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논문검색

Culture Convergence (CC)

Runway visual range prediction using Convolutional Neural Network with Weather information

원문정보

SungKwan Ku, Seungsu Kim, Seokmin Hong

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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

저자정보

  • 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

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자료제공 : 네이버학술정보

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