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Automated Intelligent real-time system for aggregate classification

Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective...

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Main Author: Md Sani, Zamani
Format: Article
Language:English
English
Published: Elsevier 2011
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://eprints.utem.edu.my/id/eprint/6586/1/1-s2.0-S030175161100038X-main.pdf
http://eprints.utem.edu.my/id/eprint/6586/2/Aggregate.pdf
http://eprints.utem.edu.my/id/eprint/6586/
http://www.journals.elsevier.com/international-journal-of-mineral-processing/
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spelling my.utem.eprints.65862022-02-03T11:10:11Z http://eprints.utem.edu.my/id/eprint/6586/ Automated Intelligent real-time system for aggregate classification Md Sani, Zamani TK Electrical engineering. Electronics Nuclear engineering Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload. Elsevier 2011-07-08 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/6586/1/1-s2.0-S030175161100038X-main.pdf text en http://eprints.utem.edu.my/id/eprint/6586/2/Aggregate.pdf Md Sani, Zamani (2011) Automated Intelligent real-time system for aggregate classification. International Journal of Mineral Processing, 100. pp. 41-50. ISSN 0301-7516 http://www.journals.elsevier.com/international-journal-of-mineral-processing/
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
English
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Md Sani, Zamani
Automated Intelligent real-time system for aggregate classification
description Traditionally, mechanical sieving and manual gauging are used to determine the quality of the aggregates. In order to obtain aggregates with better characteristics, it must pass a series of mechanical, chemical and physical tests which are often performed manually, and are slow, highly subjective and laborious. This research focuses on developing an intelligent real-time classification system called NeuralAgg which consists of 3 major subsystems namely the real-time machine vision, the intelligent classification and the database system. The image capturing system can send high quality images of moving aggregates to the image processing subsystem, and then to the intelligent system for shape classification using artificial neural network. Finally, the classification information is stored in the database system for data archive, which can be used for post analysis purposes. These 3 subsystems are integrated to work in real-time mode which takes an average of 1.23 s for a complete classification process. The system developed in this study has an accuracy of approximately 87% and has the potential to significantly reduce the processing and/or classification time and workload.
format Article
author Md Sani, Zamani
author_facet Md Sani, Zamani
author_sort Md Sani, Zamani
title Automated Intelligent real-time system for aggregate classification
title_short Automated Intelligent real-time system for aggregate classification
title_full Automated Intelligent real-time system for aggregate classification
title_fullStr Automated Intelligent real-time system for aggregate classification
title_full_unstemmed Automated Intelligent real-time system for aggregate classification
title_sort automated intelligent real-time system for aggregate classification
publisher Elsevier
publishDate 2011
url http://eprints.utem.edu.my/id/eprint/6586/1/1-s2.0-S030175161100038X-main.pdf
http://eprints.utem.edu.my/id/eprint/6586/2/Aggregate.pdf
http://eprints.utem.edu.my/id/eprint/6586/
http://www.journals.elsevier.com/international-journal-of-mineral-processing/
_version_ 1724077951572508672
score 13.211869

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