Pseudo Mask Augmented Object Detection

In this work, we present a novel and effective framework to facilitate object detection with the instance-level segmentation information that is only supervised by bounding box annotation. Starting from the joint object detection and instance segmentation network, we propose to recursively estimate the pseudo ground-truth object masks from the instance-level object segmentation network training, and then enhance the detection network with top-down segmentation feedbacks. The pseudo ground truth mask and network parameters are optimized alternatively to mutually benefit each other. To obtain the promising pseudo masks in each iteration, we embed a graphical inference that incorporates the low-level image appearance consistency and the bounding box annotations to refine the segmentation masks predicted by the segmentation network. Our approach progressively improves the object detection performance by incorporating the detailed pixel-wise information learned from the weakly-supervised segmentation network. Extensive evaluation on the detection task in PASCAL VOC 2007 and 2012 [12] verifies that the proposed approach is effective

Switching Trajectory Control for High Voltage Silicon Carbide Power Devices with Novel Active Gate Drivers

The penetration of silicon carbide (SiC) semiconductor devices is increasing in the power industry due to their lower parasitics, higher blocking voltage, and higher thermal conductivity over their silicon (Si) counterparts. Applications of high voltage SiC power devices, generally 10 kV or higher, can significantly reduce the amount of the cascaded levels of converters in the distributed system, simplify the system by reducing the number of the semiconductor devices, and increase the system reliability. However, the gate drivers for high voltage SiC devices are not available on the market. Also, the characteristics of the third generation 10 kV SiC MOSFETs with XHV-6 package which are developed by CREE are approaching those of an ideal switch with high dv/dt and di/dt. The fast switching speed of SiC devices introduces challenges for the application since electromagnetic interference (EMI) noise and overshoot voltage can be serious. Also, the insulation should be carefully designed to prevent partial discharge. To address the aforementioned issues, this work investigates the switching behaviors of SiC power MOSFETs with mathematic models and the formation of EMI noise in a power converter. Based on the theoretical analysis, a model-based switching trajectory optimizing three-level active gate driver (AGD) is proposed. The proposed AGD has five operation modes, i.e., faster/normal/slower for the turn-on process and slower/normal for the turn-off process. The availability of multiple operation modes offers an extra degree of freedom to improve the switching performance for a particular application and enables it to be more versatile. The proposed AGD can provide higher switching speed adjustment resolution than the other AGDs, and this feature will allow the proposed AGD to fine tune the switching speed of SiC power devices. In addition, a novel model-based trajectory optimization strategy is proposed to determine the optimal gate driver output voltage by trading the EMI noise against the switching energy losses. For the 10 kV SiC power MOSFET, the detailed design considerations of the proposed AGD are demonstrated in this dissertation. The functionalities of the 3-L AGD are validated through the double pulse tests results with 1.2 kV and 10 kV SiC power MOSFETs