Automated Intelligent real-time system for aggregate classification

Md Sani, Zamani (2011) Automated Intelligent real-time system for aggregate classification. International Journal of Mineral Processing, 100. pp. 41-50. ISSN 0301-7516

Text (Automated Intelligent real-time system for aggregate classification)
1-s2.0-S030175161100038X-main.pdf - Published Version

Download (1MB)
Aggregate.pdf - Published Version

Download (1MB)


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.

Item Type: Article
Uncontrolled Keywords: Aggregate classification, Intelligent system, Real-time system, Artificial neural network, Image processing, Shape classification
Subjects: T Technology > TK Electrical engineering. Electronics Nuclear engineering
Divisions: Faculty of Electrical Engineering > Department of Mechatronics Engineering
Depositing User: En Zamani Md. Sani
Date Deposited: 18 Nov 2013 03:24
Last Modified: 03 Feb 2022 11:10
Statistic Details: View Download Statistic

Actions (login required)

View Item View Item