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Auroral Oval Boundary Modeling Based on Deep Learning Method

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Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques (IScIDE 2015)

Part of the book series: Lecture Notes in Computer Science ((LNIP,volume 9243))

Abstract

Research on the location of the auroral oval is important to understand the coupling processes of the Sun-Earth system. The equatorward boundary and poleward boundary of the auroral oval are significant parameters of the auroral oval location. Thus auroral oval boundary modeling is an efficient way to study the location of auroral oval. As the location of the auroral oval boundary is subject to a variety of geomagnetic factors, there are some limitations on traditional methods, which express the auroral oval boundary as a function of only one or several geomagnetic activity index. Deep learning method is used in this paper to learn the essential features of the inputs, which are a large number of geomagnetic parameters and the former locations of aurora boundary. Furthermore, a model is established to forecast the location of the auroral oval boundary. The experiment results show that our method can model and forecast the boundary of aurora oval efficiently on the data set obtained from Ultraviolet Imager (UVI) on Polar satellite and OMNI database on NASA.

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Acknowledgments

This research is supported by the Special Scientific Research of Marine Public Welfare Industry (201005017), the Basic Foundation for Scientific Research, the Fundamental Research Funds for the Central Universities (K5051302008), the Project Sponsored by the Scientific Research Foundation for the Returned Overseas Chinese Scholars, State Education Ministry and the Open Funding of State Key Laboratory of Remote Sensing Science (OFSLRSS201415), the Project Funded by China Postdoctoral Science Foundation.

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Authors and Affiliations

  1. School of Electronic Engineering, Xidian University, Xi’an, 710071, China

    Bing Han, Xinbo Gao, Hui Liu & Ping Wang

  2. State Key Laboratory of Remote Sensing Science, Beijing, 100101, China

    Bing Han

Authors
  1. Bing Han
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  2. Xinbo Gao
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  3. Hui Liu
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  4. Ping Wang
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Corresponding author

Correspondence to Bing Han .

Editor information

Editors and Affiliations

  1. Zhejiang University, Hangzhou, China

    Xiaofei He

  2. Xidian University, Xi'an, China

    Xinbo Gao

  3. Northwestern Polytechnical University, Xi'an, China

    Yanning Zhang

  4. Nanjing University, Nanjing, China

    Zhi-Hua Zhou

  5. Chinese Academy of Sciences, Beijing, China

    Zhi-Yong Liu

  6. Suzhou University of Science and Technology, Suzhou, China

    Baochuan Fu

  7. Suzhou University of Science and Technology, Jiangsu, China

    Fuyuan Hu

  8. Suzhou University of Science and Technology, Jiangsu, China

    Zhancheng Zhang

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Han, B., Gao, X., Liu, H., Wang, P. (2015). Auroral Oval Boundary Modeling Based on Deep Learning Method. In: He, X., et al. Intelligence Science and Big Data Engineering. Big Data and Machine Learning Techniques. IScIDE 2015. Lecture Notes in Computer Science(), vol 9243. Springer, Cham. https://doi.org/10.1007/978-3-319-23862-3_10

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  • DOI: https://doi.org/10.1007/978-3-319-23862-3_10

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-319-23861-6

  • Online ISBN: 978-3-319-23862-3

  • eBook Packages: Computer ScienceComputer Science (R0)

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