Potential for improving the local realization of coordinated universal time with a convolutional neural network.
Rev Sci Instrum
; 90(12): 125111, 2019 Dec 01.
Article
em En
| MEDLINE
| ID: mdl-31893839
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.
Texto completo:
1
Coleções:
01-internacional
Base de dados:
MEDLINE
Idioma:
En
Revista:
Rev Sci Instrum
Ano de publicação:
2019
Tipo de documento:
Article
País de afiliação:
Japão