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무인항공기 영상과 딥러닝 기반의 의미론적 분할 기법을 활용한 야적퇴비 탐지 연구
김나경 ( Na-kyeong Kim ),박미소 ( Mi-so Park ),정민지 ( Min-ji Jeong ),황도현 ( Do-hyun Hwang ),윤홍주 ( Hong-joo Yoon ) 대한원격탐사학회 2021 大韓遠隔探査學會誌 Vol.37 No.3
야적퇴비는 대표적인 축산계 비점오염원으로 강우로 인해 수계로 유입될 경우 야적퇴비에 포함된 인과 질소 등의 영양염류가 하천 수질에 악영향을 미칠 수 있다. 이에 본 논문에서는 무인항공기 영상과 딥러닝 기반의 의미론적 분할 기법을 활용한 야적퇴비 탐지 방법을 제안한다. 연구지역에서 취득한 39개의 정사영상을 토대로 Data Augmentation을 통해 약 30,000개의 데이터를 확보하였다. 취득한 데이터를 U-net을 기반으로 개발된 의미론적 분할 알고리즘에 적용시킨 후 OpenCV의 필터링 기법을 적용하여 정확도를 평가하였다. 정확도 평가 결과 화소정확도는 99.97, 정밀도는 83.80%, 재현율은 60.95%, F1- Score는 70.57%의 정확도를 보였다. 정밀도에 비해 재현율이 떨어지는 것은 정성적으로 보았을 때 전체 이미지에서 가장자리에 작은 비율로 야적퇴비 픽셀이 존재하는 경우 과소추정되었기 때문이다. 향후 추가적인 데이터셋과 RGB 밴드 이외의 추가 밴드를 조합한다면 모델 정확도를 향상시킬 수 있을 것으로 판단된다. Field compost is a representative non-point pollution source for livestock. If the field compost flows into the water system due to rainfall, nutrients such as phosphorus and nitrogen contained in the field compost can adversely affect the water quality of the river. In this paper, we propose a method for detecting field compost using unmanned aerial vehicle images and deep learning-based semantic segmentation. Based on 39 ortho images acquired in the study area, about 30,000 data were obtained through data augmentation. Then, the accuracy was evaluated by applying the semantic segmentation algorithm developed based on U-net and the filtering technique of Open CV. As a result of the accuracy evaluation, the pixel accuracy was 99.97%, the precision was 83.80%, the recall rate was 60.95%, and the F1-Score was 70.57%. The low recall compared to precision is due to the underestimation of compost pixels when there is a small proportion of compost pixels at the edges of the image. After, It seems that accuracy can be improved by combining additional data sets with additional bands other than the RGB band.
김나경(Na-Kyeong Kim),이효정(Hyo-Jeong Lee),김상민(Sang-Min Kim),정래동(Rae-Dong Jeong) 한국식물병리학회 2022 Plant Pathology Journal Vol.38 No.2
Barley yellow dwarf virus (BYDV) has been a major viral pathogen causing significant losses of cereal crops including oats worldwide. It spreads naturally through aphids, and a rapid, specific, and reliable diagnostic method is imperative for disease monitoring and management. Here, we established a rapid and reliable method for isothermal reverse transcription recombinase polymerase amplification (RT-RPA) combined with a lateral flow strips (LFS) assay for the detection of BYDV-infected oat samples based on the conserved sequences of the BYDV coat protein gene. Specific primers and a probe for RT-RPA reacted and optimally incubated at 42oC for 10 min, and the end-labeled amplification products were visualized on LFS within 10 min. The RT-RPA-LFS assay showed no cross-reactivity with other major cereal viruses, including barley mild mosaic virus, barley yellow mosaic virus, and rice black streaked dwarf virus, indicating high specificity of the assay. The sensitivity of the RT-RPA-LFS assay was similar to that of reverse transcription polymerase chain reaction, and it was successfully validated to detect BYDV in oat samples from six different regions and in individual aphids. These results confirm the outstanding potential of the RT-RPA-LFS assay for rapid detection of BYDV.
창의적 교육환경 특성을 바탕으로 한 초등학교 공간 디자인 방법에 관한 연구
김나경(Kim, Na-Kyeong),김종진(Kim, Jong-Jin) 한국실내디자인학회 2016 한국실내디자인학회 학술대회논문집 Vol.2016 No.10
Creativity is fundamentally important for our society in these days. Creativity can be generated by many diverse factors. Educational environment is one of the most significant factors. According to the academic research of creativity development process in adolescence, importance of elementary education environment is crucially emphasized. However, Korea has school space form of 19th century. Because the importance of creative school space is not fully recognized, the existing school buildings that are outdated Japanese barrack style have been still used. Therefore this study aims to suggest the design methods of elementary school space for the improvement of creativity through five elements of creative educational environment. Ultimately this thesis is expected to have a positive role for development of creative educational environment.
CCTV 영상과 합성곱 신경망을 활용한 해무 탐지 기법 연구
김나경(Na-Kyeong Kim),박수호(Su-Ho Bak),정민지(Min-Ji Jeong),황도현(Do-Hyun Hwang),앵흐자리갈 운자야(Unuzaya Enkhjargal),박미소(Mi-So Park),김보람(Bo-Ram Kim),윤홍주(Hong-Joo Yoon) 한국전자통신학회 2020 한국전자통신학회 논문지 Vol.15 No.6
본 논문에서는 합성곱 신경망을 기반으로 CCTV 이미지를 통한 해무 탐지 방법을 제안한다. 학습에 필요한 자료로 시정 1km 기준으로 총 11개의 항만 또는 해수욕장(부산항, 부산신항, 평택항, 인천항, 군산항, 대산항, 목포항, 여수광양항, 울산항, 포항항, 해운대해수욕장)에서 수집된 해무와 해무가 아닌 이미지 10004장을 랜덤 추출하였다. 전체 10004장의 데이터셋 중에 80%를 추출하여 합성곱 신경망 모델 학습에 사용하였다. 사용된 모델은 16개의 합성곱층과 3개의 완전 연결층을 가지고 있으며, 마지막 완전 연결층에서 Softmax 분류를 수행하는 합성곱 신경망을 활용하였다. 나머지 20%를 이용하여 모델 정확도 평가를 수행하였고 정확도 평가 결과 약 96%의 분류 정확도를 보였다. In this paper, the method of detecting sea fog through CCTV image is proposed based on convolutional neural networks. The study data randomly extracted 1,0004 images, sea-fog and not sea-fog, from a total of 11 ports or beaches (Busan Port, Busan New Port, Pyeongtaek Port, Incheon Port, Gunsan Port, Daesan Port, Mokpo Port, Yeosu Gwangyang Port, Ulsan Port, Pohang Port, and Haeundae Beach) based on 1km of visibility. 80% of the total 1,0004 datasets were extracted and used for learning the convolutional neural network model. The model has 16 convolutional layers and 3 fully connected layers, and a convolutional neural network that performs Softmax classification in the last fully connected layer is used. Model accuracy evaluation was performed using the remaining 20%, and the accuracy evaluation result showed a classification accuracy of about 96%.