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Research on local sound field intensity control technique in metasurface based on deep neural networks

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  • Huanlong Zhao
  • Qiang Lv
  • Zhen Huang
  • Wei Chen
  • Guoqiang Hao

Abstract

The use of tunable metasurface technology to realize the underwater tracking function of submarines, which is one of the hotspots and difficulties in submarine design. The structure-to-sound-field metasurface design approach is a highly iterative process based on trial and error. The process is cumbersome and inefficient. Therefore, an inverse design method was proposed based on parallel deep neural networks. The method took the global and local target sound field feature information as input and the metasurface physical structure parameters as output. The deep neural network was trained using a kernel loss function based on a radial basis kernel function, which established an inverse mapping relationship between the desired sound field to the metasurface physical structure parameters. Finally, the sound field intensity modulation at a localized target range was achieved. The results indicated that within the regulated target range, this method achieved an average prediction error of less than 5 dB for 92.9% of the sample data.

Suggested Citation

  • Huanlong Zhao & Qiang Lv & Zhen Huang & Wei Chen & Guoqiang Hao, 2024. "Research on local sound field intensity control technique in metasurface based on deep neural networks," PLOS ONE, Public Library of Science, vol. 19(3), pages 1-17, March.
    • Handle: RePEc:plo:pone00:0301211
    DOI: 10.1371/journal.pone.0301211
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    References listed on IDEAS

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    1. Yangbo Xie & Wenqi Wang & Huanyang Chen & Adam Konneker & Bogdan-Ioan Popa & Steven A. Cummer, 2014. "Wavefront modulation and subwavelength diffractive acoustics with an acoustic metasurface," Nature Communications, Nature, vol. 5(1), pages 1-5, December.
    2. Chen, Xi & Yu, Ruyi & Ullah, Sajid & Wu, Dianming & Li, Zhiqiang & Li, Qingli & Qi, Honggang & Liu, Jihui & Liu, Min & Zhang, Yundong, 2022. "A novel loss function of deep learning in wind speed forecasting," Energy, Elsevier, vol. 238(PB).
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