Comparison Analysis Between Pneumatic and Airless Tires by Computational Modelling for Avoiding Road Traffic Accidents

Karpenko, Mykola; Prentkovskis, Olegas; Skačkauskas, Paulius

doi:10.1007/978-3-031-26655-3_28

Cited by 2 publications

(1 citation statement)

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“…Pneumatic tires were invented in England in the late 1800s [1]. However, due to the limitations and problems of pneumatic tires, punctures can occur while driving and maintaining the proper amount of air pressure is essential and requires regular inspection and maintenance [2]. To solve these problems, non-pneumatic tires were introduced by creating polygonal elastomer spokes that replace and fill the air between the wheel and the tread [3].…”

Section: Introductionmentioning

confidence: 99%

Optimizing Generative Adversarial Network (GAN) Models for Non-Pneumatic Tire Design

Seong,

Ji,

Choi

et al. 2023

Applied Sciences

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Pneumatic tires are used in diverse industries. However, their design is difficult, as it relies on the knowledge of experienced designers. In this paper, we generate images of non-pneumatic tire designs with patterns based on shapes and lines for different generative adversarial network (GAN) models and test the performance of the models. Using OpenCV, 2000 training images were generated, corresponding to spoke, curve, triangle, and honeycomb non-pneumatic tires. The images created for training were used after removing highly similar images by applying mean squared error (MSE) and structural similarity index (SSIM). To identify the best model for generating patterns of regularly shaped non-pneumatic tires, GAN, deep convolutional generative adversarial network (DCGAN), StarGAN v2, StyleGAN v2-ADA, and ProjectedGAN were compared and analyzed. In the qualitative evaluation, the GAN, DCGAN, StarGAN v2, and StyleGAN v2-ADA models distorted the circle shape and did not maintain a consistent pattern, but ProjectedGAN retained consistency in the circle, and the pattern was less distorted than in the other GAN models. When evaluating quantitative metrics, ProjectedGAN performed the best among several techniques when the difference between the generated and actual image distributions was measured.

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Section: Introductionmentioning

confidence: 99%