Deep Neural Network-Based Time Series Atmospheric Refractivity Prediction Model Using Meteorological Observation Data
Author:
Funder
Defense Acquisition Program Administration
Publisher
Korean Institute of Electromagnetic Engineering and Science
Subject
General Medicine
Link
http://jkiees.org/download/download_pdf?doi=10.5515/KJKIEES.2023.34.11.860
Reference7 articles.
1. C. Kim, D. Y. Na, and Y. B. Park, “Electromagnetic wave propagation from low-earth orbit satellite to ground station considering interpolated atmospheric environments,” IEEE Access, vol. 9, pp. 95853-95861, Jul. 2021. 10.1109/ACCESS.2021.3094470
2. J. Yang, J. Heo, J. Kim, Y. Park, H. Choo, and Y. B. Park, “Predicting atmospheric refractivity over the Korean Peninsula using deep learning techniques,” The Journal of Korean Institute of Electromagnetic Engineering and Science, vol. 34, no. 6, pp. 493-496, Jun. 2023. 10.5515/KJKIEES.2023.34.6.493
3. S. Hochreiter, J. Schmidhuber, “Long short-term memory,” Neural Computation, vol. 9, no. 8, pp. 1735-1780, Nov. 1997. 10.1162/neco.1997.9.8.1735 9377276
4. University of Wyoming College of Engineering. Available: http://weather.uwyo.edu/upperair/sounding.html
5. B. R. Bean, G. D. Thayer, “Models of the atmospheric radio refractive index,” Proceedings of the IRE, vol. 47, no. 5, pp. 740-755, May 1959. 10.1109/JRPROC.1959.287242
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