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Updated: Jan 24, 2026

Author Spotlight: Enhancement of Salient Object Detection for Smart Grid Applications

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Published on: December 15, 2023

深層ニューラルネットワークを用いた地域音響伝搬場の高速予測手法に関する研究

Chuxiong Wang^1,2, Cheng Chen^1,2, Xiao Feng^1,2

¹School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an 710000, China.

JASA express letters

|January 23, 2026

Abstract:

This study introduces a convolutional neural network based method for rapid prediction of underwater acoustic propagation fields, addressing the high computational cost of traditional methods. By analyzing regional terrain features and constructing a training dataset, the model learns acoustic transmission loss patterns across various terrain conditions. Tests in the Western Pacific demonstrate a root mean square error of 3.48 dB for non-smoothed fields, with an average prediction time of 1.95 ms per batch (10 samples). This method highlights the potential for fast acoustic propagation predictions using simplified inputs, offering a promising direction for real-time applications.

キーワード:

水中音響学伝播損失畳み込みニューラルネットワーク深層学習音響伝搬モデリングリアルタイム予測地形データ

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