title: ÐÐ¾Ð¸ÑÐº ÑÐ¾Ð±ÑÑÐ¸Ð¹ Ð¸ Ð·Ð°ÐºÐ¾Ð½Ð¾Ð¼ÐµÑÐ½Ð¾ÑÑÐµÐ¹, Ð¿ÑÐµÐ´ÑÐµÑÑÐ²ÑÑÑÐ¸Ñ ÑÑÑÐ²Ñ Ð² Ð´Ð¸Ð°Ð³Ð½Ð¾ÑÑÐ¸ÑÐµÑÐºÐ¸Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð°Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð¼ Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ð¸ Ð¾Ð±ÑÑÐµÐ½Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸
title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð¼Ð°Ð³Ð¸ÑÑÑÐ°
titleType: Subtitle
title: Search for events and patterns in diagnostic signals that precede the disruption, by interpreting the trained neural network model
Creators: name: ÐÐ¾Ð³Ð´Ð°Ð½Ð¾Ð², ÐÐ½ÑÐ¾Ð½
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2023
Subjects: ÐÐ°ÑÐ¸Ð½Ð½Ð¾Ðµ Ð¾Ð±ÑÑÐµÐ½Ð¸Ðµ
ÐÐµÐ¹ÑÐ¾Ð½Ð½ÑÐµ ÑÐµÑÐ¸
Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ðµ ÑÑÑÐ²Ð¾Ð²
Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ñ Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹
Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð°Ñ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ°
disruption prediction
model interpretation
hardware development

description: Ð Ð´Ð°Ð½Ð½Ð¾Ð¹ ÑÐ°Ð±Ð¾ÑÐµ Ð¸ÑÑÐ»ÐµÐ´ÑÐµÑÑÑ Ð²Ð¾Ð·Ð¼Ð¾Ð¶Ð½Ð¾ÑÑÑ Ð¸ÑÐ¿Ð¾Ð»ÑÐ·Ð¾Ð²Ð°Ð½Ð¸Ñ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¼Ð¾Ð´ÐµÐ»ÐµÐ¹ Ð¼Ð°ÑÐ¸Ð½Ð½Ð¾Ð³Ð¾ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð´Ð»Ñ Ð¿Ð¾Ð²ÑÑÐµÐ½Ð¸Ñ ÑÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÐ¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÑÑÐ²Ð° Ð½Ð° ÑÐ¾ÐºÐ°Ð¼Ð°ÐºÐµ ÐÐ»Ð¾Ð±ÑÑ-Ð2, Ð° ÑÐ°ÐºÐ¶Ðµ ÑÐ°Ð·ÑÐ°Ð±Ð¾ÑÐºÐ° Ð°Ð¿Ð¿Ð°ÑÐ°ÑÐ½Ð¾Ð¹ ÑÐ°ÑÑÐ¸ Ð´Ð»Ñ ÑÐµÐ°Ð»Ð¸Ð·Ð°ÑÐ¸Ð¸ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÑÑÐ²Ð° Ð² ÑÐµÐ¶Ð¸Ð¼Ðµ ÑÐµÐ°Ð»ÑÐ½Ð¾Ð³Ð¾ Ð²ÑÐµÐ¼ÐµÐ½Ð¸. ÐÐ»Ñ ÑÑÐ¾Ð³Ð¾ Ð±ÑÐ» ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½ Ð½Ð°Ð±Ð¾Ñ Ð¿ÑÐ¸Ð¼ÐµÑÐ¾Ð² Ð´Ð»Ñ Ð¾Ð±ÑÑÐµÐ½Ð¸Ñ Ð¿Ð¾Ð»Ð½Ð¾ÑÐ²ÑÐ·Ð½Ð¾Ð¹ Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð½ÐµÐ¹ÑÐ¾Ð½Ð½Ð¾Ð¹ ÑÐµÑÐ¸. ÐÐµÑÐ¾Ð´ SHAP Ð¿ÑÐ¸Ð¼ÐµÐ½ÐµÐ½ Ð´Ð»Ñ Ð¸Ð½ÑÐµÑÐ¿ÑÐµÑÐ°ÑÐ¸Ð¸ ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐ¾Ð² Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ ÑÑÑÐ²Ð° Ð² ÑÐ°Ð·ÑÑÐ´Ð°Ñ ÑÐ¾ ÑÑÐ°ÑÐ½ÑÐ¼ Ð·Ð°Ð²ÐµÑÑÐµÐ½Ð¸ÐµÐ¼ Ð¸ Ñ Ð½ÐµÐ¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐµÐ¼ÑÐ¼ ÑÑÑÐ²Ð¾Ð¼. Ð¡ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð° SHAP Ð²ÑÐ±ÑÐ°Ð½ Ð½Ð°Ð±Ð¾Ñ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð², Ð¾ÐºÐ°Ð·ÑÐ²Ð°ÑÑÐ¸Ñ Ð½Ð°Ð¸Ð±Ð¾Ð»ÑÑÐµÐµ Ð²Ð»Ð¸ÑÐ½Ð¸Ðµ Ð½Ð° ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·Ð°Ð½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ ÑÑÑÐ²Ð°. Ð­ÑÑÐµÐºÑÐ¸Ð²Ð½Ð¾ÑÑÑ Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð¾Ð³Ð¾ Ð½Ð°Ð±Ð¾ÑÐ° Ð¿ÑÐ¾Ð²ÐµÑÐµÐ½Ð° Ð¿Ð¾ ÑÑÐ°Ð²Ð½ÐµÐ½Ð¸Ñ Ñ Ð½Ð°Ð±Ð¾ÑÐ¾Ð¼, ÑÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð½ÑÐ¼ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð¼ÐµÑÐ¾Ð´Ð¾Ð² ÐºÐ¾ÑÑÐµÐ»ÑÑÐ¸Ð¾Ð½Ð½Ð¾Ð³Ð¾ Ð°Ð½Ð°Ð»Ð¸Ð·Ð°. ÐÐ¾Ð´ÐµÐ»Ñ, Ð¾Ð±ÑÑÐµÐ½Ð½Ð°Ñ Ñ Ð¿Ð¾Ð¼Ð¾ÑÑÑ Ð½Ð°Ð±Ð¾ÑÐ° ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¸Ð· Ð¼ÐµÑÐ¾Ð´Ð° SHAP, Ð½Ð° 7% ÑÑÑÐµÐºÑÐ¸Ð²Ð½ÐµÐµ Ð¿ÑÐµÐ´ÑÐºÐ°Ð·ÑÐ²Ð°ÐµÑ ÑÑÑÐ² Ð¿Ð¾ Ð¼ÐµÑÑÐ¸ÐºÐµ AUC. Ð Ð°Ð·ÑÐ°Ð±Ð¾ÑÐ°Ð½Ð° Ð¿ÑÐ¸Ð½ÑÐ¸Ð¿Ð¸Ð°Ð»ÑÐ½Ð°Ñ ÑÑÐµÐ¼Ð° Ð¸ 3D-Ð¼Ð¾Ð´ÐµÐ»Ð¸ Ð¼Ð¾Ð´ÑÐ»Ñ Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ¸ ÑÐ¸Ð³Ð½Ð°Ð»Ð¾Ð² Ð¸ Ð²ÑÑÐ¸ÑÐ»ÐµÐ½Ð¸Ñ Ð²ÐµÑÐ¾ÑÑÐ½Ð¾ÑÑÐ¸ ÑÑÑÐ²Ð°. ÐÐ°Ð´ÐµÑÐ¶ÐºÐ° Ð¼ÐµÐ¶Ð´Ñ Ð¾ÑÐ¸ÑÑÐ¾Ð²ÐºÐ¾Ð¹ ÑÐ¸Ð³Ð½Ð°Ð»Ð° Ð¸ ÑÐ¾ÑÐ¼Ð¸ÑÐ¾Ð²Ð°Ð½Ð¸ÐµÐ¼ ÑÐµÐ·ÑÐ»ÑÑÐ°Ñ, Ð¿Ð¾Ð»ÑÑÐµÐ½Ð½Ð°Ñ Ð² ÑÐµÐ·ÑÐ»ÑÑÐ°ÑÐµ ÑÐµÑÑÐ¸ÑÐ¾Ð²Ð°Ð½Ð¸Ñ, Ð¾ÐºÐ°Ð·Ð°Ð»Ð°ÑÑ ÑÐ°Ð²Ð½Ð¾Ð¹ 280 Ð¼ÐºÑ.
description: In this paper, we study the possibility of using methods for interpreting the results of machine learning models to improve the efficiency of plasma disruption prediction at the Globus-M2 tokamak, as well as the development of hardware for real-time disruption prediction. For this, a set of examples was formed for training a fully connected neural network model. The SHAP method is applied to interpret the results of disruption prediction in charges with regular termination and with unpredictable disruption. With the help of the SHAP method, a set of signals is selected that have the greatest impact on the result of predicting the relapse probability. The effectiveness of the resulting set was tested in comparison with the set formed using the methods of correlation analysis. A model trained with a set of signals from the SHAP method is 7% more effective at predicting relapse using the AUC metric. A schematic diagram and 3D models of the signal processing module and calculation of the disruption probability have been developed. The delay between the digitization of the signal and the formation of the result, obtained as a result of testing, turned out to be 280 Î¼s.