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김화영(Hwayoung Kim),김형민(Hyungmin Kim),송민수(Minsu Song),안지훈(Jihun An),김강현(Ganghyun Kim),김선민(Sunmin Kim),김찬현(Chanhyun Kim),박성우(Seoungwoo Park),이준우(Junu Lee),이준기(Joonki Rhee),임재혁(Jaehyuk Im),정승수(Seungsu Jeo 한국추진공학회 2022 한국추진공학회 학술대회논문집 Vol.2022 No.5
본 논문은 경기대학교 KURBC 팀이 설계한 모듈형 신기전K22에 대한 설계과정과 그 근거를 서술하였다. 로켓의 임무는 ‘로켓 발사 후 낙하 시 낙하산 사출 및 안전한 회수’이다. 목표 고도는 400m, 연료는 질산칼륨-소르비톨(KNSB)이다. 낙하산 사출 방법은 동체와 낙하산이 안전하게 보호되는 용수철 사출을 선택했다. 본 팀은 이번 대회에서 CFRP 카본으로 로켓의 동체를 만들고, 내부를 모듈로 구성함으로써 차별점을 두었다. 또한 RF 장거리 통신 기술을 이용하여 발사 전후의 데이터를 체계적으로 기록하여 후속 연구에 참고할 예정이다. This paper describes the design process and basis for the modular Shinkijeon-K22 designed by the KURBC team of Kyonggi University. The rockets mission is devising a method for parachute injection and safe recovery when falling after launching the rocket. The target altitude is 400 m and the fuel is potassium nitrate-solvitol (KNSB). The spring injection method was used for the parachute injection, in which the fuselage and parachute are safely protected. We made a difference by making the rockets fuselage with CFRP carbon and configuring the interior as a module. In addition, RF long-distance communication technology will be used to systematically record data before and after launch and refer it to subsequent studies.
Byoung-Hak Jeon,Jinseob Kim,Ganghyun Kim,Soochul Park,SangYun Kim,Hae-Kwan Cheong 한국역학회 2016 Epidemiology and Health Vol.38 No.-
OBJECTIVES: This study estimated the overall incidence of iatrogenic Creutzfeldt-Jakob disease (iCJD) based on dura graft cases in Korea using a mathematical model. METHODS: We estimated the number of annual dura grafts performed between 1980 and 1995 by applying the proportion of dura grafts recorded by the Health Insurance Review Agency claim dataset in Korea to the number of nationwide neurosurgery cases. The distribution of the incubation period was assumed to fall under a Weibull distribution with density function or a log-logistic distribution with density function. RESULTS: The total number of neurosurgery procedures performed from 1980 to 1995 was estimated to be 263,945, and among those operations, 37% used dura graft products. Between the years of 1980 and 2020, our model predicted that the total number of iCJD cases would be between 14.9 and 33.2 (95% confidence interval [CI], 13.4 to 50.9). Notably, we estimated that the cumulative number of iCJD cases caused by dura grafts between 1980 and 2011 was approximately 13.3 to 27.3 (95% CI, 12.2 to 40.6). CONCLUSIONS: Based on our model, we postulate that the incidence of iCJD will sharply decline from 2012 to 2020. However, additional new cases are still expected, which necessitates a strong national surveillance system.
드론영상과 YOLOv7x 모델을 이용한 활성산불 객체탐지
박강현,강종구,최소연,윤유정,김근아,이양원,Park, Ganghyun,Kang, Jonggu,Choi, Soyeon,Youn, Youjeong,Kim, Geunah,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Active fire monitoring using high-resolution drone images and deep learning technologies is now an initial stage and requires various approaches for research and development. This letter examined the detection of active fire objects using You Look Only Once Version 7 (YOLOv7), a state-of-the-art (SOTA) model that has rarely been used in fire detection with drone images. Our experiments showed a better performance than the previous works in terms of multiple quantitative measures. The proposed method can be applied to continuous monitoring of wide areas, with an integration of additional development of new technologies.
DeepLabV3+ 모델을 이용한 PlanetScope 영상의 해상 유출유 탐지
강종구,윤유정,김근아,박강현,최소연,양찬수,이종혁,이양원,Kang, Jonggu,Youn, Youjeong,Kim, Geunah,Park, Ganghyun,Choi, Soyeon,Yang, Chan-Su,Yi, Jonghyuk,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Since oil spills can be a significant threat to the marine ecosystem, it is necessary to obtain information on the current contamination status quickly to minimize the damage. Satellite-based detection of marine oil spills has the advantage of spatiotemporal coverage because it can monitor a wide area compared to aircraft. Due to the recent development of computer vision and deep learning, marine oil spill detection can also be facilitated by deep learning. Unlike the existing studies based on Synthetic Aperture Radar (SAR) images, we conducted a deep learning modeling using PlanetScope optical satellite images. The blind test of the DeepLabV3+ model for oil spill detection showed the performance statistics with an accuracy of 0.885, a precision of 0.888, a recall of 0.886, an F1-score of 0.883, and a Mean Intersection over Union (mIOU) of 0.793.
YOLOv5와 YOLOv7 모델을 이용한 해양침적쓰레기 객체탐지 비교평가
박강현,윤유정,강종구,김근아,최소연,장선웅,박수호,공신우,곽지우,이양원,Park, Ganghyun,Youn, Youjeong,Kang, Jonggu,Kim, Geunah,Choi, Soyeon,Jang, Seonwoong,Bak, Suho,Gong, Shinwoo,Kwak, Jiwoo,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Deposited Marine Debris(DMD) can negatively affect marine ecosystems, fishery resources, and maritime safety and is mainly detected by sonar sensors, lifting frames, and divers. Considering the limitation of cost and time, recent efforts are being made by integrating underwater images and artificial intelligence (AI). We conducted a comparative study of You Only Look Once Version 5 (YOLOv5) and You Only Look Once Version 7 (YOLOv7) models to detect DMD from underwater images for more accurate and efficient management of DMD. For the detection of the DMD objects such as glass, metal, fish traps, tires, wood, and plastic, the two models showed a performance of over 0.85 in terms of Mean Average Precision (mAP@0.5). A more objective evaluation and an improvement of the models are expected with the construction of an extensive image database.
김근아,윤유정,강종구,최소연,박강현,천정화,장근창,원명수,이양원,Kim, Geunah,Youn, Youjeong,Kang, Jonggu,Choi, Soyeon,Park, Ganghyun,Chun, Junghwa,Jang, Keunchang,Won, Myoungsoo,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.5
최근 지구 온난화로 인한 기후 변화와 관련된 문제의 심각성이 커지고 있으며 평균 기온 또한 상승하고 있다. 이로 인해 온도에 민감한 다양한 생물과 생물이 살아가는 환경에 영향을 미치고 있으며, 생태계의 변화 역시 감지되고 있다. 계절은 그 지역에 사는 생물의 종류, 분포, 생육 특성 등에 영향을 미치는 중요한 요인의 하나이다. 기후 변화 영향 평가의 지표 중 가장 대중적이고 쉽게 인식될 수 있는 식물 계절 중 개화일과 단풍나무 절정일의 모델링을 수행하였다. 모델링에 사용된 식물의 종류에는 봄을 대표하는 식물로 볼 수 있는 개나리와 벚나무, 가을을 대표하는 식물로 볼 수 있는 단풍 나무와 은행 나무를 사용하였다. 모델링을 수행할 때 사용된 기상 자료로는 기상청의 Automated Surface Observing System (ASOS) 관측소를 통해서 관측된 기온, 강수, 일사 자료를 사용하였으며, 개나리, 벚나무의 개화일과 약 -0.2, 은행나무, 단풍나무의 단풍 절정일과 약 0.3 정도의 상관 계수를 가지는 Moderate Resolution Imaging Spectroradiometer (MODIS) 식생지수를 사용하여 모델링을 수행하였다. 사용된 모델로는 선형 모델인 다중 회귀 모형과, 비선형 모델인 Random Forest (RF)를 사용하여 모델을 수립하였다. 또한 각 모형으로 추정된 예측 값을 공간 내삽 기법을 이용하여 등치 선도로 2003~2020년의 식물 계절 변화 경향 성을 표현하였다. 향후에 높은 시공간 해상도를 가지는 식생지수를 사용한다면 더 높은 식물 계절 모델링의 정확도를 높일 수 있을 것으로 판단된다. Recently, the seriousness of climate change-related problems caused by global warming is growing, and the average temperature is also rising. As a result, it is affecting the environment in which various temperature-sensitive creatures and creatures live, and changes in the ecosystem are also being detected. Seasons are one of the important factors influencing the types, distribution, and growth characteristics of creatures living in the area. Among the most popular and easily recognized plant seasonal phenomena among the indicators of the climate change impact evaluation, the blooming day of flower and the peak day of autumn leaves were modeled. The types of plants used in the modeling were forsythia and cherry trees, which can be seen as representative plants of spring, and maple and ginkgo, which can be seen as representative plants of autumn. Weather data used to perform modeling were temperature, precipitation, and solar radiation observed through the ASOS Observatory of the Korea Meteorological Administration. As satellite data, MODIS NDVI was used for modeling, and it has a correlation coefficient of about -0.2 for the flowering date and 0.3 for the autumn leaves peak date. As the model used, the model was established using multiple regression models, which are linear models, and Random Forest, which are nonlinear models. In addition, the predicted values estimated by each model were expressed as isopleth maps using spatial interpolation techniques to express the trend of plant seasonal changes from 2003 to 2020. It is believed that using NDVI with high spatio-temporal resolution in the future will increase the accuracy of plant phenology modeling.
DeepLabV3+와 Swin Transformer 모델을 이용한 Sentinel-2 영상의 구름탐지
강종구,박강현,김근아,윤유정,최소연,이양원,Kang, Jonggu,Park, Ganghyun,Kim, Geunah,Youn, Youjeong,Choi, Soyeon,Lee, Yangwon 대한원격탐사학회 2022 大韓遠隔探査學會誌 Vol.38 No.6
Sentinel-2 can be used as proxy data for the Korean Compact Advanced Satellite 500-4 (CAS500-4), also known as Agriculture and Forestry Satellite, in terms of spectral wavelengths and spatial resolution. This letter examined cloud detection for later use in the CAS500-4 based on deep learning technologies. DeepLabV3+, a traditional Convolutional Neural Network (CNN) model, and Shifted Windows (Swin) Transformer, a state-of-the-art (SOTA) Transformer model, were compared using 22,728 images provided by Radiant Earth Foundation (REF). Swin Transformer showed a better performance with a precision of 0.886 and a recall of 0.875, which is a balanced result, unbiased between over- and under-estimation. Deep learning-based cloud detection is expected to be a future operational module for CAS500-4 through optimization for the Korean Peninsula.