Skip to main content

ASU Electronic Theses and Dissertations


This collection includes most of the ASU Theses and Dissertations from 2011 to present. ASU Theses and Dissertations are available in downloadable PDF format; however, a small percentage of items are under embargo. Information about the dissertations/theses includes degree information, committee members, an abstract, supporting data or media.

In addition to the electronic theses found in the ASU Digital Repository, ASU Theses and Dissertations can be found in the ASU Library Catalog.

Dissertations and Theses granted by Arizona State University are archived and made available through a joint effort of the ASU Graduate College and the ASU Libraries. For more information or questions about this collection contact or visit the Digital Repository ETD Library Guide or contact the ASU Graduate College at gradformat@asu.edu.


Resource Type
  • Doctoral Dissertation
Date Range
2010 2020


The main objective of this research is to develop reliability assessment methodologies to quantify the effect of various environmental factors on photovoltaic (PV) module performance degradation. The manufacturers of these photovoltaic modules typically provide a warranty level of about 25 years for 20% power degradation from the initial specified power rating. To quantify the reliability of such PV modules, the Accelerated Life Testing (ALT) plays an important role. But there are several obstacles that needs to be tackled to conduct such experiments, since there has not been enough historical field data available. Even if some time-series performance data of maximum …

Contributors
Bala Subramaniyan, Arun, Pan, Rong, TamizhMani, GovindaSamy, et al.
Created Date
2020

One of the critical issues in the U.S. healthcare sector is attributed to medications management. Mismanagement of medications can not only bring more unfavorable medical outcomes for patients, but also imposes avoidable medical expenditures, which can be partially accounted for the enormous $750 billion that the American healthcare system wastes annually. The lack of efficiency in medical outcomes can be due to several reasons. One of them is the problem of drug intensification: a problem associated with more aggressive management of medications and its negative consequences for patients. To address this and many other challenges in regard to medications mismanagement, …

Contributors
Boloori, Alireza, Saghafian, Soroush, Fowler, John, et al.
Created Date
2019

Technology advancements in diagnostic imaging, smart sensing, and health information systems have resulted in a data-rich environment in health care, which offers a great opportunity for Precision Medicine. The objective of my research is to develop data fusion and system informatics approaches for quality and performance improvement of health care. In my dissertation, I focus on three emerging problems in health care and develop novel statistical models and machine learning algorithms to tackle these problems from diagnosis to care to system-level decision-making. The first topic is diagnosis/subtyping of migraine to customize effective treatment to different subtypes of patients. Existing clinical …

Contributors
Si, Bing, Li, Jing, Montgomery, Douglas, et al.
Created Date
2018

A quantitative analysis of a system that has a complex reliability structure always involves considerable challenges. This dissertation mainly addresses uncertainty in- herent in complicated reliability structures that may cause unexpected and undesired results. The reliability structure uncertainty cannot be handled by the traditional relia- bility analysis tools such as Fault Tree and Reliability Block Diagram due to their deterministic Boolean logic. Therefore, I employ Bayesian network that provides a flexible modeling method for building a multivariate distribution. By representing a system reliability structure as a joint distribution, the uncertainty and correlations existing between system’s elements can effectively be modeled …

Contributors
Lee, Dongjin, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2018

This dissertation proposes a new set of analytical methods for high dimensional physiological sensors. The methodologies developed in this work were motivated by problems in learning science, but also apply to numerous disciplines where high dimensional signals are present. In the education field, more data is now available from traditional sources and there is an important need for analytical methods to translate this data into improved learning. Affecting Computing which is the study of new techniques that develop systems to recognize and model human emotions is integrating different physiological signals such as electroencephalogram (EEG) and electromyogram (EMG) to detect and …

Contributors
Lujan Moreno, Gustavo A., Runger, George C, Atkinson, Robert K, et al.
Created Date
2017

Feature learning and the discovery of nonlinear variation patterns in high-dimensional data is an important task in many problem domains, such as imaging, streaming data from sensors, and manufacturing. This dissertation presents several methods for learning and visualizing nonlinear variation in high-dimensional data. First, an automated method for discovering nonlinear variation patterns using deep learning autoencoders is proposed. The approach provides a functional mapping from a low-dimensional representation to the original spatially-dense data that is both interpretable and efficient with respect to preserving information. Experimental results indicate that deep learning autoencoders outperform manifold learning and principal component analysis in reproducing …

Contributors
Howard, Phillip Ryan, Runger, George, Montgomery, Douglas, et al.
Created Date
2016

Revenue management is at the core of airline operations today; proprietary algorithms and heuristics are used to determine prices and availability of tickets on an almost-continuous basis. While initial developments in revenue management were motivated by industry practice, later developments overcoming fundamental omissions from earlier models show significant improvement, despite their focus on relatively esoteric aspects of the problem, and have limited potential for practical use due to computational requirements. This dissertation attempts to address various modeling and computational issues, introducing realistic choice-based demand revenue management models. In particular, this work introduces two optimization formulations alongside a choice-based demand modeling …

Contributors
Clough, Michael C., Gel, Esma, Jacobs, Timothy, et al.
Created Date
2016

Transfer learning refers to statistical machine learning methods that integrate the knowledge of one domain (source domain) and the data of another domain (target domain) in an appropriate way, in order to develop a model for the target domain that is better than a model using the data of the target domain alone. Transfer learning emerged because classic machine learning, when used to model different domains, has to take on one of two mechanical approaches. That is, it will either assume the data distributions of the different domains to be the same and thereby developing one model that fits all, …

Contributors
Zou, Na, Li, Jing, Baydogan, Mustafa, et al.
Created Date
2015

Accelerated life testing (ALT) is the process of subjecting a product to stress conditions (temperatures, voltage, pressure etc.) in excess of its normal operating levels to accelerate failures. Product failure typically results from multiple stresses acting on it simultaneously. Multi-stress factor ALTs are challenging as they increase the number of experiments due to the stress factor-level combinations resulting from the increased number of factors. Chapter 2 provides an approach for designing ALT plans with multiple stresses utilizing Latin hypercube designs that reduces the simulation cost without loss of statistical efficiency. A comparison to full grid and large-sample approximation methods illustrates …

Contributors
Nasir, Ehab Awad Mukhtar, Pan, Rong, Runger, George, et al.
Created Date
2014

In the three phases of the engineering design process (conceptual design, embodiment design and detailed design), traditional reliability information is scarce. However, there are different sources of information that provide reliability inputs while designing a new product. This research considered these sources to be further analyzed: reliability information from similar existing products denominated as parents, elicited experts' opinions, initial testing and the customer voice for creating design requirements. These sources were integrated with three novels approaches to produce reliability insights in the engineering design process, all under the Design for Reliability (DFR) philosophy. Firstly, an enhanced parenting process to assess …

Contributors
Mejia Sanchez, Luis, Pan, Rong, Montgomery, Douglas, et al.
Created Date
2014