发表机构
School of Computer Science and Technology, Anhui University; Department of Computer Science and Technology, Tsinghua University; Institute of Plasma Physics, Chinese Academy of Sciences（安徽大学计算机科学与技术学院; 清华大学计算机科学与技术系; 中国科学院等离子体物理研究所）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
针对深度时间序列模型在ELM识别中缺乏物理知识利用且易混淆相似类别的问题，提出KGF-ELM知识图谱融合网络，通过双层级可靠性感知融合机制增强特征并修正分类结果。
AI 中文摘要
边界局域模（ELMs）是在托卡马克等离子体中常见的瞬态磁流体力学不稳定性。尽管深度时间序列模型能够从多诊断信号中学习复杂的动态模式，但其预测主要基于数据驱动，缺乏对ELM物理、等离子体运行状态和历史放电经验的显式利用。因此，在严重的类别不平衡条件下，这些模型可能混淆具有相似时间模式的类别。为解决这一问题，我们提出了KGF-ELM，一种用于ELM识别的知识图谱融合网络，并引入知识图谱协同融合模块（KGCFM），该模块将当前时间窗口的物理证据、ELM原型、关系知识图谱表示以及相似历史放电案例组织为互补的多源知识表示。KGCFM通过物理原型匹配、图结构知识编码、历史案例检索、跨证据一致性分析和可靠性估计来协同建模这些知识源，从而生成用于增强时间特征和修正分类结果的知识表示。基于该模块，KGF-ELM采用双层级可靠性感知融合机制。在特征层面，物理残差和图引导的知识表示根据其可靠性自适应地融入时间特征。在决策层面，使用可靠性门控、少数类保护和有界知识修正来安全地细化分类结果，并减少不可靠知识对基础模型的负面影响。
英文摘要
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.
