Analysis of surveillance position error for airfield detection

Ku, SungKwan,Baik, Hojong,Kim, Taehyoung Emerald (MCB UP ) 2018 Aircraft engineering and aerospace technology Vol.90 No.6

Runway visual range prediction using Convolutional Neural Network with Weather information

SungKwan Ku,Seungsu Kim,Seokmin Hong 국제문화기술진흥원 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..

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.

Influencing factors of low-altitude unmanned aircraft navigation using AHP

SungKwan Ku,HyoJung An,DongJin Lee 국제문화기술진흥원 2020 International Journal of Advanced Culture Technolo Vol.8 No.1

This study examines whether unmanned aircraft systems (UAS) operated in the context of UAS traffic management (UTM) can be properly operated in its flight environment. In detail, this study examines the influencing navigation factors affecting UASs during flight and examines factors affecting the navigation of UASs under UTM. After deriving various factors affecting navigation, their importance are determined by applying the analytic hierarchy process technique, and the important influencing factors are examined. For low-altitude UAS navigation, errors are classified into navigation-system and flight-technical errors, and a hierarchy is constructed for their sub-factors affecting the influencers. Through this, influencing factors for precise navigation of low-altitude UAS are analyzed, and high importance items are identified

난류 예보 정보의 비교를 통한 항공기 운항중 수직 가속도 측정값 활용

구성관(SungKwan Ku),최영서(YoungSeo Choi),이승표(SeungPyo Lee),원완식(WanSik Won) 한국항공우주학회 2023 한국항공우주학회 학술발표회 논문집 Vol.2023 No.11

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 韓國航行學會論文誌 Vol.19 No.5

ADS-B는 레이더에 비하여 높은 정확도와 빠른 갱신 주기를 제공하여, 레이더를 대체하거나 보완할 수 있는 기술이다. 최근 증가하는 항공교통량과 이에 따른 정확한 감시의 요구에 의하여 ADS-B가 실제 항공기 운영에 적용되고 있다. 본 논문은 항공기가 운항 중 상시 송신하는 ADS-B 신호를 활용한 공한 인근 지역에서 정밀 감시가 가능한지 확인하기 위한 자료 수집과 일부 데이터에 대한 분석을 수행하였다. 이를 위하여 정밀계기 착륙 접근하는 항공기의 ADS-B 항적을 수집하고, 최종접근 구간에서 항적의 분포특성에 관한 분석을 수행하였다. 분석결과 ADS-B 항적은 활주로 중심선 연장선에 대하여 평균 이격 거리 -0.04 m, 표준편차 6.71 이고, 또한 비교적 정확한 감시정보의 제공이 가능함을 확인하였다. 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.

소형무인항공기 항법시스템오차 시험평가 방법

구성관 ( Sungkwan Ku ),안효정 ( Hyojung Ahn ),주요한 ( Yo-han Ju ),홍석민 ( Seokmin Hong ) 한국항행학회 2021 韓國航行學會論文誌 Vol.25 No.6

최근 무인항공기의 활용 범위와 수요가 지속적으로 증가하고 있으며, 저고도 무인항공기의 경우 유인항공기와 별개의 관리 체계 개발을 통해 별도의 운영 시스템 구축에 관한 연구가 진행되고 있다. 저고도 무인항공기의 경우도 공역을 비행하는 비행체 이므로 비행체의 운영에 필요한 기술 기준 및 인증 제도의 수립이 필수적이며, 이에 대한 연구도 함께 진행되고 있다. 비행체의 운영기준 및 인증 요건이 제시되는 경우, 이를 확인할 수 있는 시험방법도 함께 제시되어야 한다. 특히, 소형무인항공기의 경우는 비행 중 요구되는 항법의 정확도 수준이 유인항공기 또는 대형 무인항공기 보다 정밀한 비행을 요구하므로, 기존의 비행체 비행에서만 확인할 수 있는 비행 결과 정확도 산출이 아닌 별도의 항법오차의 산출이 필요할 것으로 판단하였다. 본 연구에서는 기존 유인항공기와 다른 장시간의 운영 데이터 획득이 어려운 무인항공기에 적용이 가능한 항법 오차 도출에 관한 시험 방법에 대하여 제시하였고, 실증 시험을 수행하였다. 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.

심층신경망을 활용한 활주로 가시거리 예측 모델 개발

구성관,홍석민,Ku, SungKwan,Hong, SeokMin 한국항행학회 2017 韓國航行學會論文誌 Vol.21 No.5

안개 등의 영향을 받는 활주로 시정은 비행장에서 항공기 이착륙의 가능 여부를 결정하는 주요 지표중 하나이다. 운송용 항공기가 운항되는 공항의 경우 활주로 시정을 포함한 주요 국지 기상 예보를 시행하며, 이를 항공종사자가 확인할 수 있도록 하고 있다. 본 논문은 최근 영상 처리, 음성 인식, 자연어 처리 등의 다양한 분야에 적용되고 있는 심층신경망을 활주로 시정 예측에 적용하여 국지 비행장의 활주로 시정 예측 모델을 개발하고 이를 활용한 예측을 수행하였다. 적용 대상 비행장의 과거 실제 기상 관측 값을 활용하여 신경망 학습 후 시정에 대한 예측을 수행하였고, 기존 관측 데이터와 비교한 결과 비교적 정확한 예측 결과를 확인하였다. 또한 개발된 모델은 별도의 예보 기능이 없는 해당 비행장에서 참고할 수 있는 기상정보를 생성하는데 사용될 수 있을 것이다. 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.

Improving Wind Speed Forecasts Using Deep Neural Network

Hong, Seokmin,Ku, SungKwan The International Promotion Agency of Culture Tech 2019 International Journal of Advanced Culture Technolo Vol.7 No.4

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.

Improving Wind Speed Forecasts Using Deep Neural Network

Seokmin Hong,SungKwan Ku 국제문화기술진흥원 2019 International Journal of Advanced Culture Technolo Vol.7 No.4

Wind speed data constitute important weather information for aircrafts flying at low altitudes, such as drones. Currently, the accuracy of low altitude wind predictions is much lower than that of high-altitude wind predictions. Deep neural networks are proposed in this study as a method to improve wind speed forecast information. Deep neural networks mimic the learning process of the interactions among neurons in the brain, and it is used in various fields, such as recognition of image, sound, and texts, image and natural language processing, and pattern recognition in time-series. In this study, the deep neural network model is constructed using the wind prediction values generated by the numerical model as an input to improve the wind speed forecasts. Using the ground wind speed forecast data collected at the Boseong Meteorological Observation Tower, wind speed forecast values obtained by the numerical model are compared with those obtained by the model proposed in this study for the verification of the validity and compatibility of the proposed model.