• JSTS:Journal of Semiconductor Technology and Science
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• v.17 no.3
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• pp.354-362
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• 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.

• Journal of the Korea Society of Computer and Information
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• v.28 no.9
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• pp.73-79
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• 2023

This paper describes a level of detail (LOD) based bending spring structure and damping technique that can reliably represent strain-based dynamics (SBD) on a triangular mesh. SBD models elastic energy using strain instead of energy based on the edge length of a triangular mesh. However, when a large external force occurs, the process of calculating the elastic energy based on edges results in a degenerate triangle, which stretches in the wrong direction because it calculates an unstable strain. In this paper, we introduce an LOD-based bending spring generation and energy calculation method that can efficiently handle this problem. As a result, the technique proposed in this paper can reliably and efficiently handle SBD based on bending springs, which can provide a stable representation of cloth simulation.

• Proceedings of the Korea Information Processing Society Conference
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• 2017.04a
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• pp.661-664
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• 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.