Prediction of the Major Factors for the Analysis of the Erosion Effect on Atomic Oxygen in LEO Satellite Using a Machine Learning Method (LSTM) |
Kim, You Gwang
(Korea Aerospace Research Institute)
Park, Eung Sik (Korea Aerospace Research Institute) Kim, Byung Chun (National Institute for Mathematical Sciences) Lee, Suk Hoon (National Institute for Mathematical Sciences) Lee, Seo Hyun (Insight Mining) |
1 | Y. G. Kim, E. S. Park, B. C. Kim, S. H Lee, and S. H. Lee, "The case study on the optimized forecasting methods of major factors for the analysis of the atomic oxygen erosion effect in LEO satellite," Proc. of KSAS 2019 Fall Conference, Cheju, Korea, pp. 203, November 2019. |
2 | www.spenvis.oma.be. |
3 | https://www.swpc.noaa.gov/news/solar-cycle-25-forecastupdate, Dec. 09, 2019. |
4 | ftp://ftp.geolab.nrcan.gc.ca/data/solar_flux/daily_flux_values/. |
5 | S. Hochreiter and J. Schmidhuber, "Long short-term memory," Neural Computation, vol. 9, no. 8, pp. 1735-1780, 1997. DOI |
6 | S.-K. Park, "Principle and formula calculation of LSTM," http://docs.likejazz.com/lstm/, May 30, 2018. |
7 | M. Mishra, "Unboxing ARIMA models," https://towardsdatascience.com/unboxing-arima-models-1dc09d2746f8, Jun 11, 2018. |
8 | Y. G. Kim, S. T. Lee, M. J. Baek, and S. H. Lee, "Prediction of atomic oxygen erosion for coating material of LEO satellite's solar array by using the real RAM direction accumulation method," Journal of Aerospace System Engineering, vol. 11, no. 5, pp. 1-5, 2017. DOI |
![]() |