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
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
Nonregular designs are a preferable alternative to regular resolution four designs because they avoid confounding two-factor interactions. As a result nonregular designs can estimate and identify a few active two-factor interactions. However, due to the sometimes complex alias structure of nonregular designs, standard screening strategies can fail to identify all active effects. In this research, two-level nonregular screening designs with orthogonal main effects will be discussed. By utilizing knowledge of the alias structure, a design based model selection process for analyzing nonregular designs is proposed.
The Aliased Informed Model Selection (AIMS) strategy is a design specific approach that is compared to three generic model selection methods; stepwise regression, least absolute shrinkage and selection operator (LASSO), and the Dantzig selector. The AIMS approach substantially increases the power to detect active main effects and two-factor interactions versus the aforementioned generic methodologies. This research identifies design specific model spaces; sets of models with strong heredity, all estimable, and exhibit no model confounding. These spaces are then used in the AIMS method along with design specific aliasing rules for model selection decisions. Model spaces and alias rules are identified for three designs; 16-run no-confounding 6, 7, and 8-factor designs. The designs are demonstrated with several examples as well as simulations to show the AIMS superiority in model selection.
A final piece of the research provides a method for augmenting no-confounding designs based on a model spaces and maximum average D-efficiency. Several augmented designs are provided for different situations. A final simulation with the augmented designs shows strong results for augmenting four additional runs if time and resources permit.
The Aliased Informed Model Selection (AIMS) strategy is a design specific approach that is compared to three generic model selection methods; stepwise regression, least absolute shrinkage and selection operator (LASSO), and the Dantzig selector. The AIMS approach substantially increases the power to detect active main effects and two-factor interactions versus the aforementioned generic methodologies. This research identifies design specific model spaces; sets of models with strong heredity, all estimable, and exhibit no model confounding. These spaces are then used in the AIMS method along with design specific aliasing rules for model selection decisions. Model spaces and alias rules are identified for three designs; 16-run no-confounding 6, 7, and 8-factor designs. The designs are demonstrated with several examples as well as simulations to show the AIMS superiority in model selection.
A final piece of the research provides a method for augmenting no-confounding designs based on a model spaces and maximum average D-efficiency. Several augmented designs are provided for different situations. A final simulation with the augmented designs shows strong results for augmenting four additional runs if time and resources permit.
ContributorsMetcalfe, Carly E (Author) / Montgomery, Douglas C. (Thesis advisor) / Jones, Bradley (Committee member) / Pan, Rong (Committee member) / Pedrielli, Giulia (Committee member) / Arizona State University (Publisher)
Created2020