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• W3216033613 abstract "In signal analysis and processing, underwater target recognition (UTR) is one of the most important technologies. Simply and quickly identify target types using conventional methods in underwater acoustic conditions is quite a challenging task. The problem can be conveniently handled by a deep learning network (DLN), which yields better classification results than conventional methods. In this paper, a novel deep learning method with a hybrid routing network is considered, which can abstract the features of time-domain signals. The used network comprises multiple routing structures and several options for the auxiliary branch, which promotes impressive effects as a result of exchanging the learned features of different branches. The experiment shows that the used network possesses more advantages in the underwater signal classification task." @default.
• W3216033613 created "2021-12-06" @default.
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• W3216033613 date "2021-11-24" @default.
• W3216033613 modified "2023-10-18" @default.
• W3216033613 title "Underwater Target Signal Classification Using the Hybrid Routing Neural Network" @default.
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• W3216033613 doi "https://doi.org/10.3390/s21237799" @default.
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