Optical fibres coating aging induced by the maritime environment

Domingues, M. Fátima; André, Paulo; Granada, M.

doi:10.1590/s2179-10742011000100024

Cited by 3 publications

(2 citation statements)

References 4 publications

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“…Such ions diffusion can be responsible for the optical fiber degradation and the decrease of its strength (Thirtha et al, 2002;Lindholm et al, 2004). Therefore, the behavior of the fiber strength with aging is not only dependent on the moisture present in the environment but also on the diffusion rate of the ions across the coating (Armstrong et al, 1999;Domingues et al, 2010). The implementation of optical fiber systems (as for example in sensing structures or optical networks) (Ferreira et al, 2009), is dependent on the optical fiber reliability and its lifetime degradation in abrasive environments, where ions like Na+, Cl-, or even moisture are present.…”

Section: Optical Fiber Degradation Behaviormentioning

confidence: 99%

“…In figure 11 we present the optical microscopy images, taken with an Olympus BH2 and the digital camera Sony DKC-CM30, for fibers degraded on a 35 g/l NaCl aqueous solution a) during 31 days and b) 105 days (Domingues et al, 2010 In these images we can see the difference in the protective polymer as consequence of its degradation.…”

Section: Microscopic Analysis Of the Aged Optical Fibermentioning

confidence: 99%

See 1 more Smart Citation

Mechanical Properties of Optical Fibers

Antunes¹,

Domingues²,

Granada³

et al. 2012

Selected Topics on Optical Fiber Technology

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Section: Optical Fiber Degradation Behaviormentioning

confidence: 99%

Section: Microscopic Analysis Of the Aged Optical Fibermentioning

confidence: 99%

Mechanical Properties of Optical Fibers

Antunes¹,

Domingues²,

Granada³

et al. 2012

Selected Topics on Optical Fiber Technology

View full text Add to dashboard Cite

Lifetime prediction for optical fibers aged in sodium chlorine aquoses solutions: Directions for deployment in seashore regions

Domingues

Granada

André

2020

Optical Fiber Technology

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Lifelong QoT prediction: an adaptation to real-world optical networks

Wang,

Cai,

Khan

2024

J. Opt. Commun. Netw.

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Predicting the quality of transmission (QoT) is a critical task in the management and optimization of modern fiber-optic networks. Traditional machine learning (ML) QoT prediction models, typically trained on pre-collected datasets, are designed to make long-term predictions once deployed. However, this static training strategy often falls short in the face of time-dependent network evolution and variations. We identify the root cause of these shortcomings as shifts in data distribution, which are not accounted for in conventional static models. In response to these challenges, we propose an online continual learning pipeline that is specifically designed for stable QoT prediction in optical networks. This pipeline directly addresses the problem of distribution shifts by continuously updating the prediction model in response to real-time network data. We explore and compare various strategies within this framework and demonstrate that the integration of the adaptive retraining strategy and the regularized online continual learning algorithm (OCL-REG) significantly enhances the QoT prediction stability while optimizing the resource efficiency. OCL-REG demonstrates superior adaptability and stability, achieving an average cumulative mean squared error (C-MSE) of 0.19 on a testbench with a data distribution shift sequence containing 1000 batches. Moreover, the OCL-REG model requires fewer samples for adaptation, averaging around 107 samples, compared to the conventional retraining strategy, which requires an average of 253 samples. Our approach presents a paradigm shift in QoT prediction, moving from a static to a dynamic, lifelong learning model that is more attuned to the evolving realities of real fiber-optic networks.

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Optical fibres coating aging induced by the maritime environment

Cited by 3 publications

References 4 publications

Mechanical Properties of Optical Fibers

Mechanical Properties of Optical Fibers

Lifetime prediction for optical fibers aged in sodium chlorine aquoses solutions: Directions for deployment in seashore regions

Lifelong QoT prediction: an adaptation to real-world optical networks

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