http://chineseinput.net/에서 pinyin(병음)방식으로 중국어를 변환할 수 있습니다.
변환된 중국어를 복사하여 사용하시면 됩니다.
성기정(Kie-Jeong Seong),김응태(Eung-Tai Kim),김성필(Seong-Pil Kim) 한국항공우주연구원 2008 항공우주산업기술동향 Vol.6 No.2
본 논문에서는 자율비행(Autonomous Flight)의 연구동향 및 향후 발전방향에 대하여 기술하였다. 자율비행은 항공기의 예기치 않은 임의 상황발생에 대해서도 항공기가 스스로 인지하고 판단한 후 대처하는 것을 의미한다. 현재의 자율비행기술은 무인기를 위주로 개발되고 있으나, 차세대 항공교통관제 시스템 도입과 무인기의 활용성 증대 요구에 따라 자율비행기능이 항공교통관제시스템과 결합되어야 한다. 그러므로 현 항공교통관제 시스템의 주요 개요와 향 후 변경될 차세대 항공교통관제 시스템의 주요 사항들을 살펴보았고, 자율비행기술 개발 현황을 기술하였으며 향 후 발전방향을 전망하였다. This paper describes the current research trend and future development direction of autonomous flight of the aircraft. The autonomous flight means that aircraft control system recognize and cope with the emergency situation confronted during the flight by itself. Current research for autonomous flight technology is mainly performed for the application to unmanned air vehicle. Considering advent of future air traffic management system and increasing demand of the unmanned air vehicle application. however, autonomous flight technology required to be combined with future air traffic management system. In this paper, the current air traffic management system and anticipating change in future air traffic management system was investigated and research activities of autonomous flight technology was described as well as future prospect.
NCC기법을 이용한 무인항공기용 차종 식별 알고리즘 개발
정재원(Jae-Won Jeong),김정호(Jeong-Ho Kim),허진우(Jin-Woo Heo),한동인(Dong-In Han),이대우(Dae-Woo Lee),성기정(Kie-Jeong Seong) 한국항공우주학회 2012 韓國航空宇宙學會誌 Vol.40 No.7
본 논문은 무인 항공기에서 지상의 차량을 촬영하여 차종을 인식하기 위한 알고리즘의 개발에 대해 논하고 있다. NCC(Normalized Cross-Correlation) 방법을 이용하여 영상에서 목표물의 기하학적인 정보를 정합하도록 하였고, 실제 비행영상을 통해 획득한 템플릿 이미지와 위성 지도를 통해 획득한 템플릿 이미지를 이용하여 영상의 정합을 수행하였다. 실내 기반 실험을 통해 정합 가능성을 평가하였으며, 위성 지도를 이용한 모의실험을 통해 NCC 알고리즘을 이용하여 차량의 종류를 식별할 수 있음을 확인하였다. 마지막으로 실제 비행 실험을 통해 획득한 영상을 통해 동일한 차량을 전체 영상에서 정합하는 실험을 수행하였다. 비행 실험 결과 승용차의 위치가 정확하게 탐지되었으며, 정합 결과 0.6점 이상의 유사도가 나타남을 확인할 수 있었다. 또한 유사한 색상을 지닌 트럭은 정합하지 않음으로서 이종 차량의 구분이 가능함을 확인하였다. This paper describes the algorithm recognizing car type from the image received from UAV and the recognition results between three types of car images. Using the NCC(Normalized Cross-Correlation) algorithm, geometric information is matched from template images. Template images are obtained from UAV and satellite map and indoor experiment is performed using satellite map. After verification of the possibility, experiment for verification of same car type recognition is performed using small UAV. In the experiment, same type cars are matched with 0.6 point similarity and truck with similar color distribution is not matched with template image of a sedan.
광주광역시 1개 치과병원 내원환자의 구강상태 및관련 요인과 구강건강영향지수의 관련성
정성국 ( Seong Kug Jeong ),김승희 ( Seung Hee Kim ),김동기 ( Dong Kie Kim ),이병진 ( Byoung Jin Lee ) 대한예방치과·구강보건학회 2014 大韓口腔保健學會誌 Vol.38 No.4
Objectives: The aim of this study was to determine the association between oral health status and oralhealth impact profile (OHIP-14) among patients undergoing treatment in a dental hospital, in order todevelop an oral health care method for improving oral health related quality of life (OHRQoL). Methods: A total of 980 patients aged 7-89 years were selected from a dental hospital between May2011 and March 2014. Questionnaires on oral health impact profile (OHIP-14K) were distributed to thepatients, and their dental records were examined to find out their oral health status. Results: OHIP-14 scores of patients with periodontal pockets over 4 mm and presence of chronic generaldisease were significantly higher than those without pockets and chronic disease (P<0.05). Factorssuch as age, gender, having prosthesis or dental implant, regular oral health care over a period of 1 yearwere not significantly associated with OHIP-14 scores. Conclusions: Periodontal health status and chronic general disease could be factors associated withOHRQoL. Thus, improving oral symptoms through professional oral care may help improve OHRQoL.
김성필(Seong Pil Kim),김응태(Eung Tai Kim),성기정(Kie Jeong Seong) 제어로봇시스템학회 2008 제어·로봇·시스템학회 논문지 Vol.14 No.12
This paper introduces an application of wavelet analysis to the sensor fusion of GPS/INS/baroaltimeter. Using wavelet analysis the baro-inertial altitude is decomposed into the low frequency content and the high frequency content. The high frequency components, ‘details’, represent the perturbed altitude change from the long time trend. GPS altitude is also broken down by a wavelet decomposition. The low frequency components, ‘approximations’, of the decomposed signal address the long-term trend of altitude. It is proposed that the final altitude be determined as the sum of both the details of the baro-inertial altitude and the approximations of GPS altitude. Then the final altitude exclude long-term baro-inertial errors and short-term GPS errors. Finally, it is shown from the test results that the proposed method produces continuous and sensitive altitude successfully.