• Journal of Auto-vehicle Safety Association
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• v.15 no.1
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• pp.55-62
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• 2023

In this study, a chest deflection is predicted by introducing a deep learning technique with the results of the frontal impact of the USNCAP conducted for 110 car models from MY2018 to MY2020. The 120 data are divided into training data and test data, and the training data is divided into training data and validation data to determine the hyperparameters. In this process, the deceleration data of each vehicle is averaged in units of 10 ms from crash pulses measured up to 100 ms. The performance of the deep learning model is measured by the indices of the mean squared error and the mean absolute error on the test data. A DNN (Deep Neural Network) model can give different predictions for the same hyperparameter values at every run. Considering this, the mean and standard deviation of the MSE (Mean Squared Error) and the MAE (Mean Absolute Error) are calculated. In addition, the deep learning model performance according to the inclusion of CVW (Curb Vehicle Weight) is also reviewed.

• Transactions of the Korean Society of Automotive Engineers
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• v.24 no.4
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• pp.454-462
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• 2016

This study was conducted in order to estimate the exhaust emissions analysis method of the real driving emission(RDE). The Association for Emissions Control by Catalyst(AECC) has developed a test procedure by using a random cycle method based on the chassis dynamometer. In order to confirm this approach in Korea, Euro 5(DPF), Euro 6(DPF + LNT), and Euro 6(DPF + SCR) were performed on three different vehicles to determine the exhaust gas characteristics of the random cycle, real-road driving test(PEMS), and emission certification driving mode(NEDC). Six different random cycle driving modes were generated by the vehicle specifications(e.g. curb weight, engine power, gear ratio, and maximum acceleration). The NOx emissions were increased in the NEDC, random cycle, and PEMS order in this study regardless of the test vehicles. The random cycle method has the advantage because it utilizes a chassis dynamometer in the laboratories for a repeatable data collection, and it allows any eminent emission improvement checked prior to a real-road driving test with PEMS.