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.
2
Pith papers citing it
verdicts
UNVERDICTED 2representative citing papers
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.
citing papers explorer
-
A method for detecting spatio-temporal correlation anomalies of WSN nodes based on topological information enhancement and time-frequency feature extraction
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.
-
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.