Journal article

Directed network topologies of smart grain sensors

David M Walker, Antoinette Tordesillas, Tomomichi Nakamura, Toshihiro Tanizawa



We employ a recent technique for building complex networks from time series data to construct a directed network embodying time structure to collate the predictive properties of individual granular sensors in a series of biaxial compression tests. For each grain, we reconstruct a static predictive model. This combines a subset selection algorithm and an information theory fitting criterion that selects which other grains in the assembly are best placed to predict a given grain's local stress throughout loading history. The local stress of a grain at each time step is summarized by the magnitude of its particle load vector. A directed network is constructed by representing each grain as a nod..

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Awarded by US Army Research Office

Awarded by Australian Research Council

Awarded by ARC Discovery Projects

Awarded by Japan Society for the Promotion of Science (JSPS)

Awarded by Grants-in-Aid for Scientific Research

Funding Acknowledgements

We thank the anonymous referees for insightful comments and thought-provoking suggestions which helped improve the manuscript. This work was supported by US Army Research Office (W911NF-11-1-0175), the Australian Research Council (DP0986876), ARC Discovery Projects 2012 (DP120104759), and the Melbourne Energy Institute (A. T., D. M. W.). T.T. would like to acknowledge the support of a Grant-in-Aid for Scientific Research (C) (No. 24540419) from the Japan Society for the Promotion of Science (JSPS).