@article{10902/15663,
year = {2018},
month = {6},
url = {http://hdl.handle.net/10902/15663},
abstract = {A system based on the use of artificial neural networks allowing discrimination of strain and temperature in a conventional Brillouin optical time domain analyzer setup is presented and demonstrated in this paper. This solution allows to perform an automatic discrimination of both parameters without compromising the complexity or cost of the interrogation unit. The classification results, achieved by considering a preprocessing stage with dimensionality reduction via principal component analysis and spatial filtering, improve those obtained in a previous feasibility study.},
organization = {This work was supported in part by the Projects TEC2013-47264-C2-1-R and TEC2016-76021-C2-2-R},
publisher = {IEEE-},
publisher = {The Optical Society},
publisher = {Journal of Lightwave Technology, 2018, 36(11), 2114-2121},
title = {Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks},
author = {Ruiz Lombera, Rubén and Fuentes Cayón, Alberto and Rodríguez Cobo, Luis and López Higuera, José Miguel and Mirapeix Serrano, Jesús María},
}