• The Journal of the Korea Contents Association
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• v.6 no.3
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• pp.63-74
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• 2006

Recently many ASP applications have been released which enable them to accept, save and manipulate files uploaded with a web browser. The files are uploaded via an HTML POST form using RFC 1867 In particular, the file transfer via the HTTP port is getting more important because of the current Internet security issues. In this paper, we implement a form-based ASP upload component and disclose explicitly most of the main codes. That is, the open source might be helpful to develop the new ASP applications including file upload function in the future. We also show the upload time and CPU usage time of the proposed upload component and compare with the well-known commercial ones, showing the performance metrics of the proposed component are comparable to those of commercial ones.

• Proceedings of the KIEE Conference
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• 2000.11a
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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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• v.17 no.5
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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.