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Articles 2401 - 2430 of 50610
Full-Text Articles in Entire DC Network
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Lethality And Survivability Of Autonomous Self-Sensing Uavs, Jeffrey T. Wilson
Theses and Dissertations
Unmanned Aerial Vehicles (UAVs) have seen increased usage over the past two decades during the Global War on Terrorism (GWOT), operating in low-risk environments against dispersed enemies with minimal counter-drone capabilities. However, as the U.S. military shifts focus to Multi-Domain Operations (MDO) and Large Scale Combat Operations (LSCO), UAVs face significantly higher risks, including frequent and successful attacks, as well as the exploitation of their technology. Battle damage assessment (BDA) is not new; however, autonomous self-assessment by UAVs represents a novel advancement. Currently, UAV BDA relies on manual inspection, requiring approximately eight hours per drone. By adopting self-sensing technology, UAVs …
Understanding Avionics Maintenance Training Student Outcomes In A Student-Centered Active Learning Environment, Scott M. Wyman
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.
Immersive Virtual Reality Heavy Equipment Simulator Training Effectiveness And Participant Performance Comparison, Zachary E. Calderon
Immersive Virtual Reality Heavy Equipment Simulator Training Effectiveness And Participant Performance Comparison, Zachary E. Calderon
Theses and Dissertations
Immersive Virtual Reality (IVR) simulator training is an interactive form of computer-based training that can positively promote the transfer of skills from training to trainees. Even at a lower physical fidelity IVR simulators may more effectively train individuals on the operation of virtual heavy equipment compared to written, demonstrative, and oral computer-based training. IVR simulator training may also be beneficial for implementation of heavy equipment operation training as a means to promote familiarization of controls and foster self-efficacy and confidence in trainees. A multi-stage IVR simulator was created for this study that both trained participants on wheel loader operations and …
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Evaluating Dry Air Personnel Decontamination Of Methyl Salicylate As A Chemical Agent Surrogate In Extreme Cold Environments To Reduce Airborne Risks Using A Manikin, Lance E. Campbell
Theses and Dissertations
This research investigated a mobile air shower as an alternative to water washing for personnel chemical decontamination in Arctic environments, where traditional methods like disrobing and rinsing are impractical. Interest in Arctic operations has grown with recent U.S. military focus on strategic advancements by Russia and China, yet research on air shower effectiveness for chemical decontamination remains limited. This study examined whether a commercial off-the-shelf air shower could effectively decontaminate methyl salicylate (MES), a surrogate for chemical warfare blister agents, from a manikin outfitted in military cold-weather gear. Researchers used a ppbRAE 3000 photo-ionization detector to measure MES concentrations following …
How Does An Air Force Instructor Analyze Officer Occupational Competencies And Translate Them Into Professional Development Courses?, Julia A. Howard
How Does An Air Force Instructor Analyze Officer Occupational Competencies And Translate Them Into Professional Development Courses?, Julia A. Howard
Theses and Dissertations
This thesis examines how Air Force instructors translate officer occupational competencies into professional development courses. The study aims to address a pressing need to align Civil Engineer education with the Department of the Air Force objective of competency based education in the midst of changing operational demands. The methodology uses expert elicitation, thematic analysis, qualitative and quantitative statistics to formulate a curriculum development methodology. Data collection involved an expert elicitation from subject matter experts in the Civil Engineer career field. This data helped to pair 32E competencies with course learning objectives. The expert opinions provided insight into where civil engineering …
Molecular Scale Investigations Of Interactions Between Pfas And Concrete, Eric E. Keeler
Molecular Scale Investigations Of Interactions Between Pfas And Concrete, Eric E. Keeler
Theses and Dissertations
Per- and polyfluoroalkyl substances (PFAS), widely referred to as “forever chemicals,” exhibit high environmental persistence and potential health risks due to their robust carbon-fluorine bonds. These substances are prevalent in aqueous film-forming foams (AFFF), used in industrial and military applications, and are known to contaminate environmental surfaces, including concrete. This study aims to characterize the molecular-level interaction energies of six PFAS species—PFOA, PFOS, PFHxS, PFHxA, 6:2 FTS, and PFBS—with calcium silicate, a key component of concrete, using density functional theory (DFT) calculations. Change in Gibbs free energy (ΔG) was determined for each of the interactions, revealing negative ΔG values for …
The Application Of Decision Analysis Theory For Space Allocation At The Air Force Institute Of Technology, Damian N. Soriano
The Application Of Decision Analysis Theory For Space Allocation At The Air Force Institute Of Technology, Damian N. Soriano
Theses and Dissertations
This thesis investigates the application of Decision Analysis Theory to optimize space allocation at the Air Force Institute of Technology (AFIT). Through a Multi-Objective Decision Analysis (MODA) framework, this study addresses existing methodologies for space allocation in military, academic, and office settings; the rules, limitations, and factors influencing space utilization at AFIT; and approaches to improve office and lab allocations for institutional efficiency and fairness. This research incorporates qualitative and quantitative metrics, including faculty and student data, research outputs, and historical space usage. These findings highlight significant complexities in space allocation, particularly in reconciling administrative and research requirements with structural …
Atmospheric Characterization For Optical Paths In Lunar Proximity, Patrick D. Carattini
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.
Autonomous Vehicle Path Planning Under Uncertainty, Madison C. Gillan
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 …
On The Exploration Of Crystallographic Anisotropy And Defects In Shock Loading Using Molecular Dynamics, Benjamin P. Helman
On The Exploration Of Crystallographic Anisotropy And Defects In Shock Loading Using Molecular Dynamics, Benjamin P. Helman
Theses and Dissertations
The impact of crystallographic orientation, grain boundaries, and vacancies on the shock behavior of aluminum was investigated using molecular dynamics simulations. Shock loading in the [001], [011], and [111] directions was explored, revealing anisotropic behavior in shock speed, melting, dislocation density, and unique phase changes. The Hugoniot elastic limit in the [100], [110], and [111] directions was calculated as 23.2 GPa, 24 GPa, and 18.4 GPa respectively. These results were found to be an order of magnitude larger than the compressive yield strength computed at equilibrium. Additionally, metastable melting in the [011] and [111] directions occurred roughly 1000 K below …
Visual Segmentation For Autonomous Aircraft Landing On Austere Runways, Alissa M. Owens
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.
Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock
Simulating The Impact Of Self-Sensing Materials On Aircraft Sortie Generation, Harmoni J. Blackstock
Theses and Dissertations
In conjunction with the Air Force Research Laboratory Materials Lab(AFRL-RX), this study evaluates the potential military value of the prototype material sensing composites on Unmanned Aerial Vehicle (UAV) operations in intelligence, surveillance, reconnaissance (ISR), and close air support (CAS) missions within a contested Indo-Pacific theater. Using a Simio based simulation,UAV performance was assessed under varying combat conditions, focusing on Remote Sensing, deployment strategies, initial lay-downs, and varying loss rates. Re-sults show that UAVs equipped with Remote Sensing technology significantly improved sortie generation and logistical efficiency. Scenario 17 achieved the highest sortie rate(965.5 sorties), outperforming the next-best scenario by 25 sorties. …
Evaluating Weather Effects On Sortie Generation Using Discrete Event Simulation, Markus Case
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 …
Class Imbalance: A Landscape Of Classification Models, Joshua L. Edmonds
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.
Data Lakehouse And Machine Learning Pipeline For Aircraft Fuel Efficiency Experimentation, Skyler G. Kepley
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.
Machine Learning With Flight Data Recorder Data For Flight Fuel Consumption Predictions, Adam C. Levandowski
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.
Island Nation Duress: Simulating Passive Peer-To-Peer Bluetooth Communication During Disaster Relief, Jason K. Medeiros
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.
Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff
Real-Time Decision-Making During Maritime Chokepoint Disruption Using Digital Twin, Jared M. Orendorff
Theses and Dissertations
This research develops a digital twin of the global maritime shipping system to model disruptions in major shipping lanes like the Suez and Panama Canals. By incorporating live ship-tracking data, the model simulates closures, forecasts queue lengths, and determines the best rerouting options. Findings show that canal closures cause large traffic backlogs and increased congestion at alternative chokepoints, while rerouted ships may face higher piracy risks in regions like the Gulf of Guinea and the Strait of Malacca. This tool helps decision-makers respond effectively to maritime disruptions.
Utility Of Self-Sensing Damage Technology Through A2/Ad Drone Combat Simulation, Sidhanth Venkatasubramaniam
Utility Of Self-Sensing Damage Technology Through A2/Ad Drone Combat Simulation, Sidhanth Venkatasubramaniam
Theses and Dissertations
Since the introduction of the first unmanned aerial vehicle (UAV), UAVs have consistently improved in capability and versatility. The ability to perform military operations without the risk of losing human life is crucial for the United States military. The trade-off for this versatility is cost, and several ongoing research efforts are being made to improve UAV mission success and the lifespan of UAVs. An area of research that falls under the categories mentioned is self-damage detection. The Air Force Research Laboratories (AFRL) are developing a capability to enable a UAV to assess airframe damage, enabling real-time determination of damage potentially …
Improving Zero Shot Learning By Linking Multi-Label Cnns With Llms, Michael A. Wegner
Improving Zero Shot Learning By Linking Multi-Label Cnns With Llms, Michael A. Wegner
Theses and Dissertations
Classifying previously unseen objects poses a significant challenge for traditional computer vision algorithms, which rely on extensive labeled training data. Zero-shot reasoning offers a way to overcome this limitation. This research explores a novel method for image recognition using the Animals with Attributes 2 (AWA2) dataset as a proof of concept. A multi-label ResNet50 model predicts core attributes like color, ear shape, or number of limbs. Those attributes then feed into ChatGPT which leverages its extensive knowledge base to classify the animal based on the provided attributes. This novel approach skips the need to train on every possible class. Instead, …
Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf
Analysis Of An Army Recruiter Allocation Model (Ram), Erik J. Wittendorf
Theses and Dissertations
The United States Army Recruiting Command’s mission to recruit America’s best and brightest volunteers that can deploy, fight, and win requires an effective distribution of its recruiting force to serve as local community ambassadors. This research analyzes an Army recruiter allocation model (RAM) and assesses its underlying assumptions, objective function, and constraints. A detailed study of relative market potential and production rates for up to 1,319 Army recruiting stations and 18,789 ZIP codes enables RAM modification recommendations leveraging evolving recruiting concepts and identifies areas of future work to continue improving the Army’s understanding of the recruiting environment.
An Analysis Of Development Phase Nre/Rec Costs In Dod Acquisition Efforts, Jason Aristizabal
An Analysis Of Development Phase Nre/Rec Costs In Dod Acquisition Efforts, Jason Aristizabal
Theses and Dissertations
Estimating Nonrecurring Engineering (NRE) and Recurring Engineering (REC) costs in defense acquisition programs remains challenging, especially in development. While production costs are studied, NRE/REC ratios in development receive little attention. This study analyzes NRE/REC ratios across WBS elements, commodity types, and time periods using defense program data. Results show significant variability, challenging the assumed 1:1 ratio. System Level, PME, and ST&E elements follow distinct trends, highlighting shifting cost structures. These findings stress the need for adaptive methodologies, enabling cost analysts to refine estimates based on historical trends and program-specific factors for improved resource planning.
Maintenance Strategy And Budgeting Optimization For United States Air Force Facilities, Sophia C. Hirtle
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 …
Commercial Satellite Payload Trends, Alexander B. Krawietz
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 …
Job Demands And Resources: A Look At The Vha Workforce To Support Dod Goals, Anthony J. Rivera
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 …
Quantifying The Impact Of Highly Corrosive Environments On Fsrm Budgets And Infrastructure Condition At Air Force Bases, Justin M. Weber
Quantifying The Impact Of Highly Corrosive Environments On Fsrm Budgets And Infrastructure Condition At Air Force Bases, Justin M. Weber
Theses and Dissertations
The Department of Defense (DoD) faces significant challenges in maintaining mission-critical infrastructure within highly corrosive environments, with annual costs exceeding $22.5 billion to combat corrosion. This thesis investigated the interplay between environmental severity, sustainment funding, and infrastructure performance. This research utilized Facility Sustainment, Restoration, and Modernization (FSRM) funding data, statistical analyses, and regression modeling, to evaluate how Environmental Severity Index (ESI) categories influence Base Condition Index (BCI) trends and the efficacy of the Facility Sustainment Model (FSM). The analysis revealed that installations in higher ESI categories required significantly greater investment per square foot to achieve comparable improvements in BCI. The …
Accuracy Of Time Phasing Missile And Munition Using The Continuous Distribution Function, Joseph Zobler
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.
Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson
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, …
Uranium Particle Classification Using Statistical Machine Learning And Deep Neural Networks For Nuclear Forensics, Lee C. Lambert
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 …
Statistical Analysis Of Spreading Code Authentication (Sca) Performance Under Varying Signal Conditions And Marker Quantization Schemes, Joseph Quinones-Ocasio
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.