http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
Schottky Barrier Tunnel Field-Effect Transistor using Spacer Technique
Kim, Hyun Woo,Kim, Jong Pil,Kim, Sang Wan,Sun, Min-Chul,Kim, Garam,Kim, Jang Hyun,Park, Euyhwan,Kim, Hyungjin,Park, Byung-Gook The Institute of Electronics and Information Engin 2014 Journal of semiconductor technology and science Vol.14 No.5
In order to overcome small current drivability of a tunneling field-effect transistor (TFET), a TFET using Schottky barrier (SBTFET) is proposed. The proposed device has a metal source region unlike the conventional TFET. In addition, dopant segregation technology between the source and channel region is applied to reduce tunneling resistance. For TFET fabrication, spacer technique is adopted to enable self-aligned process because the SBTFET consists of source and drain with different types. Also the control device which has a doped source region is made to compare the electrical characteristics with those of the SBTFET. From the measured results, the SBTFET shows better on/off switching property than the control device. The observed drive current is larger than those of the previously reported TFET. Also, short-channel effects (SCEs) are investigated through the comparison of electrical characteristics between the long- and short-channel SBTFET.
Interlayer-state-driven superconductivity inCaC6studied by angle-resolved photoemission spectroscopy
Kyung, Wonshik,Kim, Yeongkwan,Han, Garam,Leem, Choonshik,Kim, Chul,Koh, Yoonyoung,Kim, Beomyoung,Kim, Youngwook,Kim, Jun Sung,Kim, Keun Su,Rotenberg, Eli,Denlinger, Jonathan D.,Kim, Changyoung American Physical Society 2015 Physical review. B, Condensed matter and materials Vol.92 No.22
Schottky Barrier Tunnel Field-Effect Transistor using Spacer Technique
Hyun Woo Kim,Jong Pil Kim,Sang Wan Kim,Min-Chul Sun,Garam Kim,Jang Hyun Kim,Euyhwan Park,Hyungjin Kim,Byung-Gook Park 대한전자공학회 2014 Journal of semiconductor technology and science Vol.14 No.5
In order to overcome small current drivability of a tunneling field-effect transistor (TFET), a TFET using Schottky barrier (SBTFET) is proposed. The proposed device has a metal source region unlike the conventional TFET. In addition, dopant segregation technology between the source and channel region is applied to reduce tunneling resistance. For TFET fabrication, spacer technique is adopted to enable self-aligned process because the SBTFET consists of source and drain with different types. Also the control device which has a doped source region is made to compare the electrical characteristics with those of the SBTFET. From the measured results, the SBTFET shows better on/off switching property than the control device. The observed drive current is larger than those of the previously reported TFET. Also, short-channel effects (SCEs) are investigated through the comparison of electrical characteristics between the long- and shortchannel SBTFET .
Landsat 8 기반 SPARCS 데이터셋을 이용한 U-Net 구름탐지
강종구 ( Jonggu Kang ),김근아 ( Geunah Kim ),정예민 ( Yemin Jeong ),김서연 ( Seoyeon Kim ),윤유정 ( Youjeong Youn ),조수빈 ( Soobin Cho ),이양원 ( Yangwon Lee ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.5
컴퓨터 비전 기술이 위성영상에 적용되면서, 최근 들어 딥러닝 영상인식을 이용한 구름 탐지가 관심을 끌고 있다. 본연구에서는 SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset과 영상자료증대 기법을 활용하여 U-Net 구름탐지 모델링을 수행하고, 10폴드 교차검증을 통해 객관적인 정확도 평가를 수행하였다. 512×512 화소로 구성된 1800장의 학습자료에 대한 암맹평가 결과, Accuracy 0.821, Precision 0.847, Recall 0.821, F1-score 0.831, IoU (Intersection over Union) 0.723의 비교적 높은 정확도를 나타냈다. 그러나 구름그림자 중 14.5%, 구름 중 19.7% 정도가 땅으로 잘못 예측되기도 했는데, 이는 학습자료의 양과 질을 보다 더 향상시킴으로써 개선 가능할 것으로 보인다. 또한 최근 각광받고 있는 DeepLab V3+ 모델이나 NAS (Neural Architecture Search) 최적화 기법을 통해 차세대중형위성 1, 2, 4호 등의 구름탐지에 활용 가능할 것으로 기대한다. With a trend of the utilization of computer vision for satellite images, cloud detection using deep learning also attracts attention recently. In this study, we conducted a U-Net cloud detection modeling using SPARCS (Spatial Procedures for Automated Removal of Cloud and Shadow) Cloud Dataset with the image data augmentation and carried out 10-fold cross-validation for an objective assessment of the model. As the result of the blind test for 1800 datasets with 512 by 512 pixels, relatively high performance with the accuracy of 0.821, the precision of 0.847, the recall of 0.821, the F1-score of 0.831, and the IoU (Intersection over Union) of 0.723. Although 14.5% of actual cloud shadows were misclassified as land, and 19.7% of actual clouds were misidentified as land, this can be overcome by increasing the quality and quantity of label datasets. Moreover, a state-of-the-art DeepLab V3+ model and the NAS (Neural Architecture Search) optimization technique can help the cloud detection for CAS500 (Compact Advanced Satellite 500) in South Korea.
Kim, Garam,Mujtaba, Ghulam,Lee, Kisay The Korean Society of Phycology 2016 ALGAE Vol.31 No.3
Nitrogen is one of the most critical nutrients affecting cell growth and biochemical composition of microalgae, ultimately determining the lipid or carbohydrate productivity for biofuels. In order to investigate the effect of nitrogen sources on the cell growth and biochemical composition of the marine microalga Tetraselmis sp., nine different N sources, including NaNO<sub>3</sub>, KNO<sub>3</sub>, NH<sub>4</sub>NO<sub>3</sub>, NH<sub>4</sub>HCO<sub>3</sub>, NH<sub>4</sub>Cl, CH<sub>3</sub>COONH<sub>4</sub>, urea, glycine, and yeast extract were compared at the given concentration of 8.82 mM. Higher biomass concentration was achieved under organic nitrogen sources, such as yeast extract (2.23 g L<sup>−1</sup>) and glycine (1.62 g L<sup>−1</sup>), compared to nitrate- (1.45 g L<sup>−1</sup>) or ammonium-N (0.98 g L<sup>−1</sup>). All ammonium sources showed an inhibition of cell growth, but accumulated higher lipids, showing a maximum content of 28.3% in ammonium bicarbonate. When Tetraselmis sp. was cultivated using yeast extract, the highest lipid productivity of 36.0 mg L<sup>−1</sup> d<sup>−1</sup> was achieved, followed by glycine 21.5 mg L<sup>−1</sup> d<sup>−1</sup> and nitrate 19.9 mg L<sup>−1</sup> d<sup>−1</sup>. Ammonium bicarbonate resulted in the lowest lipid productivity of 14.4 mg L<sup>−1</sup> d<sup>−1</sup>. The major fatty acids in Tetraselmis sp. were palmitic, oleic, linoleic and linolenic acids, regardless of the nutritional compositions, indicating the suitability of this species for biodiesel production.