Presentation | 2004-11-05 Anomalous Signal Detection Using Multi-layer Neural Network for Electromagnetic Wave Radiation Akitoshi ITAI, Hiroshi YASUKAWA, Ichi TAKUMI, Masayasu HATA, |
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Abstract(in English) | It is well known that the electromagnetic waves that radiate from the earth's crust are useful for predieting earthquakes. We observe electromagnetic waves in the Extremely Low Frequency(ELF) band of 223Hz. These observed signals contain several undesired signals due to fluctuations in the magnetosphere or the ionized layer, lightning radiation from the tropics, and so on. This paper proposes a multi-layer neural network using a compression data for precursor signal detection. Input data are reduced by wavelet transform. Moreover, we discuss an implementation of the hidden layer. It is shown that the proposed neural network is useful for precursor signal detection. |
Keyword(in Japanese) | (See Japanese page) |
Keyword(in English) | electromagnetic wave / anomalous signal detection / neural network / signal processing |
Paper # | CAS2004-56,CST2004-35 |
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Committee | CST |
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Conference Date | 2004/10/29(1days) |
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Registration To | Concurrent System Technology (CST) |
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Language | JPN |
Title (in Japanese) | (See Japanese page) |
Sub Title (in Japanese) | (See Japanese page) |
Title (in English) | Anomalous Signal Detection Using Multi-layer Neural Network for Electromagnetic Wave Radiation |
Sub Title (in English) | |
Keyword(1) | electromagnetic wave |
Keyword(2) | anomalous signal detection |
Keyword(3) | neural network |
Keyword(4) | signal processing |
1st Author's Name | Akitoshi ITAI |
1st Author's Affiliation | Aichi prefectural University() |
2nd Author's Name | Hiroshi YASUKAWA |
2nd Author's Affiliation | Aichi prefectural University |
3rd Author's Name | Ichi TAKUMI |
3rd Author's Affiliation | Nagoya Institute of Technology |
4th Author's Name | Masayasu HATA |
4th Author's Affiliation | Collage of Engineering, Chubu University |
Date | 2004-11-05 |
Paper # | CAS2004-56,CST2004-35 |
Volume (vol) | vol.104 |
Number (no) | 405 |
Page | pp.pp.- |
#Pages | 6 |
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