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.
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- Creators: Pan, Rong
This research study focuses on the behavior of crystalline silicon PV module technology in the dry and hot climatic condition of Tempe/Phoenix, Arizona. A three-phase approach was developed: (1) A quantitative failure modes, effects, and criticality analysis (FMECA) was developed for prioritizing failure modes or mechanisms in a given environment; (2) A time-series approach was used to model environmental stress variables involved and prioritize their effect on the power output drop; and (3) A procedure for developing a prediction model was proposed for the climatic specific condition based on accelerated degradation testing
As part of this dissertation, a yield approximation method is developed and integrated with a mixed-integer program to estimate a region’s potential to produce non-perennial, vegetable items. This integration offers practical approximations that help decision-makers identify technologies needed to protect agricultural production, alter harvesting patterns to better match market behavior, and provide an analytical framework through which external investment entities can assess different production options.
innovation in healthcare policy over a huge variety of applications by tackling prob-
lems via the creation and optimization of descriptive mathematical models to guide
decision-making. Despite these accomplishments, models are stylized representations
of real-world applications, reliant on accurate estimations from historical data to jus-
tify their underlying assumptions. To protect against unreliable estimations which
can adversely affect the decisions generated from applications dependent on fully-
realized models, techniques that are robust against misspecications are utilized while
still making use of incoming data for learning. Hence, new robust techniques are ap-
plied that (1) allow for the decision-maker to express a spectrum of pessimism against
model uncertainties while (2) still utilizing incoming data for learning. Two main ap-
plications are investigated with respect to these goals, the first being a percentile
optimization technique with respect to a multi-class queueing system for application
in hospital Emergency Departments. The second studies the use of robust forecasting
techniques in improving developing countries’ vaccine supply chains via (1) an inno-
vative outside of cold chain policy and (2) a district-managed approach to inventory
control. Both of these research application areas utilize data-driven approaches that
feature learning and pessimism-controlled robustness.
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 in a probabilistic man- ner. This dissertation focuses on analyzing system reliability for the entire system life cycle, particularly, production stage and early design stages.
In production stage, the research investigates a system that is continuously mon- itored by on-board sensors. With modeling the complex reliability structure by Bayesian network integrated with various stochastic processes, I propose several methodologies that evaluate system reliability on real-time basis and optimize main- tenance schedules.
In early design stages, the research aims to predict system reliability based on the current system design and to improve the design if necessary. The three main challenges in this research are: 1) the lack of field failure data, 2) the complex reliability structure and 3) how to effectively improve the design. To tackle the difficulties, I present several modeling approaches using Bayesian inference and nonparametric Bayesian network where the system is explicitly analyzed through the sensitivity analysis. In addition, this modeling approach is enhanced by incorporating a temporal dimension. However, the nonparametric Bayesian network approach generally accompanies with high computational efforts, especially, when a complex and large system is modeled. To alleviate this computational burden, I also suggest to building a surrogate model with quantile regression.
In summary, this dissertation studies and explores the use of Bayesian network in analyzing complex systems. All proposed methodologies are demonstrated by case studies.
Topic 1: Design of super-HIS and tracking of quality of care (QoC). My research developed an information technology that integrates multiple HISs in radiology, and proposed QoC metrics defined upon the data that measure various dimensions of care. The DDD assisted the clinical practices and enabled an effective intervention for reducing lengthy radiologist turnaround times for patients.
Topic 2: Monitoring and change detection of QoC data streams for process improvement. With the super-HIS in place, high-dimensional data streams of QoC metrics are generated. I developed a statistical model for monitoring high- dimensional data streams that integrated Singular Vector Decomposition (SVD) and process control. The algorithm was applied to QoC metrics data, and additionally extended to another application of monitoring traffic data in communication networks.
Topic 3: Deep transfer learning of archive HIS data for computer-aided diagnosis (CAD). The novelty of the CAD system is the development of a deep transfer learning algorithm that combines the ideas of transfer learning and multi- modality image integration under the deep learning framework. Our system achieved high accuracy in breast cancer diagnosis compared with conventional machine learning algorithms.
Many studies propose to use centralized algorithms for achieving optimal solutions for resource allocation. However, the centralized algorithms may encounter the scalability problem to handle a large number of service requests in a realistically satisfactory time. Hence, this dissertation presents two studies. One study develops and tests heuristics of centralized resource allocation to produce near-optimal solutions in a scalable manner. Another study looks into decentralized methods of performing resource allocation.
The first part of this dissertation defines the resource allocation problem as a centralized optimization problem in Mixed Integer Programming (MIP) and obtains the optimal solutions for various resource-service problem scenarios. Based on the analysis of the optimal solutions, various heuristics are designed for efficient resource allocation. Extended experiments are conducted with larger numbers of user requests and service providers for performance evaluation of the resource allocation heuristics. Experimental results of the resource allocation heuristics show the comparable performance of the heuristics to the optimal solutions from solving the optimization problem. Moreover, the resource allocation heuristics demonstrate better computational efficiency and thus scalability than solving the optimization problem.
The second part of this dissertation looks into elements of service provider-user coordination first in the formulation of the centralized resource allocation problem in MIP and then in the formulation of the optimization problem in a decentralized manner for various problem cases. By examining differences between the centralized, optimal solutions and the decentralized solutions for those problem cases, the analysis of how the decentralized service provider-user coordination breaks down the optimal solutions is performed. Based on the analysis, strategies of decentralized service provider-user coordination are developed.