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- Title
Recognition of Fiber Optic Vibration Signals Based on Laplace Wavelet Transform and Deep Learning.
- Authors
Qi, Jinshui; Mo, Jiaqing; Niu, Yasen; Cui, Yiteng
- Abstract
Convolutional neural networks possess the capability of feature learning and nonlinear mapping, which has significant advantages in classifying and recognizing optical fiber vibration signals. In order to further enhance the recognition rate of vibration signals, this paper combines wavelet transform with convolutional neural networks and designs a convolutional layer based on parameterized wavelets. In this layer, the initial signal is convolved with parameterized Laplace wavelet dictionaries to complete the wavelet transform. Such customized filters make more sense than filters with randomly initialized parameters for traditional CNNs. Simultaneously, we introduce the channel attention mechanism to enhance the features of the filtered signals. Subsequently, standard CNNs are employed to extract and process signal features, ultimately utilizing a multi‐layer perceptron for recognition and classification. The experimental results show that the network model possesses better recognition refinement. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.
- Subjects
JAPAN; WAVELET transforms; DEEP learning; CONVOLUTIONAL neural networks; SIGNAL processing; OPTICAL fibers
- Publication
IEEJ Transactions on Electrical & Electronic Engineering, 2024, Vol 19, Issue 6, p1026
- ISSN
1931-4973
- Publication type
Article
- DOI
10.1002/tee.24029