{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T05:59:50Z","timestamp":1780379990289,"version":"3.54.1"},"reference-count":35,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T00:00:00Z","timestamp":1618272000000},"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>To our knowledge, this is the first report on a machine-learning-assisted Brillouin optical frequency domain analysis (BOFDA) for time-efficient temperature measurements. We propose a convolutional neural network (CNN)-based signal post-processing method that, compared to the conventional Lorentzian curve fitting approach, facilitates temperature extraction. Due to its robustness against noise, it can enhance the performance of the system. The CNN-assisted BOFDA is expected to shorten the measurement time by more than nine times and open the way for applications, where faster monitoring is essential.<\/jats:p>","DOI":"10.3390\/s21082724","type":"journal-article","created":{"date-parts":[[2021,4,13]],"date-time":"2021-04-13T05:31:37Z","timestamp":1618291897000},"page":"2724","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":16,"title":["Time-Efficient Convolutional Neural Network-Assisted Brillouin Optical Frequency Domain Analysis"],"prefix":"10.3390","volume":"21","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"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-3707-5132","authenticated-orcid":false,"given":"Aleksander","family":"Wosniok","sequence":"additional","affiliation":[{"name":"Bundesanstalt f\u00fcr Materialforschung und-Pr\u00fcfung, Unter den Eichen 87, 12205 Berlin, Germany"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-7411-8296","authenticated-orcid":false,"given":"Konstantin","family":"Hicke","sequence":"additional","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":[[2021,4,13]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","unstructured":"Hartog, A.H. (2017). An Introduction to Distributed Optical Fibre Sensors, CRC Press, Taylor & Francis Group.","DOI":"10.1201\/9781315119014"},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1109\/JSEN.2011.2141985","article-title":"Bridge Monitoring Using Brillouin Fiber-Optic Sensors","volume":"12","author":"Minardo","year":"2012","journal-title":"IEEE Sens. J."},{"key":"ref_3","first-page":"6930T","article-title":"A distributed fiber optic sensor system for dike monitoring using Brillouin optical frequency domain analysis","volume":"6933","author":"Wosniok","year":"2008","journal-title":"Smart Sens. Phenom. Technol. Netw. Syst."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1016\/j.jlp.2016.02.019","article-title":"Experimental and numerical investigation on temperature measurement of BOTDA due to drop leakage in soil","volume":"41","author":"Li","year":"2016","journal-title":"J. Loss Prevent. Proc."},{"key":"ref_5","unstructured":"Barber, K., Aanhaanen, G., Lauria, S., Waite, F., Kobayashi, S., Suyama, H., Werle, V., Orton, H., Kim, J., and Ackerwall, C. (2017). CIGRE TB 680\u2014Implementation of Long AC HV and EHV Cable System, CIGRE Working Group."},{"key":"ref_6","doi-asserted-by":"crossref","first-page":"81","DOI":"10.1016\/j.optlastec.2015.09.013","article-title":"State of the art of Brillouin fiber-optic distributed sensing","volume":"78","author":"Motil","year":"2016","journal-title":"Opt. Laser Technol."},{"key":"ref_7","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_8","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_9","doi-asserted-by":"crossref","unstructured":"Kapa, T., Schreier, A., and Krebber, K. (2019). A 100-km BOFDA Assisted by First-Order Bi-Directional Raman Amplification. Sensors, 19.","DOI":"10.3390\/s19071527"},{"key":"ref_10","first-page":"1","article-title":"Distributed Brillouin Sensing: Frequency-Domain Techniques","volume":"Volume 2018","author":"Peng","year":"2018","journal-title":"Handbook of Optical Fibers"},{"key":"ref_11","doi-asserted-by":"crossref","unstructured":"Lalam, N., and Ng, W.P. (2020, January 20\u201322). Recent development in artificial neural network based distributed fiber optic sensors. Proceedings of the 2020 12th International Symposium on Communication Systems, Networks and Digital Signal Processing (CSNDSP), Porto, Portugal.","DOI":"10.1109\/CSNDSP49049.2020.9249588"},{"key":"ref_12","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_13","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":"Photon. Res."},{"key":"ref_14","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_15","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1109\/JPHOT.2018.2880772","article-title":"Support Vector Machine based Differential Pulse-width Pair Brillouin Optical Time Domain Analyzer","volume":"10","author":"Wu","year":"2018","journal-title":"IEEE Photon. J."},{"key":"ref_16","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_17","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_18","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_19","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. Lightwave Technol."},{"key":"ref_20","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_21","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. Lightwave Technol."},{"key":"ref_22","doi-asserted-by":"crossref","first-page":"1842","DOI":"10.1109\/50.633570","article-title":"Brillouin gain spectrum characterization in single-mode optical fibers","volume":"15","author":"Nikles","year":"1997","journal-title":"J. Lightwave Technol."},{"key":"ref_23","doi-asserted-by":"crossref","first-page":"788","DOI":"10.1109\/TMTT.2007.892818","article-title":"Extension of two-signal spurious-free dynamic range of wideband digital receivers using Kaiser window and compensation method","volume":"55","author":"George","year":"2007","journal-title":"IEEE Trans. Microw. Theory Tech."},{"key":"ref_24","unstructured":"Lyons, R.G. (2004). Understanding Digital Signal Processing, Prentice Hall\/PTR."},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"2352","DOI":"10.1162\/neco_a_00990","article-title":"Deep Convolutional Neural Networks for Image Classification: A Comprehensive Review","volume":"29","author":"Rawat","year":"2017","journal-title":"Neural Comput."},{"key":"ref_26","unstructured":"Simonyan, K., and Zisserman, A. (2014). Very deep convolutional networks for large-scale image recognition. arXiv."},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"022022","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_28","unstructured":"Ioffe, S., and Szegedy, C. (2015). Batch normalization: Accelerating deep network training by reducing internal covariate shift. arXiv."},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"1803","DOI":"10.1063\/1.1144830","article-title":"Neural networks and their applications","volume":"65","author":"Bishop","year":"1994","journal-title":"Rev. Sci. Instrum."},{"key":"ref_30","unstructured":"Kingma, D.P., and Ba, J. (2014). Adam: A method for stochastic optimization. arXiv."},{"key":"ref_31","unstructured":"Abadi, M., Barham, P., Chen, J., Chen, Z., Davis, A., Dean, J., Devin, M., Ghemawat, S., Irving, G., and Isard, M. (2016, January 2\u20134). Tensorflow: A system for large-scale machine learning. Proceedings of the 12th USENIX symposium on operating systems design and implementation OSDI 16, Savannah, GA, USA."},{"key":"ref_32","unstructured":"Chollet, F. (2015). Keras, GitHub Inc."},{"key":"ref_33","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1186\/s40537-019-0197-0","article-title":"A survey on Image Data Augmentation for Deep Learning","volume":"6","author":"Shorten","year":"2019","journal-title":"J. Big Data"},{"key":"ref_34","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_35","doi-asserted-by":"crossref","unstructured":"Kapa, T., Schreier, A., and Krebber, K. (2018). 63 km BOFDA for Temperature and Strain Monitoring. 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