基于半监督异常检测的破裂前兆起始时间研究
Disruption Precursor Onset Time Study Based on Semi-supervised Anomaly Detection
AI总结：
针对托卡马克破裂前兆固定时间标注导致误标的问题，提出基于异常检测推断各炮前兆起始时间并优化标签的方法，在 J-TEXT 和 EAST 上提升了监督预测器性能。
摘要：
目前对托卡马克中等离子体破裂的全面认识仍有不足，数据驱动方法被广泛用于破裂预测。然而，现有的大多数数据驱动破裂预测器采用监督学习技术，需要带标签的训练数据。手动标注破裂前兆是一项繁琐且具有挑战性的工作，因为部分前兆难以准确识别，这限制了机器学习模型的潜力。为解决这一问题，常用标注方法假定前兆起始于破裂前的固定时间；但由于等离子体不稳定性升级速度不同，这一假定对于不同类型的破裂、甚至同一类型的破裂都可能并不一致，从而导致样本误标，并使监督学习预测器性能欠佳。本文提出一种基于异常检测的破裂预测方法，克服了正负数据样本不平衡以及破裂前兆样本标注不准确的缺陷。我们在 J-TEXT 和 EAST 上展示了基于不同算法的异常检测预测器的有效性和可靠性，以评估异常检测预测器推断的前兆起始时间的可靠性。这些预测器推断出的前兆起始时间表明，由于不同炮（shot）的起始时间不一定相同，标注方法仍有改进空间。最后，我们利用异常检测预测器推断出的起始时间优化前兆标注，并在监督学习破裂预测器上测试优化后的标签。J-TEXT 和 EAST 上的结果表明，使用优化标签训练的模型优于使用固定起始时间标签训练的模型。
英文摘要：
The full understanding of plasma disruption in tokamaks is currently lacking, and data-driven methods are extensively used for disruption prediction. However, most existing data-driven disruption predictors employ supervised learning techniques, which require labeled training data. The manual labeling of disruption precursors is a tedious and challenging task, as some precursors are difficult to accurately identify, limiting the potential of machine learning models. To address this issue, commonly used labeling methods assume that the precursor onset occurs at a fixed time before the disruption, which may not be consistent for different types of disruptions or even the same type of disruption, due to the different speeds at which plasma instabilities escalate. This leads to mislabeled samples and suboptimal performance of the supervised learning predictor. In this paper, we present a disruption prediction method based on anomaly detection that overcomes the drawbacks of unbalanced positive and negative data samples and inaccurately labeled disruption precursor samples. We demonstrate the effectiveness and reliability of anomaly detection predictors based on different algorithms on J-TEXT and EAST to evaluate the reliability of the precursor onset time inferred by the anomaly detection predictor. The precursor onset times inferred by these predictors reveal that the labeling methods have room for improvement as the onset times of different shots are not necessarily the same. Finally, we optimize precursor labeling using the onset times inferred by the anomaly detection predictor and test the optimized labels on supervised learning disruption predictors. The results on J-TEXT and EAST show that the models trained on the optimized labels outperform those trained on fixed onset time labels.
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