Theses and Dissertations
This collection includes both ASU Theses and Dissertations, submitted by graduate students, and the Barrett, Honors College theses submitted by undergraduate students.
Displaying 1 - 10 of 191
Description
The rapid escalation of technology and the widespread emergence of modern technological equipments have resulted in the generation of humongous amounts of digital data (in the form of images, videos and text). This has expanded the possibility of solving real world problems using computational learning frameworks. However, while gathering a large amount of data is cheap and easy, annotating them with class labels is an expensive process in terms of time, labor and human expertise. This has paved the way for research in the field of active learning. Such algorithms automatically select the salient and exemplar instances from large quantities of unlabeled data and are effective in reducing human labeling effort in inducing classification models. To utilize the possible presence of multiple labeling agents, there have been attempts towards a batch mode form of active learning, where a batch of data instances is selected simultaneously for manual annotation. This dissertation is aimed at the development of novel batch mode active learning algorithms to reduce manual effort in training classification models in real world multimedia pattern recognition applications. Four major contributions are proposed in this work: $(i)$ a framework for dynamic batch mode active learning, where the batch size and the specific data instances to be queried are selected adaptively through a single formulation, based on the complexity of the data stream in question, $(ii)$ a batch mode active learning strategy for fuzzy label classification problems, where there is an inherent imprecision and vagueness in the class label definitions, $(iii)$ batch mode active learning algorithms based on convex relaxations of an NP-hard integer quadratic programming (IQP) problem, with guaranteed bounds on the solution quality and $(iv)$ an active matrix completion algorithm and its application to solve several variants of the active learning problem (transductive active learning, multi-label active learning, active feature acquisition and active learning for regression). These contributions are validated on the face recognition and facial expression recognition problems (which are commonly encountered in real world applications like robotics, security and assistive technology for the blind and the visually impaired) and also on collaborative filtering applications like movie recommendation.
ContributorsChakraborty, Shayok (Author) / Panchanathan, Sethuraman (Thesis advisor) / Balasubramanian, Vineeth N. (Committee member) / Li, Baoxin (Committee member) / Mittelmann, Hans (Committee member) / Ye, Jieping (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis addresses the issue of making an economic case for energy storage in power systems. Bulk energy storage has often been suggested for large scale electric power systems in order to levelize load; store energy when it is inexpensive and discharge energy when it is expensive; potentially defer transmission and generation expansion; and provide for generation reserve margins. As renewable energy resource penetration increases, the uncertainty and variability of wind and solar may be alleviated by bulk energy storage technologies. The quadratic programming function in MATLAB is used to simulate an economic dispatch that includes energy storage. A program is created that utilizes quadratic programming to analyze various cases using a 2010 summer peak load from the Arizona transmission system, part of the Western Electricity Coordinating Council (WECC). The MATLAB program is used first to test the Arizona test bed with a low level of energy storage to study how the storage power limit effects several optimization out-puts such as the system wide operating costs. Very high levels of energy storage are then added to see how high level energy storage affects peak shaving, load factor, and other system applications. Finally, various constraint relaxations are made to analyze why the applications tested eventually approach a constant value. This research illustrates the use of energy storage which helps minimize the system wide generator operating cost by "shaving" energy off of the peak demand.
ContributorsRuggiero, John (Author) / Heydt, Gerald T (Thesis advisor) / Datta, Rajib (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2013
Description
Underground cables have been widely used in big cities. This is because underground cables offer the benefits of reducing visual impact and the disturbance caused by bad weather (wind, ice, snow, and the lightning strikes). Additionally, when placing power lines underground, the maintenance costs can also be reduced as a result. The underground cable rating calculation is the most critical part of designing the cable construction and cable installation. In this thesis, three contributions regarding the cable ampacity study have been made. First, an analytical method for rating of underground cables has been presented. Second, this research also develops the steady state and transient ratings for Salt River Project (SRP) 69 kV underground system using the commercial software CYMCAP for several typical substations. Third, to find an alternative way to predict the cable ratings, three regression models have been built. The residual plot and mean square error for the three methods have been analyzed. The conclusion is dawn that the nonlinear regression model provides the sufficient accuracy of the cable rating prediction for SRP's typical installation.
ContributorsWang, Tong (Author) / Tylavsky, Daniel (Thesis advisor) / Karady, George G. (Committee member) / Holbert, Keith E. (Committee member) / Arizona State University (Publisher)
Created2013
Description
This thesis concerns the flashover issue of the substation insulators operating in a polluted environment. The outdoor insulation equipment used in the power delivery infrastructure encounter different types of pollutants due to varied environmental conditions. Various methods have been developed by manufacturers and researchers to mitigate the flashover problem. The application of Room Temperature Vulcanized (RTV) silicone rubber is one such favorable method as it can be applied over the already installed units. Field experience has already showed that the RTV silicone rubber coated insulators have a lower flashover probability than the uncoated insulators. The scope of this research is to quantify the improvement in the flashover performance. Artificial contamination tests were carried on station post insulators for assessing their performance. A factorial experiment design was used to model the flashover performance. The formulation included the severity of contamination and leakage distance of the insulator samples. Regression analysis was used to develop a mathematical model from the data obtained from the experiments. The main conclusion drawn from the study is that the RTV coated insulators withstood much higher levels of contamination even when the coating had lost its hydrophobicity. This improvement in flashover performance was found to be in the range of 20-40%. A much better flashover performance was observed when the coating recovered its hydrophobicity. It was also seen that the adhesion of coating was excellent even after many tests which involved substantial discharge activity.
ContributorsGholap, Vipul (Author) / Gorur, Ravi S (Thesis advisor) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2013
Description
In recent years, machine learning and data mining technologies have received growing attention in several areas such as recommendation systems, natural language processing, speech and handwriting recognition, image processing and biomedical domain. Many of these applications which deal with physiological and biomedical data require person specific or person adaptive systems. The greatest challenge in developing such systems is the subject-dependent data variations or subject-based variability in physiological and biomedical data, which leads to difference in data distributions making the task of modeling these data, using traditional machine learning algorithms, complex and challenging. As a result, despite the wide application of machine learning, efficient deployment of its principles to model real-world data is still a challenge. This dissertation addresses the problem of subject based variability in physiological and biomedical data and proposes person adaptive prediction models based on novel transfer and active learning algorithms, an emerging field in machine learning. One of the significant contributions of this dissertation is a person adaptive method, for early detection of muscle fatigue using Surface Electromyogram signals, based on a new multi-source transfer learning algorithm. This dissertation also proposes a subject-independent algorithm for grading the progression of muscle fatigue from 0 to 1 level in a test subject, during isometric or dynamic contractions, at real-time. Besides subject based variability, biomedical image data also varies due to variations in their imaging techniques, leading to distribution differences between the image databases. Hence a classifier learned on one database may perform poorly on the other database. Another significant contribution of this dissertation has been the design and development of an efficient biomedical image data annotation framework, based on a novel combination of transfer learning and a new batch-mode active learning method, capable of addressing the distribution differences across databases. The methodologies developed in this dissertation are relevant and applicable to a large set of computing problems where there is a high variation of data between subjects or sources, such as face detection, pose detection and speech recognition. From a broader perspective, these frameworks can be viewed as a first step towards design of automated adaptive systems for real world data.
ContributorsChattopadhyay, Rita (Author) / Panchanathan, Sethuraman (Thesis advisor) / Ye, Jieping (Thesis advisor) / Li, Baoxin (Committee member) / Santello, Marco (Committee member) / Arizona State University (Publisher)
Created2013
Description
Currently, to interact with computer based systems one needs to learn the specific interface language of that system. In most cases, interaction would be much easier if it could be done in natural language. For that, we will need a module which understands natural language and automatically translates it to the interface language of the system. NL2KR (Natural language to knowledge representation) v.1 system is a prototype of such a system. It is a learning based system that learns new meanings of words in terms of lambda-calculus formulas given an initial lexicon of some words and their meanings and a training corpus of sentences with their translations. As a part of this thesis, we take the prototype NL2KR v.1 system and enhance various components of it to make it usable for somewhat substantial and useful interface languages. We revamped the lexicon learning components, Inverse-lambda and Generalization modules, and redesigned the lexicon learning algorithm which uses these components to learn new meanings of words. Similarly, we re-developed an inbuilt parser of the system in Answer Set Programming (ASP) and also integrated external parser with the system. Apart from this, we added some new rich features like various system configurations and memory cache in the learning component of the NL2KR system. These enhancements helped in learning more meanings of the words, boosted performance of the system by reducing the computation time by a factor of 8 and improved the usability of the system. We evaluated the NL2KR system on iRODS domain. iRODS is a rule-oriented data system, which helps in managing large set of computer files using policies. This system provides a Rule-Oriented interface langauge whose syntactic structure is like any procedural programming language (eg. C). However, direct translation of natural language (NL) to this interface language is difficult. So, for automatic translation of NL to this language, we define a simple intermediate Policy Declarative Language (IPDL) to represent the knowledge in the policies, which then can be directly translated to iRODS rules. We develop a corpus of 100 policy statements and manually translate them to IPDL langauge. This corpus is then used for the evaluation of NL2KR system. We performed 10 fold cross validation on the system. Furthermore, using this corpus, we illustrate how different components of our NL2KR system work.
ContributorsKumbhare, Kanchan Ravishankar (Author) / Baral, Chitta (Thesis advisor) / Ye, Jieping (Committee member) / Li, Baoxin (Committee member) / Arizona State University (Publisher)
Created2013
Description
There is a lack of music therapy services for college students who have problems with depression and/or anxiety. Even among universities and colleges that offer music therapy degrees, there are no known programs offering music therapy to the institution's students. Female college students are particularly vulnerable to depression and anxiety symptoms compared to their male counterparts. Many students who experience mental health problems do not receive treatment, because of lack of knowledge, lack of services, or refusal of treatment. Music therapy is proposed as a reliable and valid complement or even an alternative to traditional counseling and pharmacotherapy because of the appeal of music to young women and the potential for a music therapy group to help isolated students form supportive networks. The present study recruited 14 female university students to participate in a randomized controlled trial of short-term group music therapy to address symptoms of depression and anxiety. The students were randomly divided into either the treatment group or the control group. Over 4 weeks, each group completed surveys related to depression and anxiety. Results indicate that the treatment group's depression and anxiety scores gradually decreased over the span of the treatment protocol. The control group showed either maintenance or slight worsening of depression and anxiety scores. Although none of the results were statistically significant, the general trend indicates that group music therapy was beneficial for the students. A qualitative analysis was also conducted for the treatment group. Common themes were financial concerns, relationship problems, loneliness, and time management/academic stress. All participants indicated that they benefited from the sessions. The group progressed in its cohesion and the participants bonded to the extent that they formed a supportive network which lasted beyond the end of the protocol. The results of this study are by no means conclusive, but do indicate that colleges with music therapy degree programs should consider adding music therapy services for their general student bodies.
ContributorsAshton, Barbara (Author) / Crowe, Barbara J. (Thesis advisor) / Rio, Robin (Committee member) / Davis, Mary (Committee member) / Arizona State University (Publisher)
Created2013
Description
Renewable portfolio standards prescribe for penetration of high amounts of re-newable energy sources (RES) that may change the structure of existing power systems. The load growth and changes in power flow caused by RES integration may result in re-quirements of new available transmission capabilities and upgrades of existing transmis-sion paths. Construction difficulties of new transmission lines can become a problem in certain locations. The increase of transmission line thermal ratings by reconductoring using High Temperature Low Sag (HTLS) conductors is a comparatively new technology introduced to transmission expansion. A special design permits HTLS conductors to operate at high temperatures (e.g., 200oC), thereby allowing passage of higher current. The higher temperature capability increases the steady state and emergency thermal ratings of the transmission line. The main disadvantage of HTLS technology is high cost. The high cost may place special emphasis on a thorough analysis of cost to benefit of HTLS technology im-plementation. Increased transmission losses in HTLS conductors due to higher current may be a disadvantage that can reduce the attractiveness of this method. Studies described in this thesis evaluate the expenditures for transmission line re-conductoring using HTLS and the consequent benefits obtained from the potential decrease in operating cost for thermally limited transmission systems. Studies performed consider the load growth and penetration of distributed renewable energy sources according to the renewable portfolio standards for power systems. An evaluation of payback period is suggested to assess the cost to benefit ratio of HTLS upgrades. The thesis also considers the probabilistic nature of transmission upgrades. The well-known Chebyshev inequality is discussed with an application to transmission up-grades. The Chebyshev inequality is proposed to calculate minimum payback period ob-tained from the upgrades of certain transmission lines. The cost to benefit evaluation of HTLS upgrades is performed using a 225 bus equivalent of the 2012 summer peak Arizona portion of the Western Electricity Coordi-nating Council (WECC).
ContributorsTokombayev, Askhat (Author) / Heydt, Gerald T. (Thesis advisor) / Sankar, Lalitha (Committee member) / Karady, George G. (Committee member) / Arizona State University (Publisher)
Created2014
Description
The future grid will face challenges to meet an increased power demand by the consumers. Various solutions were studied to address this issue. One alternative to realize increased power flow in the grid is to use High Temperature Low Sag (HTLS) since it fulfills essential criteria of less sag and good material performance with temperature. HTLS conductors like Aluminum Conductor Composite Reinforced (ACCR) and Aluminum Conductor Carbon Composite (ACCC) are expected to face high operating temperatures of 150-200 degree Celsius in order to achieve the desired increased power flow. Therefore, it is imperative to characterize the material performance of these conductors with temperature. The work presented in this thesis addresses the characterization of carbon composite core based and metal matrix core based HTLS conductors. The thesis focuses on the study of variation of tensile strength of the carbon composite core with temperature and the level of temperature rise of the HTLS conductors due to fault currents cleared by backup protection. In this thesis, Dynamic Mechanical Analysis (DMA) was used to quantify the loss in storage modulus of carbon composite cores with temperature. It has been previously shown in literature that storage modulus is correlated to the tensile strength of the composite. Current temperature relationships of HTLS conductors were determined using the IEEE 738-2006 standard. Temperature rise of these conductors due to fault currents were also simulated. All simulations were performed using Microsoft Visual C++ suite. Tensile testing of metal matrix core was also performed. Results of DMA on carbon composite cores show that the storage modulus, hence tensile strength, decreases rapidly in the temperature range of intended use. DMA on composite cores subjected to heat treatment were conducted to investigate any changes in the variation of storage modulus curves. The experiments also indicates that carbon composites cores subjected to temperatures at or above 250 degree Celsius can cause permanent loss of mechanical properties including tensile strength. The fault current temperature analysis of carbon composite based conductors reveal that fault currents eventually cleared by backup protection in the event of primary protection failure can cause damage to fiber matrix interface.
ContributorsBanerjee, Koustubh (Author) / Gorur, Ravi (Committee member) / Karady, George G. (Committee member) / Ayyanar, Raja (Committee member) / Arizona State University (Publisher)
Created2014
Description
Sometimes difficult life events challenge our existing resources in such a way that routinized responses are inadequate to handle the challenge. Some individuals will persist in habitual, automatic behavior, regardless of environmental cues that indicate a mismatch between coping strategy and the demands of the stressor. Other individuals will marshal adaptive resources to construct new courses of action and reconceptualize the problem, associated goals and/or values. A mixed methods approach was used to describe and operationalize cognitive shift, a relatively unexplored construct in existing literature. The study was conducted using secondary data from a parent multi-year cross-sectional study of resilience with eight hundred mid-aged adults from the Phoenix metro area. Semi-structured telephone interviews were analyzed using a purposive sample (n=136) chosen by type of life event. Participants' beliefs, assumptions, and experiences were examined to understand how they shaped adaptation to adversity. An adaptive mechanism, "cognitive shift," was theorized as the transition from automatic coping to effortful cognitive processes aimed at novel resolution of issues. Aims included understanding when and how cognitive shift emerges and manifests. Cognitive shift was scored as a binary variable and triangulated through correlational and logistic regression analyses. Interaction effects revealed that positive personality attributes influence cognitive shift most when people suffered early adversity. This finding indicates that a certain complexity, self-awareness and flexibility of mind may lead to a greater capacity to find meaning in adversity. This work bridges an acknowledged gap in literature and provides new insights into resilience.
ContributorsRivers, Crystal T (Author) / Zautra, Alex (Thesis advisor) / Davis, Mary (Committee member) / Kurpius, Sharon (Committee member) / Arizona State University (Publisher)
Created2014