Engineering Commons^™

Afit Generative Ai Teaching Guidebook, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

AFIT is proud to highlight the Generative AI Teaching Guidebook, a resource designed to provide military educators with practical insights, strategies, and use cases for integrating Generative AI (Gen AI) into their teaching practices. Developed through a collaborative effort involving AFIT faculty across various departments within the Graduate School of Engineering and Management and the School of Systems and Logistics, this digital resource serves as a starting point for educators exploring how to leverage Gen AI in their classrooms. It offers accessible examples and best practices, ensuring utility for instructors of all technical backgrounds. The guidebook provides a comprehensive overview …

Afit Generative Ai Teaching Guidebook Synopsis, Afit Faculty Learning Community, Mark G. Bateman, Brett J. Borghetti, Allen W. Dukes, Nicholas C. Francis, Mike Frick, Bobbie Oh, Kevin Patterson, Hiren J. Patel, Mark G. Reith, Erick S. Tyndall, Teresa M. Walton, Torrey J. Wagner, Timothy S. Wolfe, Jonathan Zemmer

AFIT Documents

The main objective of this work was to bring together various perspectives on how to envision incorporating Gen AI capabilities into the learning environment and identify some best practices for their implementation. Any instructor who is interested in these capabilities but does not necessarily have a technical background can find pragmatic use of the examples provided. While the examples have a wide range of applicability, they are meant to serve as a starting point for educators to explore what would be beneficial to their educational environment, from traditional classroom settings to online continuing education courses.

Dynamic Analysis Of Additively Manufactured Tensegrity Structures, Keivan Davami, Russell A. Rowe, Ben Gulledge, Jesse Park, Ali Beheshti, Anthony N. Palazotto, Fariborz Tavangarian, Sadie Beck

Faculty Publications

Herein, we present an analysis, design, and experimental testing of modular prestressed pin-jointed structures constructed from bistable units and inspired by the classical triangular tensegrity prism. Tensegrity structures, characterized by a combination of tension members (cables) and compression members (bars) in a self-equilibrated state, have gained significant attention in engineering over the past two decades due to their unique nonlinear mechanical behavior. The discontinuity of the compression members in tensegrity structures leads to a slightly different failure behavior compared to their lattice structure counterparts, with unprecedented applications. However, traditional fabrication and assembly methods have posed challenges for their widespread adoption. …

The Boris Experience: Evaluating Omnichannel Returns And Repurchase Intention, Jianliang Hao, Robert G. Richey Jr., Tyler R. Morgan, Ian M. Slazinik

Faculty Publications

Researchers have examined the influence of the factors on reducing return rates in retailing over the years. However, the returns experience is often an overlooked way to drive customer engagement and repeat sales in the now ubiquitous omnichannel setting. The focus on returns prevention in existing research overshadows management’s need to understand better the comprehensive mechanics linking the customer in-store return experience with their repurchase actions. Recognizing the need to bridge different stages of the returns management process, this research aims to explore the facilitators and barriers of in-store return activities.

Machine Visual Perception From Sim-To-Real Transfer Learning For Autonomous Docking Maneuvers, Derek Worth, Jeffrey Choate, Ryan M. Raettig, Scott L. Nykl, Clark N. Taylor

Faculty Publications

This paper presents a comprehensive approach to enhancing autonomous docking maneuvers through machine visual perception and sim-to-real transfer learning. By leveraging relative vectoring techniques, we aim to replicate the human ability to execute precise docking operations. Our study focuses on autonomous aerial refueling as a use case, demonstrating significant advancements in relative navigation and object detection. We introduce a novel method for aligning digital twins using fiducial targets and motion capture data, which facilitates accurate pose estimation from real-world imagery. Additionally, we develop cost-efficient annotation automation techniques for generating high-quality You Only Look Once training data. Experimental results indicate that …

Extracting Permittivity And Permeability Using The Position-Insensitive And Calibration-Independent Method On A Rectangular Waveguide, James Conrad Denemark

Theses and Dissertations

Classic methods for extracting material characteristics require known measurements to accurately calibrate the network analyzer. Previous work demonstrated a position-insensitive and calibration-independent (PiCi) transmission/reflection method to extract a material’s permittivity. This thesis proposes a method with the same function, manipulated to use one empty measurement and then two samples of different thicknesses. The PiCi method is first adopted for rectangular waveguide which resulted in inaccurate permittivity data when compared to the calibrated solution. Once detector mismatch corrections were applied, the PiCi method produced accurate results. Using a 2-D numerical root search, permittivity and permeability material characteristics are now successfully extracted …

Dual-Channel Side Channel Attack: Improved Aes Key Decryption By Combining Power And Electromagnetic Side Channels With Convolutional Neural Networks, Sean P. O'Neill

Theses and Dissertations

This research introduces a novel DL approach for SCA that combines power consumption and EM signals to enhance encryption key deduction by leveraging a dual-channel CNN architecture. A new dataset, consisting of simultaneous power and EM signal collections during 128-bitAES encryption, was developed to train and evaluate the model’s effectiveness. The combined approach achieved an 88% reduction in traces needed, from 50 traces to 6, for encryption key classification, outperforming traditional methods such as random forest, DPA, DEMA,and individual side channel CNN models. These findings highlight the potential of integrating multiple side channels in SCA to improve performance without the …

Training-Informed Air Traffic Control Workforce Scheduling, Eli Winett

Theses and Dissertations

The Radar Control Facility (RCF) at Eglin Air Force Base creates monthly workforce schedules. Adherence to regulations must be verified manually and no evaluation criteria exist to compare schedules. A mixed-integer program model for air traffic controllers allocates workers across 19 positions for a user-defined number of weeks of 24-hour operations. Across eight consecutive months, this model produced schedules to nearly eliminate all shifts with critical shortages from the reference schedules. Without civilian overtime work, gains in positional coverage require a 35% increase in overtime work for military workers and 35% reduction in monthly training instances. We recommend users adopt …

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.

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 …

The Impact Of Film Cooling On The Heat Release Within A Rotating Detonation Combustor, Shreyas Ramanagar Sridhara, Antonio Andreini, Marc D. Polanka, Myles D. Bohon

Faculty Publications

Rotating detonation combustors establish a detonation wave that continuously circulates inside a small annulus. The presence of the detonation wave and the downstream oblique shock within the small annulus coupled with high mass flow induces a high heat load to the combustor wall. Preliminary analysis shows that for higher thermal power, internal air cooling alone is not sufficient to remove the heat out of the walls to maintain them below the maximum temperature of the metal. A possible solution is to use film cooling to reduce the heat flux to the combustor walls. One issue, though, is that the introduction …

Data-Driven Sparse Sensor Placement Optimization On Wings For Flight-By-Feel: Bioinspired Approach And Application, Alex C. Hollenbeck, Atticus J. Beachy, Ramana V. Grandhi, Alexander M. Pankonien

Faculty Publications

Flight-by-feel (FBF) is an approach to flight control that uses dispersed sensors on the wings of aircraft to detect flight state. While biological FBF systems, such as the wings of insects, often contain hundreds of strain and flow sensors, artificial systems are highly constrained by size, weight, and power (SWaP) considerations, especially for small aircraft. An optimization approach is needed to determine how many sensors are required and where they should be placed on the wing. Airflow fields can be highly nonlinear, and many local minima exist for sensor placement, meaning conventional optimization techniques are unreliable for this application. The …

Stereo Vision Relative Navigation Of Airborne Vehicles, Scott L. Nykl, Brian Woolley, John Pecarina

AFIT Patents

An automated aerial formation (AAF) system includes an imaging device mounted on an imaging first aircraft that receives reflected energy from an imaged second aircraft. A controller is communicatively coupled to the imaging device and a flight control system of one of the first and the second aircraft. The controller generates a three-dimensional (3D) point cloud based on the reflected energy and identifies a target 3D model in the 3D point cloud. The controller rotates and scales one of a pre-defined 3D model and the target 3D model to find a 3D point registration between the target 3D model and …

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 …

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 …

A Panel Data Regression Model For Defense Merger And Acquisition Activity, Corey D. Mack, Clay Koschnick, Michael Brown, Jonathan D. Ritschel, Brandon M. Lucas

Faculty Publications

Excerpt: This paper examines the relationship between a prime contractor's financial health and its mergers and acquisitions (M&A) spending in the defense industry. It aims to provide models that give the United States Department of Defense (DoD) indications of future M&A activity, informing decision-makers and contributing to ensuring competitive markets that benefit the consumer.

The results show a significant relationship between efficiency and M&A spending, indicating that companies with lower efficiency tend to spend more on M&As. However, there was no significant relationship between M&A spending and a company's profitability or solvency. These results were consistent with previous research and …

Investigating Hardware-Based Aes Countermeasures In The Sam4l Microcontroller For Side-Channel Attack Mitigation, Turki Mesbel Alamri

Theses and Dissertations

Side-channel attacks (SCAs) represent a sophisticated method by which attackers exploit indirect pathways, such as power leakage and electromagnetic emissions to glean sensitive information from microprocessors. These attacks analyze variation in power consumption during operations like data encryption to infer protected data, potentially compromising the security of the device. For example, observing the power draw differences when a device encrypts known data can also serve defensive purposes. Attacks often overlook emissions and power patterns, while focusing on avoiding network and host-based detection systems. This oversight presents an opportunity for security professionals to use SCAs to enhance system defenses by monitoring …

Improving Military Medical Evacuation System Performance Via Stochastic Optimization, Virbon B. Frial

Theses and Dissertations

This research highlights the importance of improving the performance of military medical evacuation systems to reduce the risk of permanent disability or death among service members in deployed environments. We employ a range of stochastic optimization techniques relating to integer programming, Markov decision process, approximate dynamic programming, and machine learning, as appropriate, to gain insights into factors that contribute to improving system performance.

Toward Adaptive And Modular Joint Multi-Domain Operational Planning, Kyle S. Wilkinson

Theses and Dissertations

This research develops a multiparametric optimization framework for modeling joint multi-domain operational planning under uncertainty. We address the application of our framework to model the doctrine of adaptive planning. We apply set-based design, which is a program management practice of maintaining maximal design options through time as a response to epistemic uncertainty. We couple this with a multiparametric optimization method yielding both sets of solutions and sensitivity profiles. We use the sensitivity profiles to quantify risk associated with changes during adaptive planning. This research also models features of military operational planning via the mathematics of category theory. We formalize intuitive …

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 …

Guidance, Navigation, And Control For Multi-Agent Inspection Of An Unknown Space Object, Mark R. Mercier

Theses and Dissertations

On-orbit inspection is a rapidly growing mission that enables extending the life of aging legacy space systems through refueling, replacing, or upgrading components. For an inspection operation, multiple agents taking observations simultaneously can more quickly map an RSO and assure an inspection is complete regardless of RSO dynamics. A multi-agent guidance, navigation, and control (GNC) scheme must be designed with cooperation in mind to achieve inspection objectives, while leveraging the unique capabilities of a distributed inspection system. This work splits the GNC system into two parts composed of an offline guidance scheme for translational reference trajectory generation and an online …

Optimal Placement Of Artificial Hair-Cell Airflow Sensors For Bioinspired Flight-By-Feel, Alex C. Hollenbeck

Theses and Dissertations

The Sparse Sensor Placement Optimization for Prediction (SSPOP) algorithm reduces highdimensional airflow data to a low-dimensional sparse approximation to identify an optimal placement for any number of sensors on airfoil or wing models of arbitrary shape and size, and outperforms conventional optimization techniques in accuracy and speed. For 2D flow this algorithm found a sensor placement solution (design point, or DP) which predicts AoA to within 0.10 degrees and ranks within the top 1 percent of the design space. On 3D wing models SSPOP found four-sensor DPs ranked well within the top 0.10 percent. Experimental validation with velocity and pressure …

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 …

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 …

An Analysis Of Electrical Energy Resilience Technologies As Applied To Air Force Operations, Eric D. Danko

Theses and Dissertations

An analysis of 46 Resilient Energy Devices and Technology Concepts was conducted to determine their suitability for use in supporting Air Force Operations both at home station and abroad. The research consisted of two endeavors: an extensive literature review and a rank-ordering matrix. The dual nature of the efforts was designed to maximize usability and understanding for the End User, who may not be familiar with some principles of energy technologies, resilience, or design. The results showed the superiority of novel Solid (Metal) Fuels and Lead-Acid Batteries for Energy Storage and Thermoelectric Generators, Solar Photovoltaic Panels, Geothermal Extraction, Diesel Generators, …

Scene Decomposed Blind Deconvolution And Neural Network Based Multi-Frame Image Restoration Techniques For Astronomical Imagery, Joshua S. Sprang

Theses and Dissertations

Ground based astronomical imaging is an important method in gaining situational awareness of orbiting and far off objects in space. This method of imaging is accessible to everyone that can look up into the sky, but the accessibility to digital telescope systems allows for more exciting methods of extracting information. A use case for these telescopes is finding nearby objects to larger brighter known objects. The number satellites in low-earth orbit and geosynchronous earth orbit is becoming more congested as these orbits increase in population. Tens of thousands of satellites and debris now exist in this orbit, with the number …

Heuristic Design Of Cislunar Space Situational Awareness Architectures, Jacob A. Dahlke

Theses and Dissertations

The growing interest in the cislunar domain, which encompasses geosynchronous Earth orbit (GEO), the Moon, and the region where Earth-Moon gravitational effects are the primary influence, necessitates the development of tools to ensure safe and successful exploration. Space situational awareness (SSA) involves monitoring an environment to detect, track, identify, and catalog space objects. While most SSA experience comes from near-Earth operations, expanding into the cislunar domain presents significant challenges due to its vast expanse—greater than 10 times the radius of GEO—and the unique dynamics governed by the Circular Restricted Three-Body Problem (CR3BP) influenced by both Earth and Moon gravitational forces. …

Enhancing Crossflow Dynamics Through The Gas Injection From Multiple Cylinders, Sahrish B. Naqvi, Sadia Siddiqa, Maciej Matyka, Rama S. R. Gorla, Md. Mamum Molla

Faculty Publications

We investigate unsteady, two-dimensional laminar fluid flow around cylinders, focusing on understanding the impact of injecting methane gas through two diametrically opposite arcs on the cylinder in the crossflow of the second fluid. This study encompasses applications in mixing and dispersion, which are crucial in various technological and natural processes. Our analysis addresses velocity field’s contribution to the spatiotemporal distribution of transported quantities. We observed that mixing induces a transition from laminar to turbulent flow. Depending on the injected-to-crossflow velocity ratios, the wake vortices downstream of cylinder arrays separate from or connect to the injected gas. This phenomenon significantly impacts …