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New artificial intelligence based tire size identification for fast and safe inflating cycle

Kahandawa, Gayan, Choudhury, T. A., Ibrahim, M. Yousef, Dzitac, Pavel and Mazid, Abdul Md 2015, New artificial intelligence based tire size identification for fast and safe inflating cycle, in ICIT 2015 : IEEE International Conference on Industrial Technology, IEEE, Piscataway, N.J., pp. 1729-1734, doi: 10.1109/ICIT.2015.7125347.

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Title New artificial intelligence based tire size identification for fast and safe inflating cycle
Author(s) Kahandawa, Gayan
Choudhury, T. A.
Ibrahim, M. Yousef
Dzitac, Pavel
Mazid, Abdul Md
Conference name IEEE Industrial Technology. International Conference (2015 : Seville, Spain)
Conference location Seville, Spain
Conference dates 17-19 Mar. 2015
Title of proceedings ICIT 2015 : IEEE International Conference on Industrial Technology
Publication date 2015
Start page 1729
End page 1734
Total pages 6
Publisher IEEE
Place of publication Piscataway, N.J.
Summary Motor vehicle accidents are one of the main killers on the road. Modern vehicles have several safety features to improve the stability and controllability. The tire condition is critical to the proper function of the designed safety features. Under or over inflated tires adversely affects the stability of vehicles. It is generally the vehicle's user responsibility to ensure the tire inflation pressure is set and maintained to the required value using a tire inflator. In the tire inflator operation, the vehicle's user sets the desired value and the machine has to complete the task. During the inflation process, the pressure sensor does not read instantaneous static pressure to ensure the target value is reached. Hence, the inflator is designed to stop repetitively for pressure reading and avoid over inflation. This makes the inflation process slow, especially for large tires. This paper presents a novel approach using artificial neural network based technique to identify the tire size. Once the tire size is correctly identified, an optimized inflation cycle can be computed to improve performance, speed and accuracy of the inflation process. The developed neural network model was successfully simulated and tested for predicting tire size from the given sets of input parameters. The test results are analyzed and discussed in this paper.
ISBN 9781479977994
Language eng
DOI 10.1109/ICIT.2015.7125347
Field of Research 091007 Manufacturing Robotics and Mechatronics (excl Automotive Mechatronics)
091302 Automation and Control Engineering
091006 Manufacturing Processes and Technologies (excl Textiles)
Socio Economic Objective 861403 Industrial Machinery and Equipment
HERDC Research category E1 Full written paper - refereed
ERA Research output type E Conference publication
Copyright notice ©2015, IEEE
Persistent URL http://hdl.handle.net/10536/DRO/DU:30082869

Document type: Conference Paper
Collection: School of Engineering
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