{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,7]],"date-time":"2026-07-07T07:32:24Z","timestamp":1783409544258,"version":"3.54.6"},"reference-count":33,"publisher":"MDPI AG","issue":"5","license":[{"start":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T00:00:00Z","timestamp":1709510400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper is dedicated to the investigation of the metrological properties of phase-sensitive reflectometric measurement systems, with a particular focus on addressing the non-uniformity of responses along optical fibers. The authors highlight challenges associated with the stochastic distribution of Rayleigh reflectors in fiber optic systems and propose a methodology for assessing response non-uniformity using both cross-correlation algorithms and machine learning approaches, using chirped-reflectometry as an example. The experimental process involves simulating deformation impact by altering the light source\u2019s wavelength and utilizing a chirped-reflectometer to estimate response non-uniformity. This paper also includes a comparison of results obtained from cross-correlation and neural network-based algorithms, revealing that the latter offers more than 34% improvement in accuracy when measuring phase differences. In conclusion, the study demonstrates how this methodology effectively evaluates response non-uniformity along different sections of optical fibers.<\/jats:p>","DOI":"10.3390\/s24051656","type":"journal-article","created":{"date-parts":[[2024,3,4]],"date-time":"2024-03-04T04:36:21Z","timestamp":1709526981000},"page":"1656","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Novel Approach to Phase-Sensitive Optical Time-Domain Reflectometry Response Analysis with Machine Learning Methods"],"prefix":"10.3390","volume":"24","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-4666-098X","authenticated-orcid":false,"given":"Vasily A.","family":"Yatseev","sequence":"first","affiliation":[{"name":"Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Science, 125009 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5464-4034","authenticated-orcid":false,"given":"Oleg V.","family":"Butov","sequence":"additional","affiliation":[{"name":"Kotelnikov Institute of Radioengineering and Electronics of Russian Academy of Science, 125009 Moscow, Russia"},{"name":"Scientific Educational Centre \u201cPhotonics and IR Engineering\u201d, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-7790-2796","authenticated-orcid":false,"given":"Alexey B.","family":"Pnev","sequence":"additional","affiliation":[{"name":"Scientific Educational Centre \u201cPhotonics and IR Engineering\u201d, Bauman Moscow State Technical University, 105005 Moscow, Russia"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2024,3,4]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"225","DOI":"10.1364\/OL.19.000225","article-title":"Interferometry with Rayleigh backscattering in a single-mode optical fiber","volume":"19","author":"Mamedov","year":"1994","journal-title":"Opt. 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