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- Genre: Academic theses
- Creators: Davulcu, Hasan
- Creators: Ayyanar, Raja
Description
Dynamic loading is the term used for one way of optimally loading a transformer. Dynamic loading means the utility takes into account the thermal time constant of the transformer along with the cooling mode transitions, loading profile and ambient temperature when determining the time-varying loading capability of a transformer. Knowing the maximum dynamic loading rating can increase utilization of the transformer while not reducing life-expectancy, delaying the replacement of the transformer. This document presents the progress on the transformer dynamic loading project sponsored by Salt River Project (SRP). A software application which performs dynamic loading for substation distribution transformers with appropriate transformer thermal models is developed in this project. Two kinds of thermal hottest-spot temperature (HST) and top-oil temperature (TOT) models that will be used in the application--the ASU HST/TOT models and the ANSI models--are presented. Brief validations of the ASU models are presented, showing that the ASU models are accurate in simulating the thermal processes of the transformers. For this production grade application, both the ANSI and the ASU models are built and tested to select the most appropriate models to be used in the dynamic loading calculations. An existing application to build and select the TOT model was used as a starting point for the enhancements developed in this work. These enhancements include: Adding the ability to develop HST models to the existing application, Adding metrics to evaluate the models accuracy and selecting which model will be used in dynamic loading calculation Adding the capability to perform dynamic loading calculations, Production of a maximum dynamic load profile that the transformer can tolerate without acceleration of the insulation aging, Provide suitable output (plots and text) for the results of the dynamic loading calculation. Other challenges discussed include: modification to the input data format, data-quality control, cooling mode estimation. Efforts to overcome these challenges are discussed in this work.
ContributorsLiu, Yi (Author) / Tylavksy, Daniel J (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2011
Description
Currently Java is making its way into the embedded systems and mobile devices like androids. The programs written in Java are compiled into machine independent binary class byte codes. A Java Virtual Machine (JVM) executes these classes. The Java platform additionally specifies the Java Native Interface (JNI). JNI allows Java code that runs within a JVM to interoperate with applications or libraries that are written in other languages and compiled to the host CPU ISA. JNI plays an important role in embedded system as it provides a mechanism to interact with libraries specific to the platform. This thesis addresses the overhead incurred in the JNI due to reflection and serialization when objects are accessed on android based mobile devices. It provides techniques to reduce this overhead. It also provides an API to access objects through its reference through pinning its memory location. The Android emulator was used to evaluate the performance of these techniques and we observed that there was 5 - 10 % performance gain in the new Java Native Interface.
ContributorsChandrian, Preetham (Author) / Lee, Yann-Hang (Thesis advisor) / Davulcu, Hasan (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2011
Description
As pointed out in the keynote speech by H. V. Jagadish in SIGMOD'07, and also commonly agreed in the database community, the usability of structured data by casual users is as important as the data management systems' functionalities. A major hardness of using structured data is the problem of easily retrieving information from them given a user's information needs. Learning and using a structured query language (e.g., SQL and XQuery) is overwhelmingly burdensome for most users, as not only are these languages sophisticated, but the users need to know the data schema. Keyword search provides us with opportunities to conveniently access structured data and potentially significantly enhances the usability of structured data. However, processing keyword search on structured data is challenging due to various types of ambiguities such as structural ambiguity (keyword queries have no structure), keyword ambiguity (the keywords may not be accurate), user preference ambiguity (the user may have implicit preferences that are not indicated in the query), as well as the efficiency challenges due to large search space. This dissertation performs an expansive study on keyword search processing techniques as a gateway for users to access structured data and retrieve desired information. The key issues addressed include: (1) Resolving structural ambiguities in keyword queries by generating meaningful query results, which involves identifying relevant keyword matches, identifying return information, composing query results based on relevant matches and return information. (2) Resolving structural, keyword and user preference ambiguities through result analysis, including snippet generation, result differentiation, result clustering, result summarization/query expansion, etc. (3) Resolving the efficiency challenge in processing keyword search on structured data by utilizing and efficiently maintaining materialized views. These works deliver significant technical contributions towards building a full-fledged search engine for structured data.
ContributorsLiu, Ziyang (Author) / Chen, Yi (Thesis advisor) / Candan, Kasim S (Committee member) / Davulcu, Hasan (Committee member) / Jagadish, H V (Committee member) / Arizona State University (Publisher)
Created2011
Description
ABSTRACT There is a body of literature--albeit largely from the UK and Australia--that examines the ways in which class and gender influence life course, including educational attainment; however, much of this literature offers explanations and analyses for why individuals choose the life course they do. By assuming a cause-effect relationship between class and gender and life course, these studies perpetuate the idea that life can be predicted and controlled. Such an approach implies there is but one way of viewing--or an "official reading" of--the experience of class and gender. This silences other readings. This study goes beneath these "interpretations" and explores the phenomenon of identity and identity making in women who grew up working-class. Included is an investigation into how these women recognize and participate in their own identity making, identifying the interpretations they created and apply to their experience and the ways in which they juxtapose their educative experience. Using semi-structured interview I interviewed 21 women with working-class habitués. The strategy of inquiry that corresponded best to the goal of this project was heuristics, a variant of empathetic phenomenology. Heuristics distinguishes itself by including the life experience of the researcher while still showing how different people may participate in an event in their lives and how these individuals may give it radically different meanings. This has two effects: (1) the researcher recognizes that their own life experience affects their interpretations of these stories and (2) it elucidates the researcher's own life as it relates to identity formation and educational experience. Two, heuristics encourages different ways of presenting findings through a variety of art forms meant to enhance the immediacy and impact of an experience rather than offer any explanation of it. As a result of the research, four themes essential to locating the experience of women who grew up working class were discovered: making, paying attention, taking care, and up. These themes have pedagogic significance as women with working-class habitués navigate from this social space: the downstream effect of which is how and what these women take up as education.
ContributorsDecker, Shannon Irene (Author) / Blumenfeld-Jones, Donald (Thesis advisor) / Richards-Young, Gillian (Committee member) / Sandlin, Jennifer (Committee member) / Arizona State University (Publisher)
Created2011
Description
The under-representation of women in science, technology, engineering and mathematics (STEM) fields indicates the presence of gender related barriers that impacted the persistence of women in science and engineering doctoral studies. The purpose of this study was to investigate the barriers of women doctoral students in STEM fields which identified supporting factors for them as well. This study also tried to determine if there was any difference in perceiving barriers among three disciplines - engineering, life sciences and natural sciences. An online questionnaire (19 Likert-type questions and one open-ended question) was sent to women STEM doctoral students studying at the Arizona State University (ASU). Questions were based on some factors which might act as obstacles or supports during their doctoral studies. Both quantitative and qualitative analyses were conducted. Factors such as work-life balance, time-management, low self-confidence, lack of female role model, fewer numbers of women in science and engineering classes, and male dominated environment revealed as significant barriers according to both the analyses but factors such as difficulty with the curriculum, gender discrimination, and two-career problem were chosen as barriers only in the free response question. Positive treatment from advisor, family support, availability of funding, and absence of sexual harassment assisted these women continuing their PhD programs at ASU. However, no significant difference was observed with respect to perceiving barriers among the three groups mentioned above. Recommendations for change in science and engineering curricula and active recruitment of female faculty are discussed to reduce or at best to remove the barriers and how to facilitate participation and retention of more women in STEM fields especially at the doctoral level.
ContributorsChaudhuri, Dola (Author) / Baker, Dale (Thesis advisor) / Sandlin, Jennifer (Committee member) / Edwards, Vicki (Committee member) / Arizona State University (Publisher)
Created2011
Description
The development of a Solid State Transformer (SST) that incorporates a DC-DC multiport converter to integrate both photovoltaic (PV) power generation and battery energy storage is presented in this dissertation. The DC-DC stage is based on a quad-active-bridge (QAB) converter which not only provides isolation for the load, but also for the PV and storage. The AC-DC stage is implemented with a pulse-width-modulated (PWM) single phase rectifier. A unified gyrator-based average model is developed for a general multi-active-bridge (MAB) converter controlled through phase-shift modulation (PSM). Expressions to determine the power rating of the MAB ports are also derived. The developed gyrator-based average model is applied to the QAB converter for faster simulations of the proposed SST during the control design process as well for deriving the state-space representation of the plant. Both linear quadratic regulator (LQR) and single-input-single-output (SISO) types of controllers are designed for the DC-DC stage. A novel technique that complements the SISO controller by taking into account the cross-coupling characteristics of the QAB converter is also presented herein. Cascaded SISO controllers are designed for the AC-DC stage. The QAB demanded power is calculated at the QAB controls and then fed into the rectifier controls in order to minimize the effect of the interaction between the two SST stages. The dynamic performance of the designed control loops based on the proposed control strategies are verified through extensive simulation of the SST average and switching models. The experimental results presented herein show that the transient responses for each control strategy match those from the simulations results thus validating them.
ContributorsFalcones, Sixifo Daniel (Author) / Ayyanar, Raja (Thesis advisor) / Karady, George G. (Committee member) / Tylavsky, Daniel (Committee member) / Tsakalis, Konstantinos (Committee member) / Arizona State University (Publisher)
Created2011
Description
Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. Genes have widely different pertinences to the etiology and pathology of diseases. Thus, they can be ranked according to their disease-significance on a genomic scale, which is the subject of gene prioritization. Given a set of genes known to be related to a disease, it is reasonable to use them as a basis to determine the significance of other candidate genes, which will then be ranked based on the association they exhibit with respect to the given set of known genes. Experimental and computational data of various kinds have different reliability and relevance to a disease under study. This work presents a gene prioritization method based on integrated biological networks that incorporates and models the various levels of relevance and reliability of diverse sources. The method is shown to achieve significantly higher performance as compared to two well-known gene prioritization algorithms. Essentially, no bias in the performance was seen as it was applied to diseases of diverse ethnology, e.g., monogenic, polygenic and cancer. The method was highly stable and robust against significant levels of noise in the data. Biological networks are often sparse, which can impede the operation of associationbased gene prioritization algorithms such as the one presented here from a computational perspective. As a potential approach to overcome this limitation, we explore the value that transcription factor binding sites can have in elucidating suitable targets. Transcription factors are needed for the expression of most genes, especially in higher organisms and hence genes can be associated via their genetic regulatory properties. While each transcription factor recognizes specific DNA sequence patterns, such patterns are mostly unknown for many transcription factors. Even those that are known are inconsistently reported in the literature, implying a potentially high level of inaccuracy. We developed computational methods for prediction and improvement of transcription factor binding patterns. Tests performed on the improvement method by employing synthetic patterns under various conditions showed that the method is very robust and the patterns produced invariably converge to nearly identical series of patterns. Preliminary tests were conducted to incorporate knowledge from transcription factor binding sites into our networkbased model for prioritization, with encouraging results. To validate these approaches in a disease-specific context, we built a schizophreniaspecific network based on the inferred associations and performed a comprehensive prioritization of human genes with respect to the disease. These results are expected to be validated empirically, but computational validation using known targets are very positive.
ContributorsLee, Jang (Author) / Gonzalez, Graciela (Thesis advisor) / Ye, Jieping (Committee member) / Davulcu, Hasan (Committee member) / Gallitano-Mendel, Amelia (Committee member) / Arizona State University (Publisher)
Created2011
Description
Michael Apple's scholarship on curriculum, educational ideology, and official knowledge continues to be influential to the study of schooling. Drawing on the sociological insights of Pierre Bourdieu and the cultural studies approaches of Raymond Williams, Apple articulates a theory of schooling that pays particular attention to how official knowledge is incorporated into the processes of schooling, including textbooks. In an effort to contribute to Apple's scholarship on textbooks, this study analyzed high school American history textbooks from the 1960s through the 2000s with specific attention to the urban riots of the late-1960s, sixties counterculture, and the women's movement utilizing Julia Kristeva's psychoanalytic concept of abjection to augment Apple's theory of knowledge incorporation. This combination reveals not only how select knowledge is incorporated as official knowledge, but also how knowledge is treated as abject, as unfit for the curricular body of official knowledge and the selective tradition of American history. To bridge the theoretical frameworks of incorporation and abjection Raymond Williams' theory of structures of feeling and Slavoj iek's theory of ideological quilting are employed to show how feelings and emotional investments maintain ideologies. The theoretical framework developed and the interpretive analyses undertaken demonstrate how textbook depictions of these historical events structure students' present educational experiences with race, class, and gender.
ContributorsKearl, Benjamin (Author) / Margolis, Eric (Thesis advisor) / Blumenfeld-Jones, Donald (Committee member) / Sandlin, Jennifer (Committee member) / Arizona State University (Publisher)
Created2011
Description
Recent changes in the energy markets structure combined with the conti-nuous load growth have caused power systems to be operated under more stressed conditions. In addition, the nature of power systems has also grown more complex and dynamic because of the increasing use of long inter-area tie-lines and the high motor loads especially those comprised mainly of residential single phase A/C motors. Therefore, delayed voltage recovery, fast voltage collapse and short term voltage stability issues in general have obtained significant importance in relia-bility studies. Shunt VAr injection has been used as a countermeasure for voltage instability. However, the dynamic and fast nature of short term voltage instability requires fast and sufficient VAr injection, and therefore dynamic VAr devices such as Static VAr Compensators (SVCs) and STATic COMpensators (STAT-COMs) are used. The location and size of such devices are optimized in order to improve their efficiency and reduce initial costs. In this work time domain dy-namic analysis was used to evaluate trajectory voltage sensitivities for each time step. Linear programming was then performed to determine the optimal amount of required VAr injection at each bus, using voltage sensitivities as weighting factors. Optimal VAr injection values from different operating conditions were weighted and averaged in order to obtain a final setting of the VAr requirement. Some buses under consideration were either assigned very small VAr injection values, or not assigned any value at all. Therefore, the approach used in this work was found to be useful in not only determining the optimal size of SVCs, but also their location.
ContributorsSalloum, Ahmed (Author) / Vittal, Vijay (Thesis advisor) / Heydt, Gerald (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2011
Description
Most existing approaches to complex event processing over streaming data rely on the assumption that the matches to the queries are rare and that the goal of the system is to identify these few matches within the incoming deluge of data. In many applications, such as stock market analysis and user credit card purchase pattern monitoring, however the matches to the user queries are in fact plentiful and the system has to efficiently sift through these many matches to locate only the few most preferable matches. In this work, we propose a complex pattern ranking (CPR) framework for specifying top-k pattern queries over streaming data, present new algorithms to support top-k pattern queries in data streaming environments, and verify the effectiveness and efficiency of the proposed algorithms. The developed algorithms identify top-k matching results satisfying both patterns as well as additional criteria. To support real-time processing of the data streams, instead of computing top-k results from scratch for each time window, we maintain top-k results dynamically as new events come and old ones expire. We also develop new top-k join execution strategies that are able to adapt to the changing situations (e.g., sorted and random access costs, join rates) without having to assume a priori presence of data statistics. Experiments show significant improvements over existing approaches.
ContributorsWang, Xinxin (Author) / Candan, K. Selcuk (Thesis advisor) / Chen, Yi (Committee member) / Davulcu, Hasan (Committee member) / Arizona State University (Publisher)
Created2011