Engineering Commons^™

Adversarial Deep Learning And Security With A Hardware Perspective, Joseph Clements

All Dissertations

Adversarial deep learning is the field of study which analyzes deep learning in the presence of adversarial entities. This entails understanding the capabilities, objectives, and attack scenarios available to the adversary to develop defensive mechanisms and avenues of robustness available to the benign parties. Understanding this facet of deep learning helps us improve the safety of the deep learning systems against external threats from adversaries. However, of equal importance, this perspective also helps the industry understand and respond to critical failures in the technology. The expectation of future success has driven significant interest in developing this technology broadly. Adversarial deep …

Multiscale Topology Optimization With A Strong Dependence On Complementary Energy, Dustin Dean Bielecki

All Dissertations

A discrete approach introduces a novel deep learning approach for generating fine resolution structures that preserve all the information from the topology optimization (TO). The proposed approach utilizes neural networks (NNs) that map the desired engineering properties to seed for determining optimized structure. This framework relies on utilizing parameters such as density and nodal deflections to predict optimized topologies. A three-stage NN framework is employed for the discrete approach to reduce computational runtime while maintaining physics constraints.

A continuous representation that uses complementary energy (CE) methods to solve a representative element's homogenized properties consists of an embedded structure that is …

Non-Destructive Terrain Evaluation And Modeling For Off-Road Autonomy, Howard Brand

All Dissertations

In recent years, there has been an increased interest in implementing intelligent robotic systems in outdoor environments. Paramount to accomplishing this objective is being able to conduct successful robotic navigation in unprepared outdoor environments. This presents unique challenges in that there is a risk of catastrophic immobilization in terrain regions which, though unoccupied, cannot provide traction support for vehicle mobility. Methods for providing prior knowledge and perception of traction support is therefore an interest and focus of research.

In the advent of ever advancing machine learning models, “learn-as-you-go” approaches have emerged as topics of interest for mobility prediction. These approaches, …

Improving The Human-Machine Interaction Of Ai Systems For System Health Monitoring, Ryan Nguyen

All Dissertations

System health monitoring aids in the longevity of fielded systems or products. Providing a fault diagnosis or a prognosis can evaluate a system's current health. A diagnosis is the type of issue that could lead to a system's end-of-life (EOL); a prognosis is the remaining useful life (RUL) between the current state and the EOL. Fault diagnosis and RUL prediction can be acquired through (1) physics-based methods (PbM), (2) data-driven methods (DDM), or (3) hybrid modeling methods. DDM accurately provide a fault diagnosis, but the amount of data required is significant. This study reduces the amount of required data by …