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.

中文翻译：

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基于拉普拉斯小波变换和深度学习的光纤振动信号识别

卷积神经网络具有特征学习和非线性映射的能力，在光纤振动信号的分类和识别方面具有显着的优势。为了进一步提高振动信号的识别率，本文将小波变换与卷积神经网络相结合，设计了基于参数化小波的卷积层。在这一层中，初始信号与参数化拉普拉斯小波字典进行卷积，完成小波变换。这种定制的滤波器比传统 CNN 的具有随机初始化参数的滤波器更有意义。同时，我们引入通道注意机制来增强滤波后信号的特征。随后，采用标准 CNN 来提取和处理信号特征，最终利用多层感知器进行识别和分类。实验结果表明，该网络模型具有较好的识别细化能力。© 2024 日本电气工程师协会和 Wiley periodicals LLC。