A CNN-based FBG demodulation method adopting the GAF-assisted ascending dimension of complicated signal

Zhao, Bin; Li, Wei; Xia, Li; Li, Shiyu; Zhao, Yang; Huang, Yuhao; Zhou, Minghui

doi:10.1016/j.optcom.2021.127296

Cited by 10 publications

(2 citation statements)

References 35 publications

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“…As the common conditions in the software training based on TensorFlow (Google's machine‐learning framework), we adopted softmax activation function for the output weights, [ 50 ] and utilized the adaptive momentum estimation optimizer [ 51 ] and the sparse categorical crossentropy loss function. [ 52 ] As shown in Figure 5f, after training, each cell weight was converted into the conductance of our artificial synapse, and quantized to a specific level in certain cases. In the hardware simulations, the quantized weight values were directly implemented as resistor elements on the synapse array.…”

Section: Resultsmentioning

confidence: 99%

Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Kim

Jang

et al. 2022

Advanced Intelligent Systems

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Section: Resultsmentioning

confidence: 99%

Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Kim

Jang

et al. 2022

Advanced Intelligent Systems

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“…This enables CNN to be used for the classification and calculation of time series signals. Zhao et al [26] use CNN to process some complex signals in fiber sensing. GAF is used to convert one-dimensional waveform signals into two-dimensional images, and then CNN is used to classify the signals under different temperature and magnetic field conditions, the results show that the effect is excellent.…”

Section: Dimensions Expandmentioning

confidence: 99%

Rotating machinery fault diagnosis using dimension expansion and AntisymNet lightweight convolutional neural network

Luo

Peng

Dong

et al. 2023

Meas. Sci. Technol.

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Deep learning-based methods have made remarkable progress in the field of fault diagnosis for rotating machinery. However, convolutional neural networks (CNNs) are not suitable for industrial applications due to their large model size and high computational complexity. To address this limitation, this paper proposes the Antisym module and constructs AntisymNet, which is combined with dimension expansion algorithms for fault diagnosis of rotating machinery. To begin with, the original vibration signal of the rolling machinery is subjected to time-frequency transformations using the Discrete Fourier Transform (DFT) and Discrete Wavelet Transform (DWT). Subsequently, each transformed time-frequency signal is expanded in dimensions, resulting in two-dimensional matrix single channel images. These single channel images are then fused into RGB images to enhance the sample features. Finally, the proposed AntisymNet is utilized for recognizing and classifying the expanded signals. To evaluate the performance of AntisymNet, the MiniImageNet image dataset is employed as a benchmark, and a comparison is made with other state-of-the-art lightweight convolutional neural networks (L-CNNs). Additionally, the effectiveness of the proposed fault diagnosis model is validated using the CWRU bearing dataset, Ottawa bearing dataset, and the hob dataset. The model achieves an impressive accuracy rate of 99.70% in the CWRU dataset, 99.26% in the Ottawa dataset, and an error rate of only 0.66% in the hob dataset. These results demonstrate the strong performance of the proposed fault diagnosis model.

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A Fault Diagnosis Method of Feature Graphical Flight Control System Based on GAF-SWT

Zhang

Wang

Tao

2023

Lecture Notes in Electrical Engineering

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A CNN-based FBG demodulation method adopting the GAF-assisted ascending dimension of complicated signal

Cited by 10 publications

References 35 publications

Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Systematic Engineering of Metal Ion Injection in Memristors for Complex Neuromorphic Computing with High Energy Efficiency

Rotating machinery fault diagnosis using dimension expansion and AntisymNet lightweight convolutional neural network

A Fault Diagnosis Method of Feature Graphical Flight Control System Based on GAF-SWT

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