Multimodal Learning for Classification of Solar Radio Spectrum

Zhuo Chen, Lin Ma, Long Xu, Ying Weng, Yihua Yan

Research output: Chapter in Book/Conference proceedingConference contributionpeer-review

8 Citations (Scopus)

Abstract

This paper proposes the first attempt to utilize multi-modal learning method for the representation learning of the solar radio spectrums. The solar radio signals sensed from differ-ent frequency channels, which present different characteristics, are regarded as different modalities. We employ a multimodal neural network to learn the representations of the solar radio spectrum, which can distinguish the differences and learn the interactions between different modalities. The original solar ra-dio spectrums are firstly pre-processed, including normalization, denoising, channel competition and etc., before being fed into the multimodal learning network. Experimental results have demon-strated that the proposed multimodal learning network can learn the representation of the solar radio spectrum more effectively, and improve the classification accuracy.

Original languageEnglish
Title of host publicationProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages1035-1040
Number of pages6
ISBN (Electronic)9781479986965
DOIs
Publication statusPublished - 12 Jan 2016
Externally publishedYes
EventIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015 - Kowloon Tong, Hong Kong
Duration: 9 Oct 201512 Oct 2015

Publication series

NameProceedings - 2015 IEEE International Conference on Systems, Man, and Cybernetics, SMC 2015

Conference

ConferenceIEEE International Conference on Systems, Man, and Cybernetics, SMC 2015
Country/TerritoryHong Kong
CityKowloon Tong
Period9/10/1512/10/15

Keywords

  • Multimodal learning; solar radio astronomy
  • solar radio spectrum; classification

ASJC Scopus subject areas

  • Artificial Intelligence
  • Computer Networks and Communications
  • Energy Engineering and Power Technology
  • Information Systems and Management
  • Control and Systems Engineering

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