학술논문

Automatic classification of ionogram with CNN
Document Type
Conference
Source
2020 IEEE International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan) Consumer Electronics - Taiwan (ICCE-Taiwan), 2020 IEEE International Conference on. :1-2 Sep, 2020
Subject
Aerospace
Communication, Networking and Broadcast Technologies
Components, Circuits, Devices and Systems
Engineered Materials, Dielectrics and Plasmas
Engineering Profession
Fields, Waves and Electromagnetics
Geoscience
Photonics and Electrooptics
Power, Energy and Industry Applications
Robotics and Control Systems
Signal Processing and Analysis
Ionosphere
Training
Neural networks
Feature extraction
Deep learning
Convolutional neural networks
Real-time systems
ionogram
classification
deep learning
CNN
Language
ISSN
2575-8284
Abstract
The ionosphere is an essential part of the near-earth environment. Ionosphere classification, especially the irregularities identification via ionograms in real time has significant meaning in the ionospheric research. In this paper, a method based on CNN is proposed to classify the ionograms automatically. To train the CNN, over 20000 ionograms from Chinese Academy of Sciences Digital Ionosonde installed at Huailai, Wuhan, Hengxian, Ganzi and Xiamen are utilized. A good performance over 83% identification accuracy was achieved by the new method.