Earticle

초록

영어

The ionosphere is the part of the Earth's atmosphere with a high concentration of free electrons and ions. The ionosphere is characterised by its variability and inhomogeneity. One of the characteristic inhomogeneities is the so-called auroral oval, which determines the range of auroral radiance. Detection of the auroral oval is an important task for forecasting auroral storms, as they affect long-range communication systems, navigation, satellite-to-ground communications, making communications complicated or impossible. Therefore, an auroral oval detection and prediction needs to be performed in order to be informed about the area of their possible influence at certain time intervals. On the basis of the available image dataset from SIMuRG, which is based on GNSS data, it is proposed to use the LSTM model and CNN architecture. The paper reviews existing implementations and proposes a method for predicting auroral oval movements in the images, using the Convolutional LSTM architecture, which combines time series processing and computer vision. The work results in a machine learning model that can make the predictions based on even small sets of data.

목차

Abstract
I. INTRODUCTION
II. METHODS
III. RESULTS
CONCLUSION
ACKNOWLEDGMENT
REFERENCES

키워드

저자

• Anastasia Lebedeva [ Institute of Mathematics and Information Technologies Irkutsk State Univercity Irkutsk, Russia ]
• Alexandr Garashchenko [ School of Information Technology and Data Science Irkutsk National Research Technical University Irkutsk, Russia ]
• Denis Sidorov [ Institute of Solar-Terrestrial Physics of the Siberian Branch of the RAS Industrial Math Lab of Baikal Sch. of BRICS Irkutsk National Research Technical University Irkutsk, Russia ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  한국차세대컴퓨팅학회 학술대회

• 간기

  반년간

• 수록기간

  2021~2025

• 십진분류

  KDC 566 DDC 004