Physical Sciences and Mathematics Commons^™

Applying Machine‐Learning Methods To Laser Acceleration Of Protons: Lessons Learned From Synthetic Data, Ronak Desai, Thomas Zhang, J. J. Felice, Ricky Oropeza, Joseph R. Smith, Alona Kryshchenko, Chris Orban, Michael L. Dexter, Anil K. Patnaik

Faculty Publications

In this study, we consider three different machine-learning methods—a three-hidden-layer neural network, support vector regression, and Gaussian process regression—and compare how well they can learn from a synthetic data set for proton acceleration in the Target Normal Sheath Acceleration regime. The synthetic data set was generated from a previously published theoretical model by Fuchs et al. 2005 that we modified. Once trained, these machine-learning methods can assist with efforts to maximize the peak proton energy, or with the more general problem of configuring the laser system to produce a proton energy spectrum with desired characteristics. In our study, we focus …

Strategies To Alleviate Flickering: Bayesian And Smoothing Methods For Deep Learning Classification In Video, Noah Miller, Glen Ryan Drumm, Lance Champagne, Bruce A. Cox, Trevor Bihl

Faculty Publications

Excerpt: Increasing reliance on autonomous systems requires confidence in the accuracies produced from computer vision classification algorithms. Computer vision (CV) for video classification provides phenomenal abilities, but it often suffers from “flickering” of results. Flickering occurs when the CV algorithm switches between declared classes over successive frames. Such behavior causes a loss of trust and confidence in their operations.

Time Domain Spectral Lidar Enabled By Cascaded Raman In A Hydrogen-Filled Transmitter, Richard K. Martin, Trevor L. Courtney, Arielle M. Adams, Daniel E. Leaird, Luke Ausley, Christian K. Keyser

Faculty Publications

We introduce what we believe to be novel spectral light detection and ranging (LiDAR) architectures that enable ultra-compact systems by a transition from spectral signal processing in space (gratings) to processing in time. The architectures leverage temporal dispersion and the unique spectro-temporal waveforms produced from the cascaded Raman scattering generated in the (H₂) filled hollow core fiber. The characterized Raman source yields as many as six Raman orders from 1.06-1.70 μm; their unique spectro-temporal waveforms are measured. System performance simulations based on measured Raman waveforms show that high accuracy measurement of range and reflectivity are possible with proper …

Limitations Of Beam-Control Compensation, Matthew Kalensky, Darren Getts, Matthias T. Banet, Derek J. Burrell, Milo W. Hyde, Mark F. Spencer

Faculty Publications

In this paper, we use wave-optics simulations to explore the limitations of beam-control compensation. We evaluate performance in terms of the normalized power in a diffraction-limited bucket for the cases of no beam-control compensation, perfect phase compensation, and perfect full-field compensation. From these results, we are able to arrive at the following conclusions: (1) without any form of beam-control compensation, performance begins to degrade when D/r₀ > 1; (2) with perfect phase compensation, performance begins to degrade when D/r₀ > 1 and (λ/r₀)/θ₀ > 1; and (3) with perfect full-field compensation, performance begins to degrade when D/r_{0 …}

Inferring Tlb Configuration With Performance Tools, Cristian Agredo, Tor J. Langehaug, Scott R. Graham

Faculty Publications

Modern computing systems are primarily designed for maximum performance, which inadvertently introduces vulnerabilities at the micro-architecture level. While cache side-channel analysis has received significant attention, other Central Processing Units (CPUs) components like the Translation Lookaside Buffer (TLB) can also be exploited to leak sensitive information. This paper focuses on the TLB, a micro-architecture component that is vulnerable to side-channel attacks. Despite the coarse granularity at the page level, advancements in tools and techniques have made TLB information leakage feasible. The primary goal of this study is not to demonstrate the potential for information leakage from the TLB but to establish …

Constraining Accreted Neutron Star Crust Shallow Heating With The Inferred Depth Of Carbon Ignition In X-Ray Superbursts, Zachary P. Meisel

Faculty Publications

Evidence has accumulated for an as-yet unaccounted for source of heat located at shallow depths within the accreted neutron star crust. However, the nature of this heat source is unknown. I demonstrate that the inferred depth of carbon ignition in X-ray superbursts can be used as an additional constraint for the magnitude and depth of shallow heating. The inferred shallow heating properties are relatively insensitive to the assumed crust composition and carbon fusion reaction rate. For low-accretion rates, the results are weakly dependent on the duration of the accretion outburst, so long as accretion has ensued for enough time to …

Rubrics Informed By The Cognitive Theory Of Multimedia Learning That Support Research On Personalized Learning Paths, Sean A. Mochocki, Mark G. Reith, Jonathan Zemmer

AFIT Documents

Personalized Learning Paths (PLP)s are a popular area of research in E-Learning where sequences of Learning Materials (LM)s and activities are returned based on a learner profile, the LM metadata, and a knowledge structure that describes the relationship between the underlying topics. Unfortunately, PLP researchers tend to not use an empirically supported cognitive science framework for their research, instead relying on such unsupported theories as learning styles or developing their own ad hoc approaches. While many of these researchers present and solve challenging PLP problems using a variety of algorithmic approaches, the PLP community in general would benefit from a …

Global Empirical Model Of Sporadic-E Occurrence Rates, Eli V. Parsch, Anthony L. Franz, Eugene V. Dao, Dong L. Wu, Nimalan Swarnalingam, Cornelius C. J. H. Salinas, Daniel J. Emmons

Faculty Publications

Intense ionization enhancements in the Earth’s ionosphere, known as sporadic-E (E_s), can degrade and severely disrupt the propagation of radio signals. Although many previous studies have analyzed the characteristics and morphologies of sporadic-E, few efforts have attempted to model global E_s occurrence rates (ORs) at high time resolutions. This study develops a global empirical model of blanketing sporadic-E occurrence rates using a Karhunen–Loéve Expansion (KLE) of a global OR climatology built with Global Navigation Satellite System radio occultation (GNSS-RO) and ionosonde observations. Using an fbE ≥ threshold of 3 MHz, the model outputs a blanketing sporadic-E …

Personalized Learning Path Problem Variations: Computational Complexity And Ai Approaches, Sean A. Mochocki, Mark Reith, Brett J. Borghetti, Gilbert L. Peterson, John Jasper, Laurence D. Merkle

Faculty Publications

E-learning courses often suffer from high dropout rates and low student satisfaction. One way to address this issue is to use personalized learning paths (PLPs), which are sequences of learning materials that meet the individual needs of students. However, creating PLPs is difficult and often involves combining knowledge graphs (KGs), student profiles, and learning materials. Researchers typically assume that the problem of creating PLPs belong to the nondeterministic polynomial (NP)-hard class of computational problems. However, previous research in this field has neither defined the different variations of the PLP problem nor formally established their computational complexity. Without clear definitions of …

Predictability Limit Of The 2021 Pacific Northwest Heatwave From Deep‐Learning Sensitivity Analysis, P. Trent Vonich, Gregory J. Hakim

Faculty Publications

The traditional method for estimating weather forecast sensitivity to initial conditions uses adjoint models, which are limited to short lead times due to linearization around a control forecast. The advent of deep‐learning frameworks enables a new approach using backpropagation and gradient descent to iteratively optimize initial conditions, minimizing forecast errors. We apply this approach to the June 2021 Pacific Northwest heatwave using the GraphCast model, yielding over 90% reduction in 10‐day forecast errors over the Pacific Northwest. Similar improvements are found for Pangu‐Weather model forecasts initialized with the GraphCast‐derived optimal, suggesting that model error is an unimportant part of the …

Numerically Efficient Coherent Mode Representations For Partially Coherent Beams With Separable Phases, Milo W. Hyde, Carolina Rickenstorff

Faculty Publications

We present a method to numerically compute the coherent mode representations (CMRs) for partially coherent beams with separable phases. This special class of random light field has the ability to self-focus and is resistant to turbulence-induced degradation, making it potentially useful in applications such as optical communications. We validate our method by generating (in simulation) two such sources from the literature using their computed CMRs. Lastly, we conclude with a summary of our approach and a discussion of potential applications.

Closed-Loop Adaptive Optics In The Presence Of Speckle And Weak Scintillation, Derek J. Burrell, Mark F. Spencer, Ronald G. Driggers

Faculty Publications

In this paper, we show that speckle averaging helps to improve adaptive-optics (AO) performance in closed loop as a result of reduced measurement error associated with a Shack–Hartmann wavefront sensor (SHWFS); however, this reduction is rendered ineffective with increasing beacon anisoplanatism. We do so operating in a weak-scintillation regime, where the SHWFS offers robust performance, and using in-plane translation of the illuminated rough surface to accomplish frame-to-frame speckle diversity. Understanding these trade-space limitations is critical when performing AO with non-cooperative, extended-source beacons.

Using Machine Learning To Predict State Compliance With International Legal Obligations For Registration Of Space Objects: Comparative Performance Of Logistic Regression And Dense Neural Network Models, Jonathan K. Sawmiller

Student Publications

Approximately 12% of satellites and other objects launched into outer space have not been registered with the United Nations (UN) as required by international law. To predict whether States will register a launched space object and understand what factors influence a registration decision, data from a UN online index of space objects was used to train and select the best machine learning model. After preparation, the dataset had 1938 datapoints with 11 features, with categorical features simplified and converted to binary.

Multiple variations of classical logistic regression models were compared to multiple variations of dense neural network models. The best …

Production Box Cost Estimating Relationships For Dod Avionics, Carla J. Cisneros, Edward D. White, Brandon M. Lucas, Jonathan D. Ritschel, Robert D. Fass, Shawn M. Valentine

Faculty Publications

The authors use historical information obtained from the Cost Assessment Data Enterprise to estimate recurring production unit cost for DoD avionics via cost estimating relationships (CERs). The specific modeled responses include mean unit cost, median unit cost, and the 100th production unit cost (T100) utilizing learning curve theory. For T100, the authors adopt both a multiplicative and an additive error for CER comparison. Recommended CERs consist of the mean unit cost and the T100 utilizing a multiplicative error. Moreover, results reveal that weight has a significant effect on cost as well as a potential underaccounting of real price change or …

Real-Time Synthesis Of A Nonuniformly Correlated, Partially Coherent Beam Using An Optical Coordinate Tansformation, Milo W. Hyde Iv

Faculty Publications

We design, build, and validate an optical system for generating light beams with complex spatial coherence properties in real time. Beams of this type self-focus and are resistant to turbulence degradation, making them potentially useful in applications such as optical communications. We begin with a general theoretical analysis of our proposed design. Our approach starts by generating a Schell-model (uniformly correlated or shift-invariant) source by spatially filtering incoherent light. We then pass this light through an optical coordinate transformer, which converts the Schell-model source into a nonuniformly correlated field. After the general analysis, we discuss system engineering, including trade-offs among …

Innovation Challenges In The Air Force Sbir Program: From The Small Businesses' Perspective, Hart J. Holt, Amy M. Cox, Scott Drylie, David S. Long, Alfred E. Thal Jr., Robert D. Fass

Faculty Publications

Every year the United States invests $3.2 billion in the Small Business Innovation Research (SBIR) program to promote innovation among the nation’s small businesses. Half of this investment is from the DoD. This research considers the challenges faced by small businesses innovating with the DoD, particularly those awarded SBIR contracts with the United States Air Force. The authors surveyed 286 unique small businesses that were previously awarded an Air Force SBIR contract. By asking the survey respondents open-ended questions and categorizing their responses, they pinpoint unaddressed challenges from the small business perspective. By categorizing survey responses through Qualitative Content Analysis, …

The Evaluation Of Machine Learning Techniques For Isotope Identification Contextualized By Training And Testing Spectral Similarity, Aaron P. Fjelsted, Tyler J. Morrow, Clayton D. Scott, Yilun Zhu, Darren E. Holland, Azaree T. Lintereur, Douglas E. Wolfe

Faculty Publications

Precise gamma-ray spectral analysis is crucial in high-stakes applications, such as nuclear security. Research efforts toward implementing machine learning (ML) approaches for accurate analysis are limited by the resemblance of the training data to the testing scenarios. The underlying spectral shape of synthetic data may not perfectly reflect measured configurations, and measurement campaigns may be limited by resource constraints. Consequently, ML algorithms for isotope identification must maintain accurate classification performance under domain shifts between the training and testing data. To this end, four different classifiers (Ridge, Random Forest, Extreme Gradient Boosting, and Multilayer Perceptron) were trained on the same dataset …

Malware Classification Through Abstract Syntax Trees And L-Moments, Anthony J. Rose, Christine M. Schubert Kabban, Scott R. Graham, Wayne C. Henry, Christopher M. Rondeau

Faculty Publications

The ongoing evolution of malware presents a formidable challenge to cybersecurity: identifying unknown threats. Traditional detection methods, such as signatures and various forms of static analysis, inherently lag behind these evolving threats. This research introduces a novel approach to malware detection by leveraging the robust statistical capabilities of L-moments and the structural insights provided by Abstract Syntax Trees (ASTs) and applying them to PowerShell. L-moments, recognized for their resilience to outliers and adaptability to diverse distributional shapes, are extracted from network analysis measures like degree centrality, betweenness centrality, and closeness centrality of ASTs. These measures provide a detailed structural representation …

Coarse-Gridded Simulation Of The Nonlinear Schrödinger Equation With Machine Learning, Benjamin F. Akers, Kristina O. F. Williams

Faculty Publications

A numerical method for evolving the nonlinear Schrödinger equation on a coarse spatial grid is developed. This trains a neural network to generate the optimal stencil weights to discretize the second derivative of solutions to the nonlinear Schrödinger equation. The neural network is embedded in a symmetric matrix to control the scheme’s eigenvalues, ensuring stability. The machine-learned method can outperform both its parent finite difference method and a Fourier spectral method. The trained scheme has the same asymptotic operation cost as its parent finite difference method after training. Unlike traditional methods, the performance depends on how close the initial data …

Estimating Dis Performance Using Mininet, Ryan D. Winz

Theses and Dissertations

Real time distributed simulation is an exceptionally useful tool for training and wargaming used by the military and industry alike. This research aims to provide scenarios and structures to evaluate the effect of distributing simulations among different compute nodes. Specific scenarios involve the analysis of performance as a function of latency and the degree network protocols and reliability affect simulation performance. Various standards exist for administering geographically separated simulations. The focus of this thesis will be on the Distributed Interactive Simulation standard, a peer-to-peer open standard for simulation messages to adhere to, but lessons can be extended to other standards.

Investigating Temperature And Emissivity Separation Techniques Through The Use Of A Spectral Emissivity Database, Mitchell A. Manzardo

Theses and Dissertations

This study evaluates two temperature and emissivity separation techniques using data from an ABB MR304 FTIR and a Telops Mid-Wave Hyper-Cam on carbon-based samples heated by a high-energy laser. The established Linear Spectral Emissivity Constraint (LSEC) and Gray Body Emissivity (GBE) methods, as well as a new Robust Ratio Linear Fitting (RRF) method are assessed for their applicability in controlled lab environments. A spectral emissivity database aids in analyzing method performance. The RRF method shows promise but has limitations, particularly with low-temperature data and complex Hyper-Cam data. Despite these challenges, the RRF method offers potential for further refinement.

Generation And Implementation Of Mixed Radiation From Laser Interactions On A Liquid Target, Benjamin M. Knight

Theses and Dissertations

The simultaneous generation of different forms of radiation from the same source is highly desirable but technically challenging. We present the generation of D-D fusion neutrons and >MeV-energy x-rays from extreme intensity (∼ 10¹⁹ W/cm² ), high repetition-rate (1 kHz) laser pulses on a thin liquid target. Total neutron fluxes of ∼ 105 neutrons/s and x-ray exposure rates up to ∼ 200 R/hr at 74 cm are measured. Evidence of D-D fusion is verified with a measurement suite including three independent neutron detection systems: an EJ-309 organic scintillator, a ³He proportional counter, and a set of 36 …

Collisional And Spatial Characterization Of Electron Broadening In Rubidium Vapor, Jordan C. Mindrup

Theses and Dissertations

Stark broadening of the 4²D → 12²F transition of rubidium is examined in a rubidium vapor cell with modulated double pump-probe spectroscopy at helium pressures of 0.001 Torr, 3.0 Torr, and 30.0 Torr. A pressure dependent critical rubidium number density was observed after which the presence of electrons becomes dominant. It is observed that after the rubidium density threshold, a linear relationship is seen between rubidium number density and the Stark broadened width of the line shapes, with a slope of 0.079 ± 0.002, 0.18 ± 0.01, and 0.9 ± 0.1 for helium pressures of 0.001 …

Advancing Robust Autonomous System Localization: Labeling Optimizations For Convolutional Neural Networks, Jeffrey L. Choate

Theses and Dissertations

AAR is increasingly critical as aircraft autonomy advances, particularly for the Global Strike mission of the USAF, enhancing operational range and endurance. Traditional methods relying on GPS and custom communication links are limited in GPS-denied environments. This dissertation advances a single camera method to estimate object pose across three interconnected studies. The system trains a CNN on synthetic imagery to predict bboxes for object components, Solve-PnP algorithm finds the 6DoF pose, then employs novel pseudo-labeling on real-world images. These findings are pivotal for the AAR community and contribute to robotics, computer vision, and CNN research. By enabling robust GPS-free autonomous …

Autonomous Experimentation For Accelerated Calibration Of Fused Deposition Modeling 3d Printers, Graig S. Ganitano

Theses and Dissertations

Additive Manufacturing (AM), also known as 3D printing, has emerged as a key component of Industry 4.0, enabling reduced cost, quick production, greater sustainability, and increased design complexity compared to its traditional manufacturing counterpart. Currently, Fused Deposition Modeling (FDM) technology dominates the AM market with respect to the number of 3D printers in use. However, the FDM process is sensitive to changes in system configuration, especially the feedstock material. Utilizing a new feedstock requires a time-consuming trial-and-error process to identify optimal settings for a large number of process parameters, acting as a barrier to the technology.

To enable greater accessibility …

Integrating Blockchain Technology Into The Software Development Life Cycle To Satisfy The Software Bill Of Materials Requirement For Government Software Systems, Walter T. Scott Ii

Theses and Dissertations

This thesis explores the integration of Blockchain Technology (BT) into the Software Development Life Cycle (SDLC) to satisfy the Software Bill of Materials (SBOM) requirement for government software systems. This study begins by synthesizing a standard SDLC definition from various government and industry references, which may provide the foundation for future efforts to standardize software development practices across the government software development community. This study proceeds to define working definitions for the software supply chain (SSC) and software supply chain management (SCM) before introducing and detailing the SBOM requirement as well as providing an overview of prior research regarding SBOMs …

Machine Visual Perception For Autonomous Docking Maneuvers, Derek B. Worth

Theses and Dissertations

This dissertation presents a novel approach to autonomous docking using machine learning for visual perception, particularly during probe and drogue aerial refueling. Autonomous vehicles have become pervasive in both civilian and defense sectors, and their ability to interact with their surroundings and each other autonomously is critical for future operations. Traditional methods relying on signals or inertial sensors face significant limitations such as interference, jamming, and drift. This research focuses on developing a computer vision-based solution to overcome these limitations. A novel pipeline, termed relative vectoring, is introduced, which utilizes dual object detection and machine learning to estimate relative positions …

Twisted Spatiotemporal Optical Vortex Beams In Dispersive Media, Milo W. Hyde Iv

Faculty Publications

We derive a closed-form expression for the mutual coherence function (MCF) of a twisted spatiotemporal optical vortex (STOV) beam after propagating a distance in a linear dispersive medium. A twisted STOV beam is a partially coherent optical field that possesses a coherent STOV and a stochastic twist coupling its space and time dimensions. These beams belong to a special class of space–time-coupled light fields that carry transverse (to the direction of propagation) orbital angular momentum, making them potentially useful in numerous applications including quantum optics, optical manipulation, and optical communications. After presenting the derivation, we validate our new general MCF …

Open-Loop Wavefront Sensing In The Presence Of Speckle And Weak Scintillation, Derek J. Burrell, Mark F. Spencer, Ronald G. Driggers

Faculty Publications

In this paper, we show that speckle averaging helps to reduce the measurement error associated with a Shack–Hartmann wavefront sensor (SHWFS); however, this reduction is rendered ineffective with increasing beacon anisoplanatism. We do so operating in a weak-scintillation regime, where the SHWFS offers robust performance, and using in-plane translation of the illuminated rough surface to accomplish frame-to-frame speckle diversity. Understanding these trade-space limitations is critical when performing wavefront sensing with noncooperative, extended-source beacons.

Strength Measurement Of The E_Α^Lab = 830 Kev Resonance In The Ne 22 ( Α , N ) Mg 25 Reaction Using A Stilbene Detector, Shahina S., R. J. Deboer, J. Gorres, R. Fang, Michael T. Febbraro, R. Kelmar, M. Matney, K. Manukyan, J. T. Nattress, E. Robles, T. J. Rutland, T. T. King, A. Sanchez, R. S. Sidhu, E. Stech, M. Wiescher

Faculty Publications

The interplay between the ²²Ne (��,��)²⁶Mg reaction and the competing ²²Ne(��,��)²⁵Mg reaction determines the efficiency of the latter as a neutron source at the temperatures of stellar helium burning. In both cases, the rates are dominated by the ��-cluster resonance at 830 keV. This resonance plays a particularly important role in determining the strength of the neutron flux for both the weak and main �� process as well as the �� process. Recent experimental studies based on transfer reactions suggest that the neutron and ��-ray strengths for this resonance are approximately equal. In this …