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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Description
Networks are a versatile modeling tool for the cyber and physical infrastructure that characterize society. They can be used to describe system spatiotemporal dynamics, including distribution of commodities, movement of agents, and data transmission. This flexibility has resulted in the widespread use of network optimization techniques for decision-making in telecommunications, transportation, commerce, among other systems. However, realistic network problems are typically large-scale and require the use of integer variables to incorporate design or logical system constraints. This makes such problems hard to solve and precludes their wide applicability in the solution of applied problems. This dissertation studies four large-scale optimization problems with underlying network structure in different domain applications, including wireless sensor networks, wastewater monitoring, and scheduling. The problems of interest are formulated using mixed-integer optimization formulations. The proposed solution approaches in this dissertation include branch-and-cut and heuristic algorithms, which are enhanced with network-based valid inequalities and network reduction techniques. The first chapter studies a relay node placement problem in wireless sensor networks, with and without the presence of transmission obstacles in the deployment region. The proposed integer linear programming approach leverages the underlying network structure to produce valid inequalities and network reduction heuristics, which are incorporated in the branch-and-bound exploration. The solution approach outperforms the equivalent nonlinear model and solves instances with up to 1000 sensors within reasonable time. The second chapter studies the continuous version of the maximum capacity (widest) path interdiction problem and introduces the first known polynomial time algorithm to solve the problem using a combination of binary search and the discrete version of the Newton’s method. The third chapter explores the service agent transport interdiction problem in autonomous vehicle systems, where an agent schedules service tasks in the presence of an adversary. This chapter proposes a single stage branch-and-cut algorithm to solve the problem, along with several enhancement techniques to improve scalability. The last chapter studies the optimal placement of sensors in a wastewater network to minimize the maximum coverage (load) of placed sensors. This chapter proposes a branch-and-cut algorithm enhanced with network reduction techniques and strengthening constraints.
ContributorsMitra, Ankan (Author) / Sefair, Jorge A (Thesis advisor) / Mirchandani, Pitu (Committee member) / Grubesic, Anthony (Committee member) / Byeon, Geunyeong (Committee member) / Arizona State University (Publisher)
Created2023
Description
During the inversion of discrete linear systems, noise in data can be amplified and result in meaningless solutions. To combat this effect, characteristics of solutions that are considered desirable are mathematically implemented during inversion. This is a process called regularization. The influence of the provided prior information is controlled by the introduction of non-negative regularization parameter(s). Many methods are available for both the selection of appropriate regularization parame- ters and the inversion of the discrete linear system. Generally, for a single problem there is just one regularization parameter. Here, a learning approach is considered to identify a single regularization parameter based on the use of multiple data sets de- scribed by a linear system with a common model matrix. The situation with multiple regularization parameters that weight different spectral components of the solution is considered as well. To obtain these multiple parameters, standard methods are modified for identifying the optimal regularization parameters. Modifications of the unbiased predictive risk estimation, generalized cross validation, and the discrepancy principle are derived for finding spectral windowing regularization parameters. These estimators are extended for finding the regularization parameters when multiple data sets with common system matrices are available. Statistical analysis of these estima- tors is conducted for real and complex transformations of data. It is demonstrated that spectral windowing regularization parameters can be learned from these new esti- mators applied for multiple data and with multiple windows. Numerical experiments evaluating these new methods demonstrate that these modified methods, which do not require the use of true data for learning regularization parameters, are effective and efficient, and perform comparably to a supervised learning method based on es- timating the parameters using true data. The theoretical developments are validated for one and two dimensional image deblurring. It is verified that the obtained estimates of spectral windowing regularization parameters can be used effectively on validation data sets that are separate from the training data, and do not require known data.
ContributorsByrne, Michael John (Author) / Renaut, Rosemary (Thesis advisor) / Cochran, Douglas (Committee member) / Espanol, Malena (Committee member) / Jackiewicz, Zdzislaw (Committee member) / Platte, Rodrigo (Committee member) / Arizona State University (Publisher)
Created2023
Description
The data explosion in the past decade is in part due to the widespread use of rich sensors that measure various physical phenomenon -- gyroscopes that measure orientation in phones and fitness devices, the Microsoft Kinect which measures depth information, etc. A typical application requires inferring the underlying physical phenomenon from data, which is done using machine learning. A fundamental assumption in training models is that the data is Euclidean, i.e. the metric is the standard Euclidean distance governed by the L-2 norm. However in many cases this assumption is violated, when the data lies on non Euclidean spaces such as Riemannian manifolds. While the underlying geometry accounts for the non-linearity, accurate analysis of human activity also requires temporal information to be taken into account. Human movement has a natural interpretation as a trajectory on the underlying feature manifold, as it evolves smoothly in time. A commonly occurring theme in many emerging problems is the need to \emph{represent, compare, and manipulate} such trajectories in a manner that respects the geometric constraints. This dissertation is a comprehensive treatise on modeling Riemannian trajectories to understand and exploit their statistical and dynamical properties. Such properties allow us to formulate novel representations for Riemannian trajectories. For example, the physical constraints on human movement are rarely considered, which results in an unnecessarily large space of features, making search, classification and other applications more complicated. Exploiting statistical properties can help us understand the \emph{true} space of such trajectories. In applications such as stroke rehabilitation where there is a need to differentiate between very similar kinds of movement, dynamical properties can be much more effective. In this regard, we propose a generalization to the Lyapunov exponent to Riemannian manifolds and show its effectiveness for human activity analysis. The theory developed in this thesis naturally leads to several benefits in areas such as data mining, compression, dimensionality reduction, classification, and regression.
ContributorsAnirudh, Rushil (Author) / Turaga, Pavan (Thesis advisor) / Cochran, Douglas (Committee member) / Runger, George C. (Committee member) / Taylor, Thomas (Committee member) / Arizona State University (Publisher)
Created2016