Browsing by Author "Wang, X."

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    Harmonic Analysis and Practical Implementation of a Two-Phase Microgrid System
    (IEEE, 2015-03) Alibeik, Maryam; dos Santos, Euzeli C., Jr.; Yang, Y.; Wang, X.; Blaabjerg, F.; Department of Electrical and Computer Engineering, School of Engineering and Technology
    This paper analyzes the harmonic contents of a non-linear load connected to a two-phase microgrid system. Although having the same harmonic content as the single-phase power system when supplying a non-linear load under balanced conditions, the two-phase microgrid system presents the following advantages: 1) constant power through the power line at the balanced condition; 2) two voltages i.e., line-to-line and phase voltages, available by using a three wire system; 3) optimized voltage utilization compared to a three-phase system; and 4) a direct connection of both symmetrical two-phase and single-phase electrical machines. This paper presents an approach for analyzing the harmonics of a two-phase non-linear load in a balanced and unbalanced cases. The mathematical model for the symmetrical component of an unbalanced two-phase system has also been presented in this paper. Finally, a practical implementation of the two-phase system has been performed, where different types of loads are connected to the two-phase power line to test the voltage control performance.
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    Predicting Hazardous Driving Events Using Multi-Modal Deep Learning Based on Video Motion Profile and Kinematics Data
    (IEEE, 2018-11) Gao, Z.; Liu, Y.; Zheng, J. Y.; Yu, R.; Wang, X.; Sun, P.; Computer and Information Science, School of Science
    As the raising of traffic accidents caused by commercial vehicle drivers, more regulations have been issued for improving their safety status. Driving record instruments are required to be installed on such vehicles in China. The obtained naturalistic driving data offer insight into the causal factors of hazardous events with the requirements to identify where hazardous events happen within large volumes of data. In this study, we develop a model based on a low-definition driving record instrument and the vehicle kinematic data for post-accident analysis by multi-modal deep learning method. With a higher camera position on commercial vehicles than cars that can observe further distance, motion profiles are extracted from driving video to capture the trajectory features of front vehicles at different depths. Then random forest is used to select significant kinematic variables which can reflect the potential crash. Finally, a multi-modal deep convolutional neural network (DCNN) combined both video and kinematic data is developed to identify potential collision risk in each 12-second vehicle trip. The analysis results indicate that the proposed multi-modal deep learning model can identify hazardous events within a large volumes of data at an AUC of 0.81, which outperforms the state-of-the-art random forest model and kinematic threshold method.