• 한국해안·해양공학회논문집
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• 제28권3호
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• pp.117-123
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• 2016

동해에서 주로 겨울철에 발생하며 파고가 크고 주기가 긴 너울성 고파의 주요 특성 중 하나는 잔잔한 상태에서 갑자기 파가 커지는 돌발성이다. 본 연구에서는 이러한 돌발성을 나타내기 위하여 돌연고파라는 용어를 도입하였다. 돌연고파의 기준을 제안하기 위하여 강릉과 왕돌초에서 2005년부터 8년간 관측된 파랑자료와 같은 기간동안 강원도와 경상북도 연안에서 발생했던 해상 사고 기록을 비교하였다. 그 결과 ${\Delta}(H^2L)/{\Delta}t$가 상위 20% 값인 $88.6m^3/hr$ 이상일 때 대부분의 사고 기록과 일치하는 것으로 나타나 이를 돌연고파의 기준으로 제안하였다. 사용된 변수는 한 파장당 파랑 에너지의 단위시간당 증가율을 나타내며, 고파의 파고 및 주기뿐만 아니라 돌발성도 포함한다.

• 한국지반환경공학회 논문집
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• 제17권5호
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• pp.5-16
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• 2016

A rainfall induced slope failure is a common natural hazard in mountainous areas worldwide. Sudden and rapid failures which have a high possibility of occurrence in a steep slope are always the most dangerous due to their suddenness and high velocities. Based on a series of experiments this study aimed to determine a critical angle which could be considered as an approximate threshold for a sudden failure. The experiments were performed using 0.42 mm mean grain size sand in a 200 cm long, 60 cm wide and 50 cm deep rectangular flume. A numerical model was created by integrating a 2D seepage flow model and a 2D slope stability analysis model to predict the failure surface and the time of occurrence. The results showed that, the failure mode for the entire material will be sudden for slopes greater than $67^{\circ}$; in contrast the failure mode becomes retrogressive. There is no clear link between the degree of saturation and the mode of failure. The simulation results in considering matric suction showed good matching with the results obtained from experiment. A subsequent discarding of the matric suction effect in calculating safety factors will result in a deeper predicted failure surface and an incorrect predicted time of occurrence.

• Journal of Multimedia Information System
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• 제8권3호
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• pp.159-166
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• 2021

COVID-19 has dramatically changed people's daily life. Wearing masks is considered as a simple but effective way to defend the spread of the epidemic. Hence, a real-time and accurate mask wearing detection system is important. In this paper, a deep learning-based mask wearing detection system is developed to help people defend against the terrible epidemic. The system consists of three important functions, which are image detection, video detection and real-time detection. To keep a high detection rate, a deep learning-based method is adopted to detect masks. Unfortunately, according to the suddenness of the epidemic, the mask wearing dataset is scarce, so a mask wearing dataset is collected in this paper. Besides, to reduce the computational cost and runtime, a simple online and real-time tracking method is adopted to achieve video detection and monitoring. Furthermore, a function is implemented to call the camera to real-time achieve mask wearing detection. The sufficient results have shown that the developed system can perform well in the mask wearing detection task. The precision, recall, mAP and F1 can achieve 86.6%, 96.7%, 96.2% and 91.4%, respectively.