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: Davulcu, Hasan
- Creators: Karl, Andrew
Description
With the explosion of autonomous systems under development, complex simulation models are being tested and relied on far more than in the recent past. This uptick in autonomous systems being modeled then tested magnifies both the advantages and disadvantages of simulation experimentation. An inherent problem in autonomous systems development is when small changes in factor settings result in large changes in a response’s performance. These occurrences look like cliffs in a metamodel’s response surface and are referred to as performance mode boundary regions. These regions represent areas of interest in the autonomous system’s decision-making process. Therefore, performance mode boundary regions are areas of interest for autonomous systems developers.Traditional augmentation methods aid experimenters seeking different objectives, often by improving a certain design property of the factor space (such as variance) or a design’s modeling capabilities. While useful, these augmentation techniques do not target areas of interest that need attention in autonomous systems testing focused on the response. Boundary Explorer Adaptive Sampling Technique, or BEAST, is a set of design augmentation algorithms. The adaptive sampling algorithm targets performance mode boundaries with additional samples. The gap filling augmentation algorithm targets sparsely sampled areas in the factor space. BEAST allows for sampling to adapt to information obtained from pervious iterations of experimentation and target these regions of interest. Exploiting the advantages of simulation model experimentation, BEAST can be used to provide additional iterations of experimentation, providing clarity and high-fidelity in areas of interest along potentially steep gradient regions.
The objective of this thesis is to research and present BEAST, then compare BEAST’s algorithms to other design augmentation techniques. Comparisons are made towards traditional methods that are already implemented in SAS Institute’s JMP software, or emerging adaptive sampling techniques, such as Range Adversarial Planning Tool (RAPT). The goal of this objective is to gain a deeper understanding of how BEAST works and where it stands in the design augmentation space for practical applications. With a gained understanding of how BEAST operates and how well BEAST performs, future research recommendations will be presented to improve BEAST’s capabilities.
ContributorsSimpson, Ryan James (Author) / Montgomery, Douglas (Thesis advisor) / Karl, Andrew (Committee member) / Pan, Rong (Committee member) / Pedrielli, Giulia (Committee member) / Wisnowski, James (Committee member) / Arizona State University (Publisher)
Created2024
Description
With the development of modern technological infrastructures, such as social networks or the Internet of Things (IoT), data is being generated at a speed that is never before seen. Analyzing the content of this data helps us further understand underlying patterns and discover relationships among different subsets of data, enabling intelligent decision making. In this thesis, I first introduce the Low-rank, Win-dowed, Incremental Singular Value Decomposition (SVD) framework to inclemently maintain SVD factors over streaming data. Then, I present the Group Incremental Non-Negative Matrix Factorization framework to leverage redundancies in the data to speed up incremental processing. They primarily tackle the challenges of using factorization models in the scenarios with streaming textual data. In order to tackle the challenges in improving the effectiveness and efficiency of generative models in this streaming environment, I introduce the Incremental Dynamic Multiscale Topic Model framework, which identifies multi-scale patterns and their evolutions within streaming datasets. While the latent factor models assume the linear independence in the latent factors, the generative models assume the observation is generated from a set of latent variables with various distributions. Furthermore, some models may not be accessible or their underlying structures are too complex to understand, such as simulation ensembles, where there may be thousands of parameters with a huge parameter space, the only way to learn information from it is to execute real simulations. When performing knowledge discovery and decision making through data- and model-driven simulation ensembles, it is expensive to operate these ensembles continuously at large scale, due to the high computational. Consequently, given a relatively small simulation budget, it is desirable to identify a sparse ensemble that includes the most informative simulations, while still permitting effective exploration of the input parameter space. Therefore, I present Complexity-Guided Parameter Space Sampling framework, which is an intelligent, top-down sampling scheme to select the most salient simulation parameters to execute, given a limited computational budget. Moreover, I also present a Pivot-Guided Parameter Space Sampling framework, which incrementally maintains a diverse ensemble of models of the simulation ensemble space and uses a pivot guided mechanism for future sample selection.
ContributorsChen, Xilun (Author) / Candan, K. Selcuk (Thesis advisor) / Davulcu, Hasan (Committee member) / Pedrielli, Giulia (Committee member) / Sapino, Maria Luisa (Committee member) / Tong, Hanghang (Committee member) / Arizona State University (Publisher)
Created2019