| Autor: |
Wei, Ziyi, Dai, Jingyi, Huang, Yi, Shen, Wei, Hu, Chengyong, Pang, Fufei, Zhang, Xiaobei, Wang, Tingyun |
| Zdroj: |
Journal of Lightwave Technology; September 2024, Vol. 42 Issue: 18 p6395-6401, 7p |
| Abstrakt: |
The phase signals, combined with time-domain signal processing methods, are often used for recognition with the phase-sensitive optical time-domain reflectometer (Φ-OTDR). Considering the advanced and sophisticated algorithms prevalent in the field of image processing, a vibration signal imaging method is proposed to enhance the adaptability of phase signals for learning by image network. The phase time series is converted to a Markov Transition Fields (MTF) matrix, from which the based matrix is extracted by Non-negative Matrix Factorization (NMF) and saved as an RGB image. One-dimensional (1-D) Convolutional Neural Network (CNN) and 2-D CNN are applied in the experiment to classify the phase signals and images, respectively. The experimental results show that the training convergence efficiency of 2-D CNN using NMF-MTF images is significantly higher than that of 1-D CNN, demonstrating the effectiveness of converting phase signals into images. In addition, the average recognition accuracy for the four fence events is improved by more than 13% by introducing the NMF algorithm on the MTF matrix. |
| Databáze: |
Supplemental Index |
| Externí odkaz: |
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