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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| Format: | Article |
| Language: | en en |
| Published: |
Elsevier
2011
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| 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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| Summary: | 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. |
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