• Information and Communications Magazine
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• v.29 no.6
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• pp.47-52
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• 2012

차세대 전력기술로 불리는 스마트그리드는 ICT와의 융합을 통해 전력을 효율적으로 운용함으로써, 에너지 소비를 줄여 탄소배출량을 줄이는 핵심 솔루션이자 Green by ICT의 대표적인 응용으로서, 글로벌 금융위기와 자원 전쟁에 대한 각국의 대응전략으로 채택되고 있다. 그러나 이와 같은 전세계적인 관심과 의욕적인 노력에도 불구하고, 전력과 정보통신의 컨버전스를 위한 표준의 부재라는 문제에 직면해 있다. [1] 이를 해결하기 위해 미국의 NIST와 유럽의 CEN/CENELEC/ETSI는 경쟁적으로 표준 개발에 전력을 다하고 있으며, 국제표준화기구인 IEC와 ITU 역시 스마트그리드 표준화에 역량을 집중하고 있는 실정이다. 본 고에서는 지금까지의 국내외 스마트그리드 국제표준화 기구의 동향과 더불어 최근 정보통신 입장에서의 스마트그리드 표준화를 진행하고 있는 ITU-T Focus Group on Smart Grid의 표준화 추진 현황을 살펴본다. ITU-T Focus Group on Smart Grid는 특히 한국의 제안으로 설립되고, 지속적이고 적극적인 참여와 주도로 한국의 스마트그리드 표준화 역량이 극적으로 발휘되고 있는 국제표준화 위원회이다.

• Proceedings of the KSR Conference
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• 2002.05a
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• pp.473-480
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• 2002

In this paper, it was proposed that hazard analysis and risk assessment about railway signal systems using FTA(Fault Tree Analysis) and ETA(Event Tree Analysis) one of the reliability analysis methods executed and output value based on the hazard baseline of CENELEC and EC 61508 producted, and also the SIL(Safety Integrity Level)/THR(Tolerable Hazard Rate) about the system set. On the basis of this principle, more systematic standardizations are required to operate railway system and in the future, we hope that safety and reliability of signal equipment will be better improved.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.170-186
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• 2016

This paper introduces a multi-phase fuzzy risk graph model, representing a method for determining for SIL values for railway industry systems. The purpose of this paper is to compensate for the shortcomings of qualitative determination, which are associated with input value ambiguity and the subjectivity problem of expert judgement. The multi-phase fuzzy risk graph model has two phases. The first involves the determination of the conventional risk graph input values of the consequence, exposure, avoidance and demand rates using fuzzy theory. For the first step of fuzzification this paper proposes detailed input parameters. The fuzzy inference and the defuzzification results from the first step will be utilized as input parameters for the second step of the fuzzy model. The second step is to determine the safety integrity level and tolerable hazard rate corresponding to be identified hazard in the railway industry. To validate the results of the proposed the multi-phase fuzzy risk graph, it is compared with the results of a safety analysis of a level crossing system in the CENELEC SC 9XA WG A0 report. This model will be adapted for determining safety requirements at the early concept design stages in the railway business.

• Journal of the Korean Society for Railway
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• v.19 no.2
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• pp.145-158
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• 2016

This paper introduces a risk graph which is one method for determining the SIL as a measure of the effectiveness of signaling system. The purpose of this research is to make up for the weakness of the qualitative determination, which has input value ambiguity and a boundary problem in the SIL range. The fuzzy input valuable consists of consequence, exposure, avoidance and demand rate. The fuzzy inference produces forty eight fuzzy rule by adapting the calibrated risk graph in the IEC 61511. The Max-min composition is utilized for the fuzzy inference. The result of the fuzzy inference is the fuzzy value. Therefore, using the de-fuzzification method, the result should be converted to a crisp value that can be utilized for real projects. Ultimately, the safety requirement for hazard is identified by proposing a SIL result with a tolerable hazard rate. For the validation the results of the proposed method, the fuzzy risk graph model is compared with the safety analysis of the signaling system in CENELEC SC 9XA WG A10 report.