Underwater acoustic target recognition based on sub-band concatenated Mel spectrogram and multidomain attention mechanism

Yang, Shuang; Jin, Anqi; Zeng, Xiangyang; Wang, Haitao; Hong, Xi; Lei, Menghui

doi:10.1016/j.engappai.2024.107983

Search citation statements

Order By: Relevance

Paper Sections

Select...

Citation Types

Supporting

Mentioning

Contrasting

Year Published

2024

Publication Types

Select...

Article3

Other1

Preprint1

Relationship

Self Cite0

Independent5

Authors

Journals

Cited by 6 publications

References 31 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

Underwater signal recognition based on integrating domain adaptation framework with the stochastic classifier

Yang,

Yan,

Wang

et al. 2024

Ocean Engineering

View full text Add to dashboard Cite

Underwater signal recognition based on integrating domain adaptation framework with the stochastic classifier

Yang,

Yan,

Wang

et al. 2024

Ocean Engineering

View full text Add to dashboard Cite

Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network

Song,

Wang,

Song

et al. 2024

Sci Rep

View full text Add to dashboard Cite

The mechanical fault diagnosis of HVCB is important to ensure the stability of electric power systems. Aiming at the problem of poor diagnostic performance of deep learning methods under limited samples, this paper proposes an HVCB operating mechanism fault diagnosis model (multi-channel CNN-SABO-SVM, MCCSS) based on multimodal data fusion features and Subtraction-Average-Based Optimizer (SABO). This model extracts and fuses features from the input two-dimensional data using a multi-channel CNN network and then uses the multimodal data fusion features to diagnose HVCB faults. Additionally, the SVM is used instead of the Softmax classifier to classify the fused features of vibration and sound, compensating for the poor diagnostic performance and generalization ability of the CNN network in small sample data scenarios. To further enhance the fault diagnosis performance of the SVM, the SABO is introduced for hyperparameter optimization of the SVM classifier. An HVCB fault test platform was established to train and test the model with limited data. The experimental results show that, compared with the multi-channel CNN-SVM and the CNN model based on unimodal signals, the proposed multi-channel CNN-SABO-SVM model improves the accuracy by 2.66% and 10.66%, respectively, and effectively addresses the challenge of circuit breaker fault diagnosis with limited samples.

show abstract

Research on Enhancing the YOLOv5 Algorithm Based on Attention Mechanisms

Yin

2024

2024 IEEE 6th International Conference on Power, Intelligent Computing and Systems (ICPICS)

View full text Add to dashboard Cite

Underwater acoustic target recognition based on sub-band concatenated Mel spectrogram and multidomain attention mechanism

Cited by 6 publications

References 31 publications

Underwater signal recognition based on integrating domain adaptation framework with the stochastic classifier

Underwater signal recognition based on integrating domain adaptation framework with the stochastic classifier

Fault diagnosis of HVCB via the subtraction average based optimizer algorithm optimized multi channel CNN-SABO-SVM network

Research on Enhancing the YOLOv5 Algorithm Based on Attention Mechanisms

Contact Info

Product

Resources

About