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TokEye: Fast Signal Extraction for Fluctuating Time Series via Offline Self-Supervised Learning From Fusion Diagnostics to Bioacoustics
arXiv – CS AI|Nathaniel Chen, Kouroche Bouchiat, Peter Steiner, Andrew Rothstein, David Smith, Max Austin, Mike van Zeeland, Azarakhsh Jalalvand, Egemen Kolemen||5 views
🤖AI Summary
Researchers developed TokEye, a self-supervised AI framework that can extract coherent signals from noisy time-series data in 0.5 seconds, initially designed for fusion reactor diagnostics. The system demonstrates applications beyond fusion research, including bioacoustics, suggesting broader potential for real-time signal processing across industries.
Key Takeaways
- →TokEye achieves 0.5-second inference latency for real-time mode identification in complex time-frequency data.
- →The framework uses self-supervised learning to automatically extract signals from high-noise environments without manual analysis.
- →Initial applications focus on fusion reactor diagnostics but extend to bioacoustics and other spectrograms.
- →The system enables automated database generation for advanced plasma control in next-generation fusion facilities.
- →The open-source tool addresses the 'data deluge' problem facing facilities that generate petabytes of sensor data daily.
#artificial-intelligence#machine-learning#signal-processing#self-supervised-learning#real-time-analysis#fusion-energy#automated-diagnostics#time-series
Read Original →via arXiv – CS AI
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