Physical Sciences and Mathematics Commons^™

Characterizing Silicate Materials Via Raman Spectroscopy And Machine Learning: Implications For Novel Approaches To Studying Melt Dynamics, Blake O. Ladouceur

Doctoral Dissertations

Silicate melt characteristics impose dramatic influence over igneous processes that operate, or have operated on, differentiated bodies: such as the Earth and Mars. Current understanding of these melt properties, such as composition, primarily comes from investigations on their volcanic byproducts. Therefore, it is imperative to innovate on modalities capable of constraining melt information in environments where a reliance on laboratory methods is severed. Recent investigations have turned to Raman Spectroscopy and amorphous volcanics as a suitable pairing for exploring these ideas. Silicate glasses are a proxy for igneous melts; and Raman spectroscopy is a robust analytical technique capable of operating …

Exact Models, Heuristics, And Supervised Learning Approaches For Vehicle Routing Problems, Zefeng Lyu

Doctoral Dissertations

This dissertation presents contributions to the field of vehicle routing problems by utilizing exact methods, heuristic approaches, and the integration of machine learning with traditional algorithms. The research is organized into three main chapters, each dedicated to a specific routing problem and a unique methodology. The first chapter addresses the Pickup and Delivery Problem with Transshipments and Time Windows, a variant that permits product transfers between vehicles to enhance logistics flexibility and reduce costs. To solve this problem, we propose an efficient mixed-integer linear programming model that has been shown to outperform existing ones. The second chapter discusses a practical …

Understanding And Simulating Wildfire Changes Using Advanced Statical And Process-Oriented Models, Rongyun Tang

Doctoral Dissertations

This study aims to investigate the spatiotemporal dynamic of global wildfires, their underlying climate-driving mechanisms, and their predictability by utilizing multiple data sources (both process-based model simulations and satellite-based observations) and multiple analytical methods including machine learning techniques (MLTs).

We first explored the global wildfire interannual variability (IAV) and its climate sensitivity across nine biomes from 1997 to 2018, leveraging the state-of-art U.S. Department of Energy’s Energy Exascale Earth System Model (E3SM) land component (ELM-v1) simulations with six sets of climate forcings. Results indicate that 1) ELM simulations could reproduce the IAV of wildfire in terms of magnitudes, distribution, bio-regional …

Imaging Normal Fluid Flow In He Ii With Neutrons And Lasers — A New Application Of Neutron Beams For Studies Of Turbulence, Xin Wen

Doctoral Dissertations

Turbulence is ubiquitous in life —from biology to astrophysics. The best direct numeric simulations (DNS) have only been benchmarked against low resolution, time-averaged experimental configurations—partly because of limitations in computing power. With time, computing power has greatly increased, so there is need for higher quality data of turbulent flow. In this dissertation, we explore a solution that enables quantitative visualization measurement of the velocity field in liquid helium, which has the potential of breaking new ground for high Reynolds number turbulence research and model testing.

Our technique involves creation of clouds of molecular tracers using 3He-neutron absorption reaction in liquid …

Power System Stability Assessment With Supervised Machine Learning, Mirka Mandich

Masters Theses

Power system stability assessment has become an important area of research due to the increased penetration of photovoltaics (PV) in modern power systems. This work explores how supervised machine learning can be used to assess power system stability for the Western Electricity Coordinating Council (WECC) service region as part of the Data-driven Security Assessment for the Multi-Timescale Integrated Dynamics and Scheduling for Solar (MIDAS) project. Data-driven methods offer to improve power flow scheduling through machine learning prediction, enabling better energy resource management and reducing demand on real-time time-domain simulations. Frequency, transient, and small signal stability datasets were created using the …

Human Fatigue Predictions In Complex Aviation Crew Operational Impact Conditions, Suresh Rangan

Doctoral Dissertations

In this last decade, several regulatory frameworks across the world in all modes of transportation had brought fatigue and its risk management in operations to the forefront. Of all transportation modes air travel has been the safest means of transportation. Still as part of continuous improvement efforts, regulators are insisting the operators to adopt strong fatigue science and its foundational principles to reinforce safety risk assessment and management. Fatigue risk management is a data driven system that finds a realistic balance between safety and productivity in an organization. This work discusses the effects of mathematical modeling of fatigue and its …

Unifying Chemistry And Machine Learning For The Study Of Noncovalent Interactions, Jacob A. Townsend

Doctoral Dissertations

Gas separations are in great demand for carbon emission reduction, natural gas purification, oxygen isolation, and much more. Many of these separations rely on cost-prohibitive methods such as cryogenic distillation or strong-binding solvents. As a result, novel materials are being developed to subvert the energetic expense of gas separation processes. These studies focus on improving the performance of alternative materials, including (but not limited to) metal-organic frameworks, covalent organic frameworks, dense polymeric membranes, porous polymers, and ionic liquids.

In this work, the atomistic effects of functional units are explored for gas separations processes using electronic structure theory and machine learning. …

Bayesian Topological Machine Learning, Christopher A. Oballe

Doctoral Dissertations

Topological data analysis encompasses a broad set of ideas and techniques that address 1) how to rigorously define and summarize the shape of data, and 2) use these constructs for inference. This dissertation addresses the second problem by developing new inferential tools for topological data analysis and applying them to solve real-world data problems. First, a Bayesian framework to approximate probability distributions of persistence diagrams is established. The key insight underpinning this framework is that persistence diagrams may be viewed as Poisson point processes with prior intensities. With this assumption in hand, one may compute posterior intensities by adopting techniques …

Improving Convolutional Neural Network Robustness To Adversarial Images Through Image Filtering, Natalie E. Bogda

Masters Theses

The field of computer vision and deep learning is known for its ability to recognize images with extremely high accuracy. Convolutional neural networks exist that can correctly classify 96\% of 1.2 million images of complex scenes. However, with just a few carefully positioned imperceptible changes to the pixels of an input image, an otherwise accurate network will misclassify this almost identical image with high confidence. These perturbed images are known as \textit{adversarial examples} and expose that convolutional neural networks do not necessarily "see" the world in the way that humans do. This work focuses on increasing the robustness of classifiers …

Neuroscience-Inspired Dynamic Architectures, Catherine Dorothy Schuman

Doctoral Dissertations

Biological brains are some of the most powerful computational devices on Earth. Computer scientists have long drawn inspiration from neuroscience to produce computational tools. This work introduces neuroscience-inspired dynamic architectures (NIDA), spiking neural networks embedded in a geometric space that exhibit dynamic behavior. A neuromorphic hardware implementation based on NIDA networks, Dynamic Adaptive Neural Network Array (DANNA), is discussed. Neuromorphic implementations are one alternative/complement to traditional von Neumann computation. A method for designing/training NIDA networks, based on evolutionary optimization, is introduced. We demonstrate the utility of NIDA networks on classification tasks, a control task, and an anomaly detection task. There …

3d Robotic Sensing Of People: Human Perception, Representation And Activity Recognition, Hao Zhang

Doctoral Dissertations

The robots are coming. Their presence will eventually bridge the digital-physical divide and dramatically impact human life by taking over tasks where our current society has shortcomings (e.g., search and rescue, elderly care, and child education). Human-centered robotics (HCR) is a vision to address how robots can coexist with humans and help people live safer, simpler and more independent lives.

As humans, we have a remarkable ability to perceive the world around us, perceive people, and interpret their behaviors. Endowing robots with these critical capabilities in highly dynamic human social environments is a significant but very challenging problem in practical …

Online Multi-Stage Deep Architectures For Feature Extraction And Object Recognition, Derek Christopher Rose

Doctoral Dissertations

Multi-stage visual architectures have recently found success in achieving high classification accuracies over image datasets with large variations in pose, lighting, and scale. Inspired by techniques currently at the forefront of deep learning, such architectures are typically composed of one or more layers of preprocessing, feature encoding, and pooling to extract features from raw images. Training these components traditionally relies on large sets of patches that are extracted from a potentially large image dataset. In this context, high-dimensional feature space representations are often helpful for obtaining the best classification performances and providing a higher degree of invariance to object transformations. …