{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T06:18:54Z","timestamp":1780640334452,"version":"3.54.1"},"reference-count":176,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2023,7,6]],"date-time":"2023-07-06T00:00:00Z","timestamp":1688601600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Bundesanstalt f\u00fcr Materialforschung und-pr\u00fcfung (BAM)"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>This paper presents reported machine learning approaches in the field of Brillouin distributed fiber optic sensors (DFOSs). The increasing popularity of Brillouin DFOSs stems from their capability to continuously monitor temperature and strain along kilometer-long optical fibers, rendering them attractive for industrial applications, such as the structural health monitoring of large civil infrastructures and pipelines. In recent years, machine learning has been integrated into the Brillouin DFOS signal processing, resulting in fast and enhanced temperature, strain, and humidity measurements without increasing the system\u2019s cost. Machine learning has also contributed to enhanced spatial resolution in Brillouin optical time domain analysis (BOTDA) systems and shorter measurement times in Brillouin optical frequency domain analysis (BOFDA) systems. This paper provides an overview of the applied machine learning methodologies in Brillouin DFOSs, as well as future perspectives in this area.<\/jats:p>","DOI":"10.3390\/s23136187","type":"journal-article","created":{"date-parts":[[2023,7,7]],"date-time":"2023-07-07T01:57:09Z","timestamp":1688695029000},"page":"6187","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":26,"title":["Machine Learning Approaches in Brillouin Distributed Fiber Optic Sensors"],"prefix":"10.3390","volume":"23","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9065-3480","authenticated-orcid":false,"given":"Christos","family":"Karapanagiotis","sequence":"first","affiliation":[{"name":"Bundesanstalt f\u00fcr Materialforschung und-Pr\u00fcfung, Unter den Eichen 87, 12205 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Katerina","family":"Krebber","sequence":"additional","affiliation":[{"name":"Bundesanstalt f\u00fcr Materialforschung und-Pr\u00fcfung, Unter den Eichen 87, 12205 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2023,7,6]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Ecke, W., N\u00f6ther, N., Peters, K.J., Wosniok, A., Krebber, K., Meyendorf, N.G., and Thiele, E. (2008, January 10\u201312). A distributed fiber optic sensor system for dike monitoring using Brillouin optical frequency domain analysis. Proceedings of the Smart Sensor Phenomena, Technology, Networks, and Systems, San Diego, CA, USA.","DOI":"10.1117\/12.775133"},{"key":"ref_2","doi-asserted-by":"crossref","unstructured":"Schenato, L. (2017). A Review of Distributed Fibre Optic Sensors for Geo-Hydrological Applications. Appl. Sci., 7.","DOI":"10.3390\/app7090896"},{"key":"ref_3","doi-asserted-by":"crossref","unstructured":"Bado, M.F., and Casas, J.R. (2021). A Review of Recent Distributed Optical Fiber Sensors Applications for Civil Engineering Structural Health Monitoring. Sensors, 21.","DOI":"10.3390\/s21051818"},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1317","DOI":"10.1007\/s13349-022-00560-w","article-title":"Large-scale distributed fiber optic sensing network for short and long-term integrity monitoring of tunnel linings","volume":"12","author":"Monsberger","year":"2022","journal-title":"J. Civ. Struct. Health"},{"key":"ref_5","doi-asserted-by":"crossref","unstructured":"Wu, T., Liu, G., Fu, S., and Xing, F. (2020). Recent Progress of Fiber-Optic Sensors for the Structural Health Monitoring of Civil Infrastructure. Sensors, 20.","DOI":"10.3390\/s20164517"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Stajanca, P., Chruscicki, S., Homann, T., Seifert, S., Schmidt, D., and Habib, A. (2018). Detection of Leak-Induced Pipeline Vibrations Using Fiber\u2014Optic Distributed Acoustic Sensing. Sensors, 18.","DOI":"10.3390\/s18092841"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"2797","DOI":"10.1038\/s41598-021-82093-8","article-title":"Detection of hydroacoustic signals on a fiber-optic submarine cable","volume":"11","author":"Matsumoto","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"e2019GL086115","DOI":"10.1029\/2019GL086115","article-title":"Urban Near-Surface Seismic Monitoring Using Distributed Acoustic Sensing","volume":"47","author":"Fang","year":"2020","journal-title":"Geophys. Res. Lett."},{"key":"ref_9","doi-asserted-by":"crossref","first-page":"5777","DOI":"10.1038\/s41467-019-13793-z","article-title":"Distributed sensing of earthquakes and ocean-solid Earth interactions on seafloor telecom cables","volume":"10","author":"Sladen","year":"2019","journal-title":"Nat. Commun."},{"key":"ref_10","doi-asserted-by":"crossref","first-page":"e2019JB018656","DOI":"10.1029\/2019JB018656","article-title":"Urban Seismic Site Characterization by Fiber-Optic Seismology","volume":"125","author":"Spica","year":"2020","journal-title":"J. Geophys. Res. Solid Earth"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"1352","DOI":"10.1109\/JLT.2019.2893038","article-title":"Subsea Cable Condition Monitoring with Distributed Optical Fiber Vibration Sensor","volume":"37","author":"Masoudi","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"107082","DOI":"10.1016\/j.optlastec.2021.107082","article-title":"Optical fiber sensing for marine environment and marine structural health monitoring: A review","volume":"140","author":"Min","year":"2021","journal-title":"Opt. Laser Technol."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"284","DOI":"10.1016\/j.snb.2017.02.027","article-title":"A fully distributed fibre optic sensor for relative humidity measurements","volume":"247","author":"Thomas","year":"2017","journal-title":"Sens. Actuators B Chem."},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Stajanca, P., Hicke, K., and Krebber, K. (2019). Distributed Fiberoptic Sensor for Simultaneous Humidity and Temperature Monitoring Based on Polyimide-Coated Optical Fibers. Sensors, 19.","DOI":"10.3390\/s19235279"},{"key":"ref_15","doi-asserted-by":"crossref","first-page":"130154","DOI":"10.1016\/j.snb.2021.130154","article-title":"Optical fibre sensor for simultaneous temperature and relative humidity measurement: Towards absolute humidity evaluation","volume":"344","author":"He","year":"2021","journal-title":"Sens. Actuators B Chem."},{"key":"ref_16","doi-asserted-by":"crossref","first-page":"102239","DOI":"10.1016\/j.yofte.2020.102239","article-title":"Distributed optical fiber pressure sensors","volume":"58","author":"Schenato","year":"2020","journal-title":"Opt. Fiber Technol."},{"key":"ref_17","unstructured":"Jaroszewicz, L.R., Kusche, N., Schukar, V., Hofmann, D., Basedau, F., Habel, W., Woschitz, H., and Lienhart, W. (2013, January 19\u201322). Field examples for optical fibre sensor condition diagnostics based on distributed fibre optic strain sensing. Proceedings of the 5th European Workshop on Optical Fibre Sensors, Cracow, Poland."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"226","DOI":"10.1016\/j.optmat.2016.05.027","article-title":"Effects of gamma radiation on perfluorinated polymer optical fibers","volume":"58","author":"Stajanca","year":"2016","journal-title":"Opt. Mater."},{"key":"ref_19","doi-asserted-by":"crossref","unstructured":"Stajanca, P., and Krebber, K. (2017). Radiation-Induced Attenuation of Perfluorinated Polymer Optical Fibers for Radiation Monitoring. Sensors, 17.","DOI":"10.3390\/s17091959"},{"key":"ref_20","unstructured":"Lewis, E., Wosniok, A., Sporea, D., Negu\u0163, D., and Krebber, K. (June, January 31). Gamma radiation influence on silica optical fibers measured by optical backscatter reflectometry and Brillouin sensing technique. Proceedings of the 6th European Workshop on Optical Fibre Sensors, Limerick, Ireland."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"14009","DOI":"10.1088\/2515-7647\/ab6a73","article-title":"Distributed and discrete hydrogen monitoring through optical fiber sensors based on optical frequency domain reflectometry","volume":"2","author":"Rizzolo","year":"2020","journal-title":"J. Phys. Photonics"},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"31568","DOI":"10.1364\/OE.25.031568","article-title":"Distributed gas sensing with optical fibre photothermal interferometry","volume":"25","author":"Lin","year":"2017","journal-title":"Opt. Express"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Hartog, A.H. (2017). An Introduction to Distributed Optical Fibre Sensors, CRC Press.","DOI":"10.1201\/9781315119014"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"527","DOI":"10.1134\/S0020441220040181","article-title":"Achievement of an 85 km Distance Range of Strain (Temperature) Measurements Using Low-Coherence Rayleigh Reflectometry","volume":"63","author":"Taranov","year":"2020","journal-title":"Instrum. Exp. Tech."},{"key":"ref_25","doi-asserted-by":"crossref","unstructured":"Lu, Z., Feng, T., Li, F., and Yao, X.S. (2023). Optical Frequency-Domain Reflectometry Based Distributed Temperature Sensing Using Rayleigh Backscattering Enhanced Fiber. Sensors, 23.","DOI":"10.3390\/s23125748"},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Pedraza, A., del R\u00edo, D., Bautista-Juzgado, V., Fern\u00e1ndez-L\u00f3pez, A., and Sanz-Andr\u00e9s, \u00c1. (2023). Study of the Feasibility of Decoupling Temperature and Strain from a \u03d5-PA-OFDR over an SMF Using Neural Networks. Sensors, 23.","DOI":"10.20944\/preprints202305.0310.v1"},{"key":"ref_27","doi-asserted-by":"crossref","unstructured":"Palmieri, L., Schenato, L., Santagiustina, M., and Galtarossa, A. (2022). Rayleigh-Based Distributed Optical Fiber Sensing. Sensors, 22.","DOI":"10.3390\/s22186811"},{"key":"ref_28","doi-asserted-by":"crossref","first-page":"2613","DOI":"10.1364\/OL.34.002613","article-title":"Dynamic strain measurement in optical fibers by stimulated Brillouin scattering","volume":"34","author":"Bernini","year":"2009","journal-title":"Opt. Lett."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"B842","DOI":"10.1364\/OE.19.00B842","article-title":"Sweep-free distributed Brillouin time-domain analyzer (SF-BOTDA)","volume":"19","author":"Voskoboinik","year":"2011","journal-title":"Opt. Express"},{"key":"ref_30","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1038\/s41377-018-0030-0","article-title":"Single-shot BOTDA based on an optical chirp chain probe wave for distributed ultrafast measurement","volume":"7","author":"Zhou","year":"2018","journal-title":"Light Sci. Appl."},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"3770","DOI":"10.1364\/AO.52.003770","article-title":"Real-time monitoring of railway traffic using slope-assisted Brillouin distributed sensors","volume":"52","author":"Minardo","year":"2013","journal-title":"Appl. Opt."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.optlastec.2015.09.013","article-title":"[INVITED] State of the art of Brillouin fiber-optic distributed sensing","volume":"78","author":"Motil","year":"2016","journal-title":"Opt. Laser Technol."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"126107","DOI":"10.1063\/5.0126068","article-title":"Ultra-long Brillouin optical time-domain analyzer based on distortion compensating pulse and hybrid lumped\u2013distributed amplification","volume":"7","author":"Sun","year":"2022","journal-title":"APL Photonics"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1016\/j.optlastec.2014.12.001","article-title":"Ultra-long dual-sideband BOTDA with balanced detection","volume":"68","author":"Zhang","year":"2015","journal-title":"Opt. Laser Technol."},{"key":"ref_35","doi-asserted-by":"crossref","first-page":"4444","DOI":"10.1364\/OE.19.004444","article-title":"Optimization of long-range BOTDA sensors with high resolution using first-order bi-directional Raman amplification","volume":"19","author":"Soto","year":"2011","journal-title":"Opt. Express"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"e16074","DOI":"10.1038\/lsa.2016.74","article-title":"Going beyond 1000000 resolved points in a Brillouin distributed fiber sensor: Theoretical analysis and experimental demonstration","volume":"5","author":"Denisov","year":"2016","journal-title":"Light Sci. Appl."},{"key":"ref_37","doi-asserted-by":"crossref","first-page":"48","DOI":"10.1109\/JPHOT.2011.2179024","article-title":"Distributed Sensing at Centimeter-Scale Spatial Resolution by BOFDA: Measurements and Signal Processing","volume":"4","author":"Bernini","year":"2012","journal-title":"IEEE Photonics J."},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"8671","DOI":"10.1364\/OE.18.008671","article-title":"High spatial resolution distributed sensing in optical fibers by Brillouin gain-profile tracing","volume":"18","author":"Sperber","year":"2010","journal-title":"Opt. Express"},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"1402","DOI":"10.1364\/OL.21.001402","article-title":"Distributed sensing technique based on Brillouin optical-fiber frequency-domain analysis","volume":"21","author":"Garus","year":"1996","journal-title":"Opt. Lett."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"111543","DOI":"10.1016\/j.measurement.2022.111543","article-title":"Fibre-optic sensor and deep learning-based structural health monitoring systems for civil structures: A review","volume":"199","author":"Jayawickrema","year":"2022","journal-title":"Measurement"},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"2975","DOI":"10.1364\/AO.444811","article-title":"Machine learning methods for identification and classification of events in \u03d5-OTDR systems: A review","volume":"61","author":"Kandamali","year":"2022","journal-title":"Appl. Opt."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"4755","DOI":"10.1109\/JLT.2019.2919713","article-title":"Efficient Processing of Distributed Acoustic Sensing Data Using a Deep Learning Approach","volume":"37","author":"Shiloh","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_43","unstructured":"Ohodnicki, P.R., Zhang, P., Lalam, N., Karki, D., Venketeswaran, A., Babaee, H., and Wright, R. (September, January 29). Fusion of Distributed Fiber Optic Sensing, Acoustic NDE, and Artificial Intelligence for Infrastructure Monitoring. Proceedings of the 27th International Conference on Optical Fiber Sensors, Alexandria, VA, USA."},{"key":"ref_44","doi-asserted-by":"crossref","unstructured":"Shiloh, L., Eyal, A., and Giryes, R. (2018, January 24\u201328). Deep Learning Approach for Processing Fiber-Optic DAS Seismic Data. Proceedings of the 26th International Conference on Optical Fiber Sensors, Lausanne, Switzerland.","DOI":"10.1364\/OFS.2018.ThE22"},{"key":"ref_45","doi-asserted-by":"crossref","first-page":"165373","DOI":"10.1016\/j.ijleo.2020.165373","article-title":"Multi-event classification for \u03a6-OTDR distributed optical fiber sensing system using deep learning and support vector machine","volume":"221","author":"Shi","year":"2020","journal-title":"Optik"},{"key":"ref_46","doi-asserted-by":"crossref","unstructured":"Shi, Y., Wang, Y., Zhao, L., and Fan, Z. (2019). An Event Recognition Method for \u03a6-OTDR Sensing System Based on Deep Learning. Sensors, 19.","DOI":"10.3390\/s19153421"},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"27277","DOI":"10.1364\/OE.397509","article-title":"Distributed fiber sensor and machine learning data analytics for pipeline protection against extrinsic intrusions and intrinsic corrosions","volume":"28","author":"Peng","year":"2020","journal-title":"Opt. Express"},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"1704","DOI":"10.1177\/1475921720930649","article-title":"A surveillance system for urban buried pipeline subject to third-party threats based on fiber optic sensing and convolutional neural network","volume":"20","author":"Li","year":"2020","journal-title":"Struct. Health Monit."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"102060","DOI":"10.1016\/j.yofte.2019.102060","article-title":"Detection and identification of external intrusion signals from 33 km optical fiber sensing system based on deep learning","volume":"53","author":"Bai","year":"2019","journal-title":"Opt. Fiber Technol."},{"key":"ref_50","doi-asserted-by":"crossref","unstructured":"Chen, J., Wu, H., Liu, X., Xiao, Y., Wang, M., Yang, M., and Rao, Y. (2018, January 18\u201320). A Real-Time Distributed Deep Learning Approach for Intelligent Event Recognition in Long Distance Pipeline Monitoring with DOFS. Proceedings of the 2018 International Conference on Cyber-Enabled Distributed Computing and Knowledge Discovery (CyberC), Zhengzhou, China.","DOI":"10.1109\/CyberC.2018.00059"},{"key":"ref_51","unstructured":"Wu, Z., Wang, Q., Gribok, A.V., and Chen, K.P. (September, January 29). Pipeline Degradation Evaluation Based on Distributed Fiber Sensors and Convolutional Neural Networks (CNNs). Proceedings of the 27th International Conference on Optical Fiber Sensors, Alexandria, VA, USA."},{"key":"ref_52","unstructured":"Wang, Q., Jian, J., Wang, M., Wu, J., Mao, Z.-H., Gribok, A.V., and Chen, K.P. (2020). Optical Fiber Sensors Conference 2020 Special Edition, Optica Publishing Group."},{"key":"ref_53","doi-asserted-by":"crossref","first-page":"4359","DOI":"10.1109\/JLT.2019.2923839","article-title":"One-Dimensional CNN-Based Intelligent Recognition of Vibrations in Pipeline Monitoring with DAS","volume":"37","author":"Wu","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_54","doi-asserted-by":"crossref","first-page":"2925","DOI":"10.1364\/OE.28.002925","article-title":"Fiber distributed acoustic sensing using convolutional long short-term memory network: A field test on high-speed railway intrusion detection","volume":"28","author":"Li","year":"2020","journal-title":"Opt. Express"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"23682","DOI":"10.1364\/OE.27.023682","article-title":"Practical multi-class event classification approach for distributed vibration sensing using deep dual path network","volume":"27","author":"Wang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_56","doi-asserted-by":"crossref","unstructured":"Kowarik, S., Hussels, M.-T., Chruscicki, S., M\u00fcnzenberger, S., L\u00e4mmerhirt, A., Pohl, P., and Schubert, M. (2020). Fiber Optic Train Monitoring with Distributed Acoustic Sensing: Conventional and Neural Network Data Analysis. Sensors, 20.","DOI":"10.3390\/s20020450"},{"key":"ref_57","unstructured":"Hamadi, A., Montarsolo, E., Kabalan, A., Garbini, G.P., and Hammi, T. (2020). Optical Fiber Sensors Conference 2020 Special Edition, Optica Publishing Group."},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"2639","DOI":"10.1109\/JLT.2021.3138724","article-title":"Deep-Learning-Based Earthquake Detection for Fiber-Optic Distributed Acoustic Sensing","volume":"40","author":"Hernandez","year":"2022","journal-title":"J. Light. Technol."},{"key":"ref_59","doi-asserted-by":"crossref","unstructured":"van den Ende, M., Lior, I., Ampuero, J.-P., Sladen, A., Ferrari, A., and Richard, C. (2021). A Self-Supervised Deep Learning Approach for Blind Denoising and Waveform Coherence Enhancement in Distributed Acoustic Sensing Data. IEEE Trans. Neural Netw. Learn. Syst., 1\u201314.","DOI":"10.31223\/X55K63"},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"2583","DOI":"10.1109\/JLT.2021.3052651","article-title":"Rapid Response DAS Denoising Method Based on Deep Learning","volume":"39","author":"Wang","year":"2021","journal-title":"J. Light. Technol."},{"key":"ref_61","doi-asserted-by":"crossref","first-page":"3004905","DOI":"10.1109\/LGRS.2021.3127637","article-title":"RCEN: A Deep-Learning-Based Background Noise Suppression Method for DAS-VSP Records","volume":"19","author":"Zhong","year":"2022","journal-title":"IEEE Geosci. Remote Sens. Lett."},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"WA91","DOI":"10.1190\/geo2022-0138.1","article-title":"Denoising of distributed acoustic sensing data using supervised deep learning","volume":"88","author":"Yang","year":"2022","journal-title":"Geophysics"},{"key":"ref_63","doi-asserted-by":"crossref","first-page":"39311","DOI":"10.1364\/OE.402789","article-title":"Long-distance fiber optic vibration sensing using convolutional neural networks as real-time denoisers","volume":"28","author":"Liehr","year":"2020","journal-title":"Opt. Express"},{"key":"ref_64","unstructured":"Wang, Y., Liu, Q., Li, B., Chen, D., Li, H., and He, Z. (2020). Optical Fiber Sensors Conference 2020 Special Edition, Optica Publishing Group."},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"7405","DOI":"10.1364\/OE.27.007405","article-title":"Real-time dynamic strain sensing in optical fibers using artificial neural networks","volume":"27","author":"Liehr","year":"2019","journal-title":"Opt. Express"},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"2100067","DOI":"10.1002\/aisy.202100067","article-title":"Recent Advances in Machine Learning for Fiber Optic Sensor Applications","volume":"4","author":"Venketeswaran","year":"2021","journal-title":"Adv. Intell. Syst."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"687","DOI":"10.1134\/S0020441222050268","article-title":"State-of-the-Art Methods for Determining the Frequency Shift of Brillouin Scattering in Fiber-Optic Metrology and Sensing (Review)","volume":"65","author":"Krivosheev","year":"2022","journal-title":"Instrum. Exp. Tech."},{"key":"ref_68","doi-asserted-by":"crossref","first-page":"1813","DOI":"10.1109\/JIOT.2022.3209674","article-title":"Enabling variable high spatial resolution retrieval from a long pulse BOTDA sensor","volume":"10","author":"Ge","year":"2022","journal-title":"IEEE Internet Things J."},{"key":"ref_69","doi-asserted-by":"crossref","unstructured":"Karapanagiotis, C., Wosniok, A., Hicke, K., and Krebber, K. (2021). Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis. Sensors, 21.","DOI":"10.3390\/s21082724"},{"key":"ref_70","doi-asserted-by":"crossref","first-page":"5027","DOI":"10.1364\/OE.480224","article-title":"Machine learning assisted BOFDA for simultaneous temperature and strain sensing in a standard optical fiber","volume":"31","author":"Karapanagiotis","year":"2023","journal-title":"Opt. Express"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"2114","DOI":"10.1109\/JLT.2018.2805362","article-title":"Simultaneous Temperature and Strain Discrimination in a Conventional BOTDA via Artificial Neural Networks","volume":"36","author":"Fuentes","year":"2018","journal-title":"J. Light. Technol."},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"2530","DOI":"10.1364\/OE.27.002530","article-title":"Deep neural networks assisted BOTDA for simultaneous temperature and strain measurement with enhanced accuracy","volume":"27","author":"Wang","year":"2019","journal-title":"Opt. Express"},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"34453","DOI":"10.1364\/OE.469342","article-title":"Integrated denoising and extraction of both temperature and strain based on a single CNN framework for a BOTDA sensing system","volume":"30","author":"Yang","year":"2022","journal-title":"Opt. Express"},{"key":"ref_74","doi-asserted-by":"crossref","first-page":"141","DOI":"10.1364\/OL.19.000141","article-title":"Combined Distributed Temperature and Strain Sensor-Based on Brillouin Loss in an Optical-Fiber","volume":"19","author":"Bao","year":"1994","journal-title":"Opt. Lett."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"1276","DOI":"10.1364\/OL.30.001276","article-title":"Simultaneous temperature and strain measurement with combined spontaneous Raman and Brillouin scattering","volume":"30","author":"Alahbabi","year":"2005","journal-title":"Opt. Lett."},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"24025","DOI":"10.1364\/OE.426427","article-title":"Hybrid Brillouin\/Rayleigh sensor for multiparameter measurements in optical fibers","volume":"29","author":"Coscetta","year":"2021","journal-title":"Opt. Express"},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"695","DOI":"10.1364\/OL.25.000695","article-title":"All-fiber system for simultaneous interrogation of distributed strain and temperature sensing by spontaneous Brillouin scattering","volume":"25","author":"Kee","year":"2000","journal-title":"Opt. Lett."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1007\/s13320-013-0136-1","article-title":"Study of Optical Fibers Strain-Temperature Sensitivities Using Hybrid Brillouin-Rayleigh System","volume":"4","author":"Kishida","year":"2014","journal-title":"Photonic Sens."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"1053","DOI":"10.1109\/JLT.2011.2168193","article-title":"Brillouin Spectrum in LEAF and Simultaneous Temperature and Strain Measurement","volume":"30","author":"Liu","year":"2012","journal-title":"J. Light. Technol."},{"key":"ref_80","doi-asserted-by":"crossref","first-page":"1217","DOI":"10.1109\/LPT.2021.3112761","article-title":"Distributed Temperature and Strain Measurement Based on Brillouin Gain Spectrum and Brillouin Beat Spectrum","volume":"33","author":"Peng","year":"2021","journal-title":"IEEE Photonic Technol. Lett."},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1007\/s13320-020-0609-y","article-title":"Simultaneous Strain and Temperature Measurement Based on Chaotic Brillouin Optical Correlation-Domain Analysis in Large-Effective-Area Fibers","volume":"11","author":"Zhang","year":"2021","journal-title":"Photonic Sens."},{"key":"ref_82","doi-asserted-by":"crossref","first-page":"1485","DOI":"10.1364\/OL.29.001485","article-title":"Dependence of the Brillouin frequency shift on strain and temperature in a photonic crystal fiber","volume":"29","author":"Zou","year":"2004","journal-title":"Opt. Lett."},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"7100","DOI":"10.1109\/JSEN.2018.2854368","article-title":"Temperature and Strain Discrimination in BOTDA Fiber Sensor by Utilizing Dispersion Compensating Fiber","volume":"18","author":"Li","year":"2018","journal-title":"IEEE Sens. J."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"12505","DOI":"10.1038\/s41598-021-91916-7","article-title":"Well-scale demonstration of distributed pressure sensing using fiber-optic DAS and DTS","volume":"11","author":"Ekechukwu","year":"2021","journal-title":"Sci. Rep."},{"key":"ref_85","first-page":"405","article-title":"Measurement of Brillouin Gain Spectrum Distribution along an Optical Fiber Using a Correlation-Based Technique: Proposal, Experiment and Simulation (Special Issue on Optical Fiber Sensors)","volume":"83","author":"Hotate","year":"2000","journal-title":"IEICE Trans. Electron."},{"key":"ref_86","doi-asserted-by":"crossref","unstructured":"Hotate, K. (2014, January 2\u20135). Recent achievements in BOCDA\/BOCDR. Proceedings of the IEEE SENSORS 2014 Proceedings, Valencia, Spain.","DOI":"10.1109\/ICSENS.2014.6984953"},{"key":"ref_87","doi-asserted-by":"crossref","first-page":"12148","DOI":"10.1364\/OE.16.012148","article-title":"Proposal of Brillouin optical correlation-domain reflectometry (BOCDR)","volume":"16","author":"Mizuno","year":"2008","journal-title":"Opt. Express"},{"key":"ref_88","doi-asserted-by":"crossref","first-page":"510","DOI":"10.1364\/OL.24.000510","article-title":"Characterization of the Brillouin-loss spectrum of single-mode fibers by use of very short (<10-ns) pulses","volume":"24","author":"Bao","year":"1999","journal-title":"Opt. Lett."},{"key":"ref_89","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3801\/IAFSS.FSS.7-221","article-title":"Brillouin Scattering Based Distributed Fiber Optic Temperature Sensing for Fire Detection","volume":"7","author":"Liu","year":"2003","journal-title":"Fire Saf. Sci."},{"key":"ref_90","doi-asserted-by":"crossref","first-page":"4275","DOI":"10.1364\/OL.36.004275","article-title":"Accurate estimation of Brillouin frequency shift in Brillouin optical time domain analysis sensors using cross correlation","volume":"36","author":"Farahani","year":"2011","journal-title":"Opt. Lett."},{"key":"ref_91","doi-asserted-by":"crossref","first-page":"4589","DOI":"10.1109\/JSEN.2013.2271254","article-title":"A Detailed Evaluation of the Correlation-Based Method Used for Estimation of the Brillouin Frequency Shift in BOTDA Sensors","volume":"13","author":"Farahani","year":"2013","journal-title":"IEEE Sens. J."},{"key":"ref_92","doi-asserted-by":"crossref","first-page":"68034","DOI":"10.1109\/ACCESS.2019.2919138","article-title":"Optimized Feedforward Neural Network Training for Efficient Brillouin Frequency Shift Retrieval in Fiber","volume":"7","author":"Liang","year":"2019","journal-title":"IEEE Access"},{"key":"ref_93","unstructured":"Bishop, C.M., and Nasrabadi, N.M. (2006). Pattern Recognition and Machine Learning, Springer."},{"key":"ref_94","doi-asserted-by":"crossref","first-page":"22022","DOI":"10.1088\/1742-6596\/1168\/2\/022022","article-title":"An Overview of Overfitting and its Solutions","volume":"1168","author":"Ying","year":"2019","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_95","unstructured":"Prechelt, L. (1998). Neural Networks: Tricks of the Trade, Springer."},{"key":"ref_96","doi-asserted-by":"crossref","first-page":"95","DOI":"10.1016\/j.jeconom.2015.02.006","article-title":"Cross-validation for selecting a model selection procedure","volume":"187","author":"Zhang","year":"2015","journal-title":"J. Econom."},{"key":"ref_97","doi-asserted-by":"crossref","first-page":"8559","DOI":"10.1109\/JSEN.2020.2985550","article-title":"Artificial Neural Network for Accurate Retrieval of Fiber Brillouin Frequency Shift with Non-Local Effects","volume":"20","author":"Lu","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_98","doi-asserted-by":"crossref","first-page":"13942","DOI":"10.1364\/OE.451877","article-title":"Wavelet convolutional neural network for robust and fast temperature measurements in Brillouin optical time domain reflectometry","volume":"30","author":"Chen","year":"2022","journal-title":"Opt. Express"},{"key":"ref_99","doi-asserted-by":"crossref","first-page":"690","DOI":"10.1364\/PRJ.389970","article-title":"Distributed Brillouin frequency shift extraction via a convolutional neural network","volume":"8","author":"Chang","year":"2020","journal-title":"Photonics Res."},{"key":"ref_100","doi-asserted-by":"crossref","unstructured":"Buber, E., and Diri, B. (2018, January 25\u201327). Performance Analysis and CPU vs GPU Comparison for Deep Learning. Proceedings of the 2018 6th International Conference on Control Engineering & Information Technology (CEIT), Istanbul, Turkey.","DOI":"10.1109\/CEIT.2018.8751930"},{"key":"ref_101","doi-asserted-by":"crossref","first-page":"7725","DOI":"10.1364\/OE.450977","article-title":"Dynamic polarization-insensitive BOTDA in direct-detection OFDM with CNN-based BFS extraction","volume":"30","author":"Qi","year":"2022","journal-title":"Opt. Express"},{"key":"ref_102","doi-asserted-by":"crossref","first-page":"124701","DOI":"10.1109\/ACCESS.2021.3110874","article-title":"Spatial Resolution Enhancement of Brillouin Optical Correlation-Domain Reflectometry Using Convolutional Neural Network: Proof of Concept","volume":"9","author":"Caceres","year":"2021","journal-title":"IEEE Access"},{"key":"ref_103","doi-asserted-by":"crossref","unstructured":"Lalam, N., Venketeswaran, A., Lu, P., Buric, M.P., Schr\u00f6der, H., and Chen, R.T. (2021, January 6\u201311). Probabilistic deep neural network based signal processing for Brillouin gain and phase spectrums of vector BOTDA system. Proceedings of the Optical Interconnects XXI, Online.","DOI":"10.1117\/12.2578509"},{"key":"ref_104","doi-asserted-by":"crossref","first-page":"31347","DOI":"10.1364\/OE.21.031347","article-title":"Modeling and evaluating the performance of Brillouin distributed optical fiber sensors","volume":"21","author":"Soto","year":"2013","journal-title":"Opt. Express"},{"key":"ref_105","doi-asserted-by":"crossref","first-page":"111966","DOI":"10.1016\/j.measurement.2022.111966","article-title":"Efficient two-stage strain\/temperature measurement method for BOTDA system based on Bayesian uncertainty quantification","volume":"203","author":"Meng","year":"2022","journal-title":"Measurement"},{"key":"ref_106","doi-asserted-by":"crossref","first-page":"273","DOI":"10.1007\/BF00994018","article-title":"Support-vector networks","volume":"20","author":"Cortes","year":"1995","journal-title":"Mach. Learn."},{"key":"ref_107","doi-asserted-by":"crossref","first-page":"15616","DOI":"10.1364\/OE.455454","article-title":"Dynamic strain measurement in Brillouin optical correlation-domain sensing facilitated by dimensionality reduction and support vector machine","volume":"30","author":"Yao","year":"2022","journal-title":"Opt. Express"},{"key":"ref_108","doi-asserted-by":"crossref","unstructured":"Zheng, H., Xiao, F., Sun, S., and Qin, Y. (2022). Brillouin Frequency Shift Extraction Based on AdaBoost Algorithm. Sensors, 22.","DOI":"10.3390\/s22093354"},{"key":"ref_109","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","article-title":"A Decision-Theoretic Generalization of On-Line Learning and an Application to Boosting","volume":"55","author":"Freund","year":"1997","journal-title":"J. Comput. Syst. Sci."},{"key":"ref_110","doi-asserted-by":"crossref","first-page":"349","DOI":"10.4310\/SII.2009.v2.n3.a8","article-title":"Multi-class AdaBoost","volume":"2","author":"Hastie","year":"2009","journal-title":"Stat. Interface"},{"key":"ref_111","unstructured":"Quinlan, J.R. (1992, January 16\u201318). Learning with Continuous Classes. Proceedings of the Australian Joint Conference on Artificial Intelligence, Hobart, Australia."},{"key":"ref_112","doi-asserted-by":"crossref","unstructured":"Dobra, A., and Gehrke, J. (2002, January 23\u201326). SECRET: A scalable linear regression tree algorithm. Proceedings of the 8th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, Edmonton, AB, Canada.","DOI":"10.1145\/775047.775117"},{"key":"ref_113","doi-asserted-by":"crossref","first-page":"238","DOI":"10.2307\/1403797","article-title":"Discriminatory Analysis. Nonparametric Discrimination: Consistency Properties","volume":"57","author":"Fix","year":"1989","journal-title":"Int. Stat. Rev. Rev. Int. Stat."},{"key":"ref_114","doi-asserted-by":"crossref","unstructured":"Zheng, H., Peng, G.-D., and He, Z. (2020, January 11\u201316). Extraction of Brillouin frequency shift in Brillouin distributed fiber sensors by neighbors-based machine learning. Proceedings of the Advanced Sensor Systems and Applications X, Online.","DOI":"10.1117\/12.2573346"},{"key":"ref_115","doi-asserted-by":"crossref","first-page":"102903","DOI":"10.1016\/j.yofte.2022.102903","article-title":"Extraction of Brillouin frequency shift from Brillouin gain spectrum in Brillouin distributed fiber sensors using K nearest neighbor algorithm","volume":"71","author":"Zheng","year":"2022","journal-title":"Opt. Fiber Technol."},{"key":"ref_116","doi-asserted-by":"crossref","unstructured":"Xiao, F., Lv, M., and Li, X. (2021). Fast Measurement of Brillouin Frequency Shift in Optical Fiber Based on a Novel Feedforward Neural Network. Photonics, 8.","DOI":"10.3390\/photonics8110474"},{"key":"ref_117","doi-asserted-by":"crossref","first-page":"20150202","DOI":"10.1098\/rsta.2015.0202","article-title":"Principal component analysis: A review and recent developments","volume":"374","author":"Jolliffe","year":"2016","journal-title":"Philos. Trans. R. Soc. A Math. Phys. Eng. Sci."},{"key":"ref_118","doi-asserted-by":"crossref","unstructured":"Abdolrasol, M.G.M., Hussain, S.M.S., Ustun, T.S., Sarker, M.R., Hannan, M.A., Mohamed, R., Ali, J.A., Mekhilef, S., and Milad, A. (2021). Artificial Neural Networks Based Optimization Techniques: A Review. Electronics, 10.","DOI":"10.3390\/electronics10212689"},{"key":"ref_119","first-page":"269","article-title":"Hyperparameter optimization in convolutional neural network using genetic algorithms","volume":"10","author":"Aszemi","year":"2019","journal-title":"Int. J. Adv. Comput. Sci. Appl."},{"key":"ref_120","unstructured":"Yu, T., and Zhu, H. (2020). Hyper-parameter optimization: A review of algorithms and applications. arXiv."},{"key":"ref_121","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.dsp.2017.10.011","article-title":"Methods for interpreting and understanding deep neural networks","volume":"73","author":"Montavon","year":"2018","journal-title":"Digit. Signal Process."},{"key":"ref_122","doi-asserted-by":"crossref","first-page":"100071","DOI":"10.1016\/j.dajour.2022.100071","article-title":"A comparative analysis of K-Nearest Neighbor, Genetic, Support Vector Machine, Decision Tree, and Long Short Term Memory algorithms in machine learning","volume":"3","author":"Bansal","year":"2022","journal-title":"Decis. Anal. J."},{"key":"ref_123","doi-asserted-by":"crossref","unstructured":"Wang, B., Guo, N., Wang, L., Yu, C., and Lu, C. (2018, January 24\u201328). Denoising and Robust Temperature Extraction for BOTDA Systems based on Denoising Autoencoder and DNN. Proceedings of the 26th International Conference on Optical Fiber Sensors, Lausanne, Switzerland.","DOI":"10.1364\/OFS.2018.WF29"},{"key":"ref_124","doi-asserted-by":"crossref","first-page":"3614","DOI":"10.1109\/JSEN.2019.2960876","article-title":"Robust and Fast Temperature Extraction for Brillouin Optical Time-Domain Analyzer by Using Denoising Autoencoder-Based Deep Neural Networks","volume":"20","author":"Wang","year":"2020","journal-title":"IEEE Sens. J."},{"key":"ref_125","doi-asserted-by":"crossref","unstructured":"Yang, Y.-n., Dong, Y., and Yu, K. (2022, January 3\u20136). SNR Improvement based on Attention-DNet for Brillouin Distributed Optical Fiber Sensors. Proceedings of the 2022 27th OptoElectronics and Communications Conference (OECC) and 2022 International Conference on Photonics in Switching and Computing (PSC), Toyama, Japan.","DOI":"10.23919\/OECC\/PSC53152.2022.9849930"},{"key":"ref_126","doi-asserted-by":"crossref","first-page":"2648","DOI":"10.1109\/JLT.2018.2876909","article-title":"Real-Time Denoising of Brillouin Optical Time Domain Analyzer with High Data Fidelity Using Convolutional Neural Networks","volume":"37","author":"Wu","year":"2019","journal-title":"J. Light. Technol."},{"key":"ref_127","doi-asserted-by":"crossref","first-page":"262","DOI":"10.1109\/JLT.2021.3117284","article-title":"Deep Learning Enhanced Long-Range Fast BOTDA for Vibration Measurement","volume":"40","author":"Zheng","year":"2022","journal-title":"J. Light. Technol."},{"key":"ref_128","doi-asserted-by":"crossref","first-page":"117","DOI":"10.1016\/j.neunet.2019.12.024","article-title":"Attention-guided CNN for image denoising","volume":"124","author":"Tian","year":"2020","journal-title":"Neural Netw."},{"key":"ref_129","doi-asserted-by":"crossref","first-page":"12068","DOI":"10.1088\/1742-6596\/2171\/1\/012068","article-title":"Attention-based CNNs for Image Classification: A Survey","volume":"2171","author":"Zheng","year":"2022","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_130","doi-asserted-by":"crossref","first-page":"5126","DOI":"10.1364\/OE.26.005126","article-title":"Brillouin optical time domain analyzer sensors assisted by advanced image denoising techniques","volume":"26","author":"Wu","year":"2018","journal-title":"Opt. Express"},{"key":"ref_131","doi-asserted-by":"crossref","first-page":"98","DOI":"10.1186\/s40537-019-0263-7","article-title":"Enlarging smaller images before inputting into convolutional neural network: Zero-padding vs. interpolation","volume":"6","author":"Hashemi","year":"2019","journal-title":"J. Big Data"},{"key":"ref_132","doi-asserted-by":"crossref","first-page":"1578","DOI":"10.1049\/el.2015.1359","article-title":"Temperature sensing in BOTDA system by using artificial neural network","volume":"51","author":"Azad","year":"2015","journal-title":"Electron. Lett."},{"key":"ref_133","doi-asserted-by":"crossref","first-page":"6769","DOI":"10.1364\/OE.24.006769","article-title":"Signal processing using artificial neural network for BOTDA sensor system","volume":"24","author":"Azad","year":"2016","journal-title":"Opt. Express"},{"key":"ref_134","doi-asserted-by":"crossref","unstructured":"Wang, L., Wang, B., Jin, C., Guo, N., Yu, C., and Lu, C. (2017, January 7\u201310). Brillouin optical time domain analyzer enhanced by artificial\/deep neural networks. Proceedings of the 2017 16th International Conference on Optical Communications and Networks (ICOCN), Wuzhen, China.","DOI":"10.1109\/ICOCN.2017.8121527"},{"key":"ref_135","doi-asserted-by":"crossref","first-page":"124418","DOI":"10.1016\/j.optcom.2019.124418","article-title":"Temperature extraction for Brillouin optical fiber sensing system based on extreme learning machine","volume":"453","author":"Wang","year":"2019","journal-title":"Opt. Commun."},{"key":"ref_136","doi-asserted-by":"crossref","first-page":"4549","DOI":"10.1364\/OE.27.004549","article-title":"Back propagation neutral network based signal acquisition for Brillouin distributed optical fiber sensors","volume":"27","author":"Cao","year":"2019","journal-title":"Opt. Express"},{"key":"ref_137","doi-asserted-by":"crossref","first-page":"6871","DOI":"10.1109\/JSEN.2022.3152254","article-title":"Enhanced Neural Network Implementation for Temperature Profile Extraction in Distributed Brillouin Scattering-Based Sensors","volume":"22","author":"Madaschi","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_138","doi-asserted-by":"crossref","first-page":"102314","DOI":"10.1016\/j.yofte.2020.102314","article-title":"Optimized neural network for temperature extraction from Brillouin scattering spectra","volume":"58","author":"Li","year":"2020","journal-title":"Opt. Fiber Technol."},{"key":"ref_139","doi-asserted-by":"crossref","first-page":"27535","DOI":"10.1364\/OE.22.027535","article-title":"Gain dependence of the linewidth of Brillouin amplification in optical fibers","volume":"22","author":"Motil","year":"2014","journal-title":"Opt. Express"},{"key":"ref_140","doi-asserted-by":"crossref","unstructured":"Wang, B., Guo, N., Khan, F.N., Azad, A.K., Wang, L., Yu, C., and Lu, C. (August, January 31). Extraction of temperature distribution using deep neural networks for BOTDA sensing system. Proceedings of the 2017 Conference on Lasers and Electro-Optics Pacific Rim (CLEO-PR), Singapore.","DOI":"10.1109\/CLEOPR.2017.8118961"},{"key":"ref_141","doi-asserted-by":"crossref","first-page":"36110","DOI":"10.1364\/OE.465460","article-title":"AIoT enabled resampling filter for temperature extraction of the Brillouin gain spectrum","volume":"30","author":"Wang","year":"2022","journal-title":"Opt. Express"},{"key":"ref_142","doi-asserted-by":"crossref","first-page":"28994","DOI":"10.1364\/OE.427998","article-title":"Sweep frequency method with variance weight probability for temperature extraction of the Brillouin gain spectrum based on an artificial neural network","volume":"29","author":"Wang","year":"2021","journal-title":"Opt. Express"},{"key":"ref_143","doi-asserted-by":"crossref","unstructured":"Zhang, Y., Li, Y., Cheng, L., Yu, L., Zhu, H., Luo, B., and Zou, X. (2020, January 24\u201327). Fast temperature extraction via Echo State Network for BOTDA sensors. Proceedings of the Asia Communications and Photonics Conference\/International Conference on Information Photonics and Optical Communications 2020 (ACP\/IPOC), Beijing, China.","DOI":"10.1364\/ACPC.2020.M4A.81"},{"key":"ref_144","doi-asserted-by":"crossref","unstructured":"Zhou, H., Zhu, H., Zhang, Y., Huang, M., Li, G., and Yang, Y. (2021, January 23\u201326). Fast and accurate temperature extraction via general regression neural network for BOTDA sensors. Proceedings of the 12th International Conference on Information Optics and Photonics, Xi\u2019an, China.","DOI":"10.1117\/12.2606620"},{"key":"ref_145","doi-asserted-by":"crossref","first-page":"111","DOI":"10.1186\/s40537-019-0268-2","article-title":"Internet of Things is a revolutionary approach for future technology enhancement: A review","volume":"6","author":"Kumar","year":"2019","journal-title":"J. Big Data"},{"key":"ref_146","doi-asserted-by":"crossref","first-page":"1537","DOI":"10.1109\/JLT.2020.3035810","article-title":"Ultrafast and Accurate Temperature Extraction via Kernel Extreme Learning Machine for BOTDA Sensors","volume":"39","author":"Zhang","year":"2021","journal-title":"J. Light. Technol."},{"key":"ref_147","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.neunet.2014.10.001","article-title":"Trends in extreme learning machines: A review","volume":"61","author":"Huang","year":"2015","journal-title":"Neural Netw."},{"key":"ref_148","doi-asserted-by":"crossref","first-page":"513","DOI":"10.1109\/TSMCB.2011.2168604","article-title":"Extreme Learning Machine for Regression and Multiclass Classification","volume":"42","author":"Hongming","year":"2012","journal-title":"IEEE Trans. Syst. Man Cybern. Part B"},{"key":"ref_149","doi-asserted-by":"crossref","first-page":"4159","DOI":"10.1109\/JLT.2017.2739421","article-title":"Brillouin Optical Time-Domain Analyzer Assisted by Support Vector Machine for Ultrafast Temperature Extraction","volume":"35","author":"Wu","year":"2017","journal-title":"J. Light. Technol."},{"key":"ref_150","doi-asserted-by":"crossref","first-page":"6802911","DOI":"10.1109\/JPHOT.2018.2858235","article-title":"Support Vector Machine based Differential Pulse-width Pair Brillouin Optical Time Domain Analyzer","volume":"10","author":"Wu","year":"2018","journal-title":"IEEE Photonics J."},{"key":"ref_151","doi-asserted-by":"crossref","unstructured":"Nordin, N.D., Abdullah, F., Zan, M.S.D., A Bakar, A.A., Krivosheev, A.I., Barkov, F.L., and Konstantinov, Y.A. (2022). Improving Prediction Accuracy and Extraction Precision of Frequency Shift from Low-SNR Brillouin Gain Spectra in Distributed Structural Health Monitoring. Sensors, 22.","DOI":"10.3390\/s22072677"},{"key":"ref_152","doi-asserted-by":"crossref","unstructured":"Nordin, N.D., Abdullah, F., Zan, M.S.D., Ismail, A., Jamaludin, M.Z., and Bakar, A.A.A. (June, January 12). Fast temperature extraction approach for BOTDA using Generalized Linear Model. Proceedings of the 2020 IEEE 8th International Conference on Photonics (ICP), Kota Bharu, Malaysia.","DOI":"10.1109\/ICP46580.2020.9206466"},{"key":"ref_153","doi-asserted-by":"crossref","first-page":"102298","DOI":"10.1016\/j.yofte.2020.102298","article-title":"Generalized linear model for enhancing the temperature measurement performance in Brillouin optical time domain analysis fiber sensor","volume":"58","author":"Nordin","year":"2020","journal-title":"Opt. Fiber Technol."},{"key":"ref_154","doi-asserted-by":"crossref","unstructured":"Nordin, N.D., Zan, M.S.D., and Abdullah, F. (2020). Comparative Analysis on the Deployment of Machine Learning Algorithms in the Distributed Brillouin Optical Time Domain Analysis (BOTDA) Fiber Sensor. Photonics, 7.","DOI":"10.3390\/photonics7040079"},{"key":"ref_155","unstructured":"Murphy, K.P. (2012). Machine Learning: A Probabilistic Perspective, MIT Press."},{"key":"ref_156","doi-asserted-by":"crossref","first-page":"75008","DOI":"10.1088\/1361-665X\/ab874e","article-title":"Deep learning method for detection of structural microcracks by brillouin scattering based distributed optical fiber sensors","volume":"29","author":"Song","year":"2020","journal-title":"Smart Mater. Struct."},{"key":"ref_157","doi-asserted-by":"crossref","unstructured":"Wei, C., Deng, Q., Yin, Y., Yan, M., Lu, M., and Deng, K. (2022). A Machine Learning Study on Internal Force Characteristics of the Anti-Slide Pile Based on the DOFS-BOTDA Monitoring Technology. Sensors, 22.","DOI":"10.3390\/s22062085"},{"key":"ref_158","doi-asserted-by":"crossref","first-page":"107019","DOI":"10.1016\/j.ymssp.2020.107019","article-title":"Robust principal component analysis and support vector machine for detection of microcracks with distributed optical fiber sensors","volume":"146","author":"Song","year":"2021","journal-title":"Mech. Syst. Signal Process."},{"key":"ref_159","doi-asserted-by":"crossref","first-page":"3383","DOI":"10.1007\/s10064-020-01749-3","article-title":"A machine learning method for inclinometer lateral deflection calculation based on distributed strain sensing technology","volume":"79","author":"Zhang","year":"2020","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_160","doi-asserted-by":"crossref","unstructured":"Ruiz-Lombera, R., Serrano, J.M., and Lopez-Higuera, J.M. (2014, January 2\u20135). Automatic strain detection in a Brillouin Optical Time Domain sensor using Principal Component Analysis and Artificial Neural Networks. Proceedings of the IEEE SENSORS 2014 Proceedings, Valencia, Spain.","DOI":"10.1109\/ICSENS.2014.6985309"},{"key":"ref_161","doi-asserted-by":"crossref","first-page":"5149","DOI":"10.1109\/JLT.2021.3078819","article-title":"Error Estimation of BFS Extraction with Optimized Neural Network & Frequency Scanning Range","volume":"39","author":"Lv","year":"2021","journal-title":"J. Light. Technol."},{"key":"ref_162","doi-asserted-by":"crossref","first-page":"2017","DOI":"10.1007\/s10586-021-03240-4","article-title":"DLBench: A comprehensive experimental evaluation of deep learning frameworks","volume":"24","author":"Elshawi","year":"2021","journal-title":"Clust. Comput."},{"key":"ref_163","doi-asserted-by":"crossref","unstructured":"Yao, Y., Set, S.Y., and Yamashita, S. (2017, January 19\u201322). Proposal of signal processing based on machine learning in Brillouin optical correlation domain analysis\/ reflectometry. Proceedings of the 2017 22nd Microoptics Conference (MOC), Tokyo, Japan.","DOI":"10.23919\/MOC.2017.8244569"},{"key":"ref_164","doi-asserted-by":"crossref","first-page":"35474","DOI":"10.1364\/OE.439215","article-title":"Neural network-assisted signal processing in Brillouin optical correlation-domain sensing for potential high-speed implementation","volume":"29","author":"Yao","year":"2021","journal-title":"Opt. Express"},{"key":"ref_165","doi-asserted-by":"crossref","unstructured":"Chen, X., Yu, H., and Huang, W. (2021, January 23\u201326). A high accurate fitting algorithm for Brillouin scattering spectrum of distributed sensing systems based on LSSVM networks. Proceedings of the 2021 International Conference on Electronic Information Engineering and Computer Science (EIECS), Changchun, China.","DOI":"10.1109\/EIECS53707.2021.9587945"},{"key":"ref_166","doi-asserted-by":"crossref","first-page":"103082","DOI":"10.1016\/j.yofte.2022.103082","article-title":"An improved lorentz fitting algorithm for BOTDR using SVM model to capture the main peak of power cumulative average data","volume":"74","author":"Wan","year":"2022","journal-title":"Opt. Fiber Technol."},{"key":"ref_167","unstructured":"Karapanagiotis, C. (2022, January 10\u201311). Evaluation of the generalization performance of a CNN-assisted BOFDA system. Proceedings of the Sensors and Measuring Systems; 21st ITG\/GMA-Symposium, Nuremberg, Germany."},{"key":"ref_168","doi-asserted-by":"crossref","unstructured":"Gyger, F., Yang, Z., Soto, M.A., Yang, F., Tow, K.H., and Th\u00e9venaz, L. (2018, January 24\u201328). High Signal-to-Noise Ratio Stimulated Brillouin Scattering Gain Spectrum Measurement. Proceedings of the 26th International Conference on Optical Fiber Sensors, Lausanne, Switzerland.","DOI":"10.1364\/OFS.2018.ThE69"},{"key":"ref_169","doi-asserted-by":"crossref","unstructured":"Rasmussen, C.E., and Williams, C.K.I. (2005). Gaussian Processes for Machine Learning, The MIT Press.","DOI":"10.7551\/mitpress\/3206.001.0001"},{"key":"ref_170","doi-asserted-by":"crossref","unstructured":"Karapanagiotis, C., Hicke, K., and Krebber, K. (2022, January 3\u20137). Temperature and humidity discrimination in Brillouin distributed fiber optic sensing using machine learning algorithms. Proceedings of the Optical Sensing and Detection VII, Strasbourg, France. Online, 9\u201315 May 2022.","DOI":"10.1117\/12.2620985"},{"key":"ref_171","doi-asserted-by":"crossref","first-page":"12484","DOI":"10.1364\/OE.453906","article-title":"Distributed humidity fiber-optic sensor based on BOFDA using a simple machine learning approach","volume":"30","author":"Karapanagiotis","year":"2022","journal-title":"Opt. Express"},{"key":"ref_172","doi-asserted-by":"crossref","first-page":"1289","DOI":"10.1109\/TIT.2006.871582","article-title":"Compressed sensing","volume":"52","author":"Donoho","year":"2006","journal-title":"IEEE Trans. Inf. Theory"},{"key":"ref_173","doi-asserted-by":"crossref","first-page":"5496","DOI":"10.1364\/OL.43.005496","article-title":"Brillouin optical time-domain analysis via compressed sensing","volume":"43","author":"Zhou","year":"2018","journal-title":"Opt. Lett."},{"key":"ref_174","doi-asserted-by":"crossref","first-page":"16414","DOI":"10.1109\/JSEN.2022.3191336","article-title":"Compressed Sensing Based on K-SVD for Brillouin Optical Fiber Distributed Sensors","volume":"22","author":"Dong","year":"2022","journal-title":"IEEE Sens. J."},{"key":"ref_175","doi-asserted-by":"crossref","first-page":"25723","DOI":"10.1109\/JSEN.2021.3117287","article-title":"Accelerated Fast BOTDA Assisted by Compressed Sensing and Image Denoising","volume":"21","author":"Zheng","year":"2021","journal-title":"IEEE Sens. J."},{"key":"ref_176","unstructured":"Calderbank, R. (2023, May 31). Compressed Learning: Universal Sparse Dimensionality Reduction and Learning in the Measurement Domain, Preprint 2009. Available online: https:\/\/www.semanticscholar.org\/paper\/Compressed-Learning-%3A-Universal-Sparse-Reduction-in-Calderbank\/627c14fe9097d459b8fd47e8a901694198be9d5d#citing-papers."}],"container-title":["Sensors"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6187\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,10,10]],"date-time":"2025-10-10T20:07:07Z","timestamp":1760126827000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/1424-8220\/23\/13\/6187"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023,7,6]]},"references-count":176,"journal-issue":{"issue":"13","published-online":{"date-parts":[[2023,7]]}},"alternative-id":["s23136187"],"URL":"https:\/\/doi.org\/10.3390\/s23136187","relation":{},"ISSN":["1424-8220"],"issn-type":[{"value":"1424-8220","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023,7,6]]}}}