cqu_5027+ATTACHMENT01+ATTACHMENT01.4.pdf (675.31 kB)

Electrofused magnesium oxide classification using digital image processing and machine learning techniques

Download (675.31 kB)
conference contribution
posted on 06.12.2017, 00:00 by A B M Shawkat Ali, Wai Keung Pun
This research is focused on using digital image processing and machine learning techniques to classify Electrofused Magnesia for industry automation. We generate the data from dfferent images by using a modern digital image process. This research proposes a new method to construct the digital image database. The propose new method is based on simple histogram mode and intensity deviation. A group of six popular machinel earning algorithms has been tested to build up an automatic system for industry. We have concluded that the best suited algorithm for magnesia industry automation from this group is the PART algorithm.

Funding

Category 1 - Australian Competitive Grants (this includes ARC, NHMRC)

History

Parent Title

Proceedings of the 2009 IEEE International Conference on Industrial Technology (ICIT'09), 10-13 February, 2009, Monash University, Gippsland, Victoria, Australia.

Start Page

1376

End Page

1381

Number of Pages

6

Start Date

01/01/2009

ISBN-10

1424435064

ISBN-13

9781424435067

Location

Victoria, Australia

Publisher

Library of Congress

Place of Publication

USA

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Faculty of Arts, Business, Informatics and Education; TBA Research Institute;

Era Eligible

Yes

Name of Conference

IEEE International Conference on Industrial Technology

Exports