Earticle

초록

영어

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..

목차

Abstract
1. Introduction
2. Runway visual range and convolutional neural network
2.1 Runway visual range
2.2 Convolutional neural network
3. Implementation on the CNN model and experiment
3.1 CNN model
3.2 Experiment
4. Conclusion
Acknowledgement
References

키워드

저자

• SungKwan Ku [ Department of Aviation industrial and System Engineering, Hanseo University ]
• Seungsu Kim [ Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University ]
• Seokmin Hong [ Department of Unmanned Aircraft Systems, Hanseo University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Advanced Culture Technology(IJACT)

• 간기

  계간

• pISSN

  2288-7202

• eISSN

  2288-7318

• 수록기간

  2013~2025

• 등재여부

  KCI 등재

• 십진분류

  KDC 600 DDC 700