• 한국콘텐츠학회논문지
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• 제6권3호
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• pp.63-74
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• 2006

오늘날 RFC 1867 표준문서를 따르는 HTML POST 폼을 사용해 웹 브라우저를 통해 업로드된 파일을 저장하고 관리하는 ASP응용들이 다양하게 출시되고 있다. 특히 인터넷의 대중화와 함께 보안이 큰 이슈로 대두되면서 HTTP 포트를 통한 파일 송수신의 중요성이 한층 대두되고 있다. 본 논문에서는 ASP 환경에서 사용 할 수 있는 'Form based ASP 업로드 컴포넌트'를 직접 제작하고 대부분의 주요 코드들을 공개함으로서 향후 업로드 기능을 포함하는 다양한 새로운 ASP 응용들을 개발하는데 활용하도록 하였다. 한편 제작된 업로드 컴포넌트의 업로드 시간 및 CUP 사용시간을 잘 알려진 기존 상용 제품과 비교 분석함으로서 타당성을 검증하였다.

• 대한전기학회:학술대회논문집
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• 대한전기학회 2000년도 추계학술대회 논문집 학회본부 A
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• pp.17-19
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• 2000

This paper deals with Load Flow Program for client/server system. Clients play roles of input and output of the data. The client upload input-data file to the server which takes the part of the function of solving the Load Flow. The developed LF COM(Component Object Model) carry out solving the Load Flow and saving the result and the input data to DataBase. It proved the developed System to be compatible through the Case Study.

• Journal of Information Processing Systems
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• 제17권5호
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• pp.1020-1033
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• 2021

Nowadays, online or mobile social network services (SNS) are very popular and widely spread in our society and daily lives to instantly share, disseminate, and search information. In particular, SNS such as YouTube, Flickr, Facebook, and Amazon allow users to upload billions of images or videos and also provide a number of multimedia information to users. Information retrieval in multimedia-rich SNS is very useful but challenging task. Content-based media retrieval (CBMR) is the process of obtaining the relevant image or video objects for a given query from a collection of information sources. However, CBMR suffers from the dimensionality curse due to inherent high dimensionality features of media data. This paper investigates the effectiveness of the kernel trick in CBMR, specifically, the kernel principal component analysis (KPCA) for dimensionality reduction. KPCA is a nonlinear extension of linear principal component analysis (LPCA) to discovering nonlinear embeddings using the kernel trick. The fundamental idea of KPCA is mapping the input data into a highdimensional feature space through a nonlinear kernel function and then computing the principal components on that mapped space. This paper investigates the potential of KPCA in CBMR for feature extraction or dimensionality reduction. Using the Gaussian kernel in our experiments, we compute the principal components of an image dataset in the transformed space and then we use them as new feature dimensions for the image dataset. Moreover, KPCA can be applied to other many domains including CBMR, where LPCA has been used to extract features and where the nonlinear extension would be effective. Our results from extensive experiments demonstrate that the potential of KPCA is very encouraging compared with LPCA in CBMR.