• Journal of the Korean Graphic Arts Communication Society
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• v.9 no.1
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• pp.1-11
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• 1991

In this reseach, double-layered photoconductor consist of the carrier generation layer(CGL) of $\varepsilon$ type copper phthalocyanine thin film by an aqueous coating method and the carrier transport layer(CGL) of polyvinyl carbazol(PVK) by spin coating. We inverstigated effect of the surfactant solution and cathod electrolysis to the crystal type of $\varepsilon$-CuPc in CGL with TEM, SEM and X- ray diffraction spectroscopy and studied the mechanism of an aqueous coating for the preparation of CGL. The effect of the washing of CGL about the electrophotographic characteristics of the $\varepsilon$-CuPc/PVK doublelayered photoconductors is studied also.

• Proceedings of the KAIS Fall Conference
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• 2011.12b
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• pp.656-659
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• 2011

X-선 검색장치는 대상체의 단면을 스캔하여 결과를 확인하기 때문에 정확성이 낮다는 것이 문제점으로 지적되어왔다. 이를 개선하기 위하여 선행연구로 스테레오 X-선 검색장치를 개발하여 단일 대상체에 대하여 윤곽선 정합 및 볼륨기반 형상복원 연구를 수행하였다. 본 연구에서는 스테레오 X-선 검색장치를 이용하여 두 개의 중첩된 대상체를 스캔하여 형상을 분리 복원하기 위한 연구를 진행하였다. 중첩 대상체에 대한 분리 복원을 위해 벡터정보의 거리값을 계산하여 내 외부 복셀을 분리하고 중첩부분에 대한 제거는 Z축을 기준으로 임계치를 두어 분리하는 알고리즘을 제안하였다. 3차원 스테레오 X-선 검색장치에 대한 스캔영상의 형상복원 알고리즘 개선을 통해 제한된 스캔환경에서 집적화된 대상체의 검색을 가능하도록 할 것이다.

• The Bulletin of The Korean Astronomical Society
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• v.44 no.1
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• pp.51.2-51.2
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• 2019

Multi-wavelength observations become very popular in astronomy. Even though there are some correlations among different sensor images, it is not easy to translate from one to the other one. In this study, we apply a deep learning method for image-to-image translation, based on conditional generative adversarial networks (cGANs), to solar images. To examine the validity of the method for scientific data, we consider several different types of pairs: (1) Generation of SDO/EUV images from SDO/HMI magnetograms, (2) Generation of backside magnetograms from STEREO/EUVI images, (3) Generation of EUV & X-ray images from Carrington sunspot drawing, and (4) Generation of solar magnetograms from Ca II images. It is very impressive that AI-generated ones are quite consistent with actual ones. In addition, we apply the convolution neural network to the forecast of solar flares and find that our method is better than the conventional method. Our study also shows that the forecast of solar proton flux profiles using Long and Short Term Memory method is better than the autoregressive method. We will discuss several applications of these methodologies for scientific research.