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Yingfeng Cai on Muck Rack

Yingfeng Cai

As seen in: Wiley Online Library, arXiv, MDPI, Taylor & Francis Online, Journal of Advanced Transportation
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Articles

Modeling vehicle dynamics with physics-informed deep operator network

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By Chenkai Tan, Yingfeng Cai, Hai Wang, Long Chen
| Taylor & Francis Online
In this paper, we present a novel data-driven approach for modelling vehicle dynamics that integrates the physics-informed deep operator network with the principles of linear time-invariant (LTI) state space modelling. This approach aims to achieve precise and robust data-driven vehicle dynamics by utilising vehicle physical models. These physical models have been validated experimentally and through rigorous theoretical derivations.
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Highway Safety with an Intelligent Headlight System for Improved Nighttime Driving

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By Yingfeng Cai, Ammar Jafaripournimchahi, Hai Wang, Jacob Kwaku Nkrumah
| MDPI
1. Introduction Headlights are essential components on vehicles whose functions become more pronounced during nighttime and adverse weather conditions while driving. The headlight is equipped with two main light beams, namely the low beam and the high beam. The low beam is employed to illuminate close and mid-range distances in front of the vehicle, offering the driver clearer visibility at short and medium ranges.
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Research on Active Trailer Steering Control Strategy of Tractor Semitrailer under Medium-/High-Speed Conditions

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By Yuxi Tang, Yingfeng Cai, Ze jun Liu, Xiaoqiang Sun
| MDPI
All articles published by MDPI are made immediately available worldwide under an open access license. No special permission is required to reuse all or part of the article published by MDPI, including figures and tables. For articles published under an open access Creative Common CC BY license, any part of the article may be reused without permission provided that the original article is clearly cited. For more information, please refer to https://www.mdpi.com/openaccess.
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