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Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.

Name: Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.
Creator: Oleg Gradov
License: https://creativecommons.org/licenses/by/4.0/
Keywords: Image Processing, Signal Processing

Citation Author(s):: Oleg
Gradov
Semenov Institute of Chemical Physics

Irene
Maclackova
Emanuel Institute of Biochemical Physics; Semenov Institute of Chemical Physics

Paul
Alexandrow
Shemyakin-Ovchinnikov Institute of Bioorganic Chemistry
Submitted by:: Oleg Gradov
Last updated:: Wed, 12/16/2020 - 11:54
DOI:: 10.21227/h2h5-2274
License:: Creative Commons Attribution

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Categories:: Image Processing
Signal Processing

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Dataset Files

For machine learning-assisted fracto-swellography (x30 000).zip (9.80 MB)

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DATASET TYPE: data
USERID: 0
DATASETID: 3330

Dataset Citation

IEEE

Oleg Gradov, Irene Maclackova , Paul Alexandrow, November 21, 2020, "Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.", IEEE Dataport, doi: https://dx.doi.org/10.21227/h2h5-2274.

BibTeX

@data{h2h5-2274-20,
doi = {10.21227/h2h5-2274},
url = {https://dx.doi.org/10.21227/h2h5-2274},
author = {Gradov, Oleg and Maclackova , Irene and Alexandrow, Paul},
publisher = {IEEE Dataport},
title = {Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.},
year = {2020} }

RIS

TY - DATA
T1 - Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.
AU - Oleg Gradov; Irene Maclackova ; Paul Alexandrow
PY - 2020
PB - IEEE Dataport
UR - 10.21227/h2h5-2274
ER -

APA

Oleg Gradov, Irene Maclackova , Paul Alexandrow. (2020). Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.. IEEE Dataport. https://dx.doi.org/10.21227/h2h5-2274

Harvard

Oleg Gradov, Irene Maclackova , Paul Alexandrow, 2020. Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.. Available at: https://dx.doi.org/10.21227/h2h5-2274.

MLA

Oleg Gradov, Irene Maclackova , Paul Alexandrow. (2020). "Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.." Web.

Vancouver

1. Oleg Gradov, Irene Maclackova , Paul Alexandrow. Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers. [Internet]. IEEE Dataport; 2020. Available from : https://dx.doi.org/10.21227/h2h5-2274

Chicago

Oleg Gradov, Irene Maclackova , Paul Alexandrow. "Between fractography and swellography of polymer materials: wave-like phenomena detection using machine learning based on TEM photomicrograph (x 30 000). Example for DST linear styrene-butadiene thermoplastic elastomers.." doi: 10.21227/h2h5-2274

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Permalink: http://test.ieee-dataport.org/documents/between-fractography-and-swellography-polymer-materials-wave-phenomena-detection-using

DOI Link: https://dx.doi.org/10.21227/h2h5-2274

Short Link: http://test.ieee-dataport.org/3330

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