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Song Seung Ha,Lee Hyunju,Lee Hoan Jong,송은송,Ahn Jong Gyun,박수은,Lee Taekjin,Cho Hye-Kyung,Lee Jina,Kim Yae-Jean,조대선,김종현,강현미,이준기,김천수,김동현,김황민,최재홍,은병욱,김남희,Cho Eun Young,Kim Yun-Kyung,Oh Chi Eun,김경효,Ma Sang 대한의학회 2023 Journal of Korean medical science Vol.38 No.16
Background: The coronavirus disease-2019 (COVID-19) pandemic has contributed to the change in the epidemiology of many infectious diseases. This study aimed to establish the pre-pandemic epidemiology of pediatric invasive bacterial infection (IBI). Methods: A retrospective multicenter-based surveillance for pediatric IBIs has been maintained from 1996 to 2020 in Korea. IBIs caused by eight bacteria (Streptococcus pneumoniae, Haemophilus influenzae, Neisseria meningitidis, Staphylococcus aureus, Streptococcus agalactiae, Streptococcus pyogenes, Listeria monocytogenes, and Salmonella species) in immunocompetent children > 3 months of age were collected at 29 centers. The annual trend in the proportion of IBIs by each pathogen was analyzed. Results: A total of 2,195 episodes were identified during the 25-year period between 1996 and 2020. S. pneumoniae (42.4%), S. aureus (22.1%), and Salmonella species (21.0%) were common in children 3 to 59 months of age. In children ≥ 5 years of age, S. aureus (58.1%), followed by Salmonella species (14.8%) and S. pneumoniae (12.2%) were common. Excluding the year 2020, there was a trend toward a decrease in the relative proportions of S. pneumoniae (rs = −0.430, P = 0.036), H. influenzae (rs = −0.922, P < 0.001), while trend toward an increase in the relative proportion of S. aureus (rs = 0.850, P < 0.001), S. agalactiae (rs = 0.615, P = 0.001), and S. pyogenes (rs = 0.554, P = 0.005). Conclusion: In the proportion of IBIs over a 24-year period between 1996 and 2019, we observed a decreasing trend for S. pneumoniae and H. influenzae and an increasing trend for S. aureus, S. agalactiae, and S. pyogenes in children > 3 months of age. These findings can be used as the baseline data to navigate the trend in the epidemiology of pediatric IBI in the post COVID-19 era.
류명걸(Myungkeol Ryu),최택진(Taekjin Choi),김동수(Dongsoo Kim),이문식(Moonsig Lee),김태훈(Taehoon Kim) 한국자동차공학회 2011 한국자동차공학회 부문종합 학술대회 Vol.2011 No.5
The target of developing engine get to improve the fuel economy and performance. An engine intake manifold is optimized by plenum shape, primary and secondary length, port diameter, mass flow rate in the cylinder chamber, etc. Intake manifold also has safety factor for PCV icing and distribution. If the icing is occurred in vehicle, it may give rise to serious accident caused by trouble of break booster, reduction of speed, etc. It becomes more and more important for base intake manifold distribution. AFIM(Air Fuel Imbalance Monitor) function is expected to be implemented in the year 2014 OBDII. Intake manifold imbalance is due to manifold leak, fuel injector trouble, cam position trouble, etc. This paper describes how to check the PCV icing at vehicle and distribution at engine dynamometer and how to improve PCV icing problem.
엄민재(Minjae Eeom),한택진(Taekjin Hahn),이후경(Hookyung Lee),최상민(Sangmin Choi) 한국연소학회 2013 한국연소학회지 Vol.18 No.3
A rotary kiln furnace is one of the most widely used gas-solid reactors in the industrial field. Although the rotary kiln is a versatile system and has different size, approach to the reactor modeling can be generalized in terms of flow motion of the solid and gas phases, heat transfer, and chemical reactions on purpose. In this paper, starting from a zero-dimensional model and extending to higher dimension and comprehensive models, overall procedure of the design development of rotary kiln reactors and considerations are presented. The approaches to performance analysis of the reactors are introduced and examples of application cases are presented.
김영민(Young-Min Kim),이지영(Jiyoung Lee),윤일로(Illo Yoon),한택진(Taekjin Han),김철연(Chulyeon Kim) 한국정보과학회 2018 정보과학회 컴퓨팅의 실제 논문지 Vol.24 No.3
본 연구는 영상 분석에서 최근 좋은 연구 성과를 내고 있는 컨볼루션 신경망 (Convolutional Neural Network: CNN) 기법을 실외 CCTV 영상 분석에 적용하여 객체 유형을 분류하는 방법론은 제안한다. 배경 차분 (background subtraction)을 사용하여 찾고자 하는 객체 후보들을 추출해내고 이를 CNN을 이용해 분류함으로써 계산량을 줄이는 효과를 얻는 방법이다. CNN 학습용 CCTV 영상 수집을 위해 범죄 발생이 주로 일어나는 골목길, 놀이터 등에서 촬영한 CCTV 영상 DB를 구축하였으며 우선적으로 사람인 객체만 검출하는 분류기를 학습하였다. 다양한 학습 데이터 사이즈와 세팅에 맞게 실험하였으며 실험 결과 약 80%의 분류 정확도를 보였으며 새로운 CCTV 영상으로 테스트했을 때 약 67.5%의 성능을 보였다. In this paper, a method to classify objects in outdoor CCTV images using Convolutional Neural Network(CNN) and background subtraction is proposed. Object candidates are extracted using background subtraction and they are classified with CNN to detect objects in the image. At the end, computation complexity is highly reduced in comparison to other object detection algorithms. A database is constructed by filming alleys and playgrounds, places where crime occurs mainly. In experiments, different image sizes and experimental settings are tested to construct a best classifier detecting person. And the final classification accuracy became 80% for same camera data and 67.5% for a different camera.