Outlier classification using autoencoders: Application for fluctuation driven flows in fusion plasmas.
Outlier classification using autoencoders: Application for fluctuation driven flows in fusion plasmas.
复制标题
使用自动编码器进行离群值分类：聚变等离子体中波动驱动流的应用。
DOI：
10.1063/1.5049519
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发表时间：
2018
期刊：
影响因子：
--
通讯作者：
B. LaBombard
中科院分区：
文献类型：
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作者：
R. Kube;Filippo Maria Bianchi;Dan Brunner;B. LaBombard
为了准确模拟真空容器部件的寿命，需要了解磁约束等离子体边界层中波动驱动流的统计数据。镜像朗缪尔探针 (MLP) 是一种新颖的诊断方法，它独特地允许我们在比等离子体参数波动的特征时间尺度更短的时间尺度上对等离子体参数进行采样。在探头偏置范围幅度不足的情况下，等离子体中突然的大幅度波动会降低 MLP 报告的等离子体参数的精度和准确度。虽然一些数据样本可以很容易地被分类为有效和无效，但我们发现，对于本研究中考虑的等离子体参数和偏置电压采样数据而言，这种分类可能不明确，高达 40%。在此贡献中，我们采用自动编码器（AE）来学习有效数据样本的低维表示。根据定义，该空间中的坐标是主要表征有效数据的特征。使用矢量数据的标准分类器在此空间中对不明确的数据样本进行分类。通过这种方式，我们可以避免定义复杂的阈值规则来识别异常值，这需要强有力的假设并在分析中引入偏差。通过删除 AE 潜在低维空间中识别的异常值，我们发现平均传导和对流径向热通量比删除阈值识别的异常值低约 5% 到 15%。对于三重相关性对径向热通量的贡献，差异高达 40%。
Understanding the statistics of fluctuation driven flows in the boundary layer of magnetically confined plasmas is desired to accurately model the lifetime of the vacuum vessel components. Mirror Langmuir probes (MLPs) are a novel diagnostic that uniquely allow us to sample the plasma parameters on a time scale shorter than the characteristic time scale of their fluctuations. Sudden large-amplitude fluctuations in the plasma degrade the precision and accuracy of the plasma parameters reported by MLPs for cases in which the probe bias range is of insufficient amplitude. While some data samples can readily be classified as valid and invalid, we find that such a classification may be ambiguous for up to 40% of data sampled for the plasma parameters and bias voltages considered in this study. In this contribution, we employ an autoencoder (AE) to learn a low-dimensional representation of valid data samples. By definition, the coordinates in this space are the features that mostly characterize valid data. Ambiguous data samples are classified in this space using standard classifiers for vectorial data. In this way, we avoid defining complicated threshold rules to identify outliers, which require strong assumptions and introduce biases in the analysis. By removing the outliers that are identified in the latent low-dimensional space of the AE, we find that the average conductive and convective radial heat fluxes are between approximately 5% and 15% lower as when removing outliers identified by threshold values. For contributions to the radial heat flux due to triple correlations, the difference is up to 40%.