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Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning.

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Title: Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning.
Authors: Yang, Gang1 (AUTHOR), Zeng, Min1 (AUTHOR), Lin, Xiaohong1 (AUTHOR), Li, Songbai2 (AUTHOR), Yang, Haoxiang2 (AUTHOR), Shen, Lingyan3 (AUTHOR) lyshen@hpu.edu.cn
Source: Earth Science Informatics. Oct2024, Vol. 17 Issue 5, p4391-4405. 15p.
Subject Terms: *Convolutional neural networks, *Machine learning, *Seismic wave velocity, *Compressed sensing, *Earthquakes, *Deep learning, *Data transmission systems
Abstract: Different geographical locations have different time series and types of earthquake early warning data of hydropower stations, and the packet loss rate in data sharing is high. In this regard, a real-time sharing algorithm of earthquake early warning data of hydropower stations based on deep learning is proposed. The compressed sensing method is used to collect the seismic data of the hydropower station, and the dictionary learning algorithm based on ordered parallel atomic updating is introduced to improve the compressed sensing process and to sparse the seismic data of the hydropower station. Combining FCOS and DNN, the seismic velocity spectrum is picked up from the collected seismic data and used as the input of the convolutional neural network. The real-time sharing of earthquake early warning data is realized using the CDMA1x network and TCP data transmission protocol. Experiments show that the algorithm can accurately pick up the regional seismic velocity spectrum of hydropower stations, the packet loss rate of earthquake early warning data transmission is low, and the sharing results contain a variety of information, which can provide a variety of data for people who need information and has strong practicability. [ABSTRACT FROM AUTHOR]
Database: Energy & Power Source
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DbLabel: Energy & Power Source
An: 180331196
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  Label: Title
  Group: Ti
  Data: Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning.
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  Data: <searchLink fieldCode="AR" term="%22Yang%2C+Gang%22">Yang, Gang</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Zeng%2C+Min%22">Zeng, Min</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lin%2C+Xiaohong%22">Lin, Xiaohong</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Li%2C+Songbai%22">Li, Songbai</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Yang%2C+Haoxiang%22">Yang, Haoxiang</searchLink><relatesTo>2</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Shen%2C+Lingyan%22">Shen, Lingyan</searchLink><relatesTo>3</relatesTo> (AUTHOR)<i> lyshen@hpu.edu.cn</i>
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  Data: <searchLink fieldCode="JN" term="%22Earth+Science+Informatics%22">Earth Science Informatics</searchLink>. Oct2024, Vol. 17 Issue 5, p4391-4405. 15p.
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  Data: *<searchLink fieldCode="DE" term="%22Convolutional+neural+networks%22">Convolutional neural networks</searchLink><br />*<searchLink fieldCode="DE" term="%22Machine+learning%22">Machine learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Seismic+wave+velocity%22">Seismic wave velocity</searchLink><br />*<searchLink fieldCode="DE" term="%22Compressed+sensing%22">Compressed sensing</searchLink><br />*<searchLink fieldCode="DE" term="%22Earthquakes%22">Earthquakes</searchLink><br />*<searchLink fieldCode="DE" term="%22Deep+learning%22">Deep learning</searchLink><br />*<searchLink fieldCode="DE" term="%22Data+transmission+systems%22">Data transmission systems</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: Different geographical locations have different time series and types of earthquake early warning data of hydropower stations, and the packet loss rate in data sharing is high. In this regard, a real-time sharing algorithm of earthquake early warning data of hydropower stations based on deep learning is proposed. The compressed sensing method is used to collect the seismic data of the hydropower station, and the dictionary learning algorithm based on ordered parallel atomic updating is introduced to improve the compressed sensing process and to sparse the seismic data of the hydropower station. Combining FCOS and DNN, the seismic velocity spectrum is picked up from the collected seismic data and used as the input of the convolutional neural network. The real-time sharing of earthquake early warning data is realized using the CDMA1x network and TCP data transmission protocol. Experiments show that the algorithm can accurately pick up the regional seismic velocity spectrum of hydropower stations, the packet loss rate of earthquake early warning data transmission is low, and the sharing results contain a variety of information, which can provide a variety of data for people who need information and has strong practicability. [ABSTRACT FROM AUTHOR]
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1007/s12145-024-01400-9
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      – Code: eng
        Text: English
    PhysicalDescription:
      Pagination:
        PageCount: 15
        StartPage: 4391
    Subjects:
      – SubjectFull: Convolutional neural networks
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: Seismic wave velocity
        Type: general
      – SubjectFull: Compressed sensing
        Type: general
      – SubjectFull: Earthquakes
        Type: general
      – SubjectFull: Deep learning
        Type: general
      – SubjectFull: Data transmission systems
        Type: general
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      – TitleFull: Real-time sharing algorithm of earthquake early warning data of hydropower station based on deep learning.
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            NameFull: Yang, Gang
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            NameFull: Zeng, Min
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            NameFull: Li, Songbai
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            NameFull: Yang, Haoxiang
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            – D: 01
              M: 10
              Text: Oct2024
              Type: published
              Y: 2024
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              Value: 17
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              Value: 5
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            – TitleFull: Earth Science Informatics
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