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Articles 31 - 60 of 2022
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An Analysis Of Cloud Computing Migration Costs And Effects For Dod Applications, Joseph S. Moore Iv
An Analysis Of Cloud Computing Migration Costs And Effects For Dod Applications, Joseph S. Moore Iv
Theses and Dissertations
The Air Force launched “Cloud One” in 2017. Cloud One provides cloud computing options for military applications. Cloud One provides common secure computing environments, standardized platforms, application migration and support services, and data management. Currently, Cloud One has over one hundred mission applications on board. Although there is information on the cost, performance, personnel requirements, risks, and migration of the commercial sector and cloud options, there is limited recorded information on the same topics for Cloud One. As such, there is a gap in the literature regarding data/feedback for mission applications that have migrated to Cloud One. This research takes …
A Comparative Analysis Of Viral Aerosol Biological Sampling Efficiency Of A Small Unmanned Aircraft System (Suas)-Mounted Aerosol Sampler And A Reference Static Biosampler®, Jonathan D. Moroz
Theses and Dissertations
Bioaerosol sampling using small unmanned aerial systems (sUAS) is a rapidly developing field that may result in a paradigm shift in emergency response and industrial hygiene sampling conventions. These technologies offer decreased sample acquisition times, larger sampling area coverage, and reduced health and safety risks to traditional human sampling teams. This potential requires a comprehensive investigation of sUAS capabilities and limitations. This study is a continuation of the characterization of an AFIT-developed sUAS-mounted aerosol sampler, proven capable of collecting viable vegetative and spore-forming bacteria through previous AFIT research. Within this study, viral biological sampling efficiency (BSE) of the sUAS-mounted aerosol …
Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas M. Crino
Garbage In ≠ Garbage Out: Exploring Gan Resilience To Image Training Set Degradations, Nicholas M. Crino
Theses and Dissertations
Generative Adversarial Networks (GANs) have received increasing attention in recent years due to their ability to capture complex, high-dimensional data distributions without the need for extensive labeling. Since their conception in 2014, a wide array of GAN variants have been proposed featuring alternative architectures, optimizers, and loss functions with the goal of improving performance and training stability. While this research has yielded GAN variants robust to training set shrinkage and corruption, our research focuses on quantifying the resilience of a GAN architecture to specific modes of image degradation. We conduct systematic experimentation to determine empirically the effects of 10 fundamental …
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Theses and Dissertations
Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Theses and Dissertations
Studies have shown a connection between early catastrophic engine failures with microtexture regions (MTRs) of a specific size and orientation on the titanium metal engine components. The MTRs can be identified through the use of Electron Backscatter Diffraction (EBSD) however doing so is costly and requires destruction of the metal component being tested. A new methodology of characterizing MTRs is needed to properly evaluate the reliability of engine components on live aircraft. The Air Force Research Lab Materials Directorate (AFRL/RX) proposed a solution of supplementing EBSD with two non-destructive modalities, Eddy Current Testing (ECT) and Scanning Acoustic Microscopy (SAM). Doing …
Fragility Of The Florida Panhandle's Electrical Transmission Grid To Hurricanes, Zachary D. Schumann
Fragility Of The Florida Panhandle's Electrical Transmission Grid To Hurricanes, Zachary D. Schumann
Theses and Dissertations
The increased frequency and intensity of extreme weather events from climate change necessitates understanding impacts on critical infrastructure, particularly electrical transmission grids. One of the foundational concepts of a grid’s resilience is its robustness to extreme weather events, such as hurricanes. Resilience of the electric grid to high wind speeds is predicated upon the location and physical characteristics of the system components. Previous modeling assessments of electric grid failure were done at the systems level with assumptions on location and type of specific components. To facilitate more explicit adaptation metrics, accurate component-level information is needed. In this study, we build …
Uncertainty Quantification In Federated Learning For Persistent Post-Traumatic Headache, Byungmoo Brian Kim
Uncertainty Quantification In Federated Learning For Persistent Post-Traumatic Headache, Byungmoo Brian Kim
Theses and Dissertations
A post-traumatic headache (PTH), resulting from a mild traumatic brain injury (mTBI), potentially develops into persistent post-traumatic headache (PPTH). Although no known cure for PPTH exists, research has shown that receiving treatment at earlier stages of PTH lowers the risk of patients developing PPTH. Previous studies have shown machine learning (ML) models capable of predicting a patient’s PTH progression, but none have considered the issue of protecting patient privacy. Due to patient privacy, ML models only have access to data within the institution. Federated learning (FL) harnesses data from separate institutions without sacrificing patient privacy as institutions can run ML …
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Theses and Dissertations
Neural networks have become increasingly popular in real time object detection algorithms. A major concern with these algorithms is their ability to quantify their own uncertainty, leading to many high profile failures. This research proposes three novel real time detection algorithms. The first of leveraging Bayesian convolutional neural layers producing a predictive distribution, the second leveraging predictions from previous frames, and the third model combining these two techniques together. These augmentations seek to mitigate the calibration problem of modern detection algorithms. These three models are compared to the state of the art YOLO architecture; with the strongest contending model achieving …
Examining Fuel Service System Failures Of The Usaf R11 Using Survival Analysis, Roed M.S. Mejia
Examining Fuel Service System Failures Of The Usaf R11 Using Survival Analysis, Roed M.S. Mejia
Theses and Dissertations
Recent events show that fuel supply is a large contributor to the success or failure of a military operation in response to a contingency. Any future near-peer conflict will stress the supply chain and require fully operational vehicles to be ready for the primary mission sets they support. In the United States Air Force (USAF), the readiness of fuel distribution trucks is crucial to meeting those mission sets in global operations. Utilizing non-parametric and semi-parametric survival models, which do not assume specific probability distributions, this study analyzes maintenance data for R-11 trucks that refuel aircraft.
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Theses and Dissertations
This research modeled and analyzed the effectiveness of different routing algorithms for penetration assets in an A2AD environment. AFSIM was used with different configurations of SAMs locations and numbers to compare the performance of AFSIM’s internal zone and shrink algorithm routers with a Dijkstra algorithm router. Route performance was analyzed through computational and operational metrics, including computational complexity, run-time, mission survivability, and simulation duration. This research also analyzed the impact of the penetration asset’s ingress altitude on those factors. Additionally, an excursion was conducted to analyze the Dijkstra algorithm router’s grid density holding altitude constant to understand its impact on …
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Theses and Dissertations
agement, development, and growth of U.S Air Force assets demand extensive organizational communication and structuring. These interactions yield substantial amounts of contracting and administrative information. Over 4 million such contracts as a means towards obtaining valuable insights on Department of Defense resource usage. This set of contracting data is largely not optimized for backend service in an analytics environment. To this end, the following research evaluates the efficiency and performance of various data structuring methods. Evaluated designs include a baseline unstructured schema, a Data Mart schema, and a snowflake schema. Overall design success metrics include ease of use by end …
Improving Accessibility And Efficiency Of Analytic Provenance Tools For Reverse Engineering, Caleb W. Richardson
Improving Accessibility And Efficiency Of Analytic Provenance Tools For Reverse Engineering, Caleb W. Richardson
Theses and Dissertations
Reverse engineering is a vital technique for identifying and mitigating cyber threats. Yet, despite its importance, reverse engineering is a time-consuming process. Provenance tools help to improve the workflow of reverse engineers by providing an accessible method of viewing their flow through a binary. The current state-of-theart provenance tool for reverse engineering software called SensorRE, leverages an external server, web browser, and a large array of javascript libraries. This thesis presents Provenance Ninja, a software reverse engineering tool developed in Python that runs directly within Binary Ninja. Provenance Ninja captures reverse engineers’ provenance data and provides an interactive graph within …
Developing And Assessing A Generalized Serious Game That Supports Customized Joint All-Domain Operations Related Learning Objectives, Jonathan D. Moore
Developing And Assessing A Generalized Serious Game That Supports Customized Joint All-Domain Operations Related Learning Objectives, Jonathan D. Moore
Theses and Dissertations
As the threat of near-peer adversaries has increased, the DoD has increased its emphasis on Joint All-Domain Operations (JADO). This emphasis on JADO highlights the need for hands-on training that can engage military members at all levels. The serious game Battlespace Next (BSN) was designed to teach high-level JADO concepts by modeling real-world military assets in the context of a strategic card game. To keep pace with the evolving landscape of warfare as well as fit the needs of a variety of Department of Defense (DoD) communities, this research introduces the Battlespace Next Education Framework (BSNEF). The BSNEF allows JADO …
Debris Survivability Study For Mega-Constellation Architectures, Joseph C. Canoy
Debris Survivability Study For Mega-Constellation Architectures, Joseph C. Canoy
Theses and Dissertations
The analysis for the overall theoretical debris survivabilty of mega-constellation architectures, with an emphasis on space-based ballistic missile defense constellation (SB-BMD), is explored via three extensive different Monte Carlo simulations: preliminary analysis of low Earth Orbit (LEO) mega-constellation survivabilty following a fragmentation event within the constellation, analysis of LEO mega-constellation survivability with a fragmentation event occurring on a satellite performing a maneuver to insert itself within the constellation, and the analysis of LEO mega-constellation survivabilty after a fragmentation event resulting from the destruction of a missile. The LEO mega-constellations represent the SB-BMD constellation. The first two analysis sections will include …
Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek
Evaluating Deep Learning Explanations On Risc-V Assembly As A Reverse Engineering Aid, Daniel F. Koranek
Theses and Dissertations
This dissertation addresses several problems surrounding the detection of malware using deep learning models trained on assembly language examples. First, it examines the feasibility of detecting examples of malice using deep learning models trained on RISC-V instruction traces. Next, it examines whether models for detecting trace features and code features in RISC-V assembly can be made explainable (providing rationale for a model’s decision based upon the model’s internal workings) or interpretable (providing additional rationale as model output to support a human’s agreement with the model output). Third, this work examines ways in which it is possible to give additional contextual …
Fitting Solar Panel Brdf Parameters To Out-Of-Plane Empirical Data, Michael R. Gross
Fitting Solar Panel Brdf Parameters To Out-Of-Plane Empirical Data, Michael R. Gross
Theses and Dissertations
The bidirectional reflectance distribution function (BRDF) describes material reflectance by describing how incident irradiance reflects into all possible scatter angles as a function of incident angle. However, a solar panel has unique features that are not featured in any of these previously known models. A previous project at the Air Force Institute of Technology (AFIT) created a novel microfacet-like BRDF to model a solar panel with a prominent diffractive feature present which had not been previously modeled. This BRDF was coded into MATLAB for modeling purposes and C++ to test its speed with a MEX function call. A previous thesis …
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Quantum Error Detection Without Using Ancilla Qubits, Nicolas Guerrero
Theses and Dissertations
Quantum computers are beset by errors from a variety of sources. Although quantum error correction and detection codes have been developed since the 1990s, these codes require mid-circuit measurements in order to operate. In order to avoid these measurements we have developed a new error detection code that only requires state collapses at the end of the circuit, which we call no ancilla error detection (NAED). We investigate some of the mathematics behind NAED such as which codes can detect which errors. We then run NAED on three separate types of circuits: Greenberger–Horne–Zeilinger circuits, phase dependent circuits, and a quantum …
Orthogonal Arrays And Legendre Pairs, Kristopher N. Kilpatrick
Orthogonal Arrays And Legendre Pairs, Kristopher N. Kilpatrick
Theses and Dissertations
Well-designed experiments greatly improve test and evaluation. Efficient experiments reduce the cost and time of running tests while improving the quality of the information obtained. Orthogonal Arrays (OAs) and Hadamard matrices are used as designed experiments to glean as much information as possible about a process with limited resources. However, constructing OAs and Hadamard matrices in general is a very difficult problem. Finding Legendre pairs (LPs) results in the construction of Hadamard matrices. This research studies the classification problem of OAs and the existence problem of LPs. In doing so, it makes two contributions to the discipline. First, it improves …
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Development Of A Security-Focused Multi-Channel Communication Protocol And Associated Quality Of Secure Service (Qoss) Metrics, Paul M. Simon
Theses and Dissertations
The threat of eavesdropping, and the challenge of recognizing and correcting for corrupted or suppressed information in communication systems is a consistent challenge. Effectively managing protection mechanisms requires an ability to accurately gauge the likelihood or severity of a threat, and adapt the security features available in a system to mitigate the threat. This research focuses on the design and development of a security-focused communication protocol at the session-layer based on a re-prioritized communication architecture model and associated metrics. From a probabilistic model that considers data leakage and data corruption as surrogates for breaches of confidentiality and integrity, a set …
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Analytic Case Study Using Unsupervised Event Detection In Multivariate Time Series Data, Jeremy M. Wightman
Theses and Dissertations
Analysis of cyber-physical systems (CPS) has emerged as a critical domain for providing US Air Force and Space Force leadership decision advantage in air, space, and cyberspace. Legacy methods have been outpaced by evolving battlespaces and global peer-level challengers. Automation provides one way to decrease the time that analysis currently takes. This thesis presents an event detection automation system (EDAS) which utilizes deep learning models, distance metrics, and static thresholding to detect events. The EDAS automation is evaluated with case study of CPS domain experts in two parts. Part 1 uses the current methods for CPS analysis with a qualitative …
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Generative Methods, Meta-Learning, And Meta-Heuristics For Robust Cyber Defense, Marc W. Chale
Theses and Dissertations
Cyberspace is the digital communications network that supports the internet of battlefield things (IoBT), the model by which defense-centric sensors, computers, actuators and humans are digitally connected. A secure IoBT infrastructure facilitates real time implementation of the observe, orient, decide, act (OODA) loop across distributed subsystems. Successful hacking efforts by cyber criminals and strategic adversaries suggest that cyber systems such as the IoBT are not secure. Three lines of effort demonstrate a path towards a more robust IoBT. First, a baseline data set of enterprise cyber network traffic was collected and modelled with generative methods allowing the generation of realistic, …
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Leveraging Subject Matter Expertise To Optimize Machine Learning Techniques For Air And Space Applications, Philip Y. Cho
Theses and Dissertations
We develop new machine learning and statistical methods that are tailored for Air and Space applications through the incorporation of subject matter expertise. In particular, we focus on three separate research thrusts that each represents a different type of subject matter knowledge, modeling approach, and application. In our first thrust, we incorporate knowledge of natural phenomena to design a neural network algorithm for localizing point defects in transmission electron microscopy (TEM) images of crystalline materials. In our second research thrust, we use Bayesian feature selection and regression to analyze the relationship between fighter pilot attributes and flight mishap rates. We …
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Learning Robust Radio Frequency Fingerprints Using Deep Convolutional Neural Networks, Jose A. Gutierrez Del Arroyo
Theses and Dissertations
Radio Frequency Fingerprinting (RFF) techniques, which attribute uniquely identifiable signal distortions to emitters via Machine Learning (ML) classifiers, are limited by fingerprint variability under different operational conditions. First, this work studied the effect of frequency channel for typical RFF techniques. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models leads to deterioration in MCC to under 0.05 (random guess), indicating that single-channel models are inadequate for realistic operation. Second, this work presented a novel way of studying fingerprint variability through Fingerprint Extraction through Distortion Reconstruction (FEDR), a …
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Analysis Of Graph Layout Algorithms For Use In Command And Control Network Graphs, Matthew R. Stone
Theses and Dissertations
This research is intended to determine which styles of layout algorithm are well suited to Command and Control (C2) network graphs to replace current manual layout methods. Manual methods are time intensive and an automated layout algorithm should decrease the time spent creating network graphs. Simulations on realistic synthetically generated graphs provide information to help infer which algorithms perform better than others on this problem. Data is generated using statistics drawn from multiple real world C2 network graphs. The three algorithms tested against this data are the Spectral algorithm, the Dot algorithm, and the Fruchterman-Reingold algorithm. The results include a …
Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando
Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando
Theses and Dissertations
Most statistical tests are fully developed for univariate data, but when inference is required for multivariate data, univariate tests risk information loss and interpretability. This research 1) derives and extends the multivariate Komolgorov Smirnov test for 2 and into m-dimensions, 2) derives small sample critical values for the KS test that are not reliant on sample size simulations or correlation between variables, 3) extends large sample estimations and current KS implementations, and 4) provides sample size and power calculations in order to enable experimental design with respect to testing for differences in distributions. Through extensive simulation, we demonstrate that our …
Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby
Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby
Theses and Dissertations
Many disparate datasets exist that provide country attributes covering political, economic, and social aspects. Unfortunately, this data often does not include all countries nor is the data complete for those countries included, as measured by the dataset’s missingness. This research addresses these dataset shortfalls in predicting country instability by considering country attributes in all aspects as well as in greater thresholds of missingness. First, a structured summary of past research is presented framed by a developed casual taxonomy and functional ontology. Additionally, a novel imputation technique for very large datasets is presented to account for moderate missingness in the expanded …
Efficiency Quantification For Pulsed-Source Digital Holographic Wavefront Sensing, Steven A. Owens
Efficiency Quantification For Pulsed-Source Digital Holographic Wavefront Sensing, Steven A. Owens
Theses and Dissertations
The efficiencies of a digital holography (DH) system in the pulsed configuration and the off-axis image plane recording geometry are analyzed. First, the system efficiencies of an infrared-wavelength DH system in a homodyne-pulsed configuration are measured and compared to those of a visible-wavelength DH system in a homodyne-continuous-wave (CW) configuration. The total-system, excess-reference-noise, shot-noise-limit, and mixing efficiencies of the pulsed-source system were found to be consistent with those of the CW-source system. This indicated no new efficiencies were necessary to characterize pulsed-source systems when no temporal delay exists between the pulses. The consistency of efficiencies also showed infrared DH systems …
Optimizing Optical Switching Of Non-Linear Optimizing Optical Switching Of Non-Linear Hyperbolic Metamaterials, James A. Ethridge
Optimizing Optical Switching Of Non-Linear Optimizing Optical Switching Of Non-Linear Hyperbolic Metamaterials, James A. Ethridge
Theses and Dissertations
Modern optical materials are engineered to be used as optical devices in specific applications, such as optical computing. For optical computing, efficient forms of a particular device, the optical switch, still have not been successfully demonstrated. This problem is addressed in this research through the use of designed optical metamaterials, specifically, hyperbolic metamaterials, which offer the possibility of large non-linear properties with a low switching intensity. One-dimensional layered hyperbolic metamaterials composed of alternating layers of metal and dielectric were used here, with ITO as the metal and SiO2 as the dielectric. The non-linear behavior of the ITO/SiO2 layered …
Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun
Statistical Inference On Desirability Function Optimal Points To Evaluate Multi-Objective Response Surfaces, Peter A. Calhoun
Theses and Dissertations
A shortfall of the Derringer and Suich (1980) desirability function is lack of inferential methods to quantify uncertainty. Most articles for addressing uncertainty usually involve robust methods, providing a point estimate that is less affected by variation. Few articles address confidence intervals or bands but not specifically for the Derringer and Suich method. This research provides two valuable contributions to the field of response surface methodology. The first contribution is evaluating the effect of correlation and plane angles on Derringer and Suich optimal solutions. The second contribution proposes and compares 8 inferential methods--both univariate and multivariate--for creating confidence intervals on …
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Analyzing Microarchitectural Residue In Various Privilege Strata To Identify Computing Tasks, Tor J. Langehaug
Theses and Dissertations
Modern multi-tasking computer systems run numerous applications simultaneously. These applications must share hardware resources including the Central Processing Unit (CPU) and memory while maximizing each application’s performance. Tasks executing in this shared environment leave residue which should not reveal information. This dissertation applies machine learning and statistical analysis to evaluate task residue as footprints which can be correlated to identify tasks. The concept of privilege strata, drawn from an analogy with physical geology, organizes the investigation into the User, Operating System, and Hardware privilege strata. In the User Stratum, an adversary perspective is taken to build an interrogator program that …