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A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis

Signal Processing 2022-02-17 v1
Authors: Khouloud Abdelli , Helmut Griesser , Carsten Tropschug , Stephan Pachnicke
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Abstract

A novel multitask learning approach based on stacked bidirectional long short-term memory (BiLSTM) networks and convolutional neural networks (CNN) for detecting, locating, characterizing, and identifying fiber faults is proposed. It outperforms conventionally employed techniques.

Keywords

neural networks for signal processing fault detection and diagnosis deep learning for detection and classification

Cite

@article{arxiv.2202.08034,
  title  = {A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis},
  author = {Khouloud Abdelli and Helmut Griesser and Carsten Tropschug and Stephan Pachnicke},
  journal= {arXiv preprint arXiv:2202.08034},
  year   = {2022}
}

Comments

2021 Optical Fiber Communications Conference and Exhibition (OFC)

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R2 v1 2026-06-24T09:40:50.274Z