币号�?No Further a Mystery
币号�?No Further a Mystery
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The research is carried out about the J-TEXT and EAST disruption database according to the prior work13,51. Discharges in the J-TEXT tokamak are useful for validating the efficiency on the deep fusion feature extractor, together with supplying a pre-trained product on J-Textual content for additional transferring to predict disruptions within the EAST tokamak. To make sure the inputs on the disruption predictor are saved the exact same, forty seven channels of diagnostics are picked from equally J-Textual content and EAST respectively, as is proven in Table 4.
尽管比特币它已经实现了加快交易速度的目标，但随着使用量的大幅增长，比特币网络仍面临着阻碍采用的成本和安全问题。
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While using the databases determined and proven, normalization is performed to remove the numerical variations in between diagnostics, also to map the inputs to an proper variety to aid the initialization in the neural community. According to the effects by J.X. Zhu et al.19, the efficiency of deep neural network is simply weakly dependent on the normalization parameters assuming that all inputs are mapped to suitable range19. Consequently the normalization procedure is done independently for both of those tokamaks. As for the two datasets of EAST, the normalization parameters are calculated independently In keeping with various education sets. The inputs are normalized Along with the z-score system, which ( X _ rm norm =frac X- rm mean (X) rm std (X) ).
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