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Tracking Multiple-person using Sparse Stereo Information
Keli Hu,Yuzhang Gu,Shigen Shen,Cheng Zhang,Yunlong Zhan 보안공학연구지원센터 2015 International Journal of Signal Processing, Image Vol.8 No.9
In this study, we address the problem of multi-person detection and tracking in challenging scenes using sparse stereo information. In each frame, only a sparse set of object feature points are extracted. All these feature points are then projected onto a plan-view map, and grouped into several clusters by employing the biometric information, the optical flow information of object feature points, as well as the width of a person. By producing clusters, the location of a possible person can be determined. In addition, a Modified Joint Probabilistic Data Association Filter (MJPDAF) is proposed for improving the performance of measurements association during the people tracking process. Compared to the traditional JPDAF, the methods for the construction of the validation matrix and the calculation of association probabilities are improved. Experiments on challenging datasets demonstrate that the proposed algorithm is robust for people detection and tracking through fixed stereo vision.
Keli Li,Yong Liao,Ren Liu,Jimiao Zhang 전력전자학회 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.6
The introduction of a high-voltage direct-current (HVDC) system based on a modular multilevel converter (MMC) for wind farm integration has stimulated studies on methods to control this type of converter. This research article focuses on the control of the AC voltage and circulating current for a wind-farm-side MMC (WFS-MMC). After theoretical analysis, emotional learning (EL) controllers are proposed for the controls. The EL controllers are derived from the learning mechanisms of the amygdala and orbitofrontal cortex which make the WFS-MMC insensitive to variance in system parameters, power change, and fault in the grid. The d-axis and q-axis currents are respectively considered for the d-axis and q-axis voltage controls to improve the performance of AC voltage control. The practicability of the proposed control is verified under various conditions with a point-to-point MMC-HVDC system. Simulation results show that the proposed method is superior to the traditional proportional-integral controller.
Li, Keli,Liao, Yong,Liu, Ren,Zhang, Jimiao The Korean Institute of Power Electronics 2016 JOURNAL OF POWER ELECTRONICS Vol.16 No.6
The introduction of a high-voltage direct-current (HVDC) system based on a modular multilevel converter (MMC) for wind farm integration has stimulated studies on methods to control this type of converter. This research article focuses on the control of the AC voltage and circulating current for a wind-farm-side MMC (WFS-MMC). After theoretical analysis, emotional learning (EL) controllers are proposed for the controls. The EL controllers are derived from the learning mechanisms of the amygdala and orbitofrontal cortex which make the WFS-MMC insensitive to variance in system parameters, power change, and fault in the grid. The d-axis and q-axis currents are respectively considered for the d-axis and q-axis voltage controls to improve the performance of AC voltage control. The practicability of the proposed control is verified under various conditions with a point-to-point MMC-HVDC system. Simulation results show that the proposed method is superior to the traditional proportional-integral controller.