Potential for improving the local realization of coordinated universal time with a convolutional neural network

Takehiko Tanabe; Jiaxing Ye; Tomonari Suzuyama; Takumi Kobayashi; Yu Yamaguchi; Masami Yasuda

doi:10.1063/1.5088533

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arXiv · 2001.02994

Potential for improving the local realization of coordinated universal time with a convolutional neural network

Takehiko Tanabe·Jiaxing Ye·Tomonari Suzuyama·Takumi Kobayashi·Yu Yamaguchi·Masami Yasuda

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Abstract

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.

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BibTeX RIS

Takehiko Tanabe, Jiaxing Ye, Tomonari Suzuyama, Takumi Kobayashi, Yu Yamaguchi, Masami Yasuda. 2020-01-08. Potential for improving the local realization of coordinated universal time with a convolutional neural network. https://doi.org/10.1063/1.5088533

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