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도시열섬 완화를 위한 나무심기운동에 따른 지표면 온도 변화 분석 - 구미시를 사례로 -
김경훈,김형수,권용하,박인선,정윤재,KIM, Kyunghun,KIM, Hung Soo,KWON, Yong-Ha,PARK, Insun,CHOUNG, Yun-Jae 한국지리정보학회 2022 한국지리정보학회지 Vol.25 No.1
Due to climate change, temperature is rising worldwide. Since rapid growth has been achieved focused on cities, South Korea is experiencing serious environmental problems such as heat island and air pollution in urban areas. To solve this problem, the central and each local government are actively promoting tree planting campaigns. This study quantitatively calculated changes in green areas and vegetation of Gumi by the tree planting campaign, and analyzed the temperature changes accordingly. For the target area, the green area, vegetation index, and ground temperature were calculated for 4 different time periods using the given Landsat satellite images. As a result of the study, the green area of was increased by 7.24km<sup>2</sup> and 4.93km<sup>2</sup> for two regions, respectively. Accordingly, the vegetation index increased by 0.14 to 0.16, and the temperature decreased by 0.8 to 1.2℃. The Tree planting campaign not only plays a role in lowering the temperature of the city but also does various roles such as air purification, carbon absorption, and providing green rest areas to citizens. Therefore the campaign should be carried out continuously.
딥러닝 기반 이미지 아웃페인팅 기술의 현황 및 최신 동향
김경훈(Kyunghun Kim),공경보(Kyeongbo Kong),강석주(Suk-ju Kang) 한국방송·미디어공학회 2021 방송공학회논문지 Vol.26 No.1
Image outpainting is a very interesting problem in that it can continuously fill the outside of a given image by considering the context of the image. There are two main challenges in this work. The first is to maintain the spatial consistency of the content of the generated area and the original input. The second is to generate high quality large image with a small amount of adjacent information. Existing image outpainting methods have difficulties such as generating inconsistent, blurry, and repetitive pixels. However, thanks to the recent development of deep learning technology, deep learning-based algorithms that show high performance compared to existing traditional techniques have been introduced. Deep learning-based image outpainting has been actively researched with various networks proposed until now. In this paper, we would like to introduce the latest technology and trends in the field of outpainting. This study compared recent techniques by analyzing representative networks among deep learning-based outpainting algorithms and showed experimental results through various data sets and comparison methods.
전단박화 효과를 고려한 R2R 공정에서의 잉크전달 과정에 관한 FSI 해석
김경훈(Kyunghun Kim),남태원(Taewon Nam),김소희(Sohee Kim),나양(Yang Na) 대한기계학회 2010 대한기계학회 춘추학술대회 Vol.2010 No.11
In most of the earlier numerical studies for the practical R2R printing system, the conductive ink was used to be idealized by a Newtonian fluid. Considering the fact that the real conductive ink can be better described by a non-Newtonian fluid with a shear thinning effect, FSI analysis was conducted to investigate the effect of variable viscosity using a CFD technique for the R2R printing process. Numerical results showed that non-Newtonian model representing a shear thinning effect tend to produce relatively lower printing quality than that of the Newtonian model in terms of amount of transferred ink. Also, it was found that non-negligible web deflection occurs in all the geometries considered in the present work but the ratio of the web deflection gets smaller as the web handling speed becomes higher, indicating that the problems associated with the higher web handling speed may be more alleviated in the range of tens of microns than in the range of a couple hundred microns.