Open Access. Powered by Scholars. Published by Universities.®
- Discipline
-
- Engineering (6523)
- Physical Sciences and Mathematics (4667)
- Social and Behavioral Sciences (3604)
- Education (3469)
- Arts and Humanities (2499)
-
- Life Sciences (1823)
- Computer Sciences (1686)
- Psychology (1596)
- Medicine and Health Sciences (1551)
- Electrical and Computer Engineering (1328)
- Aerospace Engineering (1117)
- Business (1020)
- Mechanical Engineering (877)
- Operations Research, Systems Engineering and Industrial Engineering (774)
- Physics (748)
- Civil and Environmental Engineering (655)
- Chemistry (569)
- Computer Engineering (522)
- Sociology (488)
- Religion (436)
- Special Education and Teaching (399)
- Teacher Education and Professional Development (381)
- Communication (371)
- Statistics and Probability (371)
- Mormon Studies (367)
- History (365)
- Operational Research (357)
- English Language and Literature (350)
- Educational Administration and Supervision (342)
- Earth Sciences (340)
- Institution
-
- University of North Dakota (6075)
- Brigham Young University (5726)
- Air Force Institute of Technology (4935)
- Virginia Commonwealth University (2784)
- Mississippi State University (2689)
-
- University of South Carolina (2494)
- Rowan University (1693)
- Florida Institute of Technology (896)
- American University in Cairo (891)
- Illinois State University (710)
- University of Texas Rio Grande Valley (667)
- University of Arkansas Little Rock (627)
- Pepperdine University (588)
- St. John's University (438)
- City University of New York (CUNY) (432)
- Nova Southeastern University (279)
- Columbus State University (273)
- University for Business and Technology in Kosovo (90)
- SASTRA Deemed to be University (89)
- Texas A&M International University (66)
- Seattle Pacific University (7)
- UAlaska System (3)
- CentraCare Health (2)
- Utah State University (2)
- Southern Methodist University (1)
- Keyword
-
- Education (296)
- #antcenter (271)
- Machine learning (258)
- North Dakota (223)
- United States (216)
-
- Dissertations (EdD) -- Organizational leadership (148)
- History (139)
- Leadership (139)
- Autism (138)
- Utah (122)
- Gender (119)
- Remote sensing (117)
- Simulation (111)
- Optimization (110)
- Deep learning (100)
- Children (97)
- Machine Learning (97)
- Motivation (90)
- Dissertations (PsyD) -- Psychology (85)
- Memory (84)
- Mormon Church (83)
- Women (82)
- FPGA (81)
- Psychology (81)
- Assessment (80)
- Computer vision (79)
- Reading (79)
- Technology (78)
- Mental health (77)
- Depression (75)
- Publication Year
Articles 1441 - 1470 of 32457
Full-Text Articles in Entire DC Network
Analyzing U.S. Army Recruiter Productivity Through Cohort Differentiation And Behavioral Tendency Composition, Mary M. Bates
Analyzing U.S. Army Recruiter Productivity Through Cohort Differentiation And Behavioral Tendency Composition, Mary M. Bates
Theses and Dissertations
This research analyzes differences among aggregate achievements of U.S. Army recruiting cohorts, determines which behavioral tendencies are indicative of performance level, and investigates aggregate behavioral composition with cohort achievement. Analyses require implementation of OLS regression, ANOVA, Tukey’s Test, Mann-Whitney U test, Holm-Bonferroni adjustment, XGBoost decision tree, logistic regression, and the Kolmogorov-Smirnov test. The results show insignificant achievement differences among cohorts and weak yet prevalent abilities of select measures of behavioral tendencies to indicate recruiter performance.
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 …
Graph Neural Network-Based Uav Coverage Planning For Robust And Efficient 3d Environments, Gal Tsfaty
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.
Hyperparameter Tuning For Robust Autonomous Vehicle Vision, Nico D. De Ros
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 …
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.
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. …
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.
Evaluating Learning Outcomes In A Serious Game: A Practical And Model Checking Approach, Matthew D. Douglas
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.
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.
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, …
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.
Spatiotemporal Prediction Of Atmospheric Events Through Recurrent Deep Learning Model, Brian W. F. Popick
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 …
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.
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.
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.
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.
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 …
An Agent-Based Modeling Framework For Evaluating The Linkage Between Disaster Facility Damage And Mental Health, Emily S. Reeves
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 …
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.
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 …
Hypergame Models For Cyber Defense In A Purple Team Setting, Thomas N. Whitney
Hypergame Models For Cyber Defense In A Purple Team Setting, Thomas N. Whitney
Theses and Dissertations
Hypergame theory and purple teaming are two fields that can support an increased cybersecurity posture. This research investigates a hypergame theory framework that incorporates fittingly into the purple team feedback loop. Additionally, this research integrates empirical data into the hypergames. The data and the hypergames are supported by the MITRE ATT&CK framework. This research also includes a review of available game theory and hypergame theory software, five different hypergame models, a comparison of the two hypergame formats, and an innovative analysis technique using a multi-stage hypergame to represent the cyber kill chain. One finding of this research is that a …
Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks, Michael A. Eddy
Jamming-Tolerant Low-Rate Wireless Personal Area Network For Detection Sensor Networks, Michael A. Eddy
Theses and Dissertations
This research evaluates the impact of electronic warfare, particularly jamming, on an audio-based drone detection wireless sensor network (WSN) using Monte Carlo simulations. A six-node IEEE 802.15.4 network, with five edge nodes and a central sink, is tested against jamming probabilities ranging from 0-100% in 5% increments across 30 iterations per configuration. Results show that packet delivery ratio (PDR) degrades linearly at approximately 20% per jammed node, while detection performance often exceeds PDR. Even at 80% jamming, detection success rates remain above 57%, highlighting resilience despite network degradation. The study reveals that jamming effectiveness depends on node placement relative to …
Performing Requirements Specification And Analysis Through Open Generative Pre-Trained Transformers, Harvey J. Hurst
Performing Requirements Specification And Analysis Through Open Generative Pre-Trained Transformers, Harvey J. Hurst
Theses and Dissertations
Every acquisition program begins with a requirement, and for those programs to succeed, robust requirements engineering (RE) must be implemented. RE encompasses eliciting, analyzing, specifying, and validating requirements—a critical process throughout a program's lifecycle. Despite its importance, RE faces challenges such as scope creep, ambiguity, redundancy, and inadequate automation support, often exacerbated by reliance on historical data. To address these issues, this thesis leverages advancements in Generative Technology, particularly large language models (LLMs) such as Generative Pre-Trained Transformers (GPTs). This research developed two GPT-based tools: the Single Requirement Analysis Tool and the Set of Requirements Analysis Tool. These tools were …
Atmospheric Water Generation: Bacterial And Inorganic Chemical Quality, Garrett E. Stanley
Atmospheric Water Generation: Bacterial And Inorganic Chemical Quality, Garrett E. Stanley
Theses and Dissertations
This study investigated the quality of untreated water collected from a vapor compression-based atmospheric water generator (AWG) operated outdoors in Dayton, Ohio. The concentrations of bacterial and chemical constituents were determined. The concentration of culturable bacteria was between 200 and 2300 CFU/mL, significantly higher than the reference levels described in recreational and drinking water quality guidance. Three discrete bacteria phenotypes were visually identified; two were yellow in color, rod-shaped and gram-negative while the third was milky white, circular, and gram-positive. Chemical analysis on 15 randomly selected samples revealed the presence of Barium (average = 0.16 mg/L), Magnesium (0.08 = mg/L), …
Material Classification With Spectropolarimetric Lidar, Alexander J. Watson
Material Classification With Spectropolarimetric Lidar, Alexander J. Watson
Theses and Dissertations
A method for characterizing unknown targets using a hyperspectral polarimetric light detection and ranging (LiDAR) system is presented. Light reflected from manmade objects tends to be more polarized than light reflected from objects in the natural world. As such, polarization measurements can be used in remote sensing applications to differentiate artificial and natural objects. Previous works have attempted to characterize objects through passive polarimetric imagery. Methods developed by Cain and Lemaster and Cunningham facilitate reconstruction of the Stokes Vector from returning light. Martin used multispectral polarimetry to classify targets when the angle of incidence (AOI) is close to 0º. Here, …
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
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 + …
Evaluating The Performance Of Conformal Prediction Generated Uncertainty Sets In Robust Optimization, Zion C. Johnson
Evaluating The Performance Of Conformal Prediction Generated Uncertainty Sets In Robust Optimization, Zion C. Johnson
Theses and Dissertations
Uncertainty is a major challenge in optimization, especially in problems where unpredictable costs impact decision-making. Robust optimization addresses this by modeling uncertainty via uncertainty sets. These sets are then used such that solutions hold under worst-case scenarios, with success depending on the accuracy of the uncertainty sets. This research examines the use of conformal prediction to construct uncertainty sets for RO, an approach that has not been widely explored. We test split and full conformal prediction in a robust optimization minimum cost flow problem, and comparing them to interval-based and normal-based ellipsoidal uncertainty sets. Experiments run across different network structures …
Development Of An Advanced 16-Channel High-Fidelity Multi-Frequency Software-Defined Rf Front-End For Advanced Satnav Signal Monitoring Applications, Melbourne T. Ketteridge
Development Of An Advanced 16-Channel High-Fidelity Multi-Frequency Software-Defined Rf Front-End For Advanced Satnav Signal Monitoring Applications, Melbourne T. Ketteridge
Theses and Dissertations
Multi-element antenna array technology provides significant performance advantages in satellite timing and navigation (satnav) receiver applications. It is the most effective anti-jamming method with the ability to place steep nulls in the direction of jammers. Until recently, satnav systems with 4 or more antenna elements were designated as weapons technology and restricted under ITAR regulations. This opens the door to commercial multi-element satnav receivers. Due to advancements in wireless broadband technology a receiver built entirely using commercial off-the-shelf (COTS) components is possible. This thesis presents an architecture for a high fidelity 16-channel RF front-end (RFFE) for research and development of …
Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub
Machine Learning Techniques To Predict Solar Particle Events And Radiation Of Aircrew, Haley Traub
Theses and Dissertations
Solar Particle Events (SPEs) are high-energy phenomena from the Sun that pose risks to technology, human health, and Air Force operations. Accurate prediction of SPEs exceeding 100 MeV is crucial for mitigating these risks. This thesis explores using Bayesian statistical models to predict such events, integrating prior knowledge from solar physics with the ability to update predictions based on new data. The research uses a dataset spanning three solar cycles (21–23) and incorporates attributes like flare fluence, peak flux, latitude, longitude, and class. Four Bayesian models (PyMC, Bnlearn, and two Dredge models) were compared to machine learning models. The Bayesian …
Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner
Symbology Detection And Numerical Recognition For T-38 Heads-Up Display Recordings, Ben T. Hepner
Theses and Dissertations
The extraction of symbology and numerical data from the T-38 Heads-Up Display (HUD) enhances post-flight analysis and supports real-time decision-making. This research develops a deep learning pipeline using YOLO-based object detection and Optical Character Recognition (OCR) to analyze HUD video data. Model evaluations showed mAP0.5:0.95 ranging from 0.422 (YOLOv11m, hard test set) to 0.696 (YOLOv8m, medium test set), demonstrating robust symbology detection. Numeric detection performed well (mAP0.5:0.95 = 0.764), but OCR struggled with glare and resolution limitations, achieving a recognition accuracy of 17.35%. These results validate deep learning for HUD data extraction but highlight the need for improved robustness …