Browsing by Subject "Transformer"
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Item A low-leakage, low-loss transformer structure for high-frequency applications(2021-05-10) Phanse, Ajinkya Ajit; Hanson, Alex J.Designing transformers for HF applications with low magnetizing inductance is challenging because of increased core and copper losses and losses due to stored energy in the leakage inductance. Many converter topologies and applications cannot absorb the leakage inductance of the transformer, so designs that minimize leakage are very valuable. We propose a transformer structure which has low leakage inductance and low losses making it suitable for applications requiring energy storage transformers or coupled inductors. The transformer structure achieves near zero MMF drop across the window which results in a design with low leakage. It achieves current conduction along most of the skin of the conductors and is also suitable for high turns ratio applications because of its ability to achieve equal current sharing between paralleled turns. Because of these reasons, the transformer achieves low conduction losses without requiring the use of litz wire. Step-by-step design guidelines are proposed to achieve a roughly optimized structure and the design of a transformer with L [subscript mag] =105nH and 1-10 turns ratio for use in a 400W, 20-400V coupled-inductor based boost converter is presented.Item Consumer-Data Approach to Assess the Effect of Residential Grid-Tied Photovoltaic Systems and Electric Vehicles on Distribution Transformers(IEEE, 2014-06) Uriarte, F. M.; Toliyat, A.; Kwasinski, A.; Hebner, R. E.The authors examine the impact of residential photovoltaic arrays and electric vehicles on distribution transformers by using 3-D surface and 2-D filled contour plots. These visualizations, somewhat unorthodox to power distribution analysis, elucidate the impact of hundreds of assets on distribution transformers on a single view. The visualizations are created with a smart grid computer model that accepts residential electrical recordings in one minute intervals. An analysis of simulation results shows that the electrical footprint experienced by a residential community and its distribution transformers stems from photovoltaic arrays rather than from electric vehicles. Additionally, the results indicate the existing distribution assets may be ready to support the proliferation of photovoltaic arrays and electric vehicles, a common concern across utilities in the United States.Item Large language models are post-structuralist intertexts(2023-04-22) Sui, Peiqi; Baker, Samuel, 1968-There is currently a robust body of literature that brings computational methods together with the concept of intertextuality from literary studies. However, these existing approaches’ attempts to quantify intertextuality often reduce the concept to the much more limited task of allusion detection. In doing so, they operationalize a characteristically structuralist understanding of intertextuality, and thereby undercut much of what was actually intertextuality’s literary-theoretical innovations and value. Instead of narrowly construing intertextuality as equivalent to a simple sub-task of natural language processing, I argue that the term’s poststructuralist legacy enables a knowledge contribution in the other direction: theories of intertextuality could help us gain a more robust understanding of large language models (LLMs) and their hermeneutic processes. This report represents some initial steps toward such a contribution. It necessarily begins by disentangling the complex theoretical affordances of Julia Kristeva’s theory of intertextuality, explicating how her concept embodies a more general shift from a structuralist to a poststructuralist modality. Drawing on the abundant conceptual parallels between Kristevan intertextuality and LLM’s transformer architecture, I demonstrate how recent developments in NLP overcome past quantitative methods’ structuralist limitations, breaking free of constraints in computational models and gaining the scope proper to literary theory frameworks. To these ends, the first section of this essay explicates Kristeva’s definition of intertextuality, the second surveys existing methods in quantitative intertextuality, the third demonstrates how such approaches operationalize a structuralist understanding of intertextuality, and the fourth will attempt to sketch a case for the intertextual hermeneutics of LLMsItem Leveraging pervasive data to study and support mother-infant dyads in the wild(2023-04-20) Yao, Xuewen; De Barbaro, Kaya; Thomaz, Edison; Yang, Diyi; Julien, Christine; Li, Jessy; Barber, SuzanneThe ubiquity of mobile devices and wearable sensors coupled with fast-evolving machine learning algorithms has transformed people's daily life, specifically in the healthcare domain. These advancements can be leveraged to not only detect and infer people's behavior patterns with great precision but also provide "just-in-time" support in a seamless, non-intrusive, and cost-effective fashion. The first year of a child's life is a particularly challenging period for the mother, and also a vital period for child developments. I hypothesize that pervasive data, such as motion, audio, text data, collected online or in people’s daily life, can be leveraged to provide support to postpartum women and their families in need in the wild. In my dissertation, I developed models that can detect two clinically-relevant parent and infant behaviors in naturalistic home interactions, namely, infant crying and parent holding. Traditional methods by developmental scientists rely heavily on behavioral observations and self-reported data while computer scientists build models using data collected in controlled environments, such as lab and hospital. These methods limit researchers’ understanding of the natural variation in mother-infant interactions across families, and its specific impacts on child development. In my work, I leveraged data collected in longitudinal home environments and built detection models that provide objective, unobtrusive, and continuous measurements of parent holding and infant crying with accuracy 0.870 and 0.613 respectively. Additionally, I evaluated both models based on assessment scenarios specific to developmental science such as event-based accuracy and contingency analysis. Another piece of my work focuses on using natural language processing to understand the experience of postpartum women experiencing or at risk of postpartum mood and anxiety disorders and to provide them with empathy in the form of a conversational agent, chatbot. Specifically, I collaborated with Postpartum Support International (PSI) and obtained text transcripts between trained volunteers and support seekers. After analyzing 7014 conversations using a combination of human annotations, dictionary models and unsupervised techniques, I find stark differences between the experiences of "distressed" and "healthy" mothers in psychological states, concerns, and goals. Additionally, incorporating the insights from the descriptive analysis as well as empathy and open questions, I designed, built, and evaluated three chatbots that accept open-ended user input to provide postpartum women with support.Item Residential PVs and EVs: Before and After(0000-00-00) Uriarte, F. M.; Hebner, R. E.Power distribution systems are experiencing higher load levels, unbalanced distributed generation, a wealth of load diversity, and more uncorrelated events than ever before. To provide quantitative information regarding the changes, this paper contrasts the electrical state of the largest Smart Grid residential community in Austin, Texas before and after the proliferation of its PVs and EVs. This community is the research focus of the authors, local utilities, and many others attempting to hinder detrimental consequences of the uncontrolled, fast proliferation of residential assets on the grid. The authors use surface and filled contour plots to show new electrical footprints, and show its impact on transformer utilization, feeder demand, current unbalance, and distribution losses.