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Yeon Woo Yoo,Uk Hee Nam,Yeontae Kim,Hyung Ik Lee,Jong Kyoo Park,Eungsun Byon 한국진공학회(ASCT) 2021 Applied Science and Convergence Technology Vol.30 No.1
Hafnium carbide (HfC) coatings on carbon composites have been extensively researched owing to their excellent high-temperature properties. However, it is still challenging to fabricate thick coatings. Vacuum plasma spraying is one of the methods of fabricating thick coatings. However, the formation of dense coatings is a problem because conventional sized thermal spray powders possessing ultra-high melting temperatures cannot be satisfactorily melted. In this study, a dense 50-μm-thick HfC coating was fabricated via suspension vacuum plasma spraying using nanometer-sized powders. The HfC coating was characterized through scanning electron microscopy and X-ray diffraction. The HfC coating contained hafnium oxide owing to the reaction with the oxygen present in the solvent during the spraying process. The ultra-high-temperature oxidation resistance of the HfC coating was determined by performing a laser oxidation test at 2000℃. The weight reduction rate of HfC-coated carbon composites was seven times less than that of uncoated carbon composites.
태양 에너지 수집형 IoT 엣지 컴퓨팅 환경에서 효율적인 오디오 딥러닝을 위한 에너지 적응형 데이터 전처리 기법
유연태,노동건,Yeontae Yoo,Dong Kun Noh 대한임베디드공학회 2023 대한임베디드공학회논문지 Vol.18 No.4
Solar energy harvesting IoT devices prioritize maximizing the utilization of collected energy due to the periodic recharging nature of solar energy, rather than minimizing energy consumption. Meanwhile, research on edge AI, which performs machine learning near the data source instead of the cloud, is actively conducted for reasons such as data confidentiality and privacy, response time, and cost. One such research area involves performing various audio AI applications using audio data collected from multiple IoT devices in an IoT edge computing environment. However, in most studies, IoT devices only perform sensing data transmission to the edge server, and all processes, including data preprocessing, are performed on the edge server. In this case, it not only leads to overload issues on the edge server but also causes network congestion by transmitting unnecessary data for learning. On the other way, if data preprocessing is delegated to each IoT device to address this issue, it leads to another problem of increased blackout time due to energy shortages in the devices. In this paper, we aim to alleviate the problem of increased blackout time in devices while mitigating issues in server-centric edge AI environments by determining where the data preprocessed based on the energy state of each IoT device. In the proposed method, IoT devices only perform the preprocessing process, which includes sound discrimination and noise removal, and transmit to the server if there is more energy available than the energy threshold required for the basic operation of the device.
날씨 변동에 따른 계통 불안정을 해소하기 위한 최적의 에너지 저장 장치 용량 산정
김태형(TAEHYUNG KIM),유연태(YEONTAE YOO),전인영(INYOUNG JUN),장길수(GILSOO JANG) 대한전기학회 2017 대한전기학회 학술대회 논문집 Vol.2017 No.5
신재생 에너지 발전원이 증가함에 따라 날씨에 대한 발전량 변동이 문제가 되고 있다. 본 논문에서는 이를 해결하기 위한 방안 중 하나로 에너지 저장장치를 제시한다. 태양광 발전기와 에너지 저장 장치를 연결한 DC 계통, 이를 AC 계통으로 연결할 DC-AC 인버터, AC 계통과 부하로 시스템을 구성하여 날씨 변동이 심한 서울의 장마철 날씨에 따른 태양광 출력을 확인하고 이를 해결하기 위한 최적의 에너지 저장 장치 용량을 산정한다.