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- 저자 : Ahn Yeongseo (검색결과 2 건)
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Ahn Yeongseo,Jin Sunmi,Park Gemma,Lee Hye Young,Lee Hyungyong,Shin Eunkyung,Kim Junyoung,Yoo Jaeil,Kim Yuna 질병관리청 2024 Osong Public Health and Research Persptectives Vol.15 No.1
Objectives: In March 2023, an alternative school in the Republic of Korea reported 12 cases of shigellosis. This study aims to analyze the epidemiological characteristics in order to determine the cause of the cluster outbreak of shigellosis and to develop prevention strategies.Methods: This study focused on 12 patients with confirmed Shigella infection and investigated their demographics, clinical features, epidemiology, diagnostics, and antimicrobial susceptibility. Following the identification of Shigella, we conducted follow-up rectal smear cultures to manage patients, implementing isolation and control measures.Results: This study investigated the emergence of multidrug-resistant Shigella following missionary activities in Cambodia, documenting a cluster infection within an alternative school in Daejeon, the Republic of Korea. The outbreak affected 56 participants, resulting in the confirmation of 12 cases. The incidence rates varied by gender and occupation, with higher rates among males and teachers. All 12 cases demonstrated multidrug resistance. Challenges included delayed pathogen confirmation and suboptimal adherence to isolation criteria. The incident prompted revisions in the criteria for isolation release, focusing on symptom resolution. The study underscores the necessity for strengthened surveillance, educational initiatives focusing on prevention in endemic areas, and improved oversight of unlicensed educational establishments.Conclusion: Successful response strategies included swift situation assessment, collaborative efforts, effective infection control measures, and modified criteria for isolation release. Continued surveillance of multidrug-resistant strains is recommended, especially in regions with a high prevalence.
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자율주행 안전을 위한 Mask R-CNN 기반 폐색 영역 검출
지유경(Yugyeong Ji),강예연(Yeyeon Kang),박시은(Sieun Park),안지현(Jihyeon Ahn),장영서(Yeongseo Jang),정민정(Minjung Jung),정경용(Kyungyong Chung) 한국정보기술학회 2022 Proceedings of KIIT Conference Vol.2022 No.6
자율주행 자동차의 비전 분야에서의 폐색 영역 검출은 보완해야 할 기술 결함의 중점이다. 본 연구에서는 자율주행 안전을 위한 Mask R-CNN 기반 폐색 영역 검출을 제안한다. 제안하는 방법은 Mask R-CNN 모델에 학습된 데이터를 기반으로 객체를 인식한다. 또한, 교통수단을 종류별로 분류하여 세부적으로 이미지 분할을 진행한다. 객체가 가려지거나 겹쳐졌을 때 객체 추적률을 높일 수 있다. 인식한 객체의 고유한 라벨을 기반으로 LSTM을 이용 시계열 데이터를 이용한 객체 탐지 기반 폐색 영역 검출을 제안한다. 제안하는 방법은 시간의 흐름에 따른 객체 탐지 정확도를 높이고, 이를 활용함으로써 자율주행의 안전성을 향상시킨다. Detection of occluded areas in the vision field of autonomous vehicles is the focus of technical defects to be supplemented. In this paper, we proposes the Mask R-CNN based occlusion area detection in the autonomous driving safety. The proposed method recognizes objects based on data learned in the Mask R-CNN model. In addition, transportation is classified by type and image division is carried out in detail. It is possible to increase the object tracking rate when the object is covered or overlapped. Based on the unique label of the recognized object, we propose object detection-based occluded area detection using time series data using LSTM. The proposed method improves the accuracy of object detection over time and improves the autonomous driving safety by utilizing it.
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