Digital Commons Network^™

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 …

Improving Mobility And Safety In Traditional And Intelligent Transportation Systems Using Computational And Mathematical Modeling, Shahrbanoo Rezaei

Doctoral Dissertations

In traditional transportation systems, park-and-ride (P&R) facilities have been introduced to mitigate the congestion problems and improve mobility. This study in the second chapter, develops a framework that integrates a demand model and an optimization model to study the optimal placement of P&R facilities. The results suggest that the optimal placement of P&R facilities has the potential to improve network performance, and reduce emission and vehicle kilometer traveled. In intelligent transportation systems, autonomous vehicles are expected to bring smart mobility to transportation systems, reduce traffic congestion, and improve safety of drivers and passengers by eliminating human errors. The safe operation …

Application Of Machine Learning Approaches To Empower Drug Development, Yue Shen

Doctoral Dissertations

Human health, one of the major topics in Life Science, is facing intensified challenges, including cancer, pandemic outbreaks, and antimicrobial resistance. Thus, new medicines with unique advantages, including peptide-based vaccines and permeable small molecule antimicrobials, are in urgent need. However, the drug development process is long, complex, and risky with no guarantee of success. Also, the improvements in techniques applied in genomics, proteomics, computational biology, and clinical trials significantly increase the data complexity and volume, which imposes higher requirements on the drug development pipeline. In recent years, machine learning (ML) methods were employed to support drug development in various aspects …

Advanced Air Quality Management With Machine Learning, Cheng-Pin Kuo

Doctoral Dissertations

Air pollution has been a significant health risk factor at a regional and global scale. Although the present method can provide assessment indices like exposure risks or air pollutant concentrations for air quality management, the modeling estimations still remain non-negligible bias which could deviate from reality and limit the effectiveness of emission control strategies to reduce air pollution and derive health benefits. The current development in air quality management is still impeded by two major obstacles: (1) biased air quality concentrations from air quality models and (2) inaccurate exposure risk estimations

Inspired by more available and overwhelming data, machine learning …

Improved Spatial Resolution For Double-Sided Strip Detectors Using Lithium Indium Diselenide Semiconductors, Jake Alexander Gallagher

Doctoral Dissertations

This research focuses on the evaluation of lithium indium diselenide (LISe) semiconductors in double-sided strip detector (DSSDs) designs as an example for the potential to achieve unparalleled neutron detection efficiency, spatial resolution, and timing resolution detection. LISe semiconductors offer high neutron detection efficiency due to the ~25% atomic ratio of Lithium-6, maximizing its efficiency of ~75% with 1 mm thickness at 2.8 angstroms. Furthermore, the 4.78 MeV 𝑄-value enables high intrinsic gamma discrimination in a pixelated design (electron range). These characteristics make LISe an alternative option for neutron radiography, energy-resolved imaging, and other neutron interrogation techniques. This dissertation summarizes my …

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 …

Tomato Flower Detection And Three-Dimensional Mapping For Precision Pollination, Kaitlyn Mckensie Nelms

Masters Theses

It is estimated that nearly 75% of major crops have some level of reliance on pollination. Humans are reliant on fruit and vegetable crops for many vital nutrients. With the intensification of agricultural production in response to human demand, native pollinator species are not able to provide sufficient pollination services, and managed bee colonies are in decline due to colony collapse disorder, among other issues. Previous work addresses a few of these issues by designing pollination systems for greenhouse operations or other controlled production systems but fails to address the larger need for development in other agricultural settings with less …

Multi-Objective Optimization Of The Fast Neutron Source By Machine Learning, John L. Pevey

Doctoral Dissertations

The design and optimization of nuclear systems can be a difficult task, often with prohibitively large design spaces, as well as both competing and complex objectives and constraints. When faced with such an optimization, the task of designing an algorithm for this optimization falls to engineers who must apply engineering knowledge and experience to reduce the scope of the optimization to a manageable size. When sufficient computational resources are available, unsupervised optimization can be used.

The optimization of the Fast Neutron Source (FNS) at the University of Tennessee is presented as an example for the methodologies developed in this work. …

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 …

What I Talk About When I Talk About Integration Of Single-Cell Data, Yang Xu

Doctoral Dissertations

Over the past decade, single-cell technologies evolved from profiling hundreds of cells to millions of cells, and emerged from a single modality of data to cover multiple views at single-cell resolution, including genome, epigenome, transcriptome, and so on. With advance of these single-cell technologies, the booming of multimodal single-cell data creates a valuable resource for us to understand cellular heterogeneity and molecular mechanism at a comprehensive level. However, the large-scale multimodal single-cell data also presents a huge computational challenge for insightful integrative analysis. Here, I will lay out problems in data integration that single-cell research community is interested in and …

Exploration Of The Stability Of Multicomponent Metal Halide Perovskites Utilizing Automated, High-Throughput Methods And Machine Learning, Katherine N. Higgins

Doctoral Dissertations

Because of their outstanding optoelectronic properties and low-cost, solution-based fabrication, metal halide perovskites (MHP) are appealing candidates for a variety of applications, such as photovoltaics, light-emitting diodes, photodetectors, and ionizing radiation detectors. However, concerns of this material’s stability in pure or device-integrated form under external stimuli, such as light, humidity, oxygen, and heat, have prohibited the widespread utilizations of MHPs. It is well established that alloying can lessen detrimental effects of these factors. To date, a small portion of alloyed compositions have been investigated compared to the thousands of possible perovskites proposed theoretically. Conventional approaches to materials discovery and optimization, …

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 …

Automated Intelligent Cueing Device To Improve Ambient Gait Behaviors For Patients With Parkinson's Disease, Nader Naghavi

Doctoral Dissertations

Freezing of gait (FoG) is a common motor dysfunction in individuals with Parkinson’s disease (PD). FoG impairs walking and is associated with increased fall risk. Although pharmacological treatments have shown promise during ON-medication periods, FoG remains difficult to treat during medication OFF state and in advanced stages of the disease. External cueing therapy in the forms of visual, auditory, and vibrotactile, has been effective in treating gait deviations. Intelligent (or on-demand) cueing devices are novel systems that analyze gait patterns in real-time and activate cues only at moments when specific gait alterations are detected. In this study we developed methods …

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. …

A Datacentric Algorithm For Gamma-Ray Radiation Anomaly Detection In Unknown Background Environments, James M. Ghawaly Jr

Doctoral Dissertations

The detection of anomalous radioactive sources in environments characterized by a high level of variation in the background radiation is a challenging problem in nuclear security. A variety of natural and artificial sources contribute to background radiation dynamics including variations in the absolute and relative concentrations of naturally occurring radioisotopes in different materials, the wet-deposition of $^{222}$Rn daughters during precipitation, and background suppression due to physical objects in the detector scene called ``clutter." This dissertation presents a new datacentric algorithm for radiation anomaly detection in dynamic background environments. The algorithm is based on a custom deep neural autoencoder architecture called …

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 …

Classification Of Bacterial Motility Using Machine Learning, Yue Ma

Masters Theses

Cells can display a diverse set of motility behaviors, and these behaviors may reflect a cell’s functional state. Automated, and accurate cell motility analysis is essential to cell studies where the analysis of motility pattern is required. The results of such analysis can be used for diagnostic or curative decisions. Deep learning area has made astonishing progresses in the past several years. For computer vision tasks, different convolutional neural networks (CNN) and optimizers have been proposed to fix some problems. For time sequence data, recurrent neural networks (RNN) have been widely used.

This project leveraged on these recent advances to …

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 …

Identifying Smokestacks In Remotely Sensed Imagery Via Deep Learning Algorithms, Kenneth Moss

Masters Theses

Locating smokestacks in remote sensing imagery is a crucial first step to calculating smokestack heights, which allows for the accurate modeling of dioxin pollution spread and the study of resulting health impacts. In the interest of automating this process, this thesis examines deep learning networks and how changes in input datasets and network architecture affect image detection accuracy. This initial image detection serves as the first step in automated object recognition and height calculation. While this is applicable to general land use classification, this study specifically addresses detecting smokestack images. Different dataset scenarios are generated from the massive Functional Map …

Toward More Predictive Models By Leveraging Multimodal Data, Sudarshan Srinivasan

Doctoral Dissertations

Data is often composed of structured and unstructured data. Both forms of data have information that can be exploited by machine learning models to increase their prediction performance on a task. However, integrating the features from both these data forms is a hard, complicated task. This is all the more true for models which operate on time-constraints. Time-constrained models are machine learning models that work on input where time causality has to be maintained such as predicting something in the future based on past data. Most previous work does not have a dedicated pipeline that is generalizable to different tasks …

A Neuroscience-Inspired Approach To Training Spiking Neural Networks, James Michael Ghawaly Jr.

Masters Theses

Spiking neural networks (SNNs) have recently gained a lot of attention for use in low-power neuromorphic and edge computing. On their own, SNNs are difficult to train, owing to their lack of a differentiable activation function and their inherent tendency towards chaotic behavior. This work takes a strictly neuroscience-inspired approach to designing and training SNNs. We demonstrate that the use of neuromodulated synaptic time dependent plasticity (STDP) can be used to create a variety of different learning paradigms including unsupervised learning, semi-supervised learning, and reinforcement learning. In order to tackle the highly dynamic and potentially chaotic spiking behavior of SNNs …

Deep Reinforcement Learning For Real-Time Residential Hvac Control, Evan Mckee

Masters Theses

The model-free Deep Reinforcement Learning (DRL) environment developed for this work attempts to minimize energy cost during residential heating, ventilation, and air conditioning (HVAC) operation. The HVAC load associated with heating and cooling is an ideal candidate for price optimization through automation for two reasons: Its power footprint in a typical home is sizeable, and the required level of participation from an inhabitant is passive. HVAC is difficult to accurately model and unique for every home, so online machine learning is used to allow for real-time readjustment in performance. Energy cost for the cooling unit shown in this work is …

Improving Manufacturing Data Quality With Data Fusion And Advanced Algorithms For Improved Total Data Quality Management, David Juriga

Masters Theses

Data mining and predictive analytics in the sustainable-biomaterials industries is currently not feasible given the lack of organization and management of the database structures. The advent of artificial intelligence, data mining, robotics, etc., has become a standard for successful business endeavors and is known as the ‘Fourth Industrial Revolution’ or ‘Industry 4.0’ in Europe. Data quality improvement through real-time multi-layer data fusion across interconnected networks and statistical quality assessment may improve the usefulness of databases maintained by these industries. Relational databases with a high degree of quality may be the gateway for predictive modeling and enhanced business analytics. Data quality …

Attention Mechanism For Recognition In Computer Vision, Alireza Rahimpour

Doctoral Dissertations

It has been proven that humans do not focus their attention on an entire scene at once when they perform a recognition task. Instead, they pay attention to the most important parts of the scene to extract the most discriminative information. Inspired by this observation, in this dissertation, the importance of attention mechanism in recognition tasks in computer vision is studied by designing novel attention-based models. In specific, four scenarios are investigated that represent the most important aspects of attention mechanism. First, an attention-based model is designed to reduce the visual features' dimensionality by selectively processing only a small subset …

Visual Sensing And Defect Detection Of Gas Tungsten Arc Welding, Zongyao Chen

Doctoral Dissertations

Weld imperfections or defects such as incomplete penetration and lack of fusion are critical issues that affect the integration of welding components. The molten weld pool geometry is the major source of information related to the formation of these defects. In this dissertation, a new visual sensing system has been designed and set up to obtain weld pool images during GTAW. The weld pool dynamical behavior can be monitored using both active and passive vision method with the interference of arc light in the image significantly reduced through the narrow band pass filter and laser based auxiliary light source.Computer vision …

How Artificial Intelligence And Machine Learning Will Change The Future Of Financial Auditing: An Analysis Of The University Of Tennessee's Accounting Graduate Curriculum, Kaylee M. Giles

Chancellor’s Honors Program Projects

No abstract provided.

Artificial Intelligence In Materials Science: Applications Of Machine Learning To Extraction Of Physically Meaningful Information From Atomic Resolution Microscopy Imaging, Artem Borisovich Maksov

Doctoral Dissertations

Materials science is the cornerstone for technological development of the modern world that has been largely shaped by the advances in fabrication of semiconductor materials and devices. However, the Moore’s Law is expected to stop by 2025 due to reaching the limits of traditional transistor scaling. However, the classical approach has shown to be unable to keep up with the needs of materials manufacturing, requiring more than 20 years to move a material from discovery to market. To adapt materials fabrication to the needs of the 21st century, it is necessary to develop methods for much faster processing of experimental …

Heat Flux Model Validation Utilizing Convolutional Neural Networks And Sub-Surface Thermocouples For Nstx-U, Thomas Patrick Looby

Masters Theses

A proof of concept convolutional neural network (CNN) has been developed to assist in operating tokamaks outside of existing empirical scalings for the heat flux width, λ_q [lambda-q]. NSTX-U has designed new plasma facing components (PFCs) to withstand increased halo current forces as well as elevated heat fluxes driven by increased poloidal field and neutral beam power compared to NSTX. Larger graphite tiles are castellated to 2.5 cm [centimenter] x 2.5 cm [centimeter] to reduce bending stresses. Maintaining PFCs below engineering limits will be an important consideration for operation of NSTX-U. Sub-surface thermocouples will be utilized to demonstrate validation …