CNN and hand-crafted feature networks recover magnetic field (~3.8 µT), temperature (~0.12 K), and hysteresis branch from one quantitative magneto-optical domain map of Bi:YIG.
Humaidi, Ayad Al-Dujaili, Ye Duan, Omran Al-Shamma, J
5 Pith papers cite this work, alongside 7,556 external citations. Polarity classification is still indexing.
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Neural networks predict orographic gravity wave momentum fluxes from coarse state variables with offline R² of 0.56-0.72, learn physically meaningful relationships via SHAP, and are compared to the Lott-Miller parameterization.
Measured-only STGNNs (RGATv2, RGSAGE) achieve up to 11 F1 points higher and 6x faster training than RNN baselines for fault location on the IEEE 123-bus feeder under partial observability.
A modular CNN protocol identifies minimal loading history modules for basic hysteresis, degradation, and pinching, then trains three separate networks to estimate Bouc-Wen parameters rapidly from those modules.
Cascaded neural networks classify 10 eye-movement classes from single-cycle EOG signals at 99% accuracy with sub-83 ms latency below human reaction time.
citing papers explorer
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Decoding magnetic texture
CNN and hand-crafted feature networks recover magnetic field (~3.8 µT), temperature (~0.12 K), and hysteresis branch from one quantitative magneto-optical domain map of Bi:YIG.
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Interpretable Neural Networks to Predict Momentum Fluxes of Orographic Gravity Waves
Neural networks predict orographic gravity wave momentum fluxes from coarse state variables with offline R² of 0.56-0.72, learn physically meaningful relationships via SHAP, and are compared to the Lott-Miller parameterization.
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Robustness of Spatio-temporal Graph Neural Networks for Fault Location in Partially Observable Distribution Grids
Measured-only STGNNs (RGATv2, RGSAGE) achieve up to 11 F1 points higher and 6x faster training than RNN baselines for fault location on the IEEE 123-bus feeder under partial observability.
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Deep learning-based modularized loading protocol for parameter estimation of Bouc-Wen class models
A modular CNN protocol identifies minimal loading history modules for basic hysteresis, degradation, and pinching, then trains three separate networks to estimate Bouc-Wen parameters rapidly from those modules.
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Single-Cycle Multidirectional EOG Classification Faster than Human Reaction Time for Wearable Human-Computer Interactions
Cascaded neural networks classify 10 eye-movement classes from single-cycle EOG signals at 99% accuracy with sub-83 ms latency below human reaction time.