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Marine Multiple Time Series Relevance Discovery Based on Complex Network

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Neural Information Processing (ICONIP 2018)

Part of the book series: Lecture Notes in Computer Science ((LNTCS,volume 11306))

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Abstract

Ocean measuring point is an important way to obtain many kinds of marine data. Reasonable layout of ocean measuring points can efficiently obtain marine data. At present, a marine measuring point can acquire multiple types of marine data, only by comprehensively using multiple types of ocean data we can more effectively discover the relationship between various ocean measuring points. This paper proposes a mapping method for fusion marine multiple time series into an image, and uses the similarity between different images to construct a complex network. Also, We build a complex network of marine multiple time series by selecting appropriate thresholds. Compared with the traditional method, the network constructed by our approach can find more accurate rules.

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Acknowledgments

This thesis is supported by National Key R&D Program of China (2016YFC1403200)(2016YFC1401900), youth fund project of east china sea branch of state oceanic administration (201614).

Author information

Authors and Affiliations

  1. National Marine Data and Information Service, Tianjin, China

    Lei Wang & Suixiang Shi

  2. Shanghai University, Shanghai, China

    Zongwen Huang, Lingyu Xu & Gaowei Zhang

  3. Tongji University, Shanghai, China

    Kuo Chen

Authors
  1. Lei Wang
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  2. Zongwen Huang
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  3. Suixiang Shi
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  4. Kuo Chen
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  5. Lingyu Xu
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  6. Gaowei Zhang
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Corresponding author

Correspondence to Zongwen Huang .

Editor information

Editors and Affiliations

  1. The Chinese Academy of Sciences, Beijing, China

    Long Cheng

  2. City University of Hong Kong, Kowloon, Hong Kong

    Andrew Chi Sing Leung

  3. Kobe University, Kobe, Japan

    Seiichi Ozawa

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Wang, L., Huang, Z., Shi, S., Chen, K., Xu, L., Zhang, G. (2018). Marine Multiple Time Series Relevance Discovery Based on Complex Network. In: Cheng, L., Leung, A., Ozawa, S. (eds) Neural Information Processing. ICONIP 2018. Lecture Notes in Computer Science(), vol 11306. Springer, Cham. https://doi.org/10.1007/978-3-030-04224-0_4

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  • DOI: https://doi.org/10.1007/978-3-030-04224-0_4

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  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-030-04223-3

  • Online ISBN: 978-3-030-04224-0

  • eBook Packages: Computer ScienceComputer Science (R0)

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