TY - JOUR
AU - 
AB - APPLYING NEURAL NETWORKS FOR TIRE PRESSURE MONITORING SYSTEMS A Thesis presented to the Faculty of California Polytechnic State University, San Luis Obispo In Partial Ful llment of the Requirements for the Degree Master of Science in Mechanical Engineering by Alex Kost March 2018 © 2018 Alex Kost ALL RIGHTS RESERVED ii COMMITTEE MEMBERSHIP TITLE: Applying Neural Networks for Tire Pres- sure Monitoring Systems AUTHOR: Alex Kost DATE SUBMITTED: March 2018 COMMITTEE CHAIR: Mohammad Noori, Ph.D. Professor of Mechanical Engineering COMMITTEE MEMBER: Xiao-Hua Yu, Ph.D. Professor of Electrical Engineering COMMITTEE MEMBER: Franz Kurfess, Ph.D. Professor of Computer Science COMMITTEE MEMBER: William Murray, Ph.D. Professor of Mechanical Engineering iii ABSTRACT Alex Kost A proof-of-concept indirect tire-pressure monitoring system is developed using neural networks to identify the tire pressure of a vehicle tire. A quarter-car model was developed with Matlab and Simulink to generate simulated accelerometer output data. Simulation data are used to train and evaluate a recurrent neural network with long short-term memory blocks (RNN-LSTM) and a convolutional neural network (CNN) developed in Python with Tensor ow. Bayesian Optimization via SigOpt was used to optimize training and model parameters. The predictive accuracy and training speed of the two models with various 
TI - Applying Neural Networks for Tire Pressure Monitoring Systems
JF - Structural Durability & Health Monitoring
DO - 10.32604/sdhm.2019.07025
DA - 2019-01-01
UR - https://www.deepdyve.com/lp/unpaywall/applying-neural-networks-for-tire-pressure-monitoring-systems-q7YHJjHGzW
DP - DeepDyve
ER -