| Item type |
学術雑誌論文 / Journal Article(1) |
| 公開日 |
2023-02-23 |
| タイトル |
|
|
タイトル |
Automated Identification of Mineral Types and Grain Size Using Hyperspectral Imaging and Deep Learning for Mineral Processing |
|
言語 |
en |
| 言語 |
|
|
言語 |
eng |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
mineral processing |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
mineral identification |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
CNN |
| 主題 |
|
|
主題Scheme |
Other |
|
主題 |
machine 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 |
| 作成者 |
Okada, Natsuo
Maekawa, Yohei
Owada, Narihiro
Haga, Kazutoshi
Shibayama, Atsushi
Kawamura, Youhei
|
| 内容記述 |
|
|
内容記述タイプ |
Abstract |
|
内容記述 |
In mining operations, an ore is separated into its constituents through mineral processing methods, such as flotation. Identifying the type of minerals contained in the ore in advance aids greatly in performing faster and more efficient mineral processing. The human eye can recognize visual information in three wavelength regions: red, green, and blue. With hyperspectral imaging, high resolution spectral data that contains information from the visible light wavelength region to the near infrared region can be obtained. Using deep learning, the features of the hyperspectral data can be extracted and learned, and the spectral pattern that is unique to each mineral can be identified and analyzed. In this paper, we propose an automatic mineral identification system that can identify mineral types before the mineral processing stage by combining hyperspectral imaging and deep learning. By using this technique, it is possible to quickly identify the types of minerals contained in rocks using a non-destructive method. As a result of experimentation, the identification accuracy of the minerals that underwent deep learning on the red, green, and blue (RGB) image of the mineral was approximately 30%, while the result of the hyperspectral data analysis using deep learning identified the mineral species with a high accuracy of over 90%. |
|
言語 |
en |
| 出版タイプ |
|
|
出版タイプ |
VoR |
|
出版タイプResource |
http://purl.org/coar/version/c_970fb48d4fbd8a85 |
| 書誌情報 |
en : MINERALS
巻 10,
号 9,
発行日 2020
|
| 収録物識別子 |
|
|
収録物識別子タイプ |
ISSN |
|
収録物識別子 |
2075-163X |
| 出版者 |
|
|
出版者 |
MDPI |
|
言語 |
en |
| 関連情報 |
|
|
関連タイプ |
isIdenticalTo |
|
|
識別子タイプ |
DOI |
|
|
関連識別子 |
https://doi.org/10.3390/min10090809 |
| 権利情報 |
|
|
権利情報 |
© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access
article distributed under the terms and conditions of the Creative Commons Attribution
(CC BY) license (http://creativecommons.org/licenses/by/4.0/). |
kokuA_2022_11.pdf (9.8 MB)
