Physical Sciences and Mathematics Commons^™

On Intrinsic Dimensionality Of Data Sets And Neural Networks, Ori Chachmo

Theses and Dissertations

The concept of Intrinsic Dimensionality (ID) is of special interest in the field of Neural Networks (NNs) since it promotes both (a) a deeper understanding of the underlying mechanisms, and (b) embraces parsimonious modeling (that is, building the right-sized model for the task) with associated benefits to processing speed and storage requirements. This thesis explores the concept of ID via two separate, but related, questions. First, we study the potential of NN ID prediction by exploiting easily obtained quantities measured on the data. We then explore NN ID as an independent concept by comparing the results of different methods for …

Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs

Theses and Dissertations

Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …

An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge

Theses and Dissertations

This research examines China and derives insights specific to it and the First Island Chain and the Second Island Chain. In doing so, this research demonstrates a methodology to examine other competitors and their geostrategic interests. In the first phase of analysis, it develops a value hierarchy to depict objectives within subregions of the area of interest and considers four alternative weightings of the value hierarchy. In the second phase of analysis, it applies four location-covering models to assess how the competitor would emplace a range of limited resources to deter and/or control points of interest. Results indicate that land-based …

Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski

Theses and Dissertations

The role of autonomy has evolved recently, demanding tighter integration between human and autonomous systems, particularly in highly contested A2AD environments. Near-peer adversaries have modernized their integrated air defense systems (IADS), diminishing the current advantages of the United States Air Force. To regain air dominance, efforts like the Collaborative Combat Aircraft (CCA) program are underway, aiming to deploy unmanned autonomous alongside manned next-generation fighter aircraft. This research assesses various operational concepts, focusing on autonomous tactics post-manned fighter loss, strike timing of independent teams, and weapon configuration observability. Using the Advanced Framework for Simulation, Integration and Modeling (AFSIM), an agent-based model …

U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan

Theses and Dissertations

The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.

Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering

Theses and Dissertations

Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …

Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O

Theses and Dissertations

This thesis investigates the use of machine learning and deep learning models within a federated learning framework to predict physical and mental readiness in military personnel, using wearable technology data. The collaboration with the 711th Human Performance Wing’s STRONG Lab highlights the importance of readiness as emphasized by the National Defense and Security Strategies. The study evaluates various predictive models, incorporating federated learning to ensure data privacy and security in healthcare systems. By analyzing a comprehensive dataset, the research aims to contribute to military readiness enhancement through technological advancements, supporting health and wellness initiatives to bolster the effectiveness of military …

A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae

Theses and Dissertations

A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …

A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike

Theses and Dissertations

This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …

A Staged Framework For Llm-Powered Information Extraction In Government Contracts, Jung H. Yae

Theses and Dissertations

The manual extraction of meaningful insights and conversion of content into structured forms to enhance document processing require substantial resources and are susceptible to errors. Despite numerous applications of various Natural Language Processing (NLP) models to streamline the manual process, challenges persist due to domain-specific data constraints and the deficiency of annotated data. This study attempts to address these challenges by leveraging a Large Language Model (LLM) to analyze government contracts. Through rigorous evaluation, we demonstrate the LLM’s effectiveness in information extraction and mitigating hallucinations, achieving a 87.86% accuracy in metadata extraction.

Advanced Oxidation Of Tert-Butanol With Ultraviolet Light Emitting Diodes And Hydrogen Peroxide, Lauren R. Mainolfi

Theses and Dissertations

Tert-butanol (TBA), a versatile chemical widely used in industrial processes, poses exposure risks through inhalation, ingestion, and skin contact. Environmental contamination, often from industrial activities, emphasizes the importance of robust waste management and monitoring to protect water supplies from potential TBA migration and ensure drinking water safety. This study employed hydrogen peroxide (H2O2): TBA molar ratios of 100, 200, 400, and 500:1 in a Continuous Flow Stirred-Tank Reactor (CSTR) with UV-LED as the TBA degradation mechanism in an Advanced Oxidation Process (AOP). The UV-LEDs and H2O2 were synergistically employed to generate hydroxyl radicals in an advanced oxidation process. Gas chromatography–mass …

Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil

Theses and Dissertations

This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.

A Model-Based And System-Theoretic Approach To Design Advanced Autonomy For Air Dominance Missions: A Loyal Wingman Case Study, Elizabeth S. Pennington

Theses and Dissertations

In contested air environments, safe coordination between decision-makers is paramount. Although the Department of Defense (DoD) prioritizes the development of Artificially Intelligent (AI) wingmen for air combat, a lack of methodology exists to design safe, holistic coordination between human and autonomous wingmen in the same environment. This thesis delivers a framework using Systems Theoretic Process Analysis Extended for Coordination (STPA-Coord) to analyze and design holistic coordination for the Loyal Wingman concept in an Air Dominance mission. STPA-Coord is a safety and hazard analysis process that uses Systems Theory to analyze and design coordination between decisionmakers in a system-of-systems architecture. Using …

Analyzing The Effects Of Atmospheric Turbulence On Polarization-Entangled Photon Pairs Using Quantum State Tomography, Noah S. Everett

Theses and Dissertations

To help in building a quantum-based communication link, we experimentally designed a system to simulate atmospheric turbulence and characterize its effects on a polarization-entangled photon-pair source. The simulated turbulence is constructed using two afocal optical systems with a phase plate inserted in each to mimic both weak and strong atmospheric turbulence respectively. After propagation, quantum state tomography (QST) is performed on each pair to reconstruct the density matrix of the pair’s overall polarization state. In characterization of the simulated turbulence, we were able to reach strengths up to a D/r0 of 18.2, which begins to approach the strong turbulent regime. …

Sensor-Based Vehicle Classification Using Machine Learning, Luke Mcfadden

Theses and Dissertations

This research investigates the classification of vehicles into heavy and light categories using acoustic, seismic, and magnetic sensor data. The effectiveness of using frequency domain data and classical machine learning techniques, is compared with the effectiveness of using time-series data and neural networks. The primary aim in doing so was to understand if modern neural network architectures could effectively remove the need for more traditional frequency based signals processing. A significant deliverable of this thesis was the feature importance determined for each of the three phenomenological types found within the data (acoustic, seismic, and magnetic). By analyzing the importance of …

Center Fixing Tropical Depressions And Tropical Storms Using Machine Learning-Nighttime Visible Imagery, Nathan K. Stanford

Theses and Dissertations

The first step in most TC-retrieval algorithms is determining the storm’s central position. In mature TCs, the center is highlighted by a distinct eye and curved band pattern; however, in intensifying and decaying storms, the center is often obscured by thick clouds or overlying cirrus. This study assesses the benefits of incorporating machine learning-derived nighttime visual imagery to improve analysis of center fix in intensifying and decaying TD- and TS-strength TCs during periods of darkness and when polar orbiting satellites are unavailable. The study is divided into two parts: the first, an objective analysis using the ARCHER-2 algorithm, and the …

Proving The Existence Of Equichordal Tight Fusion Frames Using The Newton–Kantorovich Theorem, Staci R. Davis

Theses and Dissertations

An equichordal tight fusion frame (ECTFF) is an example of an optimal packing of subspaces. In particular, an ECTFF is an optimal packing of points in the Grassmannian with respect to chordal distance. Equivalently, every ECTFF is an arrangement of subspaces that meet certain criteria; tightness and equichordality. The existence of an ECTFF can be rephrased as, a certain polynomial mapping as a root. Hence, one can prove the existence of an ECTFF by applying Newton–Kantorovich theorem to this polynomial mapping, given a close enough approximation of one. Newton–Kantorovich requires checking an inequality within a neighborhood of the approximate root, …

Feasibility Of A Zero-Length Spring Gravimeter For Use In Portal Monitoring, Matthew C. Stone

Theses and Dissertations

In recent years, gravimeters have become sensitive enough to theoretically measure masses of interest at distances relevant for portal monitoring. Characterizing the re[1]sponse of the gravimeter to close range masses across a large solid angle is necessary before attempting to demonstrate a portal-monitoring-like measurement due to this mission being outside the scope of typical gravimeter operations. The characterization of the gravimeter focused on three primary topics. For the position-dependent bias, it is found that the models used overestimate the expected observed gravity when the test mass is closest to the gravimeter in four out of seventeen experiments and under[1]estimate the …

Standardization Of Risk Classifications For Unmanned Space Vehicle Missions, Collin A. Gwaltney

Theses and Dissertations

This paper seeks to model risk classification levels (A-D) for 122 Space Vehicle programs. Models include multinomial logistic regression as well as random forest, a machine learning technique based on decision trees. We use independent variables (IVs) which are theoretically correlated to risk class for the regression and one random forest model. We then include all IVs and allow the random forest technique to use those which provide the most information on risk class before paring down the number of IVs to only 7. We show that the accuracy of predictions increases from 62% to 87% by using random forest …

Advanced Oxidation Of Methyl Tert Butyl Ether With Ultraviolet Light Emitting Diodes, Aaron R. Neal

Theses and Dissertations

Methyl Tert-Butyl Ether (MTBE) is a volatile, soluble, organic compound introduced to gasoline within the United States as a fuel oxygenator in 1979. MTBE is a common groundwater pollutant due to its high water solubility and resistance to bio-degradation; it commonly infiltrates groundwater sources via leaking underground storage tanks or fuel spills. This research employed ultraviolet light emitting diodes (UV-LEDs) and hydrogen peroxide (H₂O₂) induced advanced oxidation process (AOP) to treat MTBE contaminated water at the bench scale. Using four H₂O₂:MTBE molar peroxide ratios (100:1, 200:1, 400:1, 500:1), MTBE degradation was induced …

Random Forests For Detecting Weak Signals And Extracting Physical Information: A Case Study Of Magnetic Navigation, Mohammadamin Moradi, Zheng-Meng Zhai, Aaron Nielsen, Ying-Cheng Lai, Aaron P. Nielsen

Faculty Publications

It has been recently demonstrated that two machine-learning architectures, reservoir computing and time-delayed feed-forward neural networks, can be exploited for detecting the Earth’s anomaly magnetic field immersed in overwhelming complex signals for magnetic navigation in a GPS-denied environment. The accuracy of the detected anomaly field corresponds to a positioning accuracy in the range of 10–40 m. To increase the accuracy and reduce the uncertainty of weak signal detection as well as to directly obtain the position information, we exploit the machine-learning model of random forests that combines the output of multiple decision trees to give optimal values of the physical …

Basecol2023 Scientific Content, Dmitri Babikov

Chemistry Faculty Research and Publications

Context. The global context of making numerous data produced by researchers available requires collecting and organising the data, assigning meaningful metadata, and presenting the data in a meaningful and homogeneous way. The BASECOL database, which collects inelastic rate coefficients for application to the interstellar medium and to circumstellar and cometary atmospheres, meets those requirements.

Aims. We aim to present the scientific content of the BASECOL2023 edition.

Methods. While the previous versions relied on finding rate coefficients in the literature, the current version is populated with published results sent by the producers of data. The paper presents the …

Two-Electron Density Of Three Electrons In A 1d Box: A Model System For Visualizing Spin Correlation Due To Antisymmetry Of Electrons, Joseph D. Alia

Chemistry Publications

Visual models for understanding electron density distribution in molecules are useful for chemical reasoning. The Lewis structure for example compares well to one-electron density when all electrons are paired. Linnett Double Quartet structures compare well to some kinds of free radical species. These approaches do not show the behavior of the total electron wavefunction when two electrons near each other. This behavior is a two- electron property and can be visualized with the model of three electrons in a one- dimensional box. The two-electron density of the three electron wavefunction described as a slater determinent of 1D particle-in-a-box spin- orbitals …

Final Quarterly Operations And Maintenance Report Butte Treatment Lagoon System – First Quarter 2022, Pioneer Technical Services, Inc.

Silver Bow Creek/Butte Area Superfund Site

No abstract provided.

Stage And Discharge Prediction From Documentary Time-Lapse Imagery, Kenneth W. Chapman, Troy E. Gilmore, Mehrube Mehrubeoglu, Christian D. Chapman, Aaron R. Mittelstet, John E. Stranzl Jr.

Conservation and Survey Division: Faculty and Staff Publications

Imagery from fixed, ground-based cameras is rich in qualitative and quantitative information that can improve stream discharge monitoring. For instance, time-lapse imagery may be valuable for filling data gaps when sensors fail and/or during lapses in funding for monitoring programs. In this study, we used a large image archive (>40,000 images from 2012 to 2019) from a fixed, ground-based camera that is part of a documentary watershed imaging project (https://plattebasintimelapse.com/). Scalar image features were extracted from daylight images taken at one-hour intervals. The image features were fused with United States Geological Survey stage and discharge data as response variables …

First Egg Dates Of Tree Swallows (Tachycineta Bicolor) Nesting In Boxes In West Michigan Advanced With Increasing Spring Temperatures Between 1993 And 2018, Michael P. Lombardo, Nathaniel Wagner, Jared M. Laughlin, Sango Otieno, Levi Rosendall, Clara Voetberg, Alejandro Hoban

Open Access Publishing Support Funded Articles

Many bird species have advanced their first egg dates in response to recent milder winters and increases in spring temperatures. Tree Swallows (Tachycineta bicolor) begin egg laying earlier in warmer springs at most, but not all, study sites. We examined whether Tree Swallows that nested in boxes in west Michigan responded to an increase in spring temperatures from 1993 to 2018 by advancing their first egg dates. May, but not April, mean daily temperatures got significantly warmer from 1993 to 2018. We found that the first egg dates, but not mean first egg dates, of both second year …

Temperature Forecasts For The Continental United States: A Deep Learning Approach Using Multidimensional Features, Jahangir Ali, Linyin Cheng

Geosciences Faculty Publications and Presentations

Accurate weather forecasts are critical for saving lives, emergency services, and future developments. Climate models such as numerical weather prediction models have made significant advancements in weather forecasts, but these models are computationally expensive and can be subject to inaccurate representations of complex natural interconnections. Alternatively, data-driven machine learning methods have provided new dimensions in assisting weather forecasts. In this study, we used convolutional neural networks (CNN) to assess how geopotential height at different levels of the troposphere may affect the predictability of extreme surface temperature (t2m) via two cases. Specifically, we analyzed temperature forecasts over the continental United States …

Pollinator Communities And Their Ecosystem Services At Conservation Grasslands And Adjacent Croplands, Araceli Gomez Villegas

Department of Entomology: Dissertations, Theses, and Student Research

Pollinators are intrinsically linked to the success of unmanaged and managed ecosystems by providing pollination services that aid in the reproduction of wildflowers and many crops. Land use change, habitat loss, fragmentation, and related landscape-level phenomena (for example, increased pesticide exposure) threaten pollinators and have been associated with population declines. In the Midwestern region of the United States, land conversion of native prairies and grasslands to row-crop agriculture has been one of the largest contributors to pollinator habitat loss. Conservation programs, such as the Conservation Reserve Program, have worked towards removing environmentally sensitive lands from agriculture production and enrolling them …

Weakly Supervised Attention-Based Recognition Under Spectral, Turbulence, And Resource Variations, Kshitij Naresh Nikhal

Dissertations and Doctoral Documents, University of Nebraska-Lincoln, 2023–

While supervised optimization paradigms are ubiquitous across diverse recognition systems, the risk of over-fitting and increasing bias have limited their applicability.

This dissertation focuses on unsupervised learning—learning without precisely curated data—and argues that unsupervised learning methods can enable both discriminability and generalizability. Through the use of attention-based machine learning and advanced clustering, unsupervised methods are able to focus on fine-grained information in images without any explicit supervision. The dissertation introduces a domain-bridging framework for tasks like cross-spectrum matching and long-range recognition, utilizing intra-domain clustering and inter-domain matching to generate pseudo-labels. Additionally, a hash-based network is proposed to accelerate the search …

Quantum Computing And U.S. Cybersecurity: A Case Study Of The Breaking Of Rsa And Plan For Cryptographic Algorithm Transition, Helena Holland

Honors Program: Senior Projects (Public)

The invention of a cryptographically relevant quantum computer would revolutionize computing power, transforming industry and national security. While a theoretical possibility at the time of this writing, the ability of quantum algorithms to solve the factoring and discrete logarithm problems, upon which all currently employed public-key cryptography depends, presents a serious threat to digital communications. This research examines both the mathematics and government policy behind these risks and their implications for cybersecurity. Specifically, a case study of RSA, Shor’s algorithm, and the American Intelligence Community’s plan to transition toward quantum-resistant algorithms is presented to analyze quantum threats and opportunities and …