| 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 |
| Download PDF |
|
| Conference Information |
| Committee |
AIT ME MMS IEICE-IE IEICE-ITS |
| 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 |
| Keyword(7) |
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| Keyword(8) |
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| 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 |
| #Pages |
5 |
| Date of Issue |
2022-02-14 (MMS, ME, AIT) |