TE-MSTAD fuses time-frequency features with graph-enhanced topology via RWKV and dual-branch networks to detect WSN anomalies, reporting F1 scores of 92.52% and 93.28% on public and real datasets.
IEEE Access 9, 109754–109762
2 Pith papers cite this work. Polarity classification is still indexing.
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MDF-RAGAN reconstructs 3D underwater sound speed fields from multimodal surface data with estimation error below 0.3 m/s and up to 65.8% RMSE reduction versus mean profile baselines.
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A Multimodal Data Fusion Attention-Empowered Generative Adversarial Network for Real Time 3D Underwater Sound Speed Field Construction
MDF-RAGAN reconstructs 3D underwater sound speed fields from multimodal surface data with estimation error below 0.3 m/s and up to 65.8% RMSE reduction versus mean profile baselines.