• Journal of KIISE:Computing Practices and Letters
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• v.13 no.6
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• pp.407-411
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• 2007

Over the years, great attention has been paid to the question of exploiting inherent semantic of HTML in the area of web document retrieval. Although HTML is mainly presentation oriented, HTML tags implicitly contain useful semantics that can be catch meaning of text. Focusing on this idea. in this paper we define 'markup pattern' and try to improve performance of web document retrieval using markup patterns. Markup pattern is a mirror of intends of web document publisher and an internal semantic of text on web document. To discover the markup pattern and exploit it, we suggest a new scheme for extracting concepts and weighting documents. For evaluation task, we select two domains-BBC and CNN web sites, and use their search engines to gather domain documents. We re-weight and re-score documents using proposed scheme, and show the performance improvement in the two domains.

• Proceedings of the Korean Information Science Society Conference
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• 2007.06a
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• pp.77-78
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• 2007

• Proceedings of the Korea Information Processing Society Conference
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• 2004.05a
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• pp.217-220
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• 2004

클래스정보에 대한 속성의 추출 및 분류에서 주로 추출된 클래스의 정보가 단지 원시코드의 코멘트에서 추출되었기 때문에 클래스에 대한 정확한 기능 및 용도에 대한 Document가 부족하여 실제로 이용자가 최적의 부분을 추출하기가 어려웠다. 이러한 것들을 향상시키기 위하여 본 연구에서는 객체에 대한 클래스뿐만 아니라 패턴모델의 설계에서도 객체지향모델링 방법을 이용하여 메타모델과 메타데이터를 설계하였다. 그리고 XMI 메타모델로 정의된 디자인패턴의 세부적인 클래스의 메타데이터의 생성에 중점을 두었으며, 마크업언어로 XML-스키마 형식을 이용하여 심플타입(simple type)과 콤플렉스타입(complex type)으로 분류하였다. 그 결과 메타데이터 엘리먼트 단위영역별로 마크업언어를 생성하여 소프트웨어 설계에서 효과적인 재사용을 할 수 있었다.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.185-191
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• 2022

In this study, we propose a novel super-resolution neural network based on U-Net, residual neural network, and sub-pixel convolution. To prevent the loss of detailed information due to the max pooling of U-Net, we propose down-sampling and connection using sub-pixel convolution. This uses all pixels in the filter, unlike the max pooling that creates a new feature map with only the max value in the filter. As a 2×2 size filter passes, it creates a feature map consisting only of pixels in the upper left, upper right, lower left, and lower right. This makes it half the size and quadruple the number of feature maps. And we propose two methods to reduce the computation. The first uses sub-pixel convolution, which has no computation, and has better performance, instead of up-convolution. The second uses a layer that adds two feature maps instead of the connection layer of the U-Net. Experiments with a banchmark dataset show better PSNR values on all scale and benchmark datasets except for set5 data on scale 2, and well represent local area patterns.