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- 저자 : Ku SungKwan (검색결과 26 건)
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Runway visual range prediction using Convolutional Neural Network with Weather information
Ku, SungKwan,Kim, Seungsu,Hong, Seokmin The International Promotion Agency of Culture Tech 2018 International Journal of Advanced Culture Technolo Vol.6 No.4
The runway visual range is one of the important factors that decide the possibility of taking offs and landings of the airplane at local airports. The runway visual range is affected by weather conditions like fog, wind, etc. The pilots and aviation related workers check a local weather forecast such as runway visual range for safe flight. However there are several local airfields at which no other forecasting functions are provided due to realistic problems like the deterioration, breakdown, expensive purchasing cost of the measurement equipment. To this end, this study proposes a prediction model of runway visual range for a local airport by applying convolutional neural network that has been most commonly used for image/video recognition, image classification, natural language processing and so on to the prediction of runway visual range. For constituting the prediction model, we use the previous time series data of wind speed, humidity, temperature and runway visibility. This paper shows the usefulness of the proposed prediction model of runway visual range by comparing with the measured data.
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구성관 ( Sungkwan Ku ),안효정 ( Hyojung Ahn ),주요한 ( Yo-han Ju ),홍석민 ( Seokmin Hong ) 한국항행학회 2021 한국항행학회논문지 Vol.25 No.6
Recently, the range of utilization and demand for unmanned aerial vehicle (UAV) has been continuously increasing, and research on the construction of a separate operating system for low-altitude UAV is underway through the development of a management system separate from manned aircraft. Since low-altitude UAVs also fly in the airspace, it is essential to establish technical standards and certification systems necessary for the operation of the aircraft, and research on this is also in progress. If the operating standards and certification requirements of the aircraft are presented, a test method to confirm this should also be presented. In particular, the accuracy of small UAV’s navigation required during flight is required to be more precise than that of a manned aircraft or a large UAV. It was necessary to calculate a separate navigation error. In this study, we presented a test method for deriving navigation errors that can be applied to UAVs that have difficulty in acquiring long-term operational data, which is different from existing manned aircraft, and conducted verification tests.
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항공 통신 기술 : ADS-B 신호를 이용한 ILS 최종접근 구간의 항공기 항적 이격 분포 도출
구성관 ( Sungkwan Ku ),이영종 ( Young Jong Lee ),신대원 ( Daiwon Shin ) 한국항행학회 2015 한국항행학회논문지 Vol.19 No.5
ADS-B can provide high accurate position information and faster update rate than Radar system and it is a technique that can supplement or replace the Radar. Recently ADS-B has been applied to the actual aircraft operation because to increase air transportation traffic and required to high accurate surveillance. In this study, we surveyed analysis of position deviation distribution analysis and received actual ADS-B trajectory data for conformed precise surveillance on the near airport area using ADS-B message. For that, we received the precision instrument approach ADS-B trajectory data using instrument landing system, and can analyse about target deviation distribution on the final approach segment about precision instrument approach. The result of analysis is mean distance of target deviation -0.04 m and standard deviation 6.71 on between ADS-B target and extended runway centerline. Also that is to conformed the ADS-B message trajectory available to provide relatively exact surveillance information.
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구성관,홍석민,Ku, SungKwan,Hong, SeokMin 한국항행학회 2017 한국항행학회논문지 Vol.21 No.5
The runway visual range affected by fog and so on is one of the important indicators to determine whether aircraft can take off and land at the airport or not. In the case of airports where transportation airplanes are operated, major weather forecasts including the runway visual range for local area have been released and provided to aviation workers for recognizing that. This paper proposes a runway visual range estimation model with a deep neural network applied recently to various fields such as image processing, speech recognition, natural language processing, etc. It is developed and implemented for estimating a runway visual range of local airport with a deep neural network. It utilizes the past actual weather observation data of the applied airfield for constituting the learning of the neural network. It can show comparatively the accurate estimation result when it compares the results with the existing observation data. The proposed model can be used to generate weather information on the airfield for which no other forecasting function is available.
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