Temporal data-driven failure prognostics using BiGRU for optical networks
Published 2020 View Full Article
- Home
- Publications
- Publication Search
- Publication Details
Title
Temporal data-driven failure prognostics using BiGRU for optical networks
Authors
Keywords
-
Journal
Journal of Optical Communications and Networking
Volume 12, Issue 8, Pages 277
Publisher
The Optical Society
Online
2020-07-01
DOI
10.1364/jocn.390727
References
Ask authors/readers for more resources
Related references
Note: Only part of the references are listed.- Low-complexity carrier phase recovery based on principal component analysis for square-QAM modulation formats
- (2019) Júlio César Medeiros Diniz et al. OPTICS EXPRESS
- A Tutorial on Machine Learning for Failure Management in Optical Networks
- (2019) Francesco Musumeci et al. JOURNAL OF LIGHTWAVE TECHNOLOGY
- Optical spectrum feature analysis and recognition for optical network security with machine learning
- (2019) Yanlong Li et al. OPTICS EXPRESS
- Relation Classification via Keyword-Attentive Sentence Mechanism and Synthetic Stimulation Loss
- (2019) Luoqin Li et al. IEEE-ACM Transactions on Audio Speech and Language Processing
- CAM-RNN: Co-Attention Model Based RNN for Video Captioning
- (2019) Bin Zhao et al. IEEE TRANSACTIONS ON IMAGE PROCESSING
- Dual-Stage Soft Failure Detection and Identification for Low-Margin Elastic Optical Network by Exploiting Digital Spectrum Information
- (2019) Liang Shu et al. JOURNAL OF LIGHTWAVE TECHNOLOGY
- Data-Driven Multi-Hidden Markov Model-Based Power Quality Disturbance Prediction that Incorporates Weather Conditions
- (2018) Fei Xiao et al. IEEE TRANSACTIONS ON POWER SYSTEMS
- Leveraging Statistical Machine Learning to Address Failure Localization in Optical Networks
- (2018) T. Panayiotou et al. Journal of Optical Communications and Networking
- Optimization of RNN-Based Speech Activity Detection
- (2018) Gregory Gelly et al. IEEE-ACM Transactions on Audio Speech and Language Processing
- Data-Based Line Trip Fault Prediction in Power Systems Using LSTM Networks and SVM
- (2018) Senlin Zhang et al. IEEE Access
- Learning from the Optical Spectrum: Failure Detection and Identification [Invited]
- (2018) Behnam Shariati et al. JOURNAL OF LIGHTWAVE TECHNOLOGY
- Modulation Format Recognition and OSNR Estimation Using CNN-Based Deep Learning
- (2017) Danshi Wang et al. IEEE PHOTONICS TECHNOLOGY LETTERS
- LSTM network: a deep learning approach for short-term traffic forecast
- (2017) Zheng Zhao et al. IET Intelligent Transport Systems
- BER Degradation Detection and Failure Identification in Elastic Optical Networks
- (2017) Alba P. Vela et al. JOURNAL OF LIGHTWAVE TECHNOLOGY
- Soft Failure Localization During Commissioning Testing and Lightpath Operation
- (2017) A. P. Vela et al. Journal of Optical Communications and Networking
- Failure prediction using machine learning and time series in optical network
- (2017) Zhilong Wang et al. OPTICS EXPRESS
- Intelligent constellation diagram analyzer using convolutional neural network-based deep learning
- (2017) Danshi Wang et al. OPTICS EXPRESS
- Optimal Fiber Link Fault Decision for Optical 2D Coding-Monitoring Scheme in Passive Optical Networks
- (2016) Min Zhu et al. Journal of Optical Communications and Networking
- Bayesian Recurrent Neural Network for Language Modeling
- (2016) Jen-Tzung Chien et al. IEEE Transactions on Neural Networks and Learning Systems
- Highly efficient data migration and backup for big data applications in elastic optical inter-data-center networks
- (2015) Ping Lu et al. IEEE NETWORK
- Minimizing the Risk From Disaster Failures in Optical Backbone Networks
- (2014) Ferhat Dikbiyik et al. JOURNAL OF LIGHTWAVE TECHNOLOGY
- Best Effort SRLG Failure Protection for Optical WDM Networks
- (2011) Xu Shao et al. Journal of Optical Communications and Networking
Discover Peeref hubs
Discuss science. Find collaborators. Network.
Join a conversationFind the ideal target journal for your manuscript
Explore over 38,000 international journals covering a vast array of academic fields.
Search