TY - JOUR A1 - Karapanagiotis, Christos A1 - Hicke, Konstantin A1 - Wosniok, Aleksander A1 - Krebber, Katerina T1 - Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach JF - Optics Express N2 - We report, to our knowledge for the first time, on distributed relative humidity sensing in silica polyimide-coated optical fibers using Brillouin optical frequency domain analysis (BOFDA). Linear regression, which is a simple and well-interpretable algorithm in machine learning and statistics, is utilized. The algorithm is trained using as features the Brillouin frequency shifts and linewidths of the fiber’s multipeak Brillouin spectrum. To assess and improve the effectiveness of the regression algorithm, we make use of machine learning concepts to estimate the model’s uncertainties and select the features that contribute most to the model’s performance. In addition to relative humidity, the model is also able to simultaneously provide distributed temperature information addressing the well-known cross-sensitivity effects. KW - Fiber optics sensors KW - Machine learning KW - Brillouin distributed sensing KW - Humidity sensing KW - Simultaneous temperature and humidity sensing PY - 2022 UR - https://nbn-resolving.org/urn:nbn:de:kobv:b43-546294 DO - https://doi.org/10.1364/OE.453906 VL - 30 IS - 8 SP - 12484 EP - 12494 PB - OPTICA AN - OPUS4-54629 LA - eng AD - Bundesanstalt fuer Materialforschung und -pruefung (BAM), Berlin, Germany ER -