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

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

Rev Sci Instrum. 2019 Dec 1;90(12):125111. doi: 10.1063/1.5088533.

Authors

Takehiko Tanabe¹, Jiaxing Ye¹, Tomonari Suzuyama¹, Takumi Kobayashi¹, Yu Yamaguchi¹, Masami Yasuda¹

Affiliation

¹ National Metrology Institute of Japan (NMIJ), National Institute of Advanced Industrial Science and Technology (AIST), 1-1-1 Umezono, Tsukuba, Ibaraki 305-8563, Japan.

PMID: 31893839
DOI: 10.1063/1.5088533

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.