A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Gao, Zhong-Ke; Dang, Weidong; Mu, Chaoxu; Yang, Yuxuan; Li, Shan; Grebogi, Celso

doi:10.1109/tii.2017.2785384

Cited by 77 publications

(27 citation statements)

References 47 publications

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“…Network science provides a powerful framework for modelling and understanding how individual entities give rise to complex—and often unexpected—phenomena by interacting with each other [ 1 , 2 , 3 , 4 , 5 , 6 ]. Network models encapsulate the topology of interactions among entities by means of links distributed among nodes.…”

Section: Introductionmentioning

confidence: 99%

Distance Entropy Cartography Characterises Centrality in Complex Networks

Stella

Domenico

2018

Entropy

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We introduce distance entropy as a measure of homogeneity in the distribution of path lengths between a given node and its neighbours in a complex network. Distance entropy defines a new centrality measure whose properties are investigated for a variety of synthetic network models. By coupling distance entropy information with closeness centrality, we introduce a network cartography which allows one to reduce the degeneracy of ranking based on closeness alone. We apply this methodology to the empirical multiplex lexical network encoding the linguistic relationships known to English speaking toddlers. We show that the distance entropy cartography better predicts how children learn words compared to closeness centrality. Our results highlight the importance of distance entropy for gaining insights from distance patterns in complex networks.

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Section: Introductionmentioning

confidence: 99%

Distance Entropy Cartography Characterises Centrality in Complex Networks

Stella

Domenico

2018

Entropy

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“…Oil-water two-phase flow is a major two-phase flow (Gao et al 2018), which is widely present in petroleum transportation pipelines. Its flow pattern is complex and changeable, and its dynamic evolution mechanism is extremely complicated.…”

Section: Introductionmentioning

confidence: 99%

A novel method to identify the flow pattern of oil–water two-phase flow

Fan

2020

J Petrol Explor Prod Technol

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This paper presents a novel method combining extreme learning machine (ELM) and multiple empirical mode decomposition (MEMD) to identify flow patterns of oil–water two-phase flow. The proposed method can recognize accurately five typical flow patterns of horizontal oil–water two-phase flow. Taking the Lorenz system as an example, we verify the MEMD is more suitable for simultaneous decomposition of multi-channel signals than empirical mode decomposition and ensemble empirical mode decomposition. In the proposed method, we employ the MEMD to decompose the multivariate conductance signal of oil–water two-phase flow to obtain the same intrinsic mode function modes, select the normalized energy of the high-frequency components as the eigenvalue, and utilize the trained ELM to achieve a good recognition result. The experimental results show that the proposed method is not only fast and generalized, but also has high accuracy in identifying flow patterns of oil–water two-phase flow.

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“…Alternatively, it is possible to study the EPU time series from the view angle of complex networks. Indeed, quite a few methods have been developed to convert time series into complex networks [8][9][10][11]. Zhang and Small connected time series with complex networks, by constructing complex networks from pseudoperiodic time series, and investigate the relationship between the topology of the constructed network and dynamics of the raw time series [12,13].…”

Section: Introductionmentioning

confidence: 99%

Visibility graph analysis of economy policy uncertainty indices

Dai

Xiong

Zhou

2019

Physica A: Statistical Mechanics and its Applications

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Uncertainty plays an important role in the global economy. In this paper, the economic policy uncertainty (EPU) indices of the United States and China are selected as the proxy variable corresponding to the uncertainty of national economic policy. By adopting the visibility graph algorithm, the four economic policy uncertainty indices of the United States and China are mapped into complex networks, and the topological properties of the corresponding networks are studied. The Hurst exponents of all the four indices are within [0.5, 1], which implies that the economic policy uncertainty is persistent. The degree distributions of the EPU networks have power-law tails and are thus scale-free. The average clustering coefficients of the four EPU networks are high and close to each other, while these networks exhibit weak assortative mixing. We also find that the EPU network in United States based on daily data shows the small-world feature since the average shortest path length increases logarithmically with the network size such that L (N) = 0.626 ln N + 0.405. Our research highlights the possibility to study the EPU from the view angle of complex networks.

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A Novel Multiplex Network-Based Sensor Information Fusion Model and Its Application to Industrial Multiphase Flow System

Cited by 77 publications

References 47 publications

Distance Entropy Cartography Characterises Centrality in Complex Networks

Distance Entropy Cartography Characterises Centrality in Complex Networks

A novel method to identify the flow pattern of oil–water two-phase flow

Visibility graph analysis of economy policy uncertainty indices

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