论文标题
通过卷积神经网络改善协调通用时间的局部实现的潜力
Potential for improving the local realization of coordinated universal time with a convolutional neural network
论文作者
论文摘要
通过使用一种称为一维卷积神经网络（1D-CNN）的深度学习技术，可以预测协调的通用时间（UTC）（UTC）（UTC）（UTC）和氢maser的氢maser氢maser的氢键（氢maser）。关于1D-CNN获得的预测结果，我们观察到与卡尔曼过滤器获得的预测准确性相比，预测准确性的提高。尽管需要更多的研究得出结论，即1D-CNN可以作为良好的预测指标，但目前的结果表明，基于深度学习技术的计算方法可能会成为提高UTC（NMIJ）相对于UTC的同步准确性的多功能方法。
The time difference between coordinated universal time (UTC) and a hydrogen maser, which is a master oscillator for the local realization of UTC at the National Metrology Institute of Japan (NMIJ), has been predicted by using one of the deep learning techniques called a one-dimensional convolutional neural network (1D-CNN). Regarding the prediction result obtained by the 1D-CNN, we have observed improvement in the accuracy of prediction compared with that obtained by the Kalman filter. Although more investigations are required to conclude that the 1D-CNN can work as a good predictor, the present results suggest that the computational approach based on the deep learning technique may become a versatile method for improving the synchronous accuracy of UTC(NMIJ) relative to UTC.
