Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array-Reference-Cited by-同舟云学术

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Underwater Noise Target Recognition Based on Sparse Adversarial Co-Training Model with Vertical Line Array

Published:2023-09-18 Issue:5 Volume:22 Page:1201-1215
ISSN:1672-5182
Container-title:Journal of Ocean University of China
language:en
Short-container-title:J. Ocean Univ. China

Author:

Zhou Xingyue,Yang Kunde,Yan Yonghong,Li Zipeng,Duan Shunli

Publisher

Springer Science and Business Media LLC

Subject

Ocean Engineering,Oceanography

Link

https://link.springer.com/content/pdf/10.1007/s11802-023-5309-y.pdf

Reference33 articles.

1. Amini, S., Homayouni, S., and Safari, A., 2014. Semi-supervised classification of hyperspectral image using random forest algorithm. 2014 IEEE Geoscience and Remote Sensing Symposium (IGARSS). Quebec City, QC, 2866–2869.

2. Badi, A., Park, S., Han, D. K., and Ko, H., 2020. Correlation distance skip connection denoising autoencoder (CDSK-DAE) for speech feature enhancement. Applied Acoustics, 163: 107213.

3. Bianco, M. J., Gerstoft, P., Traer, J., Ozanich, E., Traer, J., Roch, M. A., et al., 2019. Machine learning in acoustics: Theory and applications. Journal of the Acoustical Society of America, 146(5): 3590–3628.

4. Dong, Y., Shen, X., Jiang, Z., and Wang, H., 2021. Recognition of imbalanced underwater acoustic datasets with exponentially weighted cross-entropy loss. Applied Acoustics, 174: 107740.

5. Duan, R., Yang, K., Li, H., and Ma, Y., 2017. Acoustic-intensity striations below the critical depth: Interpretation and modeling. Journal of the Acoustical Society of America, 142(3): EL245–EL250.

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