A BiLSTM-CNN based Multitask Learning Approach for Fiber Fault Diagnosis
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Abstract (arXiv)
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.
Key facts. Cited by 3 papers in the map (data through 7 Oct 2026). First posted on arXiv 16 Feb 2022.
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Citing papers per year: 2022: 2; 2023: 0; 2024: 0; 2025: 0; 2026: 1.
3 papers.
- 2022Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networkscited by 3
- 2022Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memorycited by 0
- 2026Evasion of IoT Malware Detection via Dummy Code Injectioncited by 0
- 2026Evasion of IoT Malware Detection via Dummy Code Injection9 Feb 2026
- 2022Optical Fiber Fault Detection and Localization in a Noisy OTDR Trace Based on Denoising Convolutional Autoencoder and Bidirectional Long Short-Term Memory19 Mar 2022
- 2022Gated Recurrent Unit based Autoencoder for Optical Link Fault Diagnosis in Passive Optical Networks19 Mar 2022