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스마트 워터 그리드 서비스 Framework 개발에 관한 연구
김성훈(Kim, Seong-Hoon),오현제(Oh, Hyunje),정진홍(Jung, Jinhong),김원재(Kim, Weonjae),윤영한(Yoon, Young H.) 한국산학기술학회 2012 한국산학기술학회논문지 Vol.13 No.12
최근 우리가 살고 있는 정보화 사회의 가장 중요한 화두 중 하나로 “스마트”가 떠오르고 있으며 통신 및 사회간접자본 분야 등을 포함한 사회의 여러 영역이 이 주제로 빠르고 접근하고 있다. 국내에서는 전력분야에서의 우선접근이 비교적 성공적인 평가를 받고 있으며 물공급 분야는 이제 막 그 첫발을 내 딛고 있는 것이 현실이다. 이 러한 관점에서 본 연구는 “스마트 워터 그리드” 서비스를 위한 프레임웤 개발에 그 목적이 있다. 연구의 절차로, 우 선 국내외 관련 연구가 조사되었고, “스마트 워터 그리드” 서비스를 구성하는 4 기술요소가 정의 되었다. 4 기술요 소분야 각각에 대해 프레임웤 모델링이 수행되었고 그 결과로 각각에 대한 TRM이 제시된다. 또한 4 요소영역을 아 우르는 전체 서비스에 대한 종합적 TRM이 제시되고 본 논문에서 제시되는 프레임웤 모델과 유사 모델을 비교하면 서 차별성 있는 연구내용의 전개와 두 모델의 연계컨셉이 정의되었다. 엔터프라이즈 통합모델, 즉, 매크로 레벨부터 마이크로 레벨 적용과 서비스를 온전히 커버하기위해 필요한 두 모델의 합체운영 컨셉과 이러한 상호연계성 위에서 작용하는 마이크로 레벨 모델의 현실화를 통해 물관리분야에서의 스마트화 구현에 현 논문이 다소간 기여하기를 기 대한다. The current society, namely information society is now moving to a specific topic which is SMART. In this sense, recently a variety of social areas including communications and SOC domains are moving fast to this topic. In Korea, The electric power area has been doing a pioneering job relatively successfully and the water supply area is just now taking the first step. The purpose of this research is to develop a technical Framework for Smart Water Grid Service. Related researches has been studied and the 4 constituting technical element areas were defined first. For each of the four areas, a framework modeling was fulfilled and as a result, a TRM(Technical Road Map) was suggested for each of the area. Finally, an Enterprise TRM covering all of the 4 areas was described. Furthermore, the currently suggested Framework model was compared to a related model and it was found that the integration of the models is desirable to wholly cover from Macro to Micro level applications and services. It is expected that the current approach contribute ,more or less, to the smart implementation in the areas of water management.
머신러닝과 통계분석 기법의 비교분석을 통한 건물에 대한 서울시 구별 지진취약도 등급화 및 위험건물 밀도분석
김상빈,김성훈,김대현,Sang-Bin Kim,Seong H. Kim,Dae-Hyeon Kim 대한산업경영학회 2023 산업융합연구 Vol.21 No.7
In the recent period, there have been numerous earthquakes both domestically and internationally, and buildings in South Korea are particularly vulnerable to seismic design and earthquake damage. Therefore, the objective of this study is to discover an effective method for assessing the seismic vulnerability of buildings and conducting a density analysis of high-risk structures. The aim is to model this approach and validate it using data from pilot area(Seoul). To achieve this, two modeling techniques were employed, of which the predictive accuracy of the statistical analysis technique was 87%. Among the machine learning techniques, Random Forest Model exhibited the highest predictive accuracy, and the accuracy of the model on the Test Set was determined to be 97.1%. As a result of the analysis, the district rating revealed that Gwangjin-gu and Songpa-gu were relatively at higher risk, and the density analysis of at-risk buildings predicted that Seocho-gu, Gwanak-gu, and Gangseo-gu were relatively at higher risk. Finally, the result of the statistical analysis technique was predicted as more dangerous than those of the machine learning technique. However, considering that about 18.9% of the buildings in Seoul are designed to withstand the Seismic intensity of 6.5 (MMI), which is the standard for seismic-resistant design in South Korea, the result of the machine learning technique was predicted to be more accurate. The current research is limited in that it only considers buildings without taking into account factors such as population density, police stations, and fire stations. Considering these limitations in future studies would lead to more comprehensive and valuable research.
인공 신경회로망을 이용한 실시간 차량 번호판 인식에 관한 연구
김성훈,장용훈,이권순 동아대학교 공과대학부설 생산기술연구소 1998 生産技術硏究所硏究論文集 Vol.3 No.1
One of the most difficult tasks in the process of car license plate is the extraction of license plate region from original image. In this paper, a real-time recognition of a car license plate based on RGB ratio adjusting method is presented. This paper is composed of two parts : one is an image preprocessing part of car images and the other is a pattern classifying part by neural networks. RGB ratio adjusting method is a color recognition method, which is not necessary hardware such as image board and is possible real-time recognition of car license plate.