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• W4313331317 endingPage "5027" @default.
• W4313331317 startingPage "5027" @default.
• W4313331317 abstract "We report, to our knowledge for the first time on simultaneous distributed temperature and strain sensing in a standard telecom optical fiber using a machine learning assisted Brillouin frequency domain analysis (BOFDA) system. The well-known temperature and strain cross-sensitivity problem is addressed by developing a BOFDA system with a high signal-to-noise ratio and applying machine learning. The spectrum consists of four highly resolved peaks, whose Brillouin frequency shifts are extracted and serve as features for the machine learning algorithms. The spectra result from a 450-m standard SMF-28 optical fiber, and particularly from a segment of 30 m. This fiber segment is coiled around a stretcher and placed in a climate chamber. The applied temperature and strain values range from 20 °C to 40 °C and from 0 µɛ to 1380 µɛ, respectively. The total measurement time to achieve a high SNR and resolve four peaks with a spatial resolution of 6 m is 16 min. To discriminate temperature and strain effects, simple frequentist and more sophisticated Bayesian-based algorithms are employed with the powerful Gaussian process regression (GPR) delivering the best performance in terms of temperature and strain errors, which are found to be 2 °C and 45 µɛ, respectively. These errors are calculated using leave-one-out cross-validation, so that an unbiased estimation of the sensor’s performance is provided." @default.
• W4313331317 created "2023-01-06" @default.
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• W4313331317 date "2023-01-27" @default.
• W4313331317 modified "2023-10-14" @default.
• W4313331317 title "Machine learning assisted BOFDA for simultaneous temperature and strain sensing in a standard optical fiber" @default.
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• W4313331317 doi "https://doi.org/10.1364/oe.480224" @default.
• W4313331317 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/36785455" @default.
• W4313331317 hasPublicationYear "2023" @default.
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