title: Multi-frequency complex network from time series for uncovering oil-water flow structure.
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.
authors: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors: Zhong-Ke
authors: ZK
authors: Gao
authors: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors: Yu-Xuan
authors: YX
authors: Yang
authors: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors: Peng-Cheng
authors: PC
authors: Fang
authors: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors: Ning-De
authors: ND
authors: Jin
authors: Key Laboratory of Computer Vision and System (Ministry of Education) and Tianjin Key Laboratory of Intelligence Computing and Novel Software Technology, Tianjin University of Technology, Tianjin 300384, China.
authors: Cheng-Yi
authors: CY
authors: Xia
authors: School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
authors: Li-Dan
authors: LD
authors: Hu
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