Machine Learning Analysis of Polarization Signatures for Distinguishing Harmful from Non-harmful Fiber Events
Author:
Affiliation:
1. Chalmers University of Technology,Department of Electrical Engineering,Gothenburg,Sweden,41296
2. Swedish Defense Material Administration,Linköping,Sweden,58663
Publisher
IEEE
Link
http://xplorestaging.ieee.org/ielx8/10647220/10647120/10648140.pdf?arnumber=10648140
Reference8 articles.
1. Eavesdropping G.652 vs. G.657 fibres: a performance comparison
2. Proactive restoration of optical links based on the classification of events;Pesic
3. Proactive Fiber Damage Detection in Real-time Coherent Receiver
4. Proactive Fiber Break Detection Based on Quaternion Time Series and Automatic Variable Selection from Relational Data
5. Breaking boundaries: harnessing unrelated image data for robust risky event classification with scarce state of polarization data
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