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Geo-Spatial Mapping Of Sentiment Analysis With Transformer-Based Models, Dugan J. Turnbow Mar 2025

Geo-Spatial Mapping Of Sentiment Analysis With Transformer-Based Models, Dugan J. Turnbow

Theses and Dissertations

The public sentiment of events of interest, and their impacts, is vital for decision makers to allocate resources. This research develops a robust algorithm for aggregating sentiment analysis from social media and published articles, while contextualizing results through spatial and temporal mapping. The methodology employs two transformer-based language models for sentiment analysis and named entity recognition (NER). Sentiment scores are generated and augmented using explicit location data, such as latitude and longitude, and implicit location data derived through NER or location features. Results are mapped using a geo-tagged location dictionary, enabling visualization of sentiment trends at state and county levels …

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Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros Mar 2025

Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros

Theses and Dissertations

Classification “flickering,” where the classification of an object changes inconsistently between consecutive video frames, remains a persistent issue in modern object classification algorithms. This problem undermines the reliability of autonomous vision systems and poses significant risks in high-stakes applications such as autonomous vehicles. This thesis explores the use of response surface methodology, a statistical design of experiments technique, to optimize hyperparameters across three object classification pipelines. The first pipeline combines YOLOv8 with SORT to establish a benchmark. The second integrates a Bayesian back-end, while the third employs an exponential smoothing back-end. Hyperparameter tuning was conducted using a two-step process: an …

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Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente Mar 2025

Evaluating Educational Benefits Of A Custom Cyber Game: ‘Hvac Attack!’, Jillian S. Valente

Theses and Dissertations

Cyber competition and conflict remain an enduring concern for the Department of Defense (DoD). Positive control of cyberspace is crucial across the vast diversity of military operations and supporting activities. Military members play an important role in cyber prevention, detection, and remediation, but most receive relatively little training outside of the annual Cyber Awareness Challenge. Particular career fields within the DoD may benefit from specialized training in cybersecurity, in particular the civil engineering (CE) community supporting critical infrastructure protection. Prior research has suggested that game-based learning (GBL) can be beneficial for teaching cyber concepts.

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Evaluating Learning Outcomes In A Serious Game: A Practical And Model Checking Approach, Matthew D. Douglas Mar 2025

Evaluating Learning Outcomes In A Serious Game: A Practical And Model Checking Approach, Matthew D. Douglas

Theses and Dissertations

This research introduces a novel computational framework to evaluate and predict the educational impact of serious games during development. By using finite state machines (FSM) and model-checking techniques, this study evaluates two serious games. Traditional evaluation approaches, often reliant on resource-intensive human trials, lack scalability and fail to provide early insight into the alignment of game mechanics with learning objectives. This study addresses these challenges of traditional evaluation methods.

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Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar Mar 2025

Calibration And Demonstration Of A Dropped Channel Polarimetric Compressive Sensing Radar System, Cameron G. Goodbar

Theses and Dissertations

The Air Force Institute of Technology (AFIT) Dropped Channel Polarimetric Compressive Sensing (DCPCS) Radar System is a polarimetric radar utilizing four horn antennae with a unique cross-coupling architecture that enables direct control of system parameters to embed signals into adjacent channels. This thesis characterizes the nature of the system, develops system calibration, and illustrates the performance of the DCPCS technique under multiple system configurations. As shown in the results, DCPCS can successfully reconstruct full-polarization data from a subset of polarization measurements. In many cases, the target estimation and signal reconstruction is precise despite less-than-ideal conditioning of the canonical target dictionary …

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Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty Mar 2025

Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty

Theses and Dissertations

This thesis addresses the challenge of generating optimized UAV waypoints for complete coverage of complex 3D environments, utilizing graph-based computational techniques. The proposed framework replaces computationally intensive steps—triangulation and three-coloring—within the Vantage Waypoint Set Generation Algorithm (VWSGA) pipeline with Graph Neural Networks (GNNs). By learning structural patterns, the GNN achieves scalable and robust triangulation and node classification, enabling enhanced coverage planning in irregular geometries. A novel penalty mechanism ensures alignment with graph structure during adjacency prediction. Experimental results demonstrate the effectiveness of GNNs in balancing accuracy, computational efficiency, and adaptability, advancing UAV coverage optimization.

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Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson Mar 2025

Evaluating A Military Digital Badging System Prototype, Benjamin T. Pederson

Theses and Dissertations

The Department of Defense is committed to developing and maintaining a highly skilled workforce capable of defending the United States and associated interests abroad. Digital badging systems, a form of micro-credentialing, offer a way to record service member competencies. By providing decision-makers with granular data, this technology could augment the military’s development of a highly skilled workforce, especially in technical career fields including cyber operations. Mixed-method data from thirty-six participants suggest that establishing a digital badging program could increase deterrence and operational effectiveness.

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Transforming Defense: A Case Study On Digital Cots Implementation, Dara A. Armstrong Mar 2025

Transforming Defense: A Case Study On Digital Cots Implementation, Dara A. Armstrong

Theses and Dissertations

While research on digital transformation efforts and their challenges are widespread, the introduction and use of digital commercial off-the-shelf (COTS) tools in the Department of Defense (DOD) remains under explored. To investigate this problem, this research focuses on a fledgling digital transformation effort within a division of the Air Force Nuclear Weapons Center. Due to inefficiencies caused by fragmented workflows, management introduced Jira and Confluence, two digital tools known for their broad use in various industries, to the workforce.

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Autonomous Vehicle Path Planning Under Uncertainty, Madison C. Gillan Mar 2025

Autonomous Vehicle Path Planning Under Uncertainty, Madison C. Gillan

Theses and Dissertations

Autonomous vehicles are increasingly being deployed for use in high-stakes and uncertain environments where safe and efficient navigation is critical. In these scenarios, traditional path planning approaches, which rely primarily on deterministic models and fixed assumptions, fall short due to the inherent uncertainty of dynamic threats, sensor inaccuracies, and incomplete information. This research addresses these challenges by developing a novel path-planning methodology that combines the Chance-Constrained Rapidly Exploring Random Tree* (CC-RRT*) algorithm with a probabilistic risk assessment heuristic. This method models uncertainty in sensor detection zones, obstacles in the environment, and the Autonomous Vehicle itself, which allows for uncertainty during …

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Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick Mar 2025

Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick

Theses and Dissertations

The main contributions of this research is to add to the growing library of literature on the use of deep learning algorithms for the spatiotemporal prediction of dangerous atmospheric and hydrologic phenomena. Specifically, we develop novel attention-based and non-attention-based recurrent neural network frameworks to produce short-range sequential forecasts for lightning and tornado occurrences. Additionally, we introduce methods that account for and include error in the model tuning process to generate more reliable models. Furthermore, we have created a lightweight spatiotemporal tornadic prediction dataset that we plan to make publicly available. The first component of this research develops three novel spatiotemporal …

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Atmospheric Characterization For Optical Paths In Lunar Proximity, Patrick D. Carattini Mar 2025

Atmospheric Characterization For Optical Paths In Lunar Proximity, Patrick D. Carattini

Theses and Dissertations

This paper presents a novel technique for estimating the Fried seeing parameter (r0) for optical paths around the Moon, where traditional methods fail due to the Moon's intensity. Using image processing, it derives the atmospheric optical transfer function (OTF) by using the Moon's edge as a step-input. A simulation chain validates the approach, achieving r0 estimation within 0.0012 m. Real-world tests confirm accuracy through visual and statistical analysis, offering an effective alternative for atmospheric characterization of optical paths close to the moon.

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Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case Mar 2025

Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case

Theses and Dissertations

United States Air Force (USAF) operations rely on sortie generation, a complex system involving aircraft maintenance, operational planning, munitions, security forces, and aircrew. Failures in any of these areas can jeopardize a mission, and extreme weather events such as lightning, high winds, and snow further complicate operations. This thesis examines the impact of extreme weather on sortie generation, focusing on developing a data-driven discrete-event simulation (DES) to predict generation timelines and identify high-risk areas. The model allows users to adjust key inputs, including the month, number of aircraft, processing times, and personnel/equipment availability. By simulating real-world conditions, the model helps …

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Understanding Avionics Maintenance Training Student Outcomes In A Student-Centered Active Learning Environment, Scott M. Wyman Mar 2025

Understanding Avionics Maintenance Training Student Outcomes In A Student-Centered Active Learning Environment, Scott M. Wyman

Theses and Dissertations

This study examines the effects of active learning compared to didactic methodologies on two soft skills, namely teamwork and self-efficacy using regression analyses and connected letter reports. Learning styles and personality traits were used as predictors. Findings indicate significant interaction effects between methodology, aural learning style, and personality traits on self-efficacy and teamwork ability. The findings highlight the nuanced role of learner traits in shaping teamwork outcomes across instructional methods. While active learning supports soft skills, individual differences must be considered in instructional design to optimize teamwork in technical education settings.

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Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley Mar 2025

Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley

Theses and Dissertations

Fuel efficiency is crucial for the U.S. Air Force, impacting mission success, aircraft performance, and cost savings. This study presents an information system that integrates flight and maintenance data using a data lakehouse. It automates ingestion, enrichment, and predictive modeling, leveraging AutoML for optimization and SHAP for transparency. A case study on C-130J aircraft shows that optimizing D Check cycles can save 11.52 pounds of fuel per flight hour. These findings highlight the effectiveness of data-driven decision-making in aviation, offering a scalable, automated solution for improving fuel efficiency and reducing costs.

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Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros Mar 2025

Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros

Theses and Dissertations

Pacific Islands under U.S. jurisdiction are highly vulnerable to natural disasters, yet many lack the infrastructure to effectively respond and recover. Clear communication during and after such events is critical for evacuation, hazard awareness, and first responders’ coordination. This research explores a simulation-based approach using Bluetooth communication to relay messages across Guam, assessing its efficiency through statistical analysis. By examining regional differences and geographic impacts on Bluetooth messaging, the study aims to identify key factors that enhance peer-to-peer communication for timely and effective disaster response.

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Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski Mar 2025

Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski

Theses and Dissertations

This study applies advanced Machine Learning (ML) to Flight Data Recorder (FDR) data for fuel consumption predictions. It explores feature engineering, model selection, and Hyper-Parameter Optimization (HPO) across all flight phases. Baseline models like Ordinary Least Squares (OLS) regression, Multi- Layer Perceptrons (MLPs), and decision trees are compared to Deep Neural Networks (DNNs), Recurrent Neural Networks (RNNs) with Gated Recurrent Unit (GRU) layers, and XGBoost. Results analyze segmentation strategies, tailored features, and model performance. A counterfactual analysis compares ML models to operational fuel predictions, demonstrating their deployment potential. Findings establish a foundation for future ML-driven advancements in aviation fuel optimization.

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Class Imbalance: A Landscape Of Classification Models, Joshua L. Edmonds Mar 2025

Class Imbalance: A Landscape Of Classification Models, Joshua L. Edmonds

Theses and Dissertations

Class imbalance poses significant challenges in machine learning classification. This study evaluates the performance of seven models (ANN, k-Means, kNN, LDA, LR, SVM, XGBoost) across multiple imbalance levels (10\%, 5\%, 1 \%, 0.5\%) and investigates the effectiveness of sampling techniques (Undersampling, SMOTE, SMOTE-ENN). ANOVA results confirm that model choice is the most critical factor, with XGBoost and SVM demonstrating superior robustness. SMOTE improves recall but reduces precision, while undersampling generally degrades overall performance. While significant, imbalance levels do not play a critical role in model effectiveness.

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Maintenance Strategy And Budgeting Optimization For United States Air Force Facilities, Sophia C. Hirtle Mar 2025

Maintenance Strategy And Budgeting Optimization For United States Air Force Facilities, Sophia C. Hirtle

Theses and Dissertations

Mission readiness in the United States Air Force (USAF) is critically dependent on the sustainment and maintenance of aging facilities. However, budgetary and manpower constraints have led to a significant backlog of deferred maintenance, estimated at $137 billion. To address these challenges, this research develops an optimization model that aids in prioritizing facility maintenance strategies within a fixed budget. The methodology incorporates existing USAF data from BUILDER, NexGenIT, and financial records to create a simulation-based optimization framework at the base level. The model evaluates four different maintenance strategies—replacement only, preventative maintenance only, corrective maintenance only, and a combination of preventative …

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Commercial Satellite Payload Trends, Alexander B. Krawietz Mar 2025

Commercial Satellite Payload Trends, Alexander B. Krawietz

Theses and Dissertations

This thesis examines the evolving trends in commercial satellite payloads with an emphasis on mass, operating power, and project duration overall and across different payload types, serving as a benchmark for Department of Defense (DoD) leaders to compare against private industry advancements. Utilizing a comprehensive dataset of open-source commercial satellite data, this study identifies significant trends that could influence cost estimation, schedule estimation, and satellite design. The analysis not only highlights the technological progression over several decades but also provides a comparative insight into how the United States and China are positioned in their race for space dominance. Beyond detecting …

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Visual Segmentation For Autonomous Aircraft Landing On Austere Runways, Alissa M. Owens Mar 2025

Visual Segmentation For Autonomous Aircraft Landing On Austere Runways, Alissa M. Owens

Theses and Dissertations

Autonomous aircraft must land without human intervention, but existing methods rely on GPS or marked runways, which may be unavailable in austere environments. This paper presents a vision-based approach using semantic segmentation to detect runways and estimate aircraft pose by comparing camera and satellite imagery. We detail the model’s training and demonstrate its effectiveness with simulated and real UAV data.

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Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler Mar 2025

Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler

Theses and Dissertations

Accurate cost and schedule estimates are crucial for maintaining the U.S. military’s technological and operational superiority, ensuring efficient resource allocation and timely development of advanced defense systems. This research examines S-curve models for time-phasing non-recurring Research, Development, Test, and Evaluation (RDT&E) expenditures in missile and munition acquisition programs. This research evaluates the commonly used 60/40 rule, which assumes 60% of expenditures occur by 50% of the schedule, for its accuracy using Cost Assessment Data Enterprise (CADE) and Earned Value Management Central Repository (EVM-CR) data from 21 missile and munition development programs.

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Job Demands And Resources: A Look At The Vha Workforce To Support Dod Goals, Anthony J. Rivera Mar 2025

Job Demands And Resources: A Look At The Vha Workforce To Support Dod Goals, Anthony J. Rivera

Theses and Dissertations

This study investigates the relationships between job demands, job resources, burnout, turnover intention, and workplace attitudes among employees of the Veterans Health Administration (VHA) through the lens of the Job Demands-Resources (JD-R) model. The findings reveal both expected and novel insights. Consistent with prior research, the study confirms that excessive job demands, without enough resources to balance then, lead to burnout. This, in turn, increases turnover intention and negatively affects how employees view leadership and the work environment. A significant discovery, however, challenges conventional assumptions: favorable job demands were positively correlated with turnover intention, suggesting that employees in favorable roles …

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Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio Mar 2025

Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio

Theses and Dissertations

This thesis analyzes Spreading Code Authentication (SCA) in the GPS L1C signal using the PyChips software-defined receiver (SDR)framework. Monte Carlo simulations evaluate authentication performance under varying signal conditions, assessing the impact of double-precision and quantized data on signal integrity. Results demonstrate that authentication remains achievable at low signal-to-noise ratio(SNR) conditions but introduces trade-offs in memory usage and authentication time. These findings provide insights into optimizing SCAfor resource-constrained environments, contributing to secure GPS operations in critical applications such as aviation, autonomous systems,and national defense.

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Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert Mar 2025

Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert

Theses and Dissertations

The classification of uranium particles from scanning electron microscopy (SEM) imagery is critical to nuclear forensics, but has traditionally relied solely on skilled analysts whose classification accuracy and procedures may vary widely. Existing morphology lexicology [1] provides standardization guidelines to aid analysts but cannot fully address analyst variability. Using a dataset of 1,906 SEM images across 13 unevenly distributed particle classes and 73 magnification levels, final accuracy between statistical and deep learning methods were compared to find the best classification techniques. Ultimately, the deep learning model achieved an impressive 82% accuracy (80% balanced accuracy) on a withheld test set. This …

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Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti Mar 2025

Cognitive Warfare In The South China Sea: Analyzing Media Influence And Public Opinion In Taiwan, Wyatt F. Blatti

Theses and Dissertations

In a time where conflict extends beyond traditional battlefields, cognitive warfare emerges as a powerful tool to influence perceptions and gain strategic advantages. This study investigates China’s cognitive warfare strategies against Taiwan through trend analysis, topic modeling, and sentiment analysis of news media articles from March 2013 to August 2024 to uncover evolving techniques and mitigation efforts. The findings highlight the potential for tracking cognitive campaigns overtime but will require more than news media alone and suggests future research to better understand indicators of cognitive warfare.

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Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson Mar 2025

Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson

Theses and Dissertations

Recent progress has been made in the development of collocation-based iterative algorithms that approximate solutions to PDEs. These algorithms rely on the ability to identify regions within a domain where a finer discretization is required. Such iterative algorithms are beneficial particularly when solution functions have highly localized behavior. This thesis proposes an indicator for node refinement that is constructed by approximating the forward error. This proposed indicator also helps to establish confidence in the accuracy of a given solution estimate. The proposed error estimator is theoretically examined and compared with contemporary refinement indicators. It is shown that an iterative algorithm, …

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Reinforcement Learning For Aeromedical Evacuation In Nonstationary Combat Environments, Micah J. Kartchner Mar 2025

Reinforcement Learning For Aeromedical Evacuation In Nonstationary Combat Environments, Micah J. Kartchner

Theses and Dissertations

This research formulates the medical evacuation (MEDEVAC) dispatching problem as a sequential decision process and investigates the application of reinforcement learning under nonstationary conditions. We model the dynamic arrival rate of MEDEVAC requests using a nonstationary Hawkes process and design a Double Deep Q-Network algorithm that incorporates belief states to anticipate future requests. Through computational experimentation, we analyze the impact of belief formulation on decision quality and system performance. Results indicate that policies incorporating belief states significantly outperform myopic dispatching policies, reducing urgent casualty wait times by up to 49.68% and increasing on-time evacuations by up to 21.91%.

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Retrospective Cohort Study Of Pure Tone Audiometry Hearing Changes Associated With Ototoxic Metals And Solvents, Continuous Noise And Impulse Noise Exposures At Robins Air Force Base From 2001-2019, Ronald Diaz Cataldo Ramos Mar 2025

Retrospective Cohort Study Of Pure Tone Audiometry Hearing Changes Associated With Ototoxic Metals And Solvents, Continuous Noise And Impulse Noise Exposures At Robins Air Force Base From 2001-2019, Ronald Diaz Cataldo Ramos

Theses and Dissertations

Utilizing DOEHRS-IH and DOEHRS-HC, a retrospective epidemiological study was performed on Robins AFB personnel to investigate relative risks of hearing loss associated with exposures to continuous noise, impulse noise, and ototoxic metals and solvents. An analysis of variance identified age, frequency octave, era and exposure type as influencers of hearing threshold shifts. Risk ratios showed higher risk of hearing loss for exposures to continuous noise + impulse noise + ototoxic metals as well as continuous noise + impulse noise at low frequencies (500Hz and 1,000Hz), and lesser risk for exposures to continuous noise + impulse noise and continuous noise + …

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An Agent-Based Modeling Framework For Evaluating The Linkage Between Disaster Facility Damage And Mental Health, Emily S. Reeves Mar 2025

An Agent-Based Modeling Framework For Evaluating The Linkage Between Disaster Facility Damage And Mental Health, Emily S. Reeves

Theses and Dissertations

This research establishes a novel agent-based modeling framework to establish the linkage between disaster-induced facility damage and mental health outcomes and the evaluation of treatment methods within the civilian and USAF mental health spheres. The study models the degradation and recovery of agent mental health using simulated data and evaluates the efficacy of three distinct treatment approaches through statistical methods. The methodology integrate agent-based modeling with resilient engineering concepts to simulate mental health resilience curves based on vulnerability, exposure, and facility damage. Agents’ mental health indices were tracked through phases of degradation, stagnation, and recovery based on the three treatments …

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Evaluating Atmospheric Water Generation For The Indo-Pacific: Predictive Modeling, Energy Considerations, And Regional Viability, Jose I. De La Serna Mar 2025

Evaluating Atmospheric Water Generation For The Indo-Pacific: Predictive Modeling, Energy Considerations, And Regional Viability, Jose I. De La Serna

Theses and Dissertations

Atmospheric Water Generator (AWG) technology presents a promising solution for extracting and harvesting water from ambient air through condensation methods. This innovative approach offers a viable alternative for water production in regions with limited or unreliable water sources. AWGs operate most effectively in hot and humid environments, typically at temperatures of 80°F and relative humidity levels of 80%. As of 2020, the Department of Defense (DoD) has identified the Indo-Pacific region as a strategic focus for addressing future greatpower competition. Within the framework of Agile Combat Employment, this study evaluates the feasibility and performance of AWG technology at pre-determined locations …

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