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진경호,채명진,이규,이교선,Chin, Kyung-Ho,Chae, Myung-Jin,Lee, Giu,Lee, Kyo-Sun 한국건설관리학회 2009 한국건설관리학회 논문집 Vol.10 No.6
Infrastructure asset management can be defined as the long term and cost effective management strategy to meet the required service level. In most developed countries, the major motivations of the introduction of asset management are increment in number of assets, extension of maintenance field, accounting approaches of public facilities, performance-based FM(Facility Management), limitations of public funds and public-private partnership, life cycle cost approach, and the development of information technology. This paper discusses the strategic and stepwise methods of introducing infrastructure asset management. Strategic approaches are suggested to develop the practical methods of condition and value assessment of assets, and long-term capital investment plan for optimized decision making(ODM). Required systematic processes are analyzed in terms of resource and technical limitations and detailed implementation plan for each development phases are suggested. 자산관리는 사회기반시설물의 요구되는 서비스수준을 만족시키기 위한 장기적인 비용 효율적 관리라고 정의할 수 있다. 선진외국의 경우 사회기반시설물의 증가와 유지관리 시장의 확대, 유지관리를 위한 회계적 접근방식, 성능 중심의 시설관리, 자원의 한계와 민간과의 협업 강화, 생애주기 비용 개념의 확대 적용, 정보화기술의 발전이 동인이 되어 자산관리가 도입되었다. 본 논문에서는 국내외 자산관리의 현황 및 문제점을 분석하여 사회기반시설물의 자산관리체계 도입 방안과 추진 전략에 관한 연구를 수행하였다. 먼저, 국가자산의 합리적인 평가와 동시에 유지관리를 포함한 장기적 최적 투자 의사결정을 수행할 수 있는 국가적 차원의 자산관리체계 구축을 위해 국내의 도입 환경을 분석하여 도입방안을 모색하였다. 그리고 성공적 사회기반시설물 자산관리체계 도입을 위해 제도적, 프로세스적, 자원적, 기술적 측면에서 요구되는 핵심 전략을 분석하여 세부전략별로 실천과제를 분석하여 단계별 추진 전략을 제시하였다.
Machine learning-based categorization of source terms for risk assessment of nuclear power plants
진경호,조재현,김성엽 한국원자력학회 2022 Nuclear Engineering and Technology Vol.54 No.9
In general, a number of severe accident scenarios derived from Level 2 probabilistic safety assessment (PSA) are typically grouped into several categories to efficiently evaluate their potential impacts on the public with the assumption that scenarios within the same group have similar source term characteristics. To date, however, grouping by similar source terms has been completely reliant on qualitative methods such as logical trees or expert judgements. Recently, an exhaustive simulation approach has been developed to provide quantitative information on the source terms of a large number of severe accident scenarios. With this motivation, this paper proposes a machine learning-based categorization method based on exhaustive simulation for grouping scenarios with similar accident consequences. The proposed method employs clustering with an autoencoder for grouping unlabeled scenarios after dimensionality reductions and feature extractions from the source term data. To validate the suggested method, source term data for 658 severe accident scenarios were used. Results confirmed that the proposed method successfully characterized the severe accident scenarios with similar behavior more precisely than the conventional grouping method