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| Content Provider | IEEE Xplore Digital Library |
|---|---|
| Author | Kahandawa, G. Choudhury, T.A. Ibrahim, M.Y. Dzitac, P. Md Mazid, A. |
| Copyright Year | 2015 |
| Description | Author affiliation: Sch. of Eng. & Technol., Central Queensland Univ., Rockhampton, QLD, Australia (Md Mazid, A.) || Sch. of Eng. & Inf. Technol., Fed. Univ. Australia, Gippsland, VIC, Australia (Kahandawa, G.; Choudhury, T.A.; Ibrahim, M.Y.) || Sch. of Eng., Deakin Univ., Geelong, VIC, Australia (Dzitac, P.) |
| Abstract | 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. |
| Starting Page | 1729 |
| Ending Page | 1734 |
| File Size | 725430 |
| Page Count | 6 |
| File Format | |
| ISBN | 9781479978007 |
| DOI | 10.1109/ICIT.2015.7125347 |
| Language | English |
| Publisher | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Publisher Date | 2015-03-17 |
| Publisher Place | Spain |
| Access Restriction | Subscribed |
| Rights Holder | Institute of Electrical and Electronics Engineers, Inc. (IEEE) |
| Subject Keyword | Tires Neurons Training Artificial neural networks Vehicles Australia Safety microcontroller applications artificial neural network back-propagation algorithm sensors tire safety |
| Content Type | Text |
| Resource Type | Article |
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