Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Deng, Kailun; Liu, Haochen; Cao, Jun; Yang, Lichao; Du, Weixiang; Xu, Yigeng; Zhao, Yifan

doi:10.1016/j.compstruct.2024.118030

Composite Structures

2024

DOI: 10.1016/j.compstruct.2024.118030

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Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Kailun Deng,

Haochen Liu,

Jun Cao

et al.

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2024

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Cited by 3 publications

References 26 publications

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AE-IRMamba: Low-Complexity Inverted Residual Mamba for Identification of Piezoelectric Ceramic and Optical Fiber Acoustic Emission Sensors Signals

Shao,

Jiang,

Dong

et al. 2024

IEEE Sensors J.

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AE-IRMamba: Low-Complexity Inverted Residual Mamba for Identification of Piezoelectric Ceramic and Optical Fiber Acoustic Emission Sensors Signals

Shao,

Jiang,

Dong

et al. 2024

IEEE Sensors J.

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Research on Online-Offline Blended Teaching of Piano Course in Higher Vocational Colleges under the Background of Internet

Zhang

2024

Applied Mathematics and Nonlinear Sciences

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Online teaching is becoming an important means of education, but for the piano course, how to organically combine online and offline teaching has been the focus of educators. To improve this situation, this paper proposes a neural network model for piano performance evaluation as an auxiliary means of blended teaching. The model uses bidirectional LSTM training data, introduces an attention mechanism to optimize data recognition efficiency, and finally generates performance evaluation through a softmax classification algorithm. After implementing online-offline hybrid piano teaching in combination with this evaluation model, 66.72% of the students believed that the new teaching method could stimulate learning initiatives. The piano performance test scores showed that the post-test times for single-note playing, mixed intervals and chords, and two-handed ensemble playing in the experimental group were reduced by 38 seconds, 45 seconds, and 7 minutes, respectively, and were significantly higher than those in the control group. This indicates that the performance evaluation model has good performance and application effectiveness.

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Research on defect recognition technology of transmission line based on visual macromodeling

Li,

Wang

et al. 2024

Applied Mathematics and Nonlinear Sciences

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In order to improve the defect recognition efficiency of transmission lines, the industry is currently using aerial images for automatic visual defect detection to ensure the safe operation of transmission lines. This paper proposes a method for defect recognition from coarse to fine, based on convolutional neural networks and connected domain algorithms, to improve recognition accuracy. The recognition speed is improved by using the knowledge distillation method of target detection networks based on decoupled features, adversarial features, and attention features. It has been found that the optimized recognition model improves the precision rate by 7%, the recall rate by 8%, and the average accuracy rate by 10%. The FPS of the model optimized by knowledge distillation is 62.5, and the average value of the FPS of other versions of this model is 47.35. It is believed that the two optimization ideas introduced in this paper can enhance the previous transmission line defect recognition algorithm in terms of accuracy and recognition speed.

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Attention mechanism enhanced spatiotemporal-based deep learning approach for classifying barely visible impact damages in CFRP materials

Cited by 3 publications

References 26 publications

AE-IRMamba: Low-Complexity Inverted Residual Mamba for Identification of Piezoelectric Ceramic and Optical Fiber Acoustic Emission Sensors Signals

AE-IRMamba: Low-Complexity Inverted Residual Mamba for Identification of Piezoelectric Ceramic and Optical Fiber Acoustic Emission Sensors Signals

Research on Online-Offline Blended Teaching of Piano Course in Higher Vocational Colleges under the Background of Internet

Research on defect recognition technology of transmission line based on visual macromodeling

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