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」。
The underside layers which happen to be closer on the inputs (the ParallelConv1D blocks within the diagram) are frozen as well as parameters will keep unchanged at further tuning the product. The layers which are not frozen (the higher levels which are nearer on the output, long small-phrase memory (LSTM) layer, and the classifier created up of entirely related layers inside the diagram) might be more trained While using the 20 EAST discharges.
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Tokamaks are quite possibly the most promising way for nuclear fusion reactors. Disruption in tokamaks can be a violent party that terminates a confined plasma and brings about unacceptable harm to the device. Equipment Understanding designs are broadly accustomed to forecast incoming disruptions. Even so, future reactors, with Substantially higher saved Power, simply cannot offer more than enough unmitigated disruption information at substantial functionality to prepare the predictor prior to harming by themselves. In this article we utilize a deep parameter-based transfer Mastering method in disruption prediction.
实际上，“¥”符号中水平线的数量在不同的字体是不同的，但其含义相同。下表提供了一些字体的情况，其中“＝”表示为双水平线，“－”表示为单水平线，“×”表示无此字符。
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As a way to validate whether or not the product did seize normal and customary patterns amongst distinctive tokamaks even with excellent distinctions in configuration and Procedure regime, along with to examine the role that each part of the model played, we further more made additional numerical experiments as is shown in Fig. six. The numerical experiments are created for interpretable investigation in the transfer design as is explained in Desk three. In Each individual scenario, a different Portion of the design is frozen. Just in case one, The underside layers from the ParallelConv1D blocks are frozen. In the event that 2, all layers on the ParallelConv1D blocks are frozen. Just in case three, all levels in ParallelConv1D blocks, together with the LSTM levels are frozen.
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Performances between the 3 versions are revealed in Table 1. The disruption predictor based upon FFE outperforms other types. The model according Click Here to the SVM with handbook attribute extraction also beats the general deep neural network (NN) model by a major margin.
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