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COMPONENT-BASED DEVELOPMENT OF OBSERVATIONAL SOFTWARE FOR KASI SOLAR IMAGING SPECTROGRAPH
SEONGHWAN CHOI,김연한,YONG-JAE MOON,KYUNG-SEOK CHO,YOUNG-DEUK PARK,BI-HO JANG,김수진,KAP-SUNG KIM 한국천문학회 2005 Journal of The Korean Astronomical Society Vol.38 No.4
In this paper, we have made the component-based development of observational software for KASIsolar imaging spectrograph (KSIS) that is able to obtain three-dimensional imaging spectrograms byusing a scanning mirror in front of the spectrograph slit. Since 2002, the KASI solar spectrographhas been successfully operated to observe solar spectra for a given slit region as well as to inspect theresponse functions of narrow band lters. To improve its capability, we have developed the KSIS thatcan perform sequential observations of solar spectra by simultaneously controlling the scanning mirrorand the CCD camera via Visual C++. Main task of this paper is to introduce the development ofthe component-based software for KSIS. Each component of the software is reusable on the level ofexecutable le instead of source code because the software was developed by using CBD (component-based development) methodology. The main advantage of such a component-based software is that keycomponents such as image processing component and display component can be applied to other similarobservational software without any modications. Using this software, we have successfully obtainedsolar imaging spectra of an active region (AR 10708) including a small sunspot. Finally, we presentsolar H spectra (6562.81A) that were obtained at an active region and a quiet region in order toconrm the validity of the developed KSIS and its software.
문서 쌍 유사도 판별을 위한 문장 상호 관계 및 그래프 기반 모델의 앙상블
최성환(Seonghwan Choi),손동현(Donghyun Son),이호창(Hochang Lee) Korean Institute of Information Scientists and Eng 2021 정보과학회논문지 Vol.48 No.11
Deriving the similarity between two documents, such as, news articles, is one of the most important factors of clustering documents. Sequence similarity models, one of the existing deep-learning based approaches to document clustering, do not reflect the entire context of documents. To address this issue, this paper uses interaction-based and graph-based approaches to construct document pair similarity models suitable for news clustering. This paper proposes four interaction-based models that measures the similarity between two documents through the aggregation of similarity information in the interaction of sentences. The experimental results demonstrated that two out of these four proposed models outperformed SVM and HAN. Ablation studies were conducted on the graph-based model through experiments on the depth of the model’s neural network and its input features. Through error analysis and ensemble of models with an interaction and graph-based approach, this paper showed that these two approaches could be complementarity due to the differences in their prediction tendencies.
최성환 ( Seonghwan Choi ),손영우 ( Youngwoo Son ),성만규 ( Mankyu Sung ) 한국정보처리학회 2017 한국정보처리학회 학술대회논문집 Vol.24 No.1
본 연구는 게임을 비롯한 여러 가지 콘텐츠에서 활용하기 위해서 단일 이미지를 이용한 복층 구조의 지형을 제작하는 방법에 대해서 제안한다. 기존의 하이트맵(Heightmap)을 이용하여 복층구조를 제작했을 때의 문제점을 제시하며 어떻게 단일이미지 하이트맵(Heightmap)을 이용하여 복층 구조의 지형을 제작할수 있는지에 대한 방향을 제시한다. 본 논문에서는 단일 이미지의 RGBA값을 이용한 복층 구조 지형 제작 방식에 대한 실험을 통해 제안한 알고리즘을 검증한다.