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Articles 601 - 630 of 5596
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Particle-In-Cell Simulations Of Ion Dynamics In A Pinched-Beam Diode, Jesse C. Foster, John W. Mcclory, S. B. B. Swanekamp, D. D. Hinshelwood, A. S. Richardson, Paul E. Adamson, J. W. Schumer, R. W. James, P. F. Ottinger, D. Mosher
Particle-In-Cell Simulations Of Ion Dynamics In A Pinched-Beam Diode, Jesse C. Foster, John W. Mcclory, S. B. B. Swanekamp, D. D. Hinshelwood, A. S. Richardson, Paul E. Adamson, J. W. Schumer, R. W. James, P. F. Ottinger, D. Mosher
Faculty Publications
article-in-cell simulations of a 1.6 MV, 800 kA, and 50 ns pinched-beam diode have been completed with emphasis placed on the quality of the ion beams produced. Simulations show the formation of multiple regions in the electron beam flow characterized by locally high charge and current density (“hot spots”). As ions flow through the electron-space-charge cloud, these hot spots electrostatically attract ions to produce a non-uniform ion current distribution. The length of the cavity extending beyond the anode-to-cathode gap (i.e., behind the cathode tip) influences both the number and amplitude of hot spots. A longer cavity length increases the number …
Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry
Identification Of Return-Oriented Programming Attacks Using Risc-V Instruction Trace Data, Daniel F. Koranek, Scott R. Graham, Brett J. Borghetti, Wayne C. Henry
Faculty Publications
An increasing number of embedded systems include dedicated neural hardware. To benefit from this specialized hardware, deep learning techniques to discover malware on embedded systems are needed. This effort evaluated candidate machine learning detection techniques for distinguishing exploited from non-exploited RISC-V program behavior using execution traces. We first developed a dataset of execution traces containing Return Oriented Programming (ROP) exploitation on the RISC-V Instruction Set Architecture (ISA) and then developed several deep learning bidirectional Long Short-Term Memory (LSTM) models capable of distinguishing exploited traces from non-exploited traces, each using subsets of features from the execution traces. An objective of this …
Aerial Radiation Detection Identification And Measurement System Detector Material Comparison Study, Benjamin C. Troxell, Kacey D. Mcgee, Christina L. Dugan
Aerial Radiation Detection Identification And Measurement System Detector Material Comparison Study, Benjamin C. Troxell, Kacey D. Mcgee, Christina L. Dugan
Faculty Publications
The 20th Chemical Biological Radiological Nuclear and Explosives Command (CBRNE) currently utilizes an airborne sodium iodide gamma and beta detection system to map radiation fields over large areas of interest. The 20th CBRNE explored emergent detector technologies utilizing two detection materials; thallium-activated cesium iodide and high purity germanium (HPGe). These detectors were simulated at various altitudes and compared to background measurements. The sodium iodide detector failed to provide isotopic discrimination at distance. The thallium-activated cesium iodide CsI(Tl) detector provided sufficient absolute efficiency and energy resolution to identify isotopics at distance. The HPGe detector provided the best energy resolution. However, current …
Survivability Analysis In The Shadow Of Apollo: Part Ii - Spacecraft Charging Vulnerability Near The Stable Earth-Moon Lagrange Points, Robert A. Bettinger, Nathan R. Boone, Nicholas S. Hamilton, Bryan D. Little
Survivability Analysis In The Shadow Of Apollo: Part Ii - Spacecraft Charging Vulnerability Near The Stable Earth-Moon Lagrange Points, Robert A. Bettinger, Nathan R. Boone, Nicholas S. Hamilton, Bryan D. Little
Faculty Publications
Though the reality of routine travel and sustained operations in space likely remains several decades away, the building blocks of space exploration and space system development necessary to realize this reality are currently being laid by peer, near-peer, and emerging space-faring nations. The pursuance of sustained cislunar space operations will necessitate a renewed examination of spacecraft survivability and the capability of spacecraft to avoid and/or withstand the natural and manmade environmental risks posed by this “new” ultimate high ground.
Evaluating Climatic Influences On The Technical Performance Of Built Infrastructure Assets, Sarah L. Brown, Steven J. Schuldt, Michael N. Grussing, Michael A. Johnson, Justin D. Delorit
Evaluating Climatic Influences On The Technical Performance Of Built Infrastructure Assets, Sarah L. Brown, Steven J. Schuldt, Michael N. Grussing, Michael A. Johnson, Justin D. Delorit
Faculty Publications
Asset performance evaluations serve as a benchmark that can be used to make asset manufacturer selection decisions, i.e., choosing the brand of manufacturer that provides the highest performance among all brand competitors. Understanding the environmental conditions to which assets are subjected provides facility managers another data point to understand asset performance. This research builds upon a previously published performance-based manufacturer selection metric by investigating the linkage between asset performance and exposure to local climate, using chiller and air handler data from 20 United States Air Force installations. The link between environmental factors such as Heating Degree Days (HDD); Cooling Degree …
Pseudo-Modal Expansions For Generating Random Electromagnetic Beams, Milo W. Hyde Iv, Olga Korotkova
Pseudo-Modal Expansions For Generating Random Electromagnetic Beams, Milo W. Hyde Iv, Olga Korotkova
Faculty Publications
Excerpt: We derive two pseudo-modal expansions that provide insight into the structure of stationary electromagnetic sources and can be used for their physical realization and in computer simulations. Both expansions are derived from the vectorial version of Bochner’s theorem of functional analysis.
Augmenting Field Nuclear Forensics Capabilities With Handheld Atomic Spectroscopy Devices For Nuclear Debris Analysis, Ashwin P. Rao, Christopher M. Sutphin, Christina L. Dugan
Augmenting Field Nuclear Forensics Capabilities With Handheld Atomic Spectroscopy Devices For Nuclear Debris Analysis, Ashwin P. Rao, Christopher M. Sutphin, Christina L. Dugan
Faculty Publications
The realm of technical nuclear forensics remains a critical part of joint counterproliferation capabilities. Since the passage of the Nuclear Forensics and Attribution Act (H.R.730) in 2010, the nuclear science community has undertaken steps to bolster national nuclear forensics capabilities under the National Technical Nuclear Forensics Center. Within the technical nuclear forensics umbrella, one particular area of interest involves developing capabilities for in-field analysis of nuclear material. This includes engaging in field training exercises across relevant branches of the military and investigating new technologies to conduct field nuclear forensic analyses. The integration of novel analytical technologies into real-world nuclear detection …
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Application Of Machine Learning To Predict The Performance Of An Emipg Reactor Using Data From Numerical Simulations, Owen Sedej, Eric G. Mbonimpa, Trevor Sleight, Jeremy Slagley
Faculty Publications
Microwave-driven plasma gasification technology has the potential to produce clean energy from municipal and industrial solid wastes. It can generate temperatures above 2000 K (as high as 30,000 K) in a reactor, leading to complete combustion and reduction of toxic byproducts. Characterizing complex processes inside such a system is however challenging. In previous studies, simulations using computational fluid dynamics (CFD) produced reproducible results, but the simulations are tedious and involve assumptions. In this study, we propose machine-learning models that can be used in tandem with CFD, to accelerate high-fidelity fluid simulation, improve turbulence modeling, and enhance reduced-order models. A two-dimensional …
Method Of Making Hinged Self-Referencing Fabry–Pérot Cavity Sensors, Jeremiah C. Williams, Hengky Chandrahalim
Method Of Making Hinged Self-Referencing Fabry–Pérot Cavity Sensors, Jeremiah C. Williams, Hengky Chandrahalim
AFIT Patents
A method is provided for fabricating a passive optical sensor on a tip of an optical fiber. The method includes perpendicularly cleaving a tip of an optical fiber and mounting the tip of the optical fiber in a specimen holder of a photosensitive polymer three-dimensional micromachining machine. The method includes forming a three-dimensional microscopic optical structure within the photosensitive polymer that comprises a two cavity Fabry-Perot Interferometer (FPI) having a hinged optical layer that is pivotally coupled to a suspended structure. The method includes removing an uncured portion of the photosensitive polymer using a solvent. The method includes depositing a …
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Global Gnss-Ro Electron Density In The Lower Ionosphere, Dong L. Wu, Daniel J. Emmons Ii, Nimalan Swarnalingam
Faculty Publications
Lack of instrument sensitivity to low electron density (Ne) concentration makes it difficult to measure sharp Ne vertical gradients (four orders of magnitude over 30 km) in the D/E-region. A robust algorithm is developed to retrieve global D/E-region Ne from the high-rate GNSS radio occultation (RO) data, to improve spatiotemporal coverage using recent SmallSat/CubeSat constellations. The new algorithm removes F-region contributions in the RO excess phase profile by fitting a linear function to the data below the D-region. The new GNSS-RO observations reveal many interesting features in the diurnal, seasonal, solar-cycle, and magnetic-field-dependent variations in the …
Copyright And Attribution Guide, Version 1.0, Air Force Institute Of Technology
Copyright And Attribution Guide, Version 1.0, Air Force Institute Of Technology
AFIT Documents
The purpose of this document is to establish a guide on the use of copyrighted material, the attribution of source material, copyright protection of covered works (limited applicability only for civilian faculty), and to educate AFIT personnel on important aspects of copyright law. This guide is intended for all AFIT employees, including all AFIT faculty, staff, contractors, and students, as well as all students reporting to AFIT regardless of the school they are attending.
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Considerations For Radio Frequency Fingerprinting Across Multiple Frequency Channels, Jose A. Gutierrez Del Arroyo, Brett J. Borghetti, Michael A. Temple
Faculty Publications
Radio Frequency Fingerprinting (RFF) is often proposed as an authentication mechanism for wireless device security, but application of existing techniques in multi-channel scenarios is limited because prior models were created and evaluated using bursts from a single frequency channel without considering the effects of multi-channel operation. Our research evaluated the multi-channel performance of four single-channel models with increasing complexity, to include a simple discriminant analysis model and three neural networks. Performance characterization using the multi-class Matthews Correlation Coefficient (MCC) revealed that using frequency channels other than those used to train the models can lead to a deterioration in performance from …
Madfam: Microarchitectural Data Framework And Methodology, Tor J. Langehaug, Scott R. Graham, Christine M. Schubert Kabban, Brett J. Borghetti
Madfam: Microarchitectural Data Framework And Methodology, Tor J. Langehaug, Scott R. Graham, Christine M. Schubert Kabban, Brett J. Borghetti
Faculty Publications
In the aftermath of Spectre and Meltdown researchers have proposed a variety of attack detection solutions by applying machine learning to data collected from hardware performance monitoring units. Although many microarchitectural attack detection systems provide high-accuracy detection results, the behavior of the underlying data collection mechanisms is not well described or understood. This research introduces the MicroArchitectural Data Framework And Methodology (MADFAM) to prescribe a systematic approach to collecting and preserving the information available in sequences of microarchitectural data. The proposed framework focuses on hardware performance counters (HPCs) as the primary data source. HPC configuration is complex, which makes it …
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Theses and Dissertations
This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a …
Monocular Pose Estimation For Automated Aerial Refueling Via Perspective-N-Point, James C. Lynch
Monocular Pose Estimation For Automated Aerial Refueling Via Perspective-N-Point, James C. Lynch
Theses and Dissertations
Any Automated Aerial Refueling (AAR) solution requires the quick and precise estimation of the relative position and rotation of the two aircraft involved. This is currently accomplished using stereo vision techniques augmented by Iterative Closest Point (ICP), but requires post-processing to account for environmental factors such as boom occlusion. This paper proposes a monocular solution, combining a custom-trained single-shot object detection Convolutional Neural Network (CNN) and Perspective-n-Point (PnP) estimation to calculate a pose estimate with a single image. This solution is capable of pose estimation at contact point (22m) within 7cm of error and a rate of 10Hz, regardless of …
Incorporating Armed Escorts To The Military Medical Evacuation Dispatching Problem Via Stochastic Optimization And Reinforcement Learning, Andrew G. Gelbard
Incorporating Armed Escorts To The Military Medical Evacuation Dispatching Problem Via Stochastic Optimization And Reinforcement Learning, Andrew G. Gelbard
Theses and Dissertations
The military medical evacuation (MEDEVAC) dispatching problem seeks to determine high-quality dispatching policies to maximize the survivability of casualties within contingency operations. This research leverages applied operations research and machine learning techniques to solve the MEDEVAC dispatching problem and evaluate system performance. More specifically, we develop an infinite-horizon, continuous-time Markov decision process (MDP) model and approximate dynamic programming (ADP) solution approach to generate high-quality policies. The ADP solution approach utilizes an approximate value iteration algorithm strategy incorporating gradient descent Q-learning to approximate the value function. A notional, synthetically-generated scenario in Africa based around the capital city of Niger, Niamey is …
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
Theses and Dissertations
Officer retention in the Air Force has been researched many times in an attempt to better predict the personnel needs of the Air Force for the future. There has been previous work done in regards to specific AFSCs and how their retention compares to specific yet similar private sector jobs. This study considers different econometric time series statistics as a feature space and an average Air Force officer separation rate as the response variable for the multivariate time series analysis deep learning techniques. The econometric indicators used in this study are New Business Formations, New Durable Good Orders, and the …
An Investigation Of Data Storage In Entity-Component Systems, Bailey V. Compton
An Investigation Of Data Storage In Entity-Component Systems, Bailey V. Compton
Theses and Dissertations
Entity-Component Systems (ECS) have grown vastly in application since their introduction more than 20 years ago. Providing the ability to efficiently manage data and optimize program execution, ECSs, as well as the wider field of data-oriented design, have attained popularity in the realms of modeling, simulation, and gaming. This manuscript aims to elucidate and document the storage frameworks commonly found in ECSs, as well as suggesting conceptual connections between ECSs and relational databases. This formal documentation of the in-memory storage formats of entity-component systems affords the United States Air Force, the Department of Defense, and the software engineering community a …
Determination Of Vortex Locations In A 2x2 Array Of Josephson Junctions For Topological Quantum Computation, Casey L. Kowalski
Determination Of Vortex Locations In A 2x2 Array Of Josephson Junctions For Topological Quantum Computation, Casey L. Kowalski
Theses and Dissertations
A large barrier to practical quantum computation exists in the form of qubit decoherence, which leads to high noise and error when implementing quantum algorithms. A potential solution to this problem is the use of topologically-protected Majorana-based qubits, as their nonlocal nature and unique non-abelian exchange statistics render them virtually immune to decoherence while still allowing the state to be easily manipulated. For such a qubit to be constructed, it is essential to know the locations of the Majorana-hosting vortices in the system. This work presents a solution for the formation locations of vortices in a 2x2 superconducting island array, …
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Bayesian Convolutional Neural Network With Prediction Smoothing And Adversarial Class Thresholds, Noah M. Miller
Theses and Dissertations
Using convolutional neural networks (CNNs) for image classification for each frame in a video is a very common technique. Unfortunately, CNNs are very brittle and have a tendency to be over confident in their predictions. This can lead to what we will refer to as “flickering,” which is when the predictions between frames jump back and forth between classes. In this paper, new methods are proposed to combat these shortcomings. This paper utilizes a Bayesian CNN which allows for a distribution of outputs on each data point instead of just a point estimate. These distributions are then smoothed over multiple …
The Impact Of Visual Feedback And Control Configuration On Pilot-Aircraft Interface Using Head Tracking Technology, Christopher M. Arnold
The Impact Of Visual Feedback And Control Configuration On Pilot-Aircraft Interface Using Head Tracking Technology, Christopher M. Arnold
Theses and Dissertations
Traditional control mechanisms restrict human input on the displays in 5th generation aircraft. This research explored methods for enhancing pilot interaction with large, information dense cockpit displays; specifically, the effects of visual feedback and control button configuration when augmenting cursor control with head tracking technology. Previous studies demonstrated that head tracking can be combined with traditional cursor control to decrease selection times but can increase pilot mental and physical workload. A human subject experiment was performed to evaluate two control button configurations and three visual feedback conditions. A Fitts Law analysis was performed to create predictive models of selection time …
Magnetic Navigation Using Online Calibration Filter Analysis, Jonnathan D. Bonifaz
Magnetic Navigation Using Online Calibration Filter Analysis, Jonnathan D. Bonifaz
Theses and Dissertations
Magnetic navigation using the Earth’s magnetic anomaly field has proven to be a promising alternative that can provide coverage for a navigation system. This research demonstrates a magnetic navigation system using an extended Kalman filter (EKF) to aid an aircraft’s inertial navigation system (INS). Traditional magnetic anomaly navigation uses a “static calibration method,” which takes post-processed data to calibrate, obtaining the Tolles-Lawson coefficients needed for magnetic anomaly navigation filter compensation. These coefficients are constant and may cause drifts in the navigation filter if not re-calibrated. The online calibration method continuously updates the Tolles-Lawson coefficients as a filter state, reducing the …
A Thematic And Reference Analysis Of Touchless Technologies, Eric R. Curia
A Thematic And Reference Analysis Of Touchless Technologies, Eric R. Curia
Theses and Dissertations
The purpose of this research is to explore the utility and current state of touchless technologies. Five categories of technologies are identified as a result of collecting and reviewing literature: facial/biometric recognition, gesture recognition, touchless sensing, personal devices, and voice recognition. A thematic analysis was conducted to evaluate the advantages and disadvantages of the five categories. A reference analysis was also conducted to determine the similarities between articles in each category. Touchless sensing showed to have the most advantages and least similar references. Gesture recognition was the opposite. Comparing analyses shows more reliable technology types are more beneficial and diverse.
Game Theory Framework To Evaluate Nuclear Deterrence, Michael A. Cevallos
Game Theory Framework To Evaluate Nuclear Deterrence, Michael A. Cevallos
Theses and Dissertations
This research game theory framework evaluates the resilience of nuclear deterrence options between two players. We use lexicographic prioritization to value four priorities of political, military, economic, and civilian casualties. The value order may be varied. We demonstrate our approach with six player choices of no nuclear strike, demonstration, counterforce, tactical military, economic, or countervalue strike. We use game theory to construct and analyze the resulting damage matrix. We conclude that credible deterrence requires having at least equivalent offensive damage capabilities.
Carbon Estimation And Decision Making In Usaf Acquisition, Robert F. Gray
Carbon Estimation And Decision Making In Usaf Acquisition, Robert F. Gray
Theses and Dissertations
Recent executive orders and international agreements require the United States to significantly reduce its carbon and greenhouse gas emissions. The DoD is a significant contributor to the carbon emissions of the USA and will be required to reduce the emissions. Therefore, in order to make appropriate programmatic decisions the DoD needs to develop an appropriate method for estimating carbon and making programmatic decisions; trading-off carbon emissions with the traditional cost-schedule-performance metrics. This thesis examines the possibility of developing a model that can be used to estimate the carbon footprint of producing a system before detailed engineering designed have been complete.
Persistence And Mitigation Of Pfas Within Concrete Stormwater Drainage Infrastructure, Jason R. Mcdonald
Persistence And Mitigation Of Pfas Within Concrete Stormwater Drainage Infrastructure, Jason R. Mcdonald
Theses and Dissertations
The persistence, fate, and transport of per- and poly-fluoroalkyl substances, which have been shown to have adverse effects on human health, have been previously studied in environmental media such as soils and groundwater. This study investigates concrete, a medium that is rarely studied but frequently present in instances where PFAS originating from AFFF releases and spills have occurred. Used heavily throughout aviation firefighting, AFFF poses environmental hazards due to the length of PFAS degradation and toxicological implications, thus its classification as a forever chemical. From the very limited reports to date, studies have suggested very slow release from concrete, potentially …
Directionally Sensitive Gamma Imaging Using Rotating Scatter Masks And Inexpensive, Scintillation Detectors, Christopher S. Charles
Directionally Sensitive Gamma Imaging Using Rotating Scatter Masks And Inexpensive, Scintillation Detectors, Christopher S. Charles
Theses and Dissertations
This work demonstrates the first instantiation of the FitzGerald Rotating Scatter Mask (RSM) as a proof-of-concept for two-dimensional source direction determination using a single, inexpensive, non-cooled scintillator, as well as an alternate mask design for comparison. A large RSM was additively manufactured from low-Z, acrylic like material, and rotated around the ubiquitous standard 3" x 3" NaI(Tl) or NaI(Tl)/CsI(Tl) phoswich detector, set internally to the mask. Smaller versions of the FitzGerald and alternate RSM designs were 3D printed for testing and used in conjunction with a LaBr detector to characterize the RSM system with a size and weight reduction applied. …
Defining Resilience, Robustness, And Vulnerability Metrics To Guide Facilities And Infrastructure Decision Making For The United States Air Force, Kelly W. Minor
Theses and Dissertations
Tyndall AFB was nearly destroyed after a category five hurricane named Michael tore through the Florida panhandle in 2018. A Program Management Office was stood up to coordinate, plan, and carry out the rebuild efforts of Tyndall AFB. The office’s primary responsibility is “building a base capable of supporting a 21st-century mission while also focusing on structural resiliency and efficiency” (Laidlaw 2019). Despite the requirement to build a resilient infrastructure portfolio, the United States Air Force has yet to fully quantify and qualify measures of resilience, including robustness and vulnerability, in the context of their current built infrastructure portfolio. This …
The Impacts Of Climate Uncertainty On Streamflow In Andes, Antioquia, Colombia, Kristen R. Roberts
The Impacts Of Climate Uncertainty On Streamflow In Andes, Antioquia, Colombia, Kristen R. Roberts
Theses and Dissertations
Natural hazards, such as hurricanes, wildfires, floods, and droughts impact human systems that rely on predictable patterns in the natural elements with which they interact. Skillful prediction of the impacts of climate change on linked, human-natural systems, like surface water resources, can help ensure physical risks within vulnerable communities are mitigated, resource sustainability is maximized, and intersectoral markets continue to contribute to socioeconomic stability. Due to water resources being a primary conduit through which climate uncertainty impacts people, economies, and ecosystems, its study is worthy of investigation; particularly, where those resources are uncertain and demanded by a variety of competitive …
Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej
Application Of Machine Learning Models With Numerical Simulations Of An Experimental Microwave Induced Plasma Gasification Reactor, Owen D. Sedej
Theses and Dissertations
This thesis aims to contribute to the future development of this technology by providing an in-depth literature review of how this technology physically operates and can be numerically modeled. Additionally, this thesis reviews literature of machine learning models that have been applied to gasification to make accurate predictions regarding the system. Finally, this thesis provides a framework of how to numerically model an experimental plasma gasification reactor in order to inform a variety of machine learning models.