<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.</p>
</abstract>

With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable faults in communication networks has become a hot topic of current concern. Firstly, a combined generative adversarial network and convolutional neural network algorithm is proposed based on a deep learning algorithm, then an improved fault diagnosis model combining generative adversarial network and convolutional neural network algorithm is built, and finally, the combined generative adversarial network and convolutional neural network model is used to verify and analyze the fiber optic cable fault diagnosis. The results show that the accuracy of the DCGAN-CNN algorithm for fiber optic cable fault diagnosis is 98.5%, and the research results verify the effectiveness of the combined generative adversarial network and convolutional neural network model for fiber optic cable fault diagnosis. This study can accurately and comprehensively solve the problem of fiber optic cable faults in communication networks and thus play a guiding reference value for developing fault diagnosis in Chinese communication networks.

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Applied Mathematics and Nonlinear Sciences

This work is licensed under the Creative Commons Attribution 4.0 International License.

With the arrival of the big data era and the development of new network technology, how to use big data technology to diagnose and locate fiber optic cable...

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Online veröffentlicht: 26. Aug. 2023

Seitenbereich: -

Eingereicht: 25. Sept. 2022

Akzeptiert: 06. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00241

Schlüsselwörter
DCGAN-CNN algorithm, Deep learning algorithm, Convolutional neural network, Adversarial network, Communication network fiber optic cable fault

© 2023 Lixia Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Deep learning-based fault diagnosis and localization method for fiber optic cables in communication networks

Online veröffentlicht: 26. Aug. 2023

Seitenbereich: -

Eingereicht: 25. Sept. 2022

Akzeptiert: 06. März 2022

DOI: https://doi.org/10.2478/amns.2023.2.00241

SchlüsselwörterDCGAN-CNN algorithm, Deep learning algorithm, Convolutional neural network, Adversarial network, Communication network fiber optic cable fault

© 2023 Lixia Zhang et al., published by Sciendo

This work is licensed under the Creative Commons Attribution 4.0 International License.

Schlüsselwörter
DCGAN-CNN algorithm, Deep learning algorithm, Convolutional neural network, Adversarial network, Communication network fiber optic cable fault