Abstract：
:Uncovering complex oil-water flow structure represents a challenge in diverse scientific disciplines. This challenge stimulates us to develop a new distributed conductance sensor for measuring local flow signals at different positions and then propose a novel approach based on multi-frequency complex network to uncover the flow structures from experimental multivariate measurements. In particular, based on the Fast Fourier transform, we demonstrate how to derive multi-frequency complex network from multivariate time series. We construct complex networks at different frequencies and then detect community structures. Our results indicate that the community structures faithfully represent the structural features of oil-water flow patterns. Furthermore, we investigate the network statistic at different frequencies for each derived network and find that the frequency clustering coefficient enables to uncover the evolution of flow patterns and yield deep insights into the formation of flow structures. Current results present a first step towards a network visualization of complex flow patterns from a community structure perspective.
journal_name
Sci Repjournal_title
Scientific reportsauthors
Gao ZK,Yang YX,Fang PC,Jin ND,Xia CY,Hu LDdoi
10.1038/srep08222subject
Has Abstractpub_date
2015-02-04 00:00:00pages
8222issn
2045-2322pii
srep08222journal_volume
5pub_type
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pub_type： 杂志文章
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更新日期：2016-09-15 00:00:00
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journal_title：Scientific reports
pub_type： 杂志文章
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更新日期：2015-02-04 00:00:00
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pub_type： 杂志文章
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更新日期：2018-03-21 00:00:00
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pub_type： 杂志文章
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pub_type： 杂志文章
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更新日期：2017-09-07 00:00:00
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pub_type： 杂志文章
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更新日期：2019-10-29 00:00:00
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更新日期：2017-08-30 00:00:00
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pub_type： 杂志文章
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更新日期：2020-09-24 00:00:00
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journal_title：Scientific reports
pub_type： 杂志文章
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更新日期：2018-01-15 00:00:00
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更新日期：2020-09-28 00:00:00
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journal_title：Scientific reports
pub_type： 杂志文章
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更新日期：2016-06-01 00:00:00
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journal_title：Scientific reports
pub_type： 杂志文章,多中心研究
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