L,H模式和无等离子状态的分类:KSTAR边缘反射仪上的卷积神经网络和变化自编码器
Boseong Kim1,2, Seong-Heon Seo2, Dong Keun Oh2
1Department of Nuclear Engineering, Seoul National University, Seoul, South Korea.
The Review of scientific instruments
|October 1, 2024
概括
机器学习模型使用边缘反射计数据准确地分类托卡马克等离子体状态. 深度学习技术显著改善了实时分析,并减少了用于稳定的核聚变能源研究的手动处理.
科学领域:
- 血物理学的等离子体物理学
- 核聚变能源的研究.
- 机器学习应用程序 机器学习应用程序
背景情况:
- 稳定的托卡马克运行依赖于将等离子体状态分类 (L模式,H模式,无等离子体).
- 边缘反射计提供了等离子体密度数据,但由于数据量和复杂性,在实时分析方面面临挑战.
- 需要高效的计算方法来实时分析复杂的反射仪数据.
研究的目的:
- 研究机器学习 (ML) 技术的有效性,用于使用边缘反射仪数据对托卡马克放电状态进行分类.
- 将ML模型的性能与不同输入数据类型 (1D原始信号与2D谱图) 的性能进行比较.
- 证明无监督ML方法对排放状态的集群能力.
主要方法:
- 使用深度卷积神经网络 (CNN) 模型进行分类任务.
- 使用变化自编码器 (VAE) 进行无监督的排放状态集群.
- 将ML模型应用于韩国超导托卡马克先进研究 (KSTAR) 边缘反射仪的原始信号数据和2D谱图.
主要成果:
- 深度CNN模型使用2D光谱输入实现了高达99%的分类准确性,优于1D原始信号输入.
- 在VAE模型中,在不需要先前标签信息的情况下,成功地将不同的排放状态聚集在潜伏空间中.
- ML模型展示了复杂的反射仪数据的有效处理,用于准确的血状态分类.
结论:
- 机器学习技术,特别是深度CNN和VAE,为边缘反射仪数据的实时分析提供了强大而高效的解决方案.
- 这种方法促进了等离子体放电状态的准确和自动分类,这对于稳定的托卡马克运行至关重要.
- 开发的机器学习模型减少了手动数据处理的需要,使得融合研究的洞察力更快.
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