Study of the Materials Microstructure using Topological Properties of Complex Networks

Semenov, Mikhail; Lelushkina, Kira

doi:10.1088/1757-899x/135/1/012040

Cited by 2 publications

(3 citation statements)

References 4 publications

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“…In contrast, complex networks, one of the tools of data science, 28 , 29 , 30 , 31 have recently been utilized to extract the descriptors of materials with a network structure. 32 , 33 Thus, it was expected that complex network science could extract the descriptors of elastomers on a larger scale 34 and could describe their properties simply, thereby enabling the discussion of hierarchical and heterogeneous structures.…”

Section: Introductionmentioning

confidence: 99%

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity

et al. 2020

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Summary The complicated structure-property relationships of materials have recently been described using a methodology of data science that is recognized as the fourth paradigm in materials science. In network polymers or elastomers, the manner of connection of the polymer chains among the crosslinking points has a significant effect on the material properties. In this study, we quantitatively evaluate the structural heterogeneity of elastomers at the mesoscopic scale based on complex network, one of the methods used in data science, to describe the elastic properties. It was determined that a unified parameter with topological and spatial information universally describes some parameters related to the stresses. This approach enables us to uncover the role of individual crosslinking points for the stresses, even in complicated structures. Based on the data science, we anticipate that the structure-property relationships of heterogeneous materials can be interpretatively represented using this type of “white box” approach.

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Section: Introductionmentioning

confidence: 99%

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity

et al. 2020

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“…However, there have been so far few works which treat images as complex networks in problems of image analysis and recognition [23,24].…”

Section: Related Workmentioning

confidence: 99%

“…To reflect gray-level intensity similarity between super pixels a color image is transformed into grey-level one in line with [23]: Y = 0.2126 · r + 0.7152 · g + 0.0722 · b…”

Section: N C ) V L ⊂V P ⊂ V G and Superpixel S Ci (I = 1 mentioning

confidence: 99%

Image Converting into Complex Networks : Scale- Level Segmentation Approach

Trufanov¹,

Kinash²,

Берестнева³

et al. 2017

Proceedings of the IV International Research Conference "Information Technologies in Science, Management, Social Sphere and Med

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Abstract-Image analysis and recognition is being a contemporary domain for successful tries to apply complex networks as an instrument for thorough studies. Researchers noted that an image having traditionally converted into a network (i.e. taking into account Euclidean distance between pixels only) possesses nodes with similar number of admissible links and the concomitant graph demonstrates a regular topology. As a rule, pixel intensity difference is considered to escape regularity and reach complex property of the network. Contrary revealing more specific traits of an image current study proposes scale segmentation views -local, medium and globalfor an image to build a genuine complex network. Case study with two sample images manifests how the scales are connected with formation of a network topology.

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Study of the Materials Microstructure using Topological Properties of Complex Networks

Cited by 2 publications

References 4 publications

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity

Complex Network Representation of the Structure-Mechanical Property Relationships in Elastomers with Heterogeneous Connectivity

Image Converting into Complex Networks : Scale- Level Segmentation Approach

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