论文标题
使用LSTM预测Aditya Tokamak的中断
Using LSTM for the Prediction of Disruption in ADITYA Tokamak
论文作者
论文摘要
Tokamak的重大破坏对船只及其周围的设备构成了严重威胁。系统检测任何可能导致破坏的行为的能力可以帮助事先提醒系统并防止其有害影响。许多机器学习技术已经在JET和ASDEX等大型Tokamak上使用，但不适合Aditya，这相对较小。通过这项工作，我们讨论了一种新的实时方法，以预测Aditya Tokamak中断的时间，并在实验数据集中验证结果。该系统使用Tokamak中选定的诊断，经过一些预处理步骤，将它们发送到时间序列长的短期内存（LSTM）网络。该模型可以提前12毫秒的预测以较少的计算成本，该预测足够快，可以部署在实时应用程序中。
Major disruptions in tokamak pose a serious threat to the vessel and its surrounding pieces of equipment. The ability of the systems to detect any behavior that can lead to disruption can help in alerting the system beforehand and prevent its harmful effects. Many machine learning techniques have already been in use at large tokamaks like JET and ASDEX, but are not suitable for ADITYA, which is comparatively small. Through this work, we discuss a new real-time approach to predict the time of disruption in ADITYA tokamak and validate the results on an experimental dataset. The system uses selected diagnostics from the tokamak and after some pre-processing steps, sends them to a time-sequence Long Short-Term Memory (LSTM) network. The model can make the predictions 12 ms in advance at less computation cost that is quick enough to be deployed in real-time applications.
