AI-Enabled-Optical Fiber Anomaly Detection
This research tries to explore the efficacy of the application of recent advancements in the field of GANs for anomaly detection for fiber optics vibration data to perform anomaly detection.
Fiber optic communication image anomaly
Optical fiber anomaly detection through SRS-induced spectral tilt in C
However, the current anomaly detection method is too complex to be implemented in deployed networks or consumes too much time during detection. This paper proposes a simple and
Unsupervised Anomaly Detection and Localization with Genera
Abstract We propose a novel unsupervised anomaly detection approach using generative adversarial networks and SOP-derived spectrograms. Demonstrating remarkable efficacy, our method achieves
Anomaly Diagnosis Using Machine Learning Method in Fiber Fault
Machine learning has emerged as a highly promising approach. Consequently, it is imperative to develop an automated and reliable algorithm that utilizes telemetry data acquired from Optical Time
Machine-learning-based anomaly detection in optical fiber monitoring
In this paper, we propose a data-driven approach to accurately and quickly detect, diagnose, and localize fiber fault anomalies, including fiber cuts and optical eavesdropping attacks.
Optical Fibre Communication Feature Analysis and Small Sample
To solve the problems of a few optical fibre line fault samples and the inefficiency of manual communication optical fibre fault diagnosis, this paper proposes a communication optical
Anomaly Detection and Localization in Optical Networks Using Vision
Optical Network Anomaly Detection and Localization Based on Forward Transmission Sensing and Route Optimization Philip N. Ji, Zilong Ye, Yue-Kai Huang, Thomas Ferreira de Lima, Yoshiaki Aono,
Machine Learning-based Anomaly Detection in Optical Fiber Monitoring
In this paper, we propose a data driven approach to accurately and quickly detect, diagnose, and localize fiber anomalies including fiber cuts, and optical eavesdropping attacks.
Anomaly Detection and Localization in Optical Networks Using Vision
We introduce an innovative vision transformer approach to identify and precisely locate high-risk events, including fiber cut precursors, in state-of-polarization derived spectrograms.
Anomaly Detection in Optical Fibers Using Machine Learning
This study introduces a data-driven approach aiming at precise, swift detection, diagnosis, and localization of fiber anomalies, spanning from fiber cuts to optical eavesdropping attacks.
Machine-learning-based anomaly detection in optical fiber
Mentioning: 18 - Secure and reliable data communication in optical networks is critical for high-speed Internet. However, optical fibers, serving as the data transmission medium providing connectivity to
ML-based Anomaly Detection in Optical Fiber Monitoring
Secure and reliable data communication in optical networks is critical for high-speed internet. We propose a data driven approach for the anomaly detection and faults identification in optical networks
Optical fiber anomaly detection through SRS-induced spectral tilt in C
This paper proposes a simple and effective fiber anomaly detection method for C+L-band fiber-optic communication systems, leveraging the spectral tilt induced by the stimulated Raman
Vision Transformers for Anomaly Classification and Localization in
Monitoring the state of polarization (SOP) in optical communication networks is vital for maintaining network reliability and performance. SOP data, influenced by environmental factors,
Resilient Anomaly Detection in Fiber-Optic Networks: A Machine
We present a thorough machine-learning framework based on real-time state-of-polarization (SOP) monitoring for robust anomaly identification in optical fiber networks.
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