1. Temperature extraction from Brillouin sensing based on temporal convolutional networks.
- Author
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Zhang, Wei, Sun, Zhihui, Chen, Xiaoan, Kong, Zhe, Jiang, Shaodong, Zhang, Faxiang, and Wang, Chang
- Subjects
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CONVOLUTIONAL neural networks , *EXTREME learning machines , *DEEP learning , *SIGNAL-to-noise ratio , *TIME-domain analysis - Abstract
• Poor real-time performance of Brillouin system during long-distance monitoring. • Changing the frequency sweep step size can affect accuracy and processing speed. • An increase in monitoring distance will lead to a decrease in signal-to-noise ratio. • The processing accuracy of temporal convolutional networks is less than 1 °C. • Temporal convolutional networks can handle data with a signal-to-noise ratio of 5 dB. The Brillouin optical time-domain sensing system has become a hot spot of research due to its ability to seamlessly monitor the temperature and strain variations in optical fibers along the line. Given its current limitations of low accuracy and inadequate real-time performance in long-distance monitoring, the Brillouin gain extraction temperature method based on temporal convolutional networks is proposed. On this basis, we established a Brillouin optical time-domain experimental system where comprehensive simulations and tests were conducted to assess the temperature extraction performance under different conditions. Besides, a comparison was made between the system and traditional methods like Lorentz fitting method and extreme learning machine method. The results have suggested that the temporal convolutional network exhibits remarkable measurement accuracy, even in scenarios with low signal-to-noise ratios and large sweep frequency steps. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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