| Paper Abstract and Keywords |
| Presentation |
2021-02-19 14:15
[Special Talk]
A Note on Electron Microscope Image Generation from Mix Proportion via Conditional Style Generative Adversarial Network for Rubber Materials
Rintaro Yanagi, Ren Togo, 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. In conventional methods, they synthesize rubber materials from ingredients, and then they collect pairs of ingredient mix proportions and rubber properties via various evaluation tests. By utilizing these pairs as training data, they realize rubber property estimation from unknown ingredient mix proportions. However, conducting the evaluation tests takes a lot of costs and then it is difficult to flexibly apply these methods for new ingredients. On the other hand, it is well known that rubber materials with similar properties possess similar electron microscope images. Therefore, image generation utilizing pairs of ingredient mix proportions and electron microscope images as training data leads to rubber property estimation without the evaluation tests. In this paper, we propose a method that can generate electron microscope images from ingredient mix proportions. In the proposed method, we train a conditional style generative adversarial network utilizing pairs of ingredient mix proportions and electron microscope images. Experimental results showed that the effectiveness of the proposed method. |
| Keyword |
(in Japanese) |
(See Japanese page) |
| (in English) |
rubber materials / electron microscope image / generative adversarial network / image generation / ingredient mix proportions / / / |
| Reference Info. |
ITE Tech. Rep., vol. 45, no. 4, ME2021-22, pp. 171-175, Feb. 2021. |
| Paper # |
ME2021-22 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |
| ISSN |
Print edition: ISSN 1342-6893 Online edition: ISSN 2424-1970 |
| Download PDF |
|
| Conference Information |
| Committee |
IEICE-IE IEICE-ITS MMS ME AIT |
| Conference Date |
2021-02-18 - 2021-02-19 |
| Place (in Japanese) |
(See Japanese page) |
| Place (in English) |
Online |
| Topics (in Japanese) |
(See Japanese page) |
| Topics (in English) |
Image Processing, etc. |
| Paper Information |
| Registration To |
ME |
| Conference Code |
2021-02-IE-ITS-MMS-ME-AIT |
| 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 via Conditional Style 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) |
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| Keyword(7) |
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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 |
Takahiro Ogawa |
| 3rd Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
| 4th Author's Name |
Miki Haseyama |
| 4th Author's Affiliation |
Hokkaido University (Hokkaido Univ.) |
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| Speaker |
Author-1 |
| Date Time |
2021-02-19 14:15:00 |
| Presentation Time |
10 minutes |
| Registration for |
ME |
| Paper # |
MMS2021-22, ME2021-22, AIT2021-22 |
| Volume (vol) |
vol.45 |
| Number (no) |
no.4 |
| Page |
pp.171-175 |
| #Pages |
5 |
| Date of Issue |
2021-02-11 (MMS, ME, AIT) |