Multimodal Learning for Classification of Solar Radio Spectrum
Allbwn ymchwil: Cyfraniad at gynhadledd › Papur
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2015. 1035-1040 Papur a gyflwynwyd yn IEEE International Conference on Systems, Man, and Cybernetics (SMC), City University Hong Kong, OCT 09-12, 2015.
Allbwn ymchwil: Cyfraniad at gynhadledd › Papur
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TY - CONF
T1 - Multimodal Learning for Classification of Solar Radio Spectrum
AU - Chen, Z.
AU - Ma, L.
AU - Xu, L.
AU - Weng, Y.
AU - Yan, Y.H.
PY - 2015/10/9
Y1 - 2015/10/9
N2 - 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 different 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 radio spectrums are firstly pre-processed, including normalization, denoising, channel competition and etc., before being fed into the multimodal learning network. Experimental results have demonstrated that the proposed multimodal learning network can learn the representation of the solar radio spectrum more effectively, and improve the classification accuracy.
AB - 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 different 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 radio spectrums are firstly pre-processed, including normalization, denoising, channel competition and etc., before being fed into the multimodal learning network. Experimental results have demonstrated that the proposed multimodal learning network can learn the representation of the solar radio spectrum more effectively, and improve the classification accuracy.
U2 - 10.1109/SMC.2015.187
DO - 10.1109/SMC.2015.187
M3 - Paper
SP - 1035
EP - 1040
T2 - IEEE International Conference on Systems, Man, and Cybernetics (SMC), City University Hong Kong, OCT 09-12, 2015
Y2 - 3 January 0001
ER -