Data Center Power System Emulation and GaN-Based High-Efficiency Rectifier with Reactive Power Regulation

Data centers are indispensable for today\u27s computing and networking society, which has a considerable power consumption and significant impact on power system. Meanwhile, the average energy usage efficiency of data centers is still not high, leading to significant power loss and system cost. In this dissertation, effective methods are proposed to investigate the data center load characteristics, improve data center power usage efficiency, and reduce the system cost. First, a dynamic power model of a typical data center ac power system is proposed, which is complete and able to predict the data center\u27s dynamic performance. Also, a converter-based data center power emulator serving as an all-in-one load is developed. The power emulator has been verified experimentally in a regional network in the HTB. Dynamic performances during voltage sag events and server load variations are emulated and discussed. Then, a gallium nitride (GaN) based critical conduction mode (CRM) totem-pole power factor correction (PFC) rectifier is designed as the single-phase front-end rectifier to improve the data center power distribution efficiency. Zero voltage switching (ZVS) modulation with ZVS time margin is developed, and a digital variable ON-time control is employed. A hardware prototype of the PFC rectifier is built and demonstrated with high efficiency. To achieve low input current total harmonic distortion (iTHD), current distortion mechanisms are analyzed, and effective solutions for mitigating current distortion are proposed and validated with experiments. The idea of providing reactive power compensation with the rack-level GaN-based front-end rectifiers is proposed for data centers to reduce data center\u27s power loss and system cost. Full-range ZVS modulation is extended into non-unity PF condition and a GaN-based T-type totem-pole rectifier with reactive power control is proposed. A hardware prototype of the proposed rectifier is built and demonstrated experimentally with high power efficiency and flexible reactive power regulation. Experimental emulation of the whole data center system also validates the capability of reactive power compensation by the front-end rectifiers, which can also generate or consume more reactive power to achieve flexible PF regulation and help support the power system

Quantitative body shape analysis for obesity evaluation

Obesity is a public health concern as it is associated with a number of diseases, such as diabetes mellitus type 2, cardiovascular disease, some forms of renal failure and certain types of cancers. Growing evidence suggests that it is not only the amount of fat, but also its distribution in the body that is important to predict metabolic risk factors and adverse changes in organs. In this respect, it is necessary to develop convenient and inexpensive measures to characterize human body fat distribution and to investigate the unknown linkage between intrinsic adiposity and external body shape. This dissertation research aims to improve the obesity assessment by developing new quantitative measurements that comprehensively characterize body shape, and are highly relevant to intrinsic abdominal adiposity conditions. The proposed body shape descriptors were defined based on three-dimensional body images reconstructed from a custom-made stereovision body imaging system, which is particularly suitable for clinical use as an obesity monitoring equipment for its high portability and affordability. In this study, we developed a fully-automated algorithm to process T1-weighted magnetic resonance imaging (MRI) slices for abdominal adiposity measurements. This algorithm dramatically reduces the processing time and workload compared with traditional manual or semi-automatic methods for MRI processing, and greatly improves the repeatability and objectivity of fat assessments. A new obesity categorization method was then defined based on MRI adiposity data to depict characteristics of abdominal fat distribution, and the associations between the body shape descriptors and the MRI abdominal adiposity were explored. It was shown that the proposed body shape descriptors are able to capture the body shape differences between the subjects with dissimilar internal fat distribution (i.e., different categories), and to provide excellent prediction for the category of fat distribution through an optimized support-vector-machine classifier. The predictive models established in this dissertation demonstrate that the novel body shape descriptors were also effective for prediction of the volumes of abdominal visceral fat and subcutaneous fat accumulated in male and female adults. This dissertation introduces an innovative approach to assess obesity and fat distribution based on newly defined shape descriptors, and provides new findings that reveal the associations of intrinsic fat distribution with external body shapes, which enable both qualitative and quantitative assessment of obesity from body shape measurements.Biomedical Engineerin

Preparing STEM Teachers through Technology Supported Collaborative Learning

There were two major goals of this mentored grant. One was to examine how technology with shared display and multi-user features influences pre-service teachers’ participation pattern and social dynamics in collaborative learning activities. The second goal was to help the Principal Investigator receive mentoring on applying for research grants from external funding agencies. Both goals were successfully achieved at the completion of this grant