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Paper Abstract and Keywords
Presentation 2022-02-21 15:20
A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, Keisuke Maeda, Takahiro Ogawa, Miki Haseyama (Hokkaido Univ.)
Abstract (in Japanese) (See Japanese page) 
(in English) Estimating the properties of rubber materials from ingredients is necessary to accelerate rubber material development. Although conventional researches realize rubber property estimation by utilizing pairs of ingredient and rubber properties, users cannot understand the relationships between ingredient and rubber properties because of these complex parameter combinations. Here, it is well known that rubber materials with similar ingredients and properties possess similar electron microscope images. Therefore, visual information of electron microscope images can be considered effective for realizing user-understandable rubber property estimation. In this paper, we propose a method that can generate electron microscope images from ingredients and rubber properties. In the proposed method, we train a generative adversarial network utilizing electron microscope images, ingredient mix proportions, and rubber properties. Our method leads to a human-understandable rubber property estimation.
Keyword (in Japanese) (See Japanese page) 
(in English) rubber materials / electron microscope image / generative adversarial network / image generation / ingredient mix proportions / material property / /  
Reference Info. ITE Tech. Rep., vol. 46, no. 6, ME2022-52, pp. 187-191, Feb. 2022.
Paper # ME2022-52 
Date of Issue 2022-02-14 (MMS, ME, AIT) 
ISSN Print edition: ISSN 1342-6893    Online edition: ISSN 2424-1970
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Conference Information
Conference Date 2022-02-21 - 2022-02-22 
Place (in Japanese) (See Japanese page) 
Place (in English) online 
Topics (in Japanese) (See Japanese page) 
Topics (in English)  
Paper Information
Registration To ME 
Conference Code 2022-02-AIT-ME-MMS-IE-ITS 
Language Japanese 
Title (in Japanese) (See Japanese page) 
Sub Title (in Japanese) (See Japanese page) 
Title (in English) A Note on Electron Microscope Image Generation from Mix Proportion and Material Property via Generative Adversarial Network for Rubber Materials 
Sub Title (in English)  
Keyword(1) rubber materials  
Keyword(2) electron microscope image  
Keyword(3) generative adversarial network  
Keyword(4) image generation  
Keyword(5) ingredient mix proportions  
Keyword(6) material property  
1st Author's Name Rintaro Yanagi  
1st Author's Affiliation Hokkaido University (Hokkaido Univ.)
2nd Author's Name Ren Togo  
2nd Author's Affiliation Hokkaido University (Hokkaido Univ.)
3rd Author's Name Keisuke Maeda  
3rd Author's Affiliation Hokkaido University (Hokkaido Univ.)
4th Author's Name Takahiro Ogawa  
4th Author's Affiliation Hokkaido University (Hokkaido Univ.)
5th Author's Name Miki Haseyama  
5th Author's Affiliation Hokkaido University (Hokkaido Univ.)
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Speaker Author-1 
Date Time 2022-02-21 15:20:00 
Presentation Time 15 minutes 
Registration for ME 
Paper # MMS2022-27, ME2022-52, AIT2022-27 
Volume (vol) vol.46 
Number (no) no.6 
Page pp.187-191 
Date of Issue 2022-02-14 (MMS, ME, AIT) 

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