• JSTS:Journal of Semiconductor Technology and Science
• /
• 제17권3호
• /
• pp.354-362
• /
• 2017

We investigated the improvement in electrical characteristics of large AlGaN/GaN on Si Schottky barrier diode (SBD) induced by structural change to achieve a better trade-off between the forward and reverse performance to obtain high power conversion efficiency in PFC converter. Using an optimized dry etch condition for a large device, we fabricated three-types of SBD with 63 mm channel width: conventional, recessed, recessed dual-anode-metal SBD. The recessed dual-anode-metal SBD exhibited a very low turn-on voltage of 0.34 V, a high forward current of 1.63 A at 1.5 V, a leakage current of $114{\mu}A$ at -15 V, a breakdown voltage of 794 V.

• 한국컴퓨터정보학회논문지
• /
• 제28권9호
• /
• pp.73-79
• /
• 2023

본 논문에서는 삼각형 메쉬(Triangular mesh) 기반에서 변형률 기반 동역학(Strain-based dynamics, SBD)을 안정적으로 표현할 수 있는 LOD(Level of detail)기반의 굽힘 스프링(Bending spring) 구조와 감쇠 기법에 대해 설명한다. SBD는 삼각형 메쉬의 에지 길이(Edge length) 기반의 에너지 대신 변형률(Strain)을 활용하여 탄성 에너지를 모델링한다. 하지만, 큰 외력이 발생하면 에지 기반으로 탄성 에너지를 계산하는 과정에서는 비정상적인 삼각형(Degenerate triangle)이 나타나고 이 문제는 불안정한 변형률을 계산하기 때문에 잘못된 방향으로 늘어나는 문제가 발생한다. 본 논문에서는 이 문제를 효율적으로 처리할 수 있는 LOD기반의 굽힘 스프링을 생성하고 에너지를 계산하는 방법에 대해 소개한다. 결과적으로 본 논문에서 제안하는 기법은 굽힘 스프링 기반의 SBD를 안정적이고 효율적으로 처리할 수 있기 때문에 옷감 시뮬레이션을 안정적으로 표현할 수 있다.

• 한국정보처리학회:학술대회논문집
• /
• 한국정보처리학회 2017년도 춘계학술발표대회
• /
• pp.661-664
• /
• 2017

Recently, during disasters occurrence, dealing with emergencies has been handled well by the early transmission of disaster relating notifications on social media networks (e.g., Twitter or Facebook). Intuitively, with their characteristics (e.g., real-time, mobility) and big communities whose users could be regarded as volunteers, social networks are proved to be a crucial role for disasters response. However, the amount of data transmitted during disasters is an obstacle for filtering informative messages; because the messages are diversity, large and very noise. This large volume of data could be seen as Social Big Data (SBD). In this paper, we proposed a big data platform for collecting and analyzing disasters' data from SBD. Firstly, we designed a collecting module; which could rapidly extract disasters' information from the Twitter; by big data frameworks supporting streaming data on distributed system; such as Kafka and Spark. Secondly, we developed an analyzing module which learned from SBD to distinguish the useful information from the irrelevant one. Finally, we also designed a real-time visualization on the web interface for displaying the results of analysis phase. To show the viability of our platform, we conducted experiments of the collecting and analyzing phases in 10 days for both real-time and historical tweets, which were about disasters happened in South Korea. The results prove that our big data platform could be applied to disaster information based systems, by providing a huge relevant data; which can be used for inferring affected regions and victims in disaster situations, from 21.000 collected tweets.