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[cited 2025 Apr 24]. Available from: https:\/\/www.thefoa.org\/tech\/ref\/testing\/OTDR\/OTDR.html."},{"key":"ref2","doi-asserted-by":"crossref","first-page":"47","DOI":"10.1109\/19.903877","article-title":"Events in fiber optics given noisy OTDR data. I. GSR\/MDL method","volume":"50","author":"Liu","year":"2001","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref3","doi-asserted-by":"crossref","first-page":"546","DOI":"10.1109\/TIM.2003.820442","article-title":"Detection and location of connection splice events in fiber optics given noisy OTDR data. Part II. R1MSDE method","volume":"53","author":"Liu","year":"2004","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref4","first-page":"2121","article-title":"Analysis of connection splice events in OTDR data using short Fourier transform method","volume":"31","author":"Man","year":"2010","journal-title":"Yi Qi Yi Biao Xue Bao\/Chin J Sci Instrum"},{"key":"ref5","series-title":"2011 4th International Congress on Image and Signal Processing","first-page":"2275","article-title":"Localization of non-reflective events in OTDR data combining DWT with template matching","volume":"4","author":"Zhang","year":"2011"},{"key":"ref6","series-title":"Proceedings of IEEE-SP International Symposium on Time-Frequency and Time-Scale Analysis","first-page":"353","article-title":"Estimation and detection in OTDR using analyzing wavelets","author":"Gu","year":"1994 Oct 25\u201328"},{"article-title":"Events detection in OTDR data based on a method combining correlation matching with STFT","series-title":"Asia Communications and Photonics Conference; 2014 Nov 11\u201314","author":"Kong","key":"ref7"},{"key":"ref8","series-title":"2019 21st International Conference on Transparent Optical Networks (ICTON)","first-page":"1","article-title":"Machine learning based laser failure mode detection","author":"Abdelli","year":"2019 Jul 9\u201313"},{"key":"ref9","series-title":"Optical Fiber Communication Conference.","article-title":"Lifetime prediction of 1550 nm DFB laser using machine learning techniques","author":"Abdelli","year":"2020"},{"key":"ref10","series-title":"2020 22nd International Conference on Transparent Optical Networks (ICTON)","first-page":"1","article-title":"Machine learning based data driven diagnostic and prognostic approach for laser reliability enhancement","author":"Abdelli","year":"2020 Jul 19\u201323"},{"key":"ref11","series-title":"Photonic Networks; 22th ITG Symposium","first-page":"1","article-title":"Convolutional neural networks for reflective event detection and characterization in fiber optical links given noisy OTDR signals","author":"Abdelli","year":"2021 May 19\u201320"},{"key":"ref12","doi-asserted-by":"crossref","first-page":"E32","DOI":"10.1364\/JOCN.423625","article-title":"Reflective fiber fault detection and characterization using long short-term memory","volume":"13","author":"Abdelli","year":"2021","journal-title":"J Opt Commun Netw"},{"key":"ref13","doi-asserted-by":"crossref","first-page":"439","DOI":"10.1016\/j.ymssp.2017.06.022","article-title":"A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load","volume":"100","author":"Zhang","year":"2018","journal-title":"Mech Syst Signal Process"},{"key":"ref14","series-title":"Optical Fiber Communication Conference","article-title":"A BiLSTM-CNN based multitask learning approach for fiber fault diagnosis","author":"Abdelli","year":"2021"},{"key":"ref15","doi-asserted-by":"crossref","first-page":"2254","DOI":"10.1109\/JLT.2021.3138268","article-title":"Optical fiber fault detection and localization in a noisy OTDR trace based on denoising convolutional autoencoder and bidirectional long short-term memory","volume":"40","author":"Abdelli","year":"2022","journal-title":"J Lightwave Technol"},{"key":"ref16","doi-asserted-by":"crossref","first-page":"6597623","DOI":"10.1155\/2023\/6597623","article-title":"Anomaly detection using multiscale C-LSTM for univariate time-series","volume":"2023","author":"Lu","year":"2023","journal-title":"Secur Commun Netw"},{"key":"ref17","article-title":"Dataset for optical fiber faults","author":"Abdelli","year":"2022","journal-title":"IEEE Dataport"},{"key":"ref18","first-page":"2697","article-title":"Optimizing optical fiber faults detection: a comparative analysis of advanced machine learning approaches","volume":"79","author":"Soothar","year":"2024","journal-title":"Comput Mater Contin"},{"key":"ref19","series-title":"2024 First International Conference on Electronics, Communication and Signal Processing (ICECSP)","first-page":"1","article-title":"Machine learning based denoising anomaly detection and localisation using BiGRU in optical fiber monitoring","author":"Prakash","year":"2024 Aug 8\u201310"},{"key":"ref20","first-page":"1","article-title":"A hybrid model integrating CNN-BiLSTM and CBAM for anchor damage events recognition of submarine cables","volume":"72","author":"Xu","year":"2023","journal-title":"IEEE Trans Instrum Meas"},{"key":"ref21","doi-asserted-by":"crossref","first-page":"1003","DOI":"10.3390\/photonics10091003","article-title":"Research on multi-source simultaneous recognition technology based on Sagnac fiber optic sound sensing system","volume":"10","author":"Zheng","year":"2023","journal-title":"Photonics"},{"key":"ref22","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 Lightwave Technol"},{"key":"ref23","unstructured":"Zhou C, Sun C, Liu Z, Lau F. A C-LSTM neural network for text classification. arXiv:1511.08630. 2015. doi:10.48550\/arXiv.1511.08630."},{"key":"ref24","doi-asserted-by":"crossref","first-page":"2219","DOI":"10.1142\/S0218127497001618","article-title":"CNN: a vision of complexity","volume":"7","author":"Chua","year":"1997","journal-title":"Int J Bifurcat Chaos"},{"key":"ref25","doi-asserted-by":"crossref","first-page":"2222","DOI":"10.1109\/TNNLS.2016.2582924","article-title":"LSTM: a search space odyssey","volume":"28","author":"Greff","year":"2016","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"key":"ref26","series-title":"2019 IEEE International Conference on Big Data (Big Data)","first-page":"3285","article-title":"The performance of LSTM and BiLSTM in forecasting time series","author":"Siami-Namini","year":"2019 Dec 9\u201312"},{"key":"ref27","series-title":"IEEE 60th International Midwest Symposium on Circuits and Systems (MWSCAS)","first-page":"1597","article-title":"Gate-variants of gated recurrent unit (GRU) neural networks","author":"Dey","year":"2017 Aug 6\u20139"},{"key":"ref28","doi-asserted-by":"crossref","unstructured":"Cho K, Van Merri\u00ebnboer B, Gulcehre C, Bahdanau D, Bougares F, Schwenk H, et al. Learning phrase representations using RNN encoder-decoder for statistical machine translation. arXiv:1406.1078. 2014. doi 10.48550\/arXiv.1406.1078.","DOI":"10.3115\/v1\/D14-1179"},{"key":"ref29","doi-asserted-by":"crossref","first-page":"1735","DOI":"10.1162\/neco.1997.9.8.1735","article-title":"Long short-term memory","volume":"9","author":"Hochreiter","year":"1997","journal-title":"Neural Comput"},{"key":"ref30","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","article-title":"Gradient-based learning applied to document recognition","volume":"86","author":"LeCun","year":"2002","journal-title":"Proc IEEE"},{"key":"ref31","doi-asserted-by":"crossref","first-page":"365","DOI":"10.1364\/JOCN.451289","article-title":"Machine-learning-based anomaly detection in optical fiber monitoring","volume":"14","author":"Abdelli","year":"2022","journal-title":"J Opt Commun Netw"},{"key":"ref32","doi-asserted-by":"crossref","first-page":"112","DOI":"10.1109\/TSMC.2020.2968516","article-title":"Anomaly detection based on convolutional recurrent autoencoder for IoT time series","volume":"52","author":"Yin","year":"2020","journal-title":"IEEE Trans Syst Man Cybern Syst"},{"key":"ref33","series-title":"6th International Conference on Mobile Computing, Applications and Services","first-page":"197","article-title":"Convolutional neural networks for human activity recognition using mobile sensors","author":"Zeng","year":"2014 Nov 6\u20137"},{"key":"ref34","first-page":"2579","article-title":"Visualizing data using t-SNE","volume":"9","author":"Van der Maaten","year":"2008","journal-title":"J Mach Learn Res"}],"container-title":["Computers, Materials &amp; Continua"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/cdn.techscience.cn\/files\/cmc\/2025\/TSP_CMC-85-1\/TSP_CMC_67518\/TSP_CMC_67518.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,11,17]],"date-time":"2025-11-17T02:02:22Z","timestamp":1763344942000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.techscience.com\/cmc\/v85n1\/63566"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025]]},"references-count":34,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025]]},"published-print":{"date-parts":[[2025]]}},"URL":"https:\/\/doi.org\/10.32604\/cmc.2025.067518","relation":{},"ISSN":["1546-2226"],"issn-type":[{"type":"electronic","value":"1546-2226"}],"subject":[],"published":{"date-parts":[[2025]]}}}