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Stepwise-Refined Interval for Deep Learning to Process Sensor-Cloud Data with Noises

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Security, Privacy, and Anonymity in Computation, Communication, and Storage (SpaCCS 2020)

Part of the book series: Lecture Notes in Computer Science ((LNISA,volume 12383))

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Abstract

Predicting behaviors of interest in sensor cloud data has been a challenging issue due to the interference of the tremendous background noises. In this paper, a large number of noisy gravitational wave data detected by the sensor array on the laser interferometer gravitational wave observatory (LIGO) measurement arm is taken as a case study, and a model is established to predict the merging time of the two neutron star collision in real-time. After injecting gravitational wave signals with noise data, combined with the current popular deep learning techniques, we first predict the probability value of the merger event within an acceptable interval, then gradually narrow the interval down, and finally make a prediction of a small interval. Our results show that the stepwise-refined interval method has a faster speed without reducing accuracy. The average absolute error of the merge time on the test set is as low as 0.003.

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Author information

Authors and Affiliations

  1. Department of Computer Science and Technology, Nanjing Tech University, Nanjing, 211816, China

    Feichou Kou

  2. Watson Group, IBM Massachusetts Lab, Littleton, MA, 02139, USA

    Fan Zhang

Authors
  1. Feichou Kou
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  2. Fan Zhang
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Corresponding author

Correspondence to Feichou Kou .

Editor information

Editors and Affiliations

  1. Guangzhou University, Guangzhou, China

    Guojun Wang

  2. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Bing Chen

  3. Computer Science, Georgia State University, Atlanta, GA, USA

    Wei Li

  4. College of Science and Engineering, Qatar Foundation Education City, Doha, Qatar

    Roberto Di Pietro

  5. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Xuefeng Yan

  6. Nanjing University of Aeronautics and Astronautics, Nanjing, China

    Hao Han

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Kou, F., Zhang, F. (2021). Stepwise-Refined Interval for Deep Learning to Process Sensor-Cloud Data with Noises. In: Wang, G., Chen, B., Li, W., Di Pietro, R., Yan, X., Han, H. (eds) Security, Privacy, and Anonymity in Computation, Communication, and Storage. SpaCCS 2020. Lecture Notes in Computer Science(), vol 12383. Springer, Cham. https://doi.org/10.1007/978-3-030-68884-4_23

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  • DOI: https://doi.org/10.1007/978-3-030-68884-4_23

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-68883-7

  • Online ISBN: 978-3-030-68884-4

  • eBook Packages: Computer ScienceComputer Science (R0)

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