Gao Zhong-Ke, Yang Yu-Xuan, Fang Peng-Cheng, Jin Ning-De, Xia Cheng-Yi, Hu Li-Dan
School of Electrical Engineering and Automation, Tianjin University, Tianjin 300072, China.
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.
Sci Rep. 2015 Feb 4;5:8222. doi: 10.1038/srep08222.
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.
揭示复杂的油水流动结构是不同科学学科面临的一项挑战。这一挑战促使我们开发一种新的分布式电导传感器，用于测量不同位置的局部流动信号，然后提出一种基于多频复杂网络的新方法，从实验多变量测量中揭示流动结构。特别是，基于快速傅里叶变换，我们展示了如何从多变量时间序列中导出多频复杂网络。我们在不同频率下构建复杂网络，然后检测群落结构。我们的结果表明，群落结构忠实地代表了油水流动模式的结构特征。此外，我们研究了每个导出网络在不同频率下的网络统计量，发现频率聚类系数能够揭示流动模式的演变，并深入了解流动结构的形成。目前的结果是从群落结构角度对复杂流动模式进行网络可视化的第一步。
