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Simultaneous temperature and strain discrimination in a conventional BOTDA via artificial neural networks

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.

Otras publicaciones de la misma revista o congreso con autores/as de la Universidad de Cantabria

 Autoría: Ruiz-Lombera R., Fuentes A., Rodriguez-Cobo L., Lopez-Higuera J., Mirapeix J.,

 Fuente: Journal of Lightwave Technology, 2018, 36(11), 2114-2121

Editorial: OSA e IEEE

 Fecha de publicación: 01/06/2018

Nº de páginas: 8

Tipo de publicación: Artículo de Revista

 DOI: 10.1109/JLT.2018.2805362

ISSN: 0733-8724,1558-2213

Proyecto español: TEC2013-47264-C2-1-R ; TEC2016-76021-C2-2-R

Url de la publicación: https://doi.org/10.1109/JLT.2018.2805362