國立虎尾科技大學 |

An Analysis of the Boundary Explorer Adaptive Sampling Technique.

紀錄類型:	書目-語言資料,手稿 : Monograph/item
正題名/作者:	An Analysis of the Boundary Explorer Adaptive Sampling Technique./
作者:	Simpson, Ryan J.
面頁冊數:	1 online resource (139 pages)
附註:	Source: Masters Abstracts International, Volume: 85-06.
Contained By:	Masters Abstracts International85-06.
標題:	Industrial engineering. -
電子資源:	click for full text (PQDT)
ISBN:	9798381157819

An Analysis of the Boundary Explorer Adaptive Sampling Technique.
Simpson, Ryan J.

An Analysis of the Boundary Explorer Adaptive Sampling Technique. - 1 online resource (139 pages)

Source: Masters Abstracts International, Volume: 85-06.

Thesis (M.S.)--Arizona State University, 2024.

Includes bibliographical references

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.

Electronic reproduction.
Ann Arbor, Mich. :
ProQuest,
2024

Mode of access: World Wide Web

ISBN: 9798381157819Subjects--Topical Terms:

679492
Industrial engineering.
Subjects--Index Terms:

Autonomous systemsIndex Terms--Genre/Form:

554714
Electronic books.

An Analysis of the Boundary Explorer Adaptive Sampling Technique.
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