| Item type |
学術雑誌論文 / Journal Article.(1) |
| 公開日 |
2019-07-16 |
| 書誌情報 |
en : IEEE Transactions on Industrial Informatics
巻 14,
号 10,
p. 4665-4673,
発行日 2018-06-01
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| タイトル |
|
|
タイトル |
Deep Learning for Smart Industry:Efficient Manufacture Inspection Systemwith Fog Computing |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
Fog computing |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
manufacture inspection |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
smart industry |
| キーワード |
|
|
言語 |
en |
|
主題Scheme |
Other |
|
主題 |
deep learning |
| 資源タイプ |
|
|
資源タイプ識別子 |
http://purl.org/coar/resource_type/c_6501 |
|
資源タイプ |
journal article |
| アクセス権 |
|
|
アクセス権 |
open access |
|
アクセス権URI |
http://purl.org/coar/access_right/c_abf2 |
| 著者 |
李, 良知
太田, 香
董, 冕雄
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| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
太田 香（OTA Kaoru） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000140_ja.html |
| 室蘭工業大学研究者データベースへのリンク |
|
|
表示名 |
董 冕雄（DONG Mianxiong） |
|
URL |
http://rdsoran.muroran-it.ac.jp/html/100000145_ja.html |
| 抄録 |
|
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内容記述タイプ |
Abstract |
|
内容記述 |
With the rapid development of Internet of things devices and network infrastructure, there have been a lot of sensors adopted in the industrial productions, resulting in a large size of data. One of the most popular examples is the manufacture inspection, which is to detect the defects of the products. In order to implement a robust inspection system with higher accuracy, we propose a deep learning based classification model in this paper, which can find the possible defective products. As there may be many assembly lines in one factory, one huge problem in this scenario is how to process such big data in real time. Therefore, we design our system with the concept of fog computing. By offloading the computation burden from the central server to the fog nodes, the system obtains the ability to deal with extremely large data. There are two obvious advantages in our system. The first one is that we adapt the convolutional neural network model to the fog computing environment, which significantly improves its computing efficiency. The other one is that we work out an inspection model, which can simultaneously indicate the defect type and its degree. The experiments well prove that the proposed method is robust and efficient. |
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言語 |
en |
| 出版者 |
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出版者 |
IEEE |
|
言語 |
en |
| 出版者版へのリンク |
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表示名 |
10.1109/TII.2018.2842821 |
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URL |
https://doi.org/10.1109/TII.2018.2842821 |
| DOI |
|
|
関連タイプ |
isVersionOf |
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|
識別子タイプ |
DOI |
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|
関連識別子 |
10.1109/TII.2018.2842821 |
| 日本十進分類法 |
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|
主題Scheme |
NDC |
|
主題 |
007 |
| ISSN |
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収録物識別子タイプ |
PISSN |
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収録物識別子 |
1551-3203 |
| 書誌レコードID |
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|
収録物識別子タイプ |
NCID |
|
収録物識別子 |
AA12023428 |
| 権利 |
|
|
言語 |
en |
|
権利情報 |
© 2018 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works. |
| 著者版フラグ |
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|
出版タイプ |
AM |
|
出版タイプResource |
http://purl.org/coar/version/c_ab4af688f83e57aa |
| フォーマット |
|
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内容記述タイプ |
Other |
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内容記述 |
application/pdf |
IEEETII_14_10_4665_4673 (577.4 kB)
