2014 한국도시설계학회 충청지회(박철희,김승근)

O 주 제 : 지역자산을 활용한 마을 만들기 O 목 적 : 지역차원의 주요 도시설계 이슈르르 선정하고, 이를 논의하기 위함 O 주제발표 - 발제 1) 지역자산을 활용한 충청남도 헤비타트 모델과 적용방안             -박철희 박사(충남발전연구원 문화관광디자인연구부 부장) - 발제 2) 주민이 행복한 (문화+복지) 농촌마을 만들기             - 김승근 교수(극동대학교) - 이후 생략지역자산을 활용한 충청남도 해비타트 모델과 적용방안-박철희 연구위원 01. 연구개요 02. 관련이론.정책 및 사업추진실태 03. 해비타트 및 주거공동체 만들기 사례 04. 충청남도 해비타트 모델 및 적용방안 05. 결론 및 정책제언 주민이 행복한(문화+복지) 농촌마을 만들기-김승근(강동대학교) 01. 주민참여 마을 만들기 02. 주민참여 03. 문화 농촌 04. 복지 농촌 05. 맺음

서포트 벡터 분류 및 서포트 벡터 회귀를 이용한 중대사고 진입시간 예측

학위논문(석사) - 한국과학기술원 : 원자력및양자공학과, 2014.8, [ vi, 60 p. ]To protect nuclear power plants (NPPs) from dangerous situations, operators are trained for various transient types and many procedures are prepared. However, operators could commit mistakes and make wrong decisions since there are so many kind of plant status variables to check and working memory is limited. In order to solve this kind of problem, many kind of operation support systems (OSSs) such as fault diagnosis system and computerized procedure system were developed. Although the transient mitigation failure probability is very low, still there is a possibility of failure that leads to severe accident occurrence. Also, recent occurrence of Fukushima accident shows that severe accident could happen even if there are many safety related systems. Therefore, support systems related to monitoring and prediction of severe accident are necessary in order to enhance the safety of NPPs. However, the researches about support systems which conduct severe accident monitoring and prediction are not sufficient. In this research, occurrence time of several events that could happen in the process of severe accident (time when maximum core temperature exceeds 1200℃, reactor vessel failure time, containment failure time) were predicted by using support vector classification (SVC) and support vector regression (SVR). And for preliminary step, break location and break size of loss of coolant accident (LOCA) were identified. SVC and SVR are included in support vector machine (SVM), which is a machine-learning algorithm that has been successfully used for solving classification problem and conducting regression analysis. Firstly, break location of LOCA is identified by using SVC which is capable to classifies cold leg LOCA and hot leg LOCA with 13 kinds of plant status variables (broken side steam generator pressure, level, and temperature, unbroken side steam generator pressure, level, and temperature, pressurizer pressure and level, core water temperature and...한국과학기술원 : 원자력및양자공학과

해양시스템연구부 NAP 기획연구 심해탐사를 위한 독도 복합 시험기지 구축 기획연구

한국해양연구