DNN-Based Prediction of Standard Driving Posture for Vehicle Takeover

Gou, Junjie; Zhao, Mingming; Wang, Hongyan; Wang, Xian

doi:10.1109/access.2023.3294275

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) Constructing a Time-varying Uncertainty Analysis Model1

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2023

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“…In this paper, the DNN model with three hidden layers were built. The number of neurons in each layer, the following empirical formula was applied 29 :…”

Section: ) Constructing a Time-varying Uncertainty Analysis Modelmentioning

confidence: 99%

A prediction model of bolted joint loosening based on deep learning network

Liu,

Yan,

Chen

et al. 2023

Proceedings of the Institution of Mechanical Engineers, Part C:

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The Bolted joint loosening is challenging to describe with an accurate mathematical model. Therefore, a prediction model of bolted joint loosening was proposed based on a deep learning network. Firstly, the loosening experiments of the bolted joint were carried out with an orthogonal test. And the nonlinear and uncertain characteristics of bolted joint loosening process were further analyzed. Furthermore, a prediction model of bolted joint loosening based on a deep learning network was established. The proposed model was trained and verified using the experimental data. The results showed that compared with the traditional mathematical and regression models, the model could not only obtain the change of the mean value of preload but also describe the confidence interval of the change of preload in the sense of probability synchronously. The experiment data and prediction data are in good agreement, which can be used to validate the rationality of the model.

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“…In this paper, the DNN model with three hidden layers were built. The number of neurons in each layer, the following empirical formula was applied 29 :…”

Section: ) Constructing a Time-varying Uncertainty Analysis Modelmentioning

confidence: 99%