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정보경,Seung Keun Ham,김재경 대한임상검사과학회 2022 대한임상검사과학회지(KJCLS) Vol.54 No.2
In December 2019, the coronavirus disease 2019 (COVID-19) caused by the virus, severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in China and spread rapidly around the world, infecting millions of people. Cases of COVID-19 infection were observed to lead to viral pneumonia. Thirty-five patients admitted to the Gyeonggi Medical Center, South Korea, between November 2020 to January 2021, were found to have been infected with the influenza virus A and B, which cause symptoms similar to COVID-19. The records of these patients and those of COVID-19 patients who visited the hospital for medical examination were compared. The study patients included thirty patients with COVID-19 and/or influenza, five of those with influenza alone. A group of 121 patients without infection was used as control. Patients with COVID-19 and influenza had significantly higher lactate dehydrogenase levels than the patients with COVID-19 alone. The erythrocyte sedimentation rate (ESR) was higher in patients with COVID-19 alone than in other groups. Significant clinical outliers were observed in the COVID-19 and influenza infection group compared with the COVID-19 alone group. These results are expected to play an important role in the analysis of the hematological data of infected patients and the comparison of simultaneous and single infection data to determine clinical symptoms and other signs. These results may also assist in the development of vaccines and treatments for COVID-19.
다중객체추적 알고리즘을 활용한 드론 항공영상 기반 미시적 교통데이터 추출
정보경,서성혁,박부기,배상훈,Jung, Bokyung,Seo, Sunghyuk,Park, Boogi,Bae, Sanghoon 한국ITS학회 2021 한국ITS학회논문지 Vol.20 No.5
4차 산업혁명의 도래와 함께 자율주행자동차의 주행관리 및 주행 전략과 관련된 연구들이 대두되고 있다. 이러한 연구를 위해서는 차량의 미시적 교통데이터의 확보가 필수적이나, 기존 교통정보 수집 방식은 개별차량의 주행행태를 수집할 수 없다. 본 연구에서는 미시적 교통정보를 수집 가능한 항공에서 내려다보는 관점의 교통정보 수집을 위해 드론 항공영상을 활용하였다. 관련 연구의 한계점을 극복하기 위하여 딥러닝 기반 다중객체추적 알고리즘과 영상정합을 활용하여 미시적 교통데이터를 추출하였다. 그 결과로 속도는 MAE 3.49km/h, RMSE 4.43km/h, MAPE 5.18km/h의 오차율과 교통량 Precision 98.07%, Recall 97.86%의 정확도를 획득하였다. With the advent of the fourth industrial revolution, studies on driving management and driving strategies of autonomous vehicles are emerging. While obtaining microscopic traffic data on vehicles is essential for such research, we also see that conventional traffic data collection methods cannot collect the driving behavior of individual vehicles. In this study, UAV videos were used to collect traffic data from the viewpoint of the aerial base that is microscopic. To overcome the limitations of the related research in the literature, the micro-traffic data were estimated using the multiple object tracking of deep learning and an image registration technique. As a result, the speed obtained error rates of MAE 3.49 km/h, RMSE 4.43 km/h, and MAPE 5.18 km/h, and the traffic obtained a precision of 98.07% and a recall of 97.86%.