此條目介紹的是货币符号。关于形近的西里尔字母，请见「Ұ」。关于形近的注音符號，请见「ㆾ」。
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In our scenario, the pre-skilled product through the J-TEXT tokamak has by now been established its effectiveness in extracting disruptive-connected attributes on J-TEXT. To even more test its potential for predicting disruptions across tokamaks according to transfer Discovering, a bunch of numerical experiments is completed on a different goal tokamak EAST. When compared to the J-TEXT tokamak, EAST contains a much larger size, and operates in constant-point out divertor configuration with elongation and triangularity, with A great deal increased plasma effectiveness (see Dataset in Techniques).
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To even further confirm the FFE’s capability to extract disruptive-associated options, two other products are qualified using the exact same input indicators and discharges, and examined utilizing the exact same discharges on J-TEXT for comparison. The 1st is actually a deep neural community product making use of very similar construction Along with the FFE, as is shown in Fig. five. The primary difference is that, all diagnostics are resampled to one hundred kHz and so are sliced into Click Here 1 ms size time Home windows, instead of addressing different spatial and temporal functions with unique sampling level and sliding window length. The samples are fed into your product straight, not taking into consideration features�?heterogeneous nature. The other model adopts the guidance vector machine (SVM).
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The goal of this exploration will be to Enhance the disruption prediction performance on goal tokamak with primarily awareness within the supply tokamak. The model general performance on goal domain mainly is dependent upon the efficiency in the model within the supply domain36. Consequently, we 1st require to obtain a significant-general performance pre-experienced model with J-Textual content information.