U.S. Army Cadet Command Branch Prediction Model,
2024
Air Force Institute of Technology
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,
2024
Air Force Institute of Technology
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,
2024
Air Force Institute of Technology
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,
2024
Air Force Institute of Technology
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 …
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability,
2024
Air Force Institute of Technology
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham
Theses and Dissertations
Neural networks, despite their prowess in computer vision, often exhibit "flickering". Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network's confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness.
Federated Analysis Of Wearables Data For United States Air Force Mental And Physical Readiness,
2024
Air Force Institute of Technology
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 …
Factors Affecting The Retention Of Active Duty Airmen,
2024
Air Force Institute of Technology
Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi
Theses and Dissertations
This study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons …
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem,
2024
Air Force Institute of Technology
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 Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence,
2024
Air Force Institute of Technology
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 …
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity,
2024
Air Force Institute of Technology
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw
Theses and Dissertations
This research examines the operations of behavioral health clinics in Air Force Continental United States facilities, with a focus on operational efficiency and capacity through Data Envelopment Analysis. Most facilities operate near capacity, demonstrating efficient resource usage, but concerns arise with the potential of increased demand. Days-to-care and leakage are identified as major sources of inefficiency, suggesting that addressing these issues can enhance operations and reduce costs. The study also explores the impact of COVID-19 on care delivery.
Knowledge Management Modeling And Decision Analysis For Usmepcom,
2024
Air Force Institute of Technology
Knowledge Management Modeling And Decision Analysis For Usmepcom, Luke G. Wunderlich
Theses and Dissertations
This thesis explores the adoption of Value-Focused Thinking (VFT) in enhancing the Knowledge Management (KM) program at USMEPCOM, aiming to align decision-making with the organization’s values and goals. Through evaluating the current knowledge flow and policy drafts, it proposes categorizing command messages, establishing a centralized information repository, and scheduling a daily order release to improve information accessibility and operational readiness. Although no alternative offers a perfect solution, implementing Command Message Categorization is expected to significantly enhance operational efficiency and prepare USMEPCOM for future challenges.
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance,
2024
Air Force Institute of Technology
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.
An Assignment Model For Lateral Transfers Matching Base Repair Facilities To Xf3 Coded Nsns Requiring Repair,
2024
Air Force Institute of Technology
An Assignment Model For Lateral Transfers Matching Base Repair Facilities To Xf3 Coded Nsns Requiring Repair, George D. Valaika
Theses and Dissertations
This research addresses challenges in efficiently distributing reparable National Stock Numbers (NSNs) to base-level repair facilities, aiming to ease strain on depot resources. It establishes a network integrating bases with similar repair capabilities for NSNs and allocates NSNs to balance repair capacities. Drawing from USAF reparable inventory modeling and inventory management theory, it generates data mimicking historical data to maximize total expected part repairs by assigning NSNs based on the best percentage of base repair (PBR).
An Introduction Of Adaptive Training Aid Concepts And Its Application To Accelerated Training For Air Battle Managers,
2024
Air Force Institute of Technology
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 …
Cloud One Migration Schedule Drivers And Schedule Growth,
2024
Air Force Institute of Technology
Cloud One Migration Schedule Drivers And Schedule Growth, Ryan J. Jansen
Theses and Dissertations
Cloud One, chartered in 2017 under the guidance of Air Force Life Cycle Management Center (AFLCMC) leadership, continues to serve as the USAF’s leading cloud services and hosting platform by providing secure computing environments, application migration assistance, and data management. Prior research has yielded qualitative insights regarding Cloud One’s personnel requirements, application of technical performance, requirements fulfillment, security risks, and various other cost metrics, but schedule improvement recommendations based on the quantitative analysis of migration schedule data has yet to be provided. This research identifies trends within migration sprint schedules and completed schedule data for Cloud One’s completed application migrations. …
Assessing Military Parking: A Deep Learning Approach To Evaluating Standards And Impacts,
2024
Air Force Institute of Technology
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,
2024
Air Force Institute of Technology
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.
Digital Airworthiness: Development Of A Sysml Based Framework For The Usaf Airworthiness Process,
2024
Air Force Institute of Technology
Digital Airworthiness: Development Of A Sysml Based Framework For The Usaf Airworthiness Process, Justin T. Moore
Theses and Dissertations
In 2018 USAF decided to move towards a digital transition for programs and processes. To maximize the impact of initial transition efforts the airworthiness process was identified as a candidate for digital implementation. The airworthiness process is mandated for nearly every system or modification that flies. The airworthiness process identifies specific tasks that must be performed and artifacts that must be produced. This research worked to develop a framework that utilizes SysML to pattern these processes and the required artifacts. A system agnostic, importable library was developed using custom stereotypes and relationships for the purpose of application to digital system …
Artificial Intelligence And Perception: An Empirical Study,
2024
Air Force Institute of Technology
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,
2024
Air Force Institute of Technology
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 …
