Degree Type
Dissertation
Date of Award
2017
Degree Name
Doctor of Philosophy
Department
Mechanical Engineering
Major
Mechanical Engineering
First Advisor
Soumik Sarkar
Second Advisor
Baskar Ganapathysubramanian
Abstract
With the advent of ubiquitous sensing, robust communication and advanced computation, data-driven modeling is increasingly becoming popular for many engineering problems. Eliminating difficulties of physics-based modeling, avoiding simplifying assumptions and ad hoc empirical models are significant among many advantages of data-driven approaches, especially for large-scale complex systems. While classical statistics and signal processing algorithms have been widely used by the engineering community, advanced machine learning techniques have not been sufficiently explored in this regard. This study summarizes various categories of machine learning tools that have been applied or may be a candidate for addressing engineering problems. While there are increasing number of machine learning algorithms, the main steps involved in applying such techniques to the problems consist in: data collection and pre-processing, feature extraction, model training and inference for decision-making. To support decision-making processes in many applications, hierarchical feature extraction is key. Among various feature extraction principles, recent studies emphasize hierarchical approaches of extracting salient features that is carried out at multiple abstraction levels from data. In this context, the focus of the dissertation is towards developing hierarchical feature extraction algorithms within the framework of machine learning in order to solve challenging cyber-physical problems in various domains such as electromechanical systems and agricultural systems. Furthermore, the feature extraction techniques are described using the spatial, temporal and spatiotemporal data types collected from the systems. The wide applicability of such features in solving some selected real-life domain problems are demonstrated throughout this study.
DOI
https://doi.org/10.31274/etd-180810-5693
Copyright Owner
Adedotun John Akintayo
Copyright Date
2017
Language
en
File Format
application/pdf
File Size
275 pages
Recommended Citation
Akintayo, Adedotun John, "Hierarchical feature extraction from spatiotemporal data for cyber-physical system analytics" (2017). Graduate Theses and Dissertations. 16064.
https://lib.dr.iastate.edu/etd/16064
Included in
Artificial Intelligence and Robotics Commons, Biostatistics Commons, Databases and Information Systems Commons