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Creators/Authors contains: "Zhang, Xue-Hao"

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  1. ; ; ; ; (, 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2023))
  2. ; ; ; ; (, 5th International Conference on Advances in Signal Processing and Artificial Intelligence (ASPAI' 2023))
    The classification of frequently occurring terrestrial-origin transient signals, called glitches, in the time series data from gravitational wave detectors is important for mitigating their adverse effects on searches for rare and valuable astrophysical signals. While formally a time series classification problem, recent successes in glitch classification have all come from using their time-frequency image representations. Using transfer learning with the VGG16 deep convolutional neural network for image classification, we compare the efficacy of different types of image representations for classifying simulated glitches. We find the novel result that training the network with 2D plots of the noisy glitch time series provides better classification accuracy than their time-frequency images. 
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    Accepted Manuscript Full Text Available
  3. ; ; ; (, Physical Review D)
    Full Text Available