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김영창(Youngchang Kim),장재우(Jaewoo Chang) 한국정보과학회 2006 한국정보과학회 학술발표논문집 Vol.33 No.2C
최근, 지리 정보 사용에 대한 관심과 응용 분야에 대한 개발이 증가함에따라서, 지리 정보의 공유 및 상호운용성에 대한 필요성이 증가하고 있다. 이에 따라 OGC(Open GIS Consortium)에서는 지리 정보의 교환 표준으로 GML 언어를 제시하였다. GML은 지리 정보를 전송 및 저장하기 위해 XML로 인코딩한 언어이다. 따라서, 지리 정보를 위한 공간 데이터베이스에 GML 문서를 저장하기 위해서는 효과적인 GML 문서의 파싱이 필수적이다. 본 논문에서는 대표적인 XML 파서인 Xerces를 확장하여 GML 문서를 효과적으로 파싱할 수 있는 GML 파서를 개발한다. 이를 위해 GML 스키마에서 제공하는 지리 정보 데이터 타입을 Xerces 파서의 내부 데이터 타입으로 제공하여, GML 응용 문서의 스캔(scan) 및 Validation을 위해 소요되는 GML 스키마 문서의 파싱 비용을 효과적으로 줄일 수 있다.
공간 네트워크에서 이동객체의 위치정보관리를 위한 동적 분산 그리드 기법
김영창(YoungChang Kim),홍승태(SeungTae Hong),조경진(KyungJin Jo),장재우(JaeWoo Chang) 한국정보과학회 2009 정보과학회 컴퓨팅의 실제 논문지 Vol.15 No.12
최근 공간 네트워크에서 대용량 이동객체의 위치정보를 관리하기 위한 DS-GRID(distributed S-GRID)가 제안되었다[1]. 그러나 DS-GRID는 균일 크기의 그리드 셀을 이용하기 때문에, 실제 응용에서 빈번히 발생하는 이동객체의 쏠림 현상을 효율적으로 관리하지 못하는 단점을 지닌다. 이를 해결하기 위해, 본 논문에서는 이동객체의 밀도에 따라 그리드 셀을 동적으로 분할하는 동적 분산 그리드 기법을 제안한다. 아울러 이를 위한 k-최근접 질의처리 알고리즘을 제안한다. 마지막으로 성능 평가를 통해 이동객체의 쏠림 현상이 발생하였을 경우, 제안하는 동적 분산 그리드 기법이 검색 및 업데이트 성능 측면에서 DS-GRID보다 우수함을 입증한다. Recently, a new distributed grid scheme, called DS-GRID(distributed S-GRID), has been proposed to manage the location information of moving objects in a spatial network[1]. However, because DS-GRID uses uniform grid cells, it cannot handle skewed data which frequently occur in the real application. To solve this problem, we propose a dynamic distributed grid scheme which splits a grid cell dynamically based on the density of moving objects. In addition, we propose a k-nearest neighbor processing algorithm for the proposed scheme. Finally, it is shown from the performance analysis that our scheme achieves better retrieval and update performance than the DS-GRID when the moving objects are skewed.
순서정보및 Materialization 기법을 이용한 최근접 질의처리 알고리즘의 설계 및 구현
김영국(Youngguk Kim),김용기(Yongki Kim),김영창(Youngchang Kim),장재우(Jaewoo Chang) 한국정보과학회 2005 한국정보과학회 학술발표논문집 Vol.32 No.1
최근 LBS(location-based service) 및 텔레매틱스(telematics) 응용의 효과적인 지원을 위해, 이상적인 유클리디언(Euclidean) 공간 대신, 실제 도로나 철도와 같은 공간 네트워크(network)를 고려한 연구가 활발하게 수행중이다. 본 논문에서는 공간 네트워크를 고려한 기존 k-최근접 질의 처리 알고리즘의 문제점을 제시하고, 공간 네트워크 데이터베이스에 보다 효율적인 새로운 k-최근접 질의 처리 알고리즘을 제안한다. 제안하는 질의처리 알고리즘은 순서정보 및 Materialization 기법에 근거하며 기존 방법의 검색 성능을 향상시킨 방법이다. 마지막으로 제안하는 k-최근접 알고리즘을 기존의 알고리즘과 성능 비교를 수행한다.
인공신경망을 이용한 저항 점용접 너겟 직경 예측에 관한 연구
김종규(Jongkyu Kim),구자훈(JaHun Ku),박영도(Yeongdo Park),김영창(Youngchang Kim),황영민(Youngmin Hwang),김희수(Heesoo Kim),Siva Prasad Murugan,구남국(Namkug Ku) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
Resistance spot welding, which has the advantages of low cost and high productivity, is the most common method used in the automobile industry for joining steel sheets. However, in practice, resistance spot welds are typically tested for welding quality using destructive rather than non-destructive inspection methods because of their lower cost. However, in destructive inspection, quality defects can be found only after the completion of the process. Accordingly, several studies are currently being conducted to predict the quality of welding in real time. Welding quality is determined by the diameter of the nugget, and its size depends on several independent variables. In this study, a linear regression model and artificial neural network model were constructed to predict the nugget diameter. An electric power pattern was obtained from the results of a welding experiment, and nine types of electric power characteristic values were extracted from the obtained electric power pattern as independent variables. From the nine electric power characteristic values, six having the highest correlation with the nugget diameter were determined as final independent variables through correlation analysis. The linear regression model was constructed using multiple linear regression analysis, and the artificial neural network model was built using a deep neural network model with two hidden layers and nodes of 64 and 16. In this study, the error between the actual measured and predicted nugget diameters was taken as 0.2 ㎜ or less as a good predictive value. When the linear regression model was used to predict the nugget diameter, only approximately 36% were predicted well. By contrast, when the artificial neural network was used, approximately 86% were predicted well. Thus, the artificial neural network model yielded better results. It was determined that with more welding data and information on steel types, the proposed welding quality prediction system could be improved.
박종훈,김영창,문동석,박귀화,채수진,유효현,안덕선,Park, Jonghoon,Kim, Youngchang,Moon, Dongseok,Park, Kwihwa,Chae, Sujin,Yoo, Hyohyun,Ahn, Ducksun 연세대학교 의과대학 2015 의학교육논단 Vol.17 No.1
To produce well-qualified medical doctors, clinical training is a crucial part of medical education. To this end, teaching hospitals should be carefully selected and professionally managed. However, in Korea, there are no regulations or standards for training hospitals. Instead, some of the regulations that target teaching interns and residents are applied to teaching hospitals. In this study, we reviewed standards and regulations for training hospitals in other countries as a basis for proposing new standards for teaching hospitals in Korea. These new standards take into account the current environment of Korean medicine with the aim of designing appropriate educational programs for students and professional development systems for professors as well as providing educational resources and addressing the local community and international exchange opportunities.
콘텐츠들 간의 유의어 태그매핑을 이용한 확장된 추천기법의 연구
김지연,김영창,정종진,Kim, Jiyeon,Kim, Youngchang,Jung, Jongjin 대한전기학회 2017 전기학회논문지 Vol.66 No.1
Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people's desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.
콘텐츠들 간의 유의어 태그매핑을 이용한 확장된 추천기법의 연구
김지연(Jiyeon Kim),김영창(Youngchang Kim),정종진(Jongjin Jung) 대한전기학회 2017 전기학회논문지 Vol.66 No.2
Recently recommendation methods need personalization and diversity as well as accuracy whereas the traditional researches have been mainly focused on the accuracy of recommendation in terms of quality. The diversity of recommendation is also important to people in terms of quantity in addition to quality since people’s desire for content consumption have been stronger rapidly than past. In this paper, we pay attention to similarity of data gathered simultaneously among different types of contents. With this motivation, we propose an enhanced recommendation method using correlation analysis with considering data similarity between two types of contents which are movie and music. Specifically, we regard folksonomy tags for music as correlated data of genres for movie even though they are different attributes depend on their contents. That is, we make result of new recommendation movie items through mapping music folksonomy tags to movie genres in addition to the recommendation items from the typical collaborative filtering. We evaluate effectiveness of our method by experiments with real data set. As the result of experimentation, we found that the diversity of recommendation could be extended by considering data similarity between music contents and movie contents.