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For an Electrical Vehicle, the power is delivered from the battery pack to the electric motor through the use of power converter. Many research projects have been conducted in improving the efficiency of traction inverters. Inverter topologies are categorized based on the number of voltage levels of the inverter output phase voltage. The conventional 2-level Voltage Source Inverter (VSI) is commonly used as a traction inverter due to its simple structure. Due to the recent trend in utilizing higher DClink voltage in traction motor drives to achieve a higher power rating, multi-level inverters are gaining attention to replace the conventional VSI in EV powertrain. Multi-level voltage source inverters (MLVSI) have been widely adopted in high-power converters and medium-voltage drives. There are four major categories of MLVSI: the Flying Capacitor (FC), Neutral Point Clamped (NPC), Cascaded and Hybrid. The power rating of the MLVSI increases with the increase of inverter levels, but the size, number of switching devices, cost and control difficulty also increases. Due to the above reasons, 3-level NPC can be a good solution for traction inverters. Due to the structure and control limitation, Diode Clamp NPC suffers from uneven loss distribution and neutral point voltage balancing issues. This issue can be resolved with Active Clamped NPC (ANPC). In this thesis, the design, simulation, prototyping and testing of a 70kw 3-level ANPC traction is introduced. / Thesis / Master of Science (MSc)

Anomaly detection is the process of finding unusual events in a given dataset. Anomaly detection is often performed on datasets with a fixed set of predefined features. As a result of this, if the normal features bear a close resemblance to the anomalous features, most anomaly detection algorithms exhibit poor performance. This work seeks to answer the question, can we deform these features so as to make the anomalies standout and hence improve the anomaly detection outcome? We employ a Deep Learning and an Active Learning framework to learn features for anomaly detection. In Active Learning, an Oracle (usually a domain expert) labels a small amount of data over a series of training rounds. The deep neural network is trained after each round to incorporate the feedback from the Oracle into the model. Results on the MNIST, CIFAR-10 and Galaxy Zoo datasets show that our algorithm, Ahunt, significantly outperforms other anomaly detection algorithms used on a fixed, static, set of features. Ahunt can therefore overcome a poor choice of features that happen to be suboptimal for detecting anomalies in the data, learning more appropriate features. We also explore the role of the loss function and Active Learning query strategy, showing these are important, especially when there is a significant variation in the anomalies.

In this paper, we will introduce partial results from our 3-year NSF funded grant titled “Inverted and Active Learning Pedagogies (IALP) for Student Success.” We will present our results comparing student achievement between inverted (flipped) classrooms and traditional lecture formats in statistics classes at Texas Lutheran University. Included are a brief introduction, the study design, data gathering, faculty and students’ surveys, and the methodology used for the study. We will share results from instruments that were developed to measure different levels of cognitive understanding across multiple sections of the same introductory statistics classes. We will also examine grades, withdrawals, potential professor effects, student characteristics, class size effects, video saturation, accountability of students for watching videos, and student evaluations of various activities in the classroom.

An alternate method for mitigating the depredating physiological affects of a soldiers marksmanship due to combat stressors can be achieved through the design and implementation of a active stabilization system for small arms weapons. The INSTAR system is an innovative active stabilization system designed to decouple the shooter's disturbance effects from the barrel movement. The INSTAR system uses an piezoelectric actuator separating the barrel of the rifle from its stock to stabilize barrel movement. This paper uses various control techniques to develop control algorithms for simulation. The level of performance for each control algorithm is based on how well each they measure up to the criteria developed from the INSTAR system. This paper furthers research on INSTAR by developing and comparing four control designs that may be implemented within the INSTAR system. / Master of Science

Yes / The launch of the Solar Dynamics Observatory (SDO) in early 2010 has provided the solar physics community with the most detailed view of the Sun to date. However, this presents new challenges for the analysis of solar data. Currently, SDO sends over 1 terabyte of data per day back to Earth and methods for fast and reliable analysis are more important than ever. This article details four algorithms developed separately at the Universities of Bradford and Glasgow, the Royal Observatory of Belgium and Trinity College Dublin for the purposes of automated detection of solar active regions (ARs) and sunspots at different levels of the solar atmosphere.

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Active participation in the learning process enhance students’ critical thinking and problem-solving skills. We implemented peerocused, active learning, recitation sessions with the large-enrollment sections for General Chemistry I courses at Mississippi State University (MSState) over a period of four semesters beginning in Spring 2016. The peerocused recitation program was a success improving student final (standardized ACS) exam scores, pass/fail rates for the course, and continuation on to General Chemistry II (CH 1223) courses. Peerocused collaborative learning and students possessing ownership over their learning significantly enhanced academic outcomes of our program. Worked-example effect is the best known and apparently the most effective cognitive load reducing technique. We incorporated a modified version of worked examples, employing “incorrect worked examples” and studied the impact of incorrect worked examples vs correct worked examples. We hypothesized that looking for errors in incorrect worked examples would achieve greater attention and would prompt students to actively engage on calculation steps than correct worked examples. Eye-tracking results showed that incorrect worked example format was effective at obtaining student attention and engaging students actively on calculation steps. Survey results showed that incorrect worked example format inspired students’ motivation and enhanced student engagement and attentiveness to examine the worked examples intensively. This research provided insights on student focus while reading and learning chemistry worked examples, and opened new avenues for supporting online learning and usage of tablet PC in the learning process. Laboratory experiments provide students the opportunity to obtain hands-on experience on laboratory techniques and instrumentation. We created a biochemistry laboratory course (CH4990) for third-year chemistry major undergraduate students at Mississippi State University. I wrote the biochemistry lab manual consisting of eleven experiments, which involved protein and DNA extraction, ion-exchange chromatography, UV/vis spectroscopy, SDS PAGE electrophoresis, and enzyme kinetics experiments. A new laboratory experiment was incorporated which allowed students exposure to peptide sequencing and proteomics experiments in conjunction with mass spectrometry. The CH4990 biochemistry lab course is open for enrollment in Fall semesters since Fall 2018.