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우주쓰레기 제거용 (초)소형위성을 활용한 우주안보 역량 강화
황영민(Youngmin Hwang),최성환(Seunghwan Choi) 항공우주시스템공학회 2023 항공우주시스템공학회 학술대회 발표집 Vol.2023 No.10
우주산업의 활성화는 우주쓰레기 급증 문제를 야기하고 있다. 21년도 국제우주감시학회 (AMOS)에 발표된 논문에서는 저궤도 우주공간의 물체간 충돌가능성은 발사체 상단부, 폐위성 등 우주쓰레기에 의한 확률이 높다고 분석하고 있다. 22년 9월 유엔총회에서는 우주쓰레기 대량생산의 원인인 ASAT 실험을 금지하는 결의안을 채택하는 등 변화를 유도하고 있다. 그러나, 매년 급격히 증가하는 위성 등 여전히 우주공간에 잔재하는 물체로 인한 우주쓰레기 문제가 대두되고 있고, 이를 제거하기 위한 위성 활용과 관련기술 개발이 활성화 되고 있다. 그러나, 이러한 호의적 기술을 악용한 무기화를 시도 (Co-Orbital Threat)하고 있는 정황들이 나타나고 있다. 이에 본고에서는 현재 확대되는 초소형위성을 활용하여 적성 위협의 활동을 억제할 수 있는 군사적 활용 가능성에 대해 고찰하였다.
인공신경망을 이용한 저항 점용접 너겟 직경 예측에 관한 연구
김종규(Jongkyu Kim),구자훈(JaHun Ku),박영도(Yeongdo Park),김영창(Youngchang Kim),황영민(Youngmin Hwang),김희수(Heesoo Kim),Siva Prasad Murugan,구남국(Namkug Ku) 대한용접·접합학회 2021 대한용접·접합학회지 Vol.39 No.6
Resistance spot welding, which has the advantages of low cost and high productivity, is the most common method used in the automobile industry for joining steel sheets. However, in practice, resistance spot welds are typically tested for welding quality using destructive rather than non-destructive inspection methods because of their lower cost. However, in destructive inspection, quality defects can be found only after the completion of the process. Accordingly, several studies are currently being conducted to predict the quality of welding in real time. Welding quality is determined by the diameter of the nugget, and its size depends on several independent variables. In this study, a linear regression model and artificial neural network model were constructed to predict the nugget diameter. An electric power pattern was obtained from the results of a welding experiment, and nine types of electric power characteristic values were extracted from the obtained electric power pattern as independent variables. From the nine electric power characteristic values, six having the highest correlation with the nugget diameter were determined as final independent variables through correlation analysis. The linear regression model was constructed using multiple linear regression analysis, and the artificial neural network model was built using a deep neural network model with two hidden layers and nodes of 64 and 16. In this study, the error between the actual measured and predicted nugget diameters was taken as 0.2 ㎜ or less as a good predictive value. When the linear regression model was used to predict the nugget diameter, only approximately 36% were predicted well. By contrast, when the artificial neural network was used, approximately 86% were predicted well. Thus, the artificial neural network model yielded better results. It was determined that with more welding data and information on steel types, the proposed welding quality prediction system could be improved.