• Reproductive and Developmental Biology
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• v.31 no.1
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• pp.29-33
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• 2007

This study was conducted to examine the development of in vitro fertilized (IVF) and nuclear transfer (NT) embryos following vitrification IVF and NT embryos developed to the blastocyst stage were equilibrated by 3 steps, vitrified and thawed, and their survival and hatching rates were examined. In IVF embryos, higher survival (82.1%, 96/117) and hatching rates (64.1%, 75/117) were obtained respectively after thawing and culture in expanded blastocysts compared to blastocysts (p<0.05). High survival and hatching rates were also obtained by vitrification of NT blastocysts, especially in expanded and hatching blastocysts (81.1 and 78.3%, respectively). The result of this study shows that IVF and NT blastocysts, especially late stage blastocysts, are successfully cryopreserved by vitrification.

• Proceedings of the Optical Society of Korea Conference
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• 2007.07a
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• pp.243-244
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• 2007

본 연구에서는 성형용 코어 가공에서 초경합금(WC, Co 0.5%)의 초정밀 가공특성을 파악하기 위하여 다이아몬드 휠의 메시, 주축 회전속도, 터빈 회전속도, 이송속도 및 연삭깊이에 따른 표면거칠기를 측정하여 최적연삭조건을 규명하였다. 규명된 최적연삭가공조건을 활용하여 페러렐 연삭법으로 초정밀 연삭가공을 수행하였다. 연삭가공은 초정밀가공기(ASP01, Nachi-Fujikoshi Co., Japan)를 사용하였다. 최종 정삭가공을 수행한 비구면 성형용 코어의 형상측정결과 형상정도(PV; ${\varphi}$ 3.0mm) 0.15${\mu}m$(비구면), 0.10${\mu}m$(평면)으로 3M급 이상의 고화질 카메라폰에 채용되고 있는 비구면 Glass렌즈 양산용 성형용 코어 규격에 만족한 결과로서 본 연구에 수행된 초정밀 가공조건 및 측정방법이 매우 유효함을 알 수 있었다. 형상정도(PV) 및 표면조도(Ra) 측정은 초정밀 자유곡면 측정기(UA3P, Panasonic Co., Japan)와 3차원 표면조도 측정기(NewView5000, Zygo Co., USA)를 각각 사용하였다. 초정밀 가공된 성형용 코어면에 이온증착법을 활용하여 DLC 코팅을 수행하였다. 코팅 전후의 성형용코어를 활용하여 Glass소재(K-BK7, Sumita Co., Japan)를 최적의 성형조건(성형온도, 압력, 냉각속도)으로 성형하였다. DLC 코팅과 성형은 DLC 코팅기(NC400, Nanotech Co., Japan)와 Glass렌즈 성형기(Nano Press-S, Sumitomo Co., Japan)을 각각 사용하였다. Fig. 1은 초정밀 연삭가공, DLC 코팅막 구조, 코팅된 성형용 코어, 그리고, 성형된 비구면Glass렌즈를 각각 나타낸다.

• Journal of the Korean Vacuum Society
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• v.11 no.3
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• pp.166-170
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• 2002

Artificial $BaTiO_3$(BTO)/$SrTiO_3$(STO) oxide superlattice have been deposited on MgO (100) single crystal substrate by pulsed laser deposition(PLD) method. The stacking periodicity of BTO/STO superlattice structure was varied from $BTO_{1\;unit\; cell}/STO_{1\;unit\; cell}$ to $BTO_{125\;unit\; cell}/STO_{125 \;unit \;cell}$ thickness with the total thickness of 100 nm. The result of X-ray diffraction showed the characteristics of superlattice in the BTO/STO multilayer structure. we have also confirmed that there was no interdiffusion at the interface between BTO and STO layers by high resolution transmission electron microscopy(HRTEM). The dielectric constant of superlattice increased with decreasing stacking periodicity of the BTO/STO superlattice within the critical thickness. The dielectric constant of the BTO/STO superlattice reached a maximum i.e., 1230 at a stacking perioicity of $BTO_{2\;unit\; cell}/STO_{2\;unit\; cell}$ .

• Journal of the Korean association of regional geographers
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• v.15 no.5
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• pp.616-634
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• 2009

Criticizing the existing social science approaches to transnational migration for their ignorance of spatial perspectives and the resultant limits in the understanding of the concrete processes of international migration and settlement, this paper aims to examine how spatial perspectives and geographical epistemology can positively contribute to the understanding and conceptualization of transnational migration. In particular, it emphasizes that the processes of transnational migration cannot be solely understood in terms of 1) global capitalist restructuring and economic rationality, 2) the impacts of deterritoralized transnational networks, or 3) the operation of immigration regimes constructed at the national scale. Alternatively, this paper argues that the conceptualization of 'transnational space', which is based on the understanding of the socio-spatial dimensions - that is, place, territory, scale and networks - that affect the processes of transnational migration, could significantly contribute to the understanding of the transnational migration.

• Journal of the Korea Convergence Society
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• v.10 no.8
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• pp.277-282
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• 2019

This study was conducted to compare the lap time of high school boys' K-2 1000m final at the $99^{th}$ National Sports Festival with the lap time of the World Championship final held in 2018 and to find an optimal pacing strategy to enhance the performance. The high school boys' average final record was 242.89 seconds, and the top international's 199.58 seconds. There was 43 seconds difference in records and by lap-time it were 9, 12, 9, and 13 seconds behind every 250m. World Championship players used the Super Fast-Even Pacing-Even Pacing-Spurt strategy. The $1^{st}$ to $3^{rd}$ ranked high school boys used Slow-Fast-Super Slow-Super Fast strategy, and $4^{th}$ to $9^{th}$ ranked boys used Fast-Slow-Fast-Slow strategy. The high school boys need to modify their pacing strategies to achieve world-class performance.

• Journal of the Korea Convergence Society
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• v.11 no.12
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• pp.15-22
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• 2020

In this paper, we proposed a deep learning based super-resolution method that combines Channel Attention and Spatial Attention feature enhancement methods. It is important to restore high-frequency components, such as texture and features, that have large changes in surrounding pixels during super-resolution processing. We proposed a super-resolution method using feature enhancement that combines Channel Attention and Spatial Attention. The existing CNN (Convolutional Neural Network) based super-resolution method has difficulty in deep network learning and lacks emphasis on high frequency components, resulting in blurry contours and distortion. In order to solve the problem, we used an emphasis block that combines Channel Attention and Spatial Attention to which Skip Connection was applied, and a Residual Block. The emphasized feature map extracted by the method was extended through Sub-pixel Convolution to obtain the super resolution. As a result, about PSNR improved by 5%, SSIM improved by 3% compared with the conventional SRCNN, and by comparison with VDSR, about PSNR improved by 2% and SSIM improved by 1%.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.774-784
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• 2021

The object detection and recognition process is a very important task in the field of computer vision, and related research is actively being conducted. However, in the actual object recognition process, the recognition accuracy is often degraded due to the resolution mismatch between the training image data and the test image data. To solve this problem, in this paper, we designed and developed an integrated object recognition and super-resolution framework by proposing an image super-resolution technique to improve object recognition accuracy. In detail, 11,231 license plate training images were built by ourselves through web-crawling and artificial-data-generation, and the image super-resolution artificial neural network was trained by defining an objective function to be robust to the image flip. To verify the performance of the proposed algorithm, we experimented with the trained image super-resolution and recognition on 1,999 test images, and it was confirmed that the proposed super-resolution technique has the effect of improving the accuracy of character recognition.

• Korean Journal of Remote Sensing
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• v.38 no.5_1
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• pp.559-570
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• 2022

When a satellite image has low spatial resolution, it is difficult to detect small objects. In this research, we aim to check the effect of super resolution on object detection. Super resolution is a software method that increases the resolution of an image. Unpaired super resolution network is used to improve Sentinel-2's spatial resolution from 10 m to 3.2 m. Faster-RCNN, RetinaNet, FCOS, and S²ANet were used to detect vessels in the Sentinel-2 images. We experimented the change in vessel detection performance when super resolution is applied. As a result, the Average Precision (AP) improved by at least 12.3% and up to 33.3% in the ship detection models trained with the super-resolution image. False positive and false negative cases also decreased. This implies that super resolution can be an important pre-processing step in object detection, and it is expected to greatly contribute to improving the accuracy of other image-based deep learning technologies along with object detection.

• Journal of Digital Policy
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• v.2 no.3
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• pp.1-7
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• 2023

In this paper, I proposes a method for performing single image super resolution by separating texture-spatial domains and then classifying features based on detailed information. In CNN (Convolutional Neural Network) based super resolution, the complex procedures and generation of redundant feature information in feature estimation process for enhancing details can lead to quality degradation in super resolution. The proposed method reduced procedural complexity and minimizes generation of redundant feature information by splitting input image into two channels: texture and spatial. In texture channel, a feature refinement process with step-wise skip connections is applied for detail restoration, while in spatial channel, a method is introduced to preserve the structural features of the image. Experimental results using proposed method demonstrate improved performance in terms of PSNR and SSIM evaluations compared to existing super resolution methods, confirmed the enhancement in quality.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.417-417
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• 2023

도시홍수는 도시의 주요 기능을 마비시킬 수 있는 수재해로서, 최근 집중호우로 인해 홍수 및 침수 위험도가 증가하고 있다. 집중호우는 한정된 지역에 단시간 동안 집중적으로 폭우가 발생하는 현상을 의미하며, 도시 지역에서 강우 추정 및 예보를 위해 레이더의 활용이 증대되고 있다. 레이더는 수상체 또는 구름으로부터 반사되는 신호를 분석해서 강우량을 측정하는 장비이다. 기상청의 기상레이더(S밴드)의 주요 목적은 남한에 발생하는 기상현상 탐지 및 악기상 대비이다. 관측반경이 넓기에 도시 지역에 적합하지 않는 반면, X밴드 이중편파레이더는 높은 시공간 해상도를 갖는 관측자료를 제공하기에 도시 지역에 대한 강우 추정 및 예보의 정확도가 상대적으로 높다. 따라서, 본 연구에서는 딥러닝 기반 초해상화(Super Resolution) 기술을 활용하여 저해상도(Low Resolution. LR) 영상인 S밴드 레이더 자료로부터 고해상도(High Resolution, HR) 영상을 생성하는 기술을 개발하였다. 초해상도 연구는 Nearest Neighbor, Bicubic과 같은 간단한 보간법(interpolation)에서 시작하여, 최근 딥러닝 기반의 초해상화 알고리즘은 가장 일반화된 합성곱 신경망(CNN)을 통해 연구가 이루어지고 있다. X밴드 레이더 반사도 자료를 고해상도(HR), S밴드 레이더 반사도 자료를 저해상도(LR) 입력자료로 사용하여 초해상화 모형을 구성하였다. 2018~2020년에 발생한 서울시 호우 사례를 중심으로 데이터를 구축하였다. 구축된 데이터로부터 훈련된 초해상도 심층신경망 모형으로부터 저해상도 이미지를 고해상도로 변환한 결과를 PSNR(Peak Signal-to-noise Ratio), SSIM(Structural SIMilarity)와 같은 평가지표로 결과를 평가하였다. 본 연구를 통해 기존 방법들에 비해 높은 공간적 해상도를 갖는 레이더 자료를 생산할 수 있을 것으로 기대된다.