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Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors

Brillouin optical time-domain analyzer (BOTDA) is used to monitor the temperature and strain along a fiber. So far, neural network and machine learning methods have been successfully applied for temperature extraction. But for different frequency scanning steps, different networks should be designed and trained. Here, a BOTDA assisted by kernel extreme learning machine (K-ELM) with high generalization is proposed and experimentally demonstrated. By utilizing K-ELM, the raw Brillouin gain spectra measured from BOTDA system are classified into different temperature classes. The performance of K-ELM is investigated both in simulation and experiment under different cases of signal-to-noise ratios, pump pulse widths, and frequency scanning steps. Compared with curve fitting methods, the K-ELM algorithm has better measurement accuracy of 0.3 °C and it realizes great improvement of the processing speed over 120 times. The ultrafast processing speed, high accuracy and generality make K-ELM become a highly competitive candidate for the high-speed BOTDA sensing system.

Ketersediaan

Informasi Detail

Judul Seri

JOURNAL OF LIGHTWAVE TECHNOLOGY; Vol.39 No.5 March 1 2021

No. Panggil

-

Penerbit

: .,

Deskripsi Fisik

p. 1537 - 1543

Bahasa

English

ISBN/ISSN

-

Klasifikasi

-

Tipe Isi

-

Tipe Media

-

Tipe Pembawa

-

Edisi

-

Subjek

KERNEL EXTREME LEARNING MACHINE
BRILLOUIN OPTICAL TIME DOMAIN ANALYZER
SIMULATED BRILLOUIN SCATTERING
DISTRIBUTED OPTICAL FIBER SENSORS

Info Detail Spesifik

DOI: 10.1109/JLT.2020.3035810

Pernyataan Tanggungjawab

Yufeng Zhang, Lei Yu, Zhengliang Hu, Le Cheng, Hao Sui, Hongna Zhu, Guangming Li, Bin Luo, Xihua Zou, Lianshan Yan

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