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Simulating Human-Autonomous Aircraft Teams In An Anti-Access Area Denial (A2ad) Environment, Michael Kaminski
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 …
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Theses and Dissertations
As the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
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.
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
Theses and Dissertations
Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …
Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering
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 …
A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development, Kathleen A. Maclean
A Reinforcement Learning Self-Play Approach For Informing Wargaming Analysis & Development, Kathleen A. Maclean
Theses and Dissertations
The integration of RL into wargames to learn strategic and operational insights is of interest to the United States Air Force. This thesis explores the application of a RL SARSA(λ) algorithm to the wargame Stratagem MIST. The primary objective is to select air and ground combat policies for the Blue Agent to effectively counter various opponent strategies across different terrains. This testing enables a comprehensive evaluation of the Blue Agent’s adaptability and performance under varying combat conditions. The use of basis functions, linear value function approximations, and specific air and ground strategies simplifies the state and action spaces of the …
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness, Sung Yong O
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 Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
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 …
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance, Ebony N. Williams
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance, Ebony N. Williams
Theses and Dissertations
This thesis investigates advanced navigation in complex environments for urban and military applications, focusing on overcoming obstacles through algorithms like Dijkstra's. It highlights the role of adaptable cost functions in customizing strategies for different scenarios. The research identifies effective algorithm-cost function combinations, improving route planning and safety in civilian and defense sectors. It advances pathfinding knowledge and sets the groundwork for future enhancements with Python simulations and AFSIM.
A Staged Framework For Llm-Powered Information Extraction In Government Contracts, Jung H. Yae
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.
The Impact Of Advancing Technology On Fire Control Radar Costs, Dustin M. Brewer
The Impact Of Advancing Technology On Fire Control Radar Costs, Dustin M. Brewer
Theses and Dissertations
Radar systems are integral components of fighter aircraft, gaining heightened importance with the emergence of fifth-generation variants. Among these systems, fire control radar plays a crucial role by providing pilots with unparalleled long-range sensing capabilities vital for navigation and target identification. The rapid evolution of radar technology is deemed essential to align with the evolving mission requirements, a factor anticipated to significantly impact radar system costs. This research investigates the influence of evolving technology on fire control radar development and production costs. To accomplish this objective, radars are categorized into generations based on existing literature, and a comparative analysis is …
Cost Estimating Relationships For Avionics Recurring Production Box Costs, Carla J. Cisneros
Cost Estimating Relationships For Avionics Recurring Production Box Costs, Carla J. Cisneros
Theses and Dissertations
This research investigates a dataset of 286 Department of Defense (DoD) avionics boxes, employing regression methods to establish seven cost estimation relationships (CERs) for predicting avionics recurring production box costs. Not confined to T100 costs, three baseline models use Ordinary Least Squares (OLS) T100 cost, explaining 83%, 86%, and 83% of dataset variation. Additionally, four robust models depict mean and median, T100 OLS, and T100 non-linear learning curves, explaining 67%, 65%, 97%, and 87% of the variation. These models strike a balance between simple baseline and overly complex computational models. Identified cost drivers include weight, year of first flight, volume, …
Development Of Cost Factors For Satellite Rechargeable Batteries, Katelyn A. Clements
Development Of Cost Factors For Satellite Rechargeable Batteries, Katelyn A. Clements
Theses and Dissertations
Factors are often used by the cost estimating profession to predict costs of future projects or project components. Cost factors can be useful when little to no cost data is available from which to build an initial estimate or as an easy-to-use cross check of a primary estimate. Specific to satellites, previous studies have examined cost factors at higher Work Breakdown Structure (WBS) levels (e.g., Level 2 or Level 3), while this research analyzes cost factors developed using data specific to rechargeable batteries at USCM WBS Level 4 and its parent, the Electrical Power System (EPS) at Level 3. Within …
An Introduction Of Adaptive Training Aid Concepts And Its Application To Accelerated Training For Air Battle Managers, John C. Gillispie
An Introduction Of Adaptive Training Aid Concepts And Its Application To Accelerated Training For Air Battle Managers, John C. Gillispie
Theses and Dissertations
Over the years, the integration of artificial intelligence (AI) to enable autonomous systems has undergone transformative shifts in the Department of Defense (DoD), revolutionizing capabilities and strategic approaches. To further optimize these advancements, varying levels of autonomy have been introduced across critical military applications, spanning intelligence, surveillance, reconnaissance (ISR), air battle management, and offensive/defensive air operations. As the technological landscape expands, so do the opportunities for autonomy to augment operations through human-agent teaming. Within the Air Force, one notably cognitively demanding role that stands to benefit from these strides is that of the Air Battle Manager (ABM). In support of …
Optimizing Hub And Spoke Selections For Adaptive Basing In Agile Combat Employment, Alan Heng
Optimizing Hub And Spoke Selections For Adaptive Basing In Agile Combat Employment, Alan Heng
Theses and Dissertations
Advancing technologies and emerging competitors within the Indo-Pacific region have rendered the use of fixed installations for United States Air Force (USAF) operations less than optimal for generating airpower. The adoption of Agile Combat Employment (ACE) is imperative and emphasizes a hub-and-spoke model. However, the methodology for selection of contingency locations remains undecided. Current selection of optimal sites hinges on an optimization framework, employing either rank-based or mathematical approaches that consider various criteria to assess and quantify each location. These criteria are then addressed through objectives and constraints, forming the basis for an effective decision-making process. This paper introduces two …
Quantifying Resilience And Redundancy For Air Force Linear Infrastructure, Ryan C. Jakovich
Quantifying Resilience And Redundancy For Air Force Linear Infrastructure, Ryan C. Jakovich
Theses and Dissertations
The historical underfunding of USAF infrastructure necessitates a strategic approach to reduce excess infrastructure while preserving resilience in the face of potential first-strike scenarios. This thesis formulates an infrastructure resilience metric that assesses network performance under varying degrees of damage. Furthermore, the study calculates the impact of redundant infrastructure by introducing added structural elements and measuring the resultant reduction in performance loss during damage scenarios. Utilizing USAF geospatial infrastructure data, adjacency matrices representing graph theory networks were constructed in Arc GIS Pro and evaluated using coding languages from the IGraph library and algorithms developed in RStudio. The newly proposed performance …
Advanced Oxidation Of Tert-Butanol With Ultraviolet Light Emitting Diodes And Hydrogen Peroxide, Lauren R. Mainolfi
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 …
Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde
Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts, Ryan D. Lalonde
Theses and Dissertations
Current United States Department of Defense (DoD) standards require a minimum amount of parking for each building. This requirement defines how much off-street parking to construct. However, the impact of these requirements remains unclear. This study builds upon the emerging field of overhead imagery analytics by directly tying it to parking on military installations. Specifically, this study leverages a pretrained deep learning car detection model, Car Detection – USA, developed by Esri for use within ArcGIS, and couples it with open-access temporal imagery sourced from Google Earth Pro to assess selected parking lots across Area B, Wright-Patterson Air Force Base, …
A Framework For Dynamic Combat Support Exercises, Sean R. Mccarty
A Framework For Dynamic Combat Support Exercises, Sean R. Mccarty
Theses and Dissertations
Future armed conflict will be characterized by the unknown as adversaries innovate and evolve—surprise is inevitable. Current exercises provide inadequate opportunities for combat support forces to develop improvisational skills. This research proposes an extensible framework for human-in-the-loop control of exercises using a graph network for modeling combined with topological analysis and the resource-constrained project scheduling problem. This framework is assessed using the United States Air Force Silver Flag exercise as a case study with promising results.
Analyzing Cost Risk In Department Of Defense Program Office Estimates, Todd L. Millard
Analyzing Cost Risk In Department Of Defense Program Office Estimates, Todd L. Millard
Theses and Dissertations
This research studies cost risk for Department of Defense (DoD) acquisition programs during development and has three overarching objectives: 1) analyze the degree of change in cost risk (quantified as risk dollars) as programs mature, 2) analyze the accuracy of cost risk estimates (quantified as risk error) as programs progress, and 3) adopt risk analysis techniques common in the commercial banking industry to analyze cost risk in DoD programs. For the first objective, some evidence of significant positive trends in relative risk dollars is found for ACAT 1, 2, and 3 programs. For the second objective, there is no strong …
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
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
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 …
Exploring Fiscal Year Budget Assessments: A Look At Sbir/Sttr And Congressional Rescissions, Bryan N. Palmer
Exploring Fiscal Year Budget Assessments: A Look At Sbir/Sttr And Congressional Rescissions, Bryan N. Palmer
Theses and Dissertations
This study examines the relationship between acquisition programs within the Air Force Life Cycle Management Center (LCMC) and budgetary assessments from both the Small Business Innovation Research and Small Business Technology Transfer (SBIR & STTR) programs, as well as Congressional Rescissions. It analyzes the tendency and severity of budget cuts across different programs, considering factors such as completion time, budget activities, and a variable akin to true Acquisition Category (ACAT) levels. Key findings include a significant difference in the rates of proportion of programs assessed between appropriations 3010 (23.65%) and 3600 (67.05%) from fiscal years 2010-2023. The research indicates a …
Human Capital Impacts In Military Acquisition, David K. Smith
Human Capital Impacts In Military Acquisition, David K. Smith
Theses and Dissertations
The Department of Defense has historically struggled to control both cost and schedule growth within acquisitions programs. Many studies have investigated these issues, but very few have explored the impact of human capital in improving performance outcomes. This study provides a description of current acquisition team manning and performs contingency table analysis to evaluate the impact of personnel, base, and ACAT on cost and schedule performance. The results of the study suggest that personnel has little to no impact on performance metrics of any kind, indicating that teams are allocated effectively. The study also suggests that ACAT 3 programs are …
Development Of A Metric To Quantify Facility Hardening, Jesse C. Solorzano
Development Of A Metric To Quantify Facility Hardening, Jesse C. Solorzano
Theses and Dissertations
This research investigates the intricate aspect of infrastructure resilience, focusing on the distinction between hardening and resilience. Hardening, often used interchangeably, with enhancing resilience, involves strategies to strengthen facilities, networks, or systems against various threats. Despite its recognized importance in mitigating physical disruptions and enhancing facility functionality amid different threats, a significant vagueness persists in quantitatively defining hardening. This thesis addresses this gap by developing a quantifiable metric for facility hardening, separate from general resilience measures. The study begins by exploring the historical context of facility hardening, linked to catastrophic events and the need for resilient infrastructure. It explores various …
Accelerating Homomorphic Encryption In Risc-V Architectures With Hardware Based Number Theory Transform, Zachary Legg
Accelerating Homomorphic Encryption In Risc-V Architectures With Hardware Based Number Theory Transform, Zachary Legg
Theses and Dissertations
Fully Homomorphic Encryption is an encryption paradigm enabling computations on encrypted data without the need for decryption. Such behavior is promising for securing DoD cloud computing but is also computationally demanding. To alleviate some of the computational load and accelerate FHE schemes, this research proposes adding a hardware accelerator, for the Number Theory Transform (NTT) operation, to a RISC-V processor. Thus enhancing FHE capabilities for potentially resource-constrained devices.
Sensor-Based Vehicle Classification Using Machine Learning, Luke Mcfadden
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 …
Analyzing The Effects Of Atmospheric Turbulence On Polarization-Entangled Photon Pairs Using Quantum State Tomography, Noah S. Everett
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. …
Atomistic Simulation: A Powerful And Effective Tool For Exploring The Stability And Thermomechanical Properties Of Materials, Lucas A. Heaton
Atomistic Simulation: A Powerful And Effective Tool For Exploring The Stability And Thermomechanical Properties Of Materials, Lucas A. Heaton
Theses and Dissertations
No abstract provided.
Center Fixing Tropical Depressions And Tropical Storms Using Machine Learning-Nighttime Visible Imagery, Nathan K. Stanford
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 …