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作者:

Lu, Cheng (Lu, Cheng.) | Peng, Lei (Peng, Lei.) | Bi, Jing (Bi, Jing.) | Yuan, Haitao (Yuan, Haitao.)

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摘要:

In light curves, the brightness of stars is associated with time, and it is an image of the brightness with respect to time. The traditional data processing methods cannot effectively handle real-time and large-volume data of various light curves. To address this issue, this work develops a deep neural network, named Dropout Recurrent Neural Networks (DRNN). It extracts complicated characteristics of all images captured by Mini Ground-based Wide-Angle Camera array (Mini-GWAC) for point source extraction and cross-certification through Long Short-Term Memory units. Furthermore, this work optimizes the training model by combining a dropout method, which predicts changes of the star brightness in advance. Extensive experiments with Mini-GWAC dataset demonstrate that DRNN outperforms several typical baseline methods with respective to forecasting performance of star brightness in large-scale astronomical light curves. © 2018 IEEE.

关键词:

Cloud computing Data handling Deep neural networks Forecasting Large dataset Luminance Recurrent neural networks Stars Time series

作者机构:

  • [ 1 ] [Lu, Cheng]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 2 ] [Peng, Lei]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 3 ] [Bi, Jing]Faculty of Information Technology, Beijing University of Technology, Beijing; 100124, China
  • [ 4 ] [Yuan, Haitao]School of Software Engineering, Beijing Jiaotong University, Beijing; 100044, China

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年份: 2019

页码: 117-121

语种: 英文

被引次数:

WoS核心集被引频次: 0

SCOPUS被引频次: 8

ESI高被引论文在榜: 0 展开所有

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