A neural network model with a classical annotation method has been used on the EXL-50 tokamak to predict impending disruption. However, the results revealed issues of overfitting and overconfidence in predictions caused by inaccurate labeling. To mitigate these issues, an improved training framework has been proposed. In this approach, soft labels from previous training serve as teachers to supervise the further learning process; this has lead to a significant improvement in predictive model performance. Notably, this enhancement is primarily attributed to the coupling effect of the soft labels and correction mechanism. This improved training framework introduces an instance-specific label smoothing method, which reflects a more nuanced model assessment on the likelihood of a disruption. It presents a possible solution to effectively address the challenges associated with accurate labeling across different machines.

中文翻译：

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改进的中断预测神经网络模型训练框架及其在 EXL-50 上的应用

EXL-50 托卡马克装置采用了采用经典注释方法的神经网络模型来预测即将发生的破坏。然而，结果揭示了由于标签不准确而导致的过度拟合和过度自信的预测问题。为了缓解这些问题，提出了改进的培训框架。在这种方法中，之前训练的软标签充当老师来监督进一步的学习过程；这导致预测模型性能的显着提高。值得注意的是，这种增强主要归因于软标签和校正机制的耦合效应。这种改进的训练框架引入了特定于实例的标签平滑方法，该方法反映了对中断可能性的更细致的模型评估。它提供了一种可能的解决方案，可以有效解决与跨不同机器进行准确贴标相关的挑战。