{
  "abstract": "Edge-localized modes (ELMs) are transient magnetohydrodynamic instabilities commonly observed in tokamak plasmas. Although deep time-series models can learn complex dynamic patterns from multi-diagnostic signals, their predictions are mainly data-driven and lack explicit utilization of ELM physics, plasma operating states, and historical discharge experience. As a result, they may confuse categories with similar temporal patterns, particularly under severe class imbalance. To address this issue, we propose KGF-ELM, a Knowledge Graph Fusion Network for ELM Recognition, and introduce a Knowledge Graph Collaborative Fusion Module (KGCFM) that organizes physical evidence from the current time window, ELM prototypes, relational knowledge graph representations, and similar historical discharge cases into complementary multi-source knowledge representations. KGCFM collaboratively models these knowledge sources through physics prototype matching, graph-structured knowledge encoding, historical case retrieval, cross-evidence consistency analysis, and reliability estimation, thereby generating knowledge representations for enhancing temporal features and correcting classification results. Based on this module, KGF-ELM employs a dual-level reliability-aware fusion mechanism. At the feature level, physical residuals and graph-guided knowledge representations are adaptively incorporated into temporal features according to their reliability. At the decision level, reliability gating, minority-class protection, and bounded knowledge correction are used to safely refine the classification results and reduce the negative influence of unreliable knowledge on the base model.",
  "abstract_shown": true,
  "affiliations": [],
  "article_type": null,
  "arxiv": "2610.00244",
  "author_total": 8,
  "authors": [
    {
      "name": "Wanli Lyu"
    },
    {
      "name": "Laiqi Qian"
    },
    {
      "name": "Xiao Wang"
    },
    {
      "name": "Qingquan Yang"
    },
    {
      "name": "Zehua Chen"
    },
    {
      "name": "Dengdi Sun"
    },
    {
      "name": "Guosheng Xu"
    },
    {
      "name": "Jin Tang"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/am4nmfhp4jfx6.bib",
  "cited_by": 0,
  "doi": "10.48550/arxiv.2610.00244",
  "fulltext_url": null,
  "id": "am4nmfhp4jfx6",
  "issns": [],
  "landing_url": "https://arxiv.org/abs/2610.00244",
  "language": null,
  "licence": "LicenseRef-arXiv-nonexclusive-1.0",
  "licence_label": "arXiv licence",
  "licence_source": "article",
  "licence_url": "http://arxiv.org/licenses/nonexclusive-distrib/1.0/",
  "metadata_licence": "CC0-1.0",
  "native_subjects": [
    [
      "arxiv",
      "physics.plasm-ph"
    ],
    [
      "openalex",
      "3106|Magnetic confinement fusion research"
    ]
  ],
  "openalex": "W7216511427",
  "pdf_url": "https://arxiv.org/pdf/2610.00244",
  "pmcid": null,
  "pmid": null,
  "published": "2026-10-01",
  "publisher": null,
  "ris": "https://research.draconitic.com/paper/am4nmfhp4jfx6.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:26:03Z",
      "native_id": "2610.00244",
      "source": "arxiv",
      "url": "https://arxiv.org/abs/2610.00244"
    }
  ],
  "subjects": [
    {
      "field": "physics",
      "origin": "arxiv:physics.plasm-ph",
      "subfield": "plasma"
    }
  ],
  "title": "Nuclear Fusion Knowledge Graph Guided Edge-Localized Mode Recognition on EAST",
  "title_text": "Nuclear Fusion Knowledge Graph Guided Edge-Localized Mode Recognition on EAST",
  "type": "preprint",
  "updated_at": "2026-10-30T12:26:03Z",
  "url": "https://research.draconitic.com/paper/am4nmfhp4jfx6",
  "venue": "arXiv",
  "versions": []
}