains the deadline information. Consequently, we propose certain formulas of state automata properly transformed by fuzzy-random variables, and present numerical examples applying the formulas as well.

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