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Full text release has been delayed at the author's request until December 16, 2025

ETD Abstract Container

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Application of Bayesian Machine Learning and Uncertainty Quantification in Structured Light System and Pipeline External Corrosion Assessment

Sreeharan, Sreelakshmi
http://orcid.org/0000-0002-6316-0365
http://rave.ohiolink.edu/etdc/view?acc_num=dayton1702455881779018

Abstract Details

2023, Doctor of Philosophy (Ph.D.), University of Dayton, Electrical and Computer Engineering.
Bayesian methods offer a powerful framework for predicting and quantifying uncertainty in various practical systems. This study explores the application of Bayesian approaches in two distinct domains: structured light (SL) systems and pipeline corrosion analysis. In structured light systems, Bayesian method is utilized to build a complete coordinate uncertainty model from the root cause. Details on sources of aleatoric and epistemic uncertainty and its propagation to final reconstructed coordinates is discussed. Certain practical implementation issues and possible solutions are also discussed. Conversely, in pipeline corrosion analysis, Bayesian methodologies are employed to model uncertainty derived from acquired data. This application involves leveraging Bayesian inference to understand, model, and mitigate uncertainties arising from environmental factors and material behaviors. Through these two applications, Bayesian methods prove instrumental in navigating and managing uncertainties in practical systems, offering insights, predictions, and solutions vital for decision-making and system optimization.
Hui Wang (Advisor)
Tarek Taha (Committee Member)
Brad Ratliff (Committee Member)
Feng Ye (Committee Member)
124 p.
Civil Engineering; Electrical Engineering; Engineering
structured light; bayesian inference; uncertainty quantification; pipeline corrosion; pipeline reliability; aleatoric uncertainty; epistemic uncertainty

Recommended Citations

Citations

  • Sreeharan, S. (2023). Application of Bayesian Machine Learning and Uncertainty Quantification in Structured Light System and Pipeline External Corrosion Assessment [Doctoral dissertation, University of Dayton]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1702455881779018

    APA Style (7th edition)

  • Sreeharan, Sreelakshmi. Application of Bayesian Machine Learning and Uncertainty Quantification in Structured Light System and Pipeline External Corrosion Assessment. 2023. University of Dayton, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=dayton1702455881779018.

    MLA Style (8th edition)

  • Sreeharan, Sreelakshmi. "Application of Bayesian Machine Learning and Uncertainty Quantification in Structured Light System and Pipeline External Corrosion Assessment." Doctoral dissertation, University of Dayton, 2023. http://rave.ohiolink.edu/etdc/view?acc_num=dayton1702455881779018

    Chicago Manual of Style (17th edition)

dayton1702455881779018
© 2023, all rights reserved.
This open access ETD is published by University of Dayton and OhioLINK.