Physical Sciences and Mathematics Commons^™

Novelty Detection Of Machinery Using A Non-Parametric Machine Learning Approach, Enrique Angola

Graduate College Dissertations and Theses

A novelty detection algorithm inspired by human audio pattern recognition is conceptualized and experimentally tested. This anomaly detection technique can be used to monitor the health of a machine or could also be coupled with a current state of the art system to enhance its fault detection capabilities. Time-domain data obtained from a microphone is processed by applying a short-time FFT, which returns time-frequency patterns. Such patterns are fed to a machine learning algorithm, which is designed to detect novel signals and identify windows in the frequency domain where such novelties occur. The algorithm presented in this paper uses one-dimensional …

Fluvial Processes In Motion: Measuring Bank Erosion And Suspended Sediment Flux Using Advanced Geomatic Methods And Machine Learning, Scott Douglas Hamshaw

Graduate College Dissertations and Theses

Excessive erosion and fine sediment delivery to river corridors and receiving waters degrade aquatic habitat, add to nutrient loading, and impact infrastructure. Understanding the sources and movement of sediment within watersheds is critical for assessing ecosystem health and developing management plans to protect natural and human systems. As our changing climate continues to cause shifts in hydrological regimes (e.g., increased precipitation and streamflow in the northeast U.S.), the development of tools to better understand sediment dynamics takes on even greater importance. In this research, advanced geomatics and machine learning are applied to improve the (1) monitoring of streambank erosion, (2) …

Improving Scalability Of Evolutionary Robotics With Reformulation, Anton Bernatskiy

Graduate College Dissertations and Theses

Creating systems that can operate autonomously in complex environments is a challenge for contemporary engineering techniques. Automatic design methods offer a promising alternative, but so far they have not been able to produce agents that outperform manual designs. One such method is evolutionary robotics. It has been shown to be a robust and versatile tool for designing robots to perform simple tasks, but more challenging tasks at present remain out of reach of the method.

In this thesis I discuss and attack some problems underlying the scalability issues associated with the method. I present a new technique for evolving modular …