Nuclear Fusion Knowledge Graph Guided Edge-Localized Mode Recognition on EAST

Recognizing edge-localized modes (ELMs) and their surrounding plasma states is essential for understanding tokamak operation and supporting plasma monitor- ing. The task requires distinguishing physically different states with similar diag- nostic waveforms, resolving brief events, and learning from strongly imbalanced classes. Human recognition combines diagnostic evidence, physical rules, and experience from previous discharges, providing structured information beyond temporal patterns alone. We propose KGF-ELM, a reliability-aware knowledge fusion framework for point-wise recognition of four ELM-related plasma states on EAST. An ELM knowledge graph abstracts the manual recognition workflow into rules that guide historical case construction from 100 training discharges. The Knowledge Graph Collaborative Fusion Module (KGCFM) brings together physi- cal evidence, prototype activations, relational graph representations, and retrieved cases. It coordinates their predictions using source-quality scores and cross-source agreement, then incorporates knowledge through feature enhancement and gated decision correction. A minority-group safeguard attenuates selected collabora- tive updates, while joint training aligns the knowledge pathways with a replace- able temporal backbone. Experiments on EAST-ELM640 show Macro-F1 gains of 2.4–10.4 percentage points across five backbones, averaging 4.7 points. With CrossLinear, KGF-ELM achieves 91.7% Accuracy, 81.6% Macro-F1, and 98.0% Macro-AUC, leading the compared temporal models on these metrics. Abla- tions show that the complete framework outperforms its evaluated fusion and knowledge-component variants. These results establish structured domain knowl- edge as an effective complement to temporal modeling for ELM-related plasma- state recognition.