Development of parametric eco-driving models for fuel savings: A novel parameter calibration approach

The existing conventional traffic flow models aims to simulate human-driven following vehicles in real world. In this era of emerging transport solutions, controlling or intervening traffic flow to achieve high fuel efficiency along with good driving safety and travel efficiency becomes a reality. As such, it is worth exploring the possibility of developing eco-driving models to optimise vehicle movements for fuel consumption minimisation, while maintaining safety and efficiency. In this study, we propose a modified genetic algorithm (GA) based calibration method that enables the calibrated parametric traffic flow (car following) models to simulate or control vehicles in an eco-driving manner. By developing a novel objective function for the GA method based on the widely-used VT-Micro fuel consumption model, the proposed method can calibrate model parameters towards improving fuel efficiency. Besides, by subtly using heavy fuel consumptions as a surrogate index to represent low travel efficiency or dangerous driving strategies, the modified GA method with the novel objective function can guide the calibrated model towards achieving complete eco-driving requirements. Experimental simulation results further indicate that traffic flow models calibrated by the modified GA-based method can also alleviate traffic disturbances and oscillations in a more effective manner

Accurate and fast two-step phase shifting algorithm based on principle component analysis and Lissajous ellipse fitting with random phase shift and no pre-filtering

To achieve high measurement accuracy with less computational time-in-phase shifting interferometry, a random phase-shifting algorithm based on principal component analysis and Lissajous ellipse fitting (PCA& LEF) is proposed. It doesn't need pre-filtering and can obtain relatively accurate phase distribution with only two phase shifted interferograms and less computational time and is suitable for different background intensity, modulation amplitude distributions and noises. Moreover, it can obtain absolutely accurate result when the background intensity and modulation amplitude are perfect and can partly suppress the effect of imperfect background intensity and modulation amplitude. Last but not least, it removes the restriction that PCA needs more than three interferograms with welldistributed phase shifts to subtract relatively accurate mean. The simulations and experiments verify the correctness and feasibility of PCA& LEF. (C) 2019 Optical Society of America under the terms of the OSA Open Access Publishing AgreementNational Natural Science Foundation of China (NSFC) [11304034]; Department of Science and Technology of Jilin Province [20190701018GH]; Education Department of Jilin Province [JJKH20190691KJ]; State Key Laboratory of Applied OpticsOpen access journalThis item from the UA Faculty Publications collection is made available by the University of Arizona with support from the University of Arizona Libraries. If you have questions, please contact us at [email protected]

Consumption prediction of bearing spare parts based on a hybrid model

Aiming at improving the accuracy of consumption prediction, a hybrid model was constructed, which designs an empirical wavelet filter bank to remove noise factors in original data. Besides the value prediction, the EWT-PGPR model can also give a certain credible interval, which effectively improves the practicability of the model