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人工智能将带来怎样的学习未来—基于国际教育核心期刊和发展报告的质性元分析研究

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fifty%) will neither exploit the limited information from EAST nor the general knowledge from J-TEXT. Just one probable explanation would be that the EAST discharges are usually not representative plenty of and also the architecture is flooded with J-TEXT info. Scenario 4 is properly trained with twenty EAST discharges (10 disruptive) from scratch. To avoid about-parameterization when coaching, we used L1 and L2 regularization for the design, and altered the learning charge program (see Overfitting handling in Methods). The general performance (BA�? 60.28%) implies that utilizing just the limited information with the focus on area isn't sufficient for extracting basic characteristics of disruption. Scenario 5 makes use of the pre-qualified model from J-Textual content instantly (BA�? 59.44%). Using the supply product along would make the overall expertise about disruption be contaminated by other know-how specific to your resource area. To conclude, the freeze & fantastic-tune procedure will be able to achieve the same functionality applying only 20 discharges Together with the complete data baseline, and outperforms all other scenarios by a sizable margin. Applying parameter-based mostly transfer Mastering method to combine the two the source tokamak model and data in the goal tokamak appropriately may possibly help make much better use of data from both domains.

All discharges are break up into consecutive temporal sequences. A time threshold right before disruption is outlined for various tokamaks in Table five to indicate the precursor of a disruptive discharge. The “unstable�?sequences of disruptive discharges are labeled as “disruptive�?and various sequences from non-disruptive discharges are labeled as “non-disruptive�? To ascertain time threshold, we very first acquired a time span based on prior discussions and consultations with tokamak operators, who delivered precious insights to the time span inside which disruptions could possibly be reliably predicted.

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Within our scenario, the pre-educated product in the J-Textual content tokamak has previously been proven its efficiency in extracting disruptive-related characteristics on J-Textual content. To further check its skill for predicting disruptions across tokamaks determined by transfer Finding out, a group of numerical experiments is performed on a whole new target tokamak EAST. As compared to the J-TEXT tokamak, EAST contains a much larger sizing, and operates in constant-point out divertor configuration with elongation and triangularity, with much increased plasma overall performance (see Dataset Go to Website in Solutions).

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Performances in between the a few versions are shown in Table one. The disruption predictor based on FFE outperforms other styles. The model based upon the SVM with manual characteristic extraction also beats the final deep neural community (NN) model by a huge margin.

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