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Articles 1531 - 1560 of 13804
Full-Text Articles in Engineering
Federated Medical Scoring Systems, Jacob F. Bryant
Federated Medical Scoring Systems, Jacob F. Bryant
Theses and Dissertations
Federated Learning (FL) is a recent framework of machine learning implementation that trains models on a distributed network of clients as opposed to housing and analyzing this data centrally. This has data communication and practical data privacy advantages, the latter of which is particularly attractive to the medical community where patient privacy is closely safeguarded. We apply FL to a family of sparse linear integer models called Medical Scoring Systems (MSSs). We create a novel methodology for creating these MSSs in a simulated federated environment that involves an lo constrained Logistic Regression (LR), loss-surface examination, and rounding procedure. We tested …
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Reinforcement Learning For Team Based Air Combat Maneuvering Decisions With Directed Energy Weaponry, Joshua D. Combs
Theses and Dissertations
Leveraging the Advanced Framework for Simulation, Integration, and Modeling (AFSIM) we investigate the use of reinforcement learning (RL) techniques for imbuing AUCAV agents with high-quality behaviors for the within-visual-range air combat maneuvering problem (ACMP). We formulate the 2v2 WVR ACMP as a Markov decision process wherein friendly AUCAVs are equipped with DEW capabilities and operate with 6 degrees of freedom. We utilize the Double Deep Q-Network RL algorithm, which centrally trains two friendly AUCAVs and employ a phased learning approach, initially exposing the AUCAVs to a dense reward environment for early training, followed by a sparse reward environment to encourage …
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port, Abdulaziz A. Aljalahma
Enhancing Port Efficiency And Lead Time Reduction Through Predictive Analysis: A Case Study Of Container Management At Khalifa Bin Salman Port, Abdulaziz A. Aljalahma
Theses and Dissertations
Khalifa bin Salman Port (KBSP), a key pillar in Bahrain's maritime infrastructure, is the focal point of this study, highlighting the significant role of predictive analytics in optimizing port operations. This thesis analyzes container throughput data from 2017 to 2022, provided by Bahrain's Ministry of Transportation database. This data forms the basis for forecasting the 2023 throughput. The study thoroughly compares these predictions with the actual 2023 data, assessing the predictive model's accuracy. The findings underscore the importance of predictive analytics in strategic decision-making for port management, focusing on enhancing operational efficiency and reducing lead times. This research offers a …
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Usmepcom Prescreens: A Value-Focused Thinking Approach, Phillip M. Koenig
Theses and Dissertations
As the recruiting crisis continues to impact the United States Armed Forces, the United States Military Entrance Processing Command (USMEPCOM) continues to search for ways to increase its capability to efficiently determine which applicants are suited for military service. Unfortunately, USMEPCOM does not have a way to evaluate newly suggested alternatives. By leveraging Value-Focused Thinking (VFT), this research describes 28 fundamental objectives that can be applied to a variety of current and future decision problems. Further, this research applies these fundamental objectives to analyze a current decision problem: reengineering the prescreen process to decrease the time from prescreen submission to …
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
The Use Of Deep Learning And Transfer Learning In Complex Problems, Jacob S. Lang
Theses and Dissertations
Deep neural networks and transfer learning show potential in addressing complex problems such as the Tower of Hanoi and knapsack problems. The primary aim is to examine how the use of deep neural networks and transfer learning can enhance the ability of artificial learning systems to generalize. Transfer learning plays a crucial role in machine learning, particularly in the domain of artificial neural networks, as it helps overcome the challenges associated with limited data, computational efficiency, and generalization. The methodology used in this research involves the creation of data sets for the Tower of Hanoi and knapsack problems. To predict …
Knowledge Management Modeling And Decision Analysis For Usmepcom, Luke G. Wunderlich
Knowledge Management Modeling And Decision Analysis For Usmepcom, Luke G. Wunderlich
Theses and Dissertations
This thesis explores the adoption of Value-Focused Thinking (VFT) in enhancing the Knowledge Management (KM) program at USMEPCOM, aiming to align decision-making with the organization’s values and goals. Through evaluating the current knowledge flow and policy drafts, it proposes categorizing command messages, establishing a centralized information repository, and scheduling a daily order release to improve information accessibility and operational readiness. Although no alternative offers a perfect solution, implementing Command Message Categorization is expected to significantly enhance operational efficiency and prepare USMEPCOM for future challenges.
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Artificial Intelligence And Perception: An Empirical Study, Anthony J. Neil
Theses and Dissertations
This thesis investigates the impact of adjusting artificial intelligence explainability levels’ outputs on user perception. The overarching study extends within the Explainable Artificial Intelligence (XAI) domain. It examines a spectrum of variables, including performance, cognizance, familiarity, transparency, system bias, and the overall impact of AI, to understand their collective and individual effects that enable effective professional use in an organization. The study aims to illuminate the relationship between the degree of explainability provided by large language models such as ChatGPT, Bard, and Bing AI and the performance of these models when tasked with XAI adjustments.
Estimating Stimulated Raman Scattering Noise In Cwdm O-Band Channels Induced By Two Classical Dwdm Sources In A Quantum Network Fiber Segment, Kurt T. Spranger Ii
Estimating Stimulated Raman Scattering Noise In Cwdm O-Band Channels Induced By Two Classical Dwdm Sources In A Quantum Network Fiber Segment, Kurt T. Spranger Ii
Theses and Dissertations
The purpose of this research is to estimate the stimulated Raman scattering noise induced in CWDM O-band channels by two DWDM classical sources in a terrestrial quantum optical network containing classical and quantum optical signal coexistence in the same fiber segment. A use case is defined and analyzed which extracts a single fiber segment from a notional Bell state measurement found in a notional terrestrial quantum network. A stimulated Raman scattering noise model is employed in a Python simulation to estimate and rank-order the five O-band channels with the least amount of relative induced stimulated Raman scattering noise when given …
Root Cause Analysis For Troop Construction Schedule Delays And Cost Overruns, Richard H. Wilkens
Root Cause Analysis For Troop Construction Schedule Delays And Cost Overruns, Richard H. Wilkens
Theses and Dissertations
Troop construction can be an effective tool when a project is simple enough to execute projects in a timely and cost effective manner. However, underlying variables plague these projects causing them to miss anticipated deadlines which in return could make them more costly. Significant variables that convey impact to cost and schedule are Equipment Operating Issues, Edits During Construction, Design Flaws, Lack of Communication, Poor User Coordination, Improper Documentation, Inaccurate Submittals, Low Quality Control, and Lack of Experience. Project engineers and project managers must provide effective continuity as well as improve their own competence and situational awareness to effectively mitigate …
Training Schedule For The 56th Maintenance Group, Samantha K. O'Rourke
Training Schedule For The 56th Maintenance Group, Samantha K. O'Rourke
Theses and Dissertations
The 56th Equipment Maintenance Squadron (56 EMS) provides equipment maintenance and back shop maintenance for the F-35 Joint Strike Fighter. The squadron executes thousands of sorties and flight hours annually. This operations tempo requires maintenance to prevent equipment failures, minimization of aircraft downtime, insurance of safety and compliance, and training of maintenance personnel. The squadron incorporates periodic training sessions to train maintenance personnel skills needed by airmen. This research investigates the optimization of these training sessions employing mixed integer programming (MIP). A multi-objective MIP model is developed to address the complex needs of various training activities, such as: training regiments, …
Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes
Performance Of Humans And Agents In A Systems Modeling Language V2 Task: A User-Centered Evaluation Approach, Allen W. Dukes
Theses and Dissertations
The Department of Defense is adopting Digital Engineering practices for its workforce. Simultaneously, the larger Systems Engineering community strives to modernize and define those Digital Engineering practices. These efforts to move from traditionally document-based approaches to pure digital implementations will require enhanced capabilities to manage and digitally track the lifecycle of a program or product. However, this growth must address tool design through an iterative process focusing on usability for many user types. Many tools and technologies exist but often lack an assessment of usability when engineers design tools for other engineers. Including usability when developing solutions for Digital Engineering …
Human Performance Modeling Architecture With Hc-130j Mission Application, Stephanie G. Slimp
Human Performance Modeling Architecture With Hc-130j Mission Application, Stephanie G. Slimp
Theses and Dissertations
The DOD emphasizes digital engineering using Model-Based Systems Engineering (MBSE), where MBSE includes SysML-based models of systems. Analysis of system impacts on the human operators and their performance typically occurs through non-MBSE approaches, if at all. One current process, considered the As-Is process for this research, evaluates human performance and workload using IMPRINT, a discrete event simulation (DES) tool. IMPRINT primarily exists as a standalone tool with limited built-in functionality to integrate with an MBSE tool. Using the HC-130J and its crew during a CSAR mission as the system, this research develops a generalizable and updateable human performance modeling architecture …
Rotorcraft Assisted Aircraft Inspection System: Creation And Component Assessment, Adam J. Warren
Rotorcraft Assisted Aircraft Inspection System: Creation And Component Assessment, Adam J. Warren
Theses and Dissertations
Aircraft require frequent inspections to perform their missions safely. Current visual inspection methods are time-consuming and dangerous for inspection personnel, but they are necessary to spot flaws that could endanger the aircraft during flight. This research explores a set of methods for transforming a set of target inspection criteria into camera specifications that will allow a UAS aircraft inspection system to perform inspections on the top skin of an aircraft. Given the minimum distance for the UAS to fly above the aircraft, minimum flaw size to search for on the aircraft, minimum number of pixels to display that flaw size, …
Laboratory Evaluation Of High-Temperature Resistant Lysine-Based Polymer Gel Systems For Leakage Control, Tao Song, Xuyang Tian, Baojun Bai, Yugandhara Eriyagama, Mohamed Ahdaya, Adel Alotibi, Thomas P. Schuman
Laboratory Evaluation Of High-Temperature Resistant Lysine-Based Polymer Gel Systems For Leakage Control, Tao Song, Xuyang Tian, Baojun Bai, Yugandhara Eriyagama, Mohamed Ahdaya, Adel Alotibi, Thomas P. Schuman
Geosciences and Geological and Petroleum Engineering Faculty Research & Creative Works
In-situ crosslinking gel known for its cost-effectiveness, has been employed for decades to plug high-permeability features in subsurface environments. However, some commonly used crosslinkers are being phased out due to the increasingly rigorous environmental regulations. As a newly discovered environmentally friendly crosslinker, lysine can crosslink the partially hydrolyzed polyacrylamide through transamidation reaction. The present work aimed to study the effect of polymer composition and concentration on the gelation behavior of lysine and high molecular weight acrylamide-based polymers. Several commercial high molecular weight polymers with different contents of 2-Acrylamido-2-methyl-1-propane sulfonic acid (AMPS) including AN-105/125, SAV-55/37/28, and SAV-10 were deployed in this …
Designing Explainable Ai To Improve Human-Ai Team Performance: A Medical Stakeholder-Driven Scoping Review, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank
Designing Explainable Ai To Improve Human-Ai Team Performance: A Medical Stakeholder-Driven Scoping Review, Harishankar V. Subramanian, Casey I. Canfield, Daniel Burton Shank
Engineering Management and Systems Engineering Faculty Research & Creative Works
The rise of complex AI systems in healthcare and other sectors has led to a growing area of research called Explainable AI (XAI) designed to increase transparency. In this area, quantitative and qualitative studies focus on improving user trust and task performance by providing system- and prediction-level XAI features. We analyze stakeholder engagement events (interviews and workshops) on the use of AI for kidney transplantation. From this we identify themes which we use to frame a scoping literature review on current XAI features. The stakeholder engagement process lasted over nine months covering three stakeholder group's workflows, determining where AI could …
Cloud One Migration Schedule Drivers And Schedule Growth, Ryan J. Jansen
Cloud One Migration Schedule Drivers And Schedule Growth, Ryan J. Jansen
Theses and Dissertations
Cloud One, chartered in 2017 under the guidance of Air Force Life Cycle Management Center (AFLCMC) leadership, continues to serve as the USAF’s leading cloud services and hosting platform by providing secure computing environments, application migration assistance, and data management. Prior research has yielded qualitative insights regarding Cloud One’s personnel requirements, application of technical performance, requirements fulfillment, security risks, and various other cost metrics, but schedule improvement recommendations based on the quantitative analysis of migration schedule data has yet to be provided. This research identifies trends within migration sprint schedules and completed schedule data for Cloud One’s completed application migrations. …
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans, Madison E. Hofmann
Analysis And Visualization Of Military Convoy And Supply Utilization In Support Of State-Specific Freight Plans, Madison E. Hofmann
Theses and Dissertations
Researching the United States military’s use of highways and interstates is necessary for the allocation of federal infrastructure spending. As of today, there is no established method, tool, or process routinely utilized by the Surface Deployment and Distribution Command to effectively show a system-wide view of military convoy and supply route usage across CONUS. This research advances this endeavor by providing a by-state characterization of interstates and major highways in 2022, categorized by the volume of military freight they support. Some notable methodologies used to conduct the analyses are the shortest path problem, map matching algorithms, and Global Positioning System …
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
U.S. Army Cadet Command Branch Prediction Model, Daniel M. Krizan
Theses and Dissertations
The current system for providing US Army ROTC cadets their branches leaves significant uncertainty until the final pronouncement of branch assigned. This uncertainty can be alleviated by providing a prediction model for cadets to input personal data and desired branch to identify likelihood of receiving the request. This thesis produces a machine learning model capable of producing branch prediction for cadets.
Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering
Automated Image Registration For Titanium Aircraft Components Via Resolution-Robust Parallel Neural Networks, Paige T. Luebbering
Theses and Dissertations
Titanium alloys are vital to the structural integrity of military and commercial aircraft, comprising numerous critical components. These components are composed of microtexture regions (MTRs) that, at a specific size and orientation, can lead to aircraft failure. Existing MTR testing methods, such as Electron Backscatter Diffraction, often fall short in effectively detecting these MTRs without causing damage to the component. Addressing this gap, this thesis develops a Parallel Convolutional Neural Network (CNN) model tailored for multi-resolution image registration of Polarized Light Microscopy (PLM) images to enhance MTR identification in a non-invasive manner. The findings reveal a significant enhancement in the …
Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi
Factors Affecting The Retention Of Active Duty Airmen, Gregory A. Picardi
Theses and Dissertations
This study investigates the factors influencing the early exit of active duty Airmen, particularly in the context of the recruitment challenges faced by the USAF in Fiscal Year 2023. The research highlights the significant impact of the implementation of MHS Genesis, a healthcare administration program, on recruitment processes and the broader issues affecting military recruitment, including physical and emotional trauma concerns among potential recruits. Through a survey conducted at Wright-Patterson Air Force Base involving 251 participants, the study utilizes a Chi-square test to explore the primary and secondary reasons for leaving active duty, with family pressure, stability, and financial reasons …
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
An Analysis Of China’S Perceived Geographic Locations Of Interest By Use Of Value Informed Facility Location Models, Layton C. Hedge
Theses and Dissertations
This research examines China and derives insights specific to it and the First Island Chain and the Second Island Chain. In doing so, this research demonstrates a methodology to examine other competitors and their geostrategic interests. In the first phase of analysis, it develops a value hierarchy to depict objectives within subregions of the area of interest and considers four alternative weightings of the value hierarchy. In the second phase of analysis, it applies four location-covering models to assess how the competitor would emplace a range of limited resources to deter and/or control points of interest. Results indicate that land-based …
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
A Reinforcement Learning Approach To The 2v2 Beyond Visual Range Air Combat Maneuvering Problem, Jacob J. Pike
Theses and Dissertations
This research examines a 2v2 air combat maneuvering problem (ACMP) in a Beyond Visual Range (BVR) environment. A discrete-time, infinite-horizon Markov Decision Process (MDP) model represents the BVR-ACMP, seeking to determine high-quality policies for a pair of autonomous aircraft to execute tactical maneuvers and firing decisions. The Advanced Framework for Simulation, Integration, and Modeling (AFSIM) characterizes the complex six-degree of freedom (6-DOF) aircraft operations, encompassing kinematics, sensors, and weapons. Given the high dimensionality and continuous nature of the state and decision variables, a deep reinforcement learning (RL) solution approach is adopted wherein the value function is approximated via a Neural …
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw
Measuring The Comparative Performance Of Usaf Behavioral Health Clinic Operations: Emphasis On Capacity, Daniel L. Straw
Theses and Dissertations
This research examines the operations of behavioral health clinics in Air Force Continental United States facilities, with a focus on operational efficiency and capacity through Data Envelopment Analysis. Most facilities operate near capacity, demonstrating efficient resource usage, but concerns arise with the potential of increased demand. Days-to-care and leakage are identified as major sources of inefficiency, suggesting that addressing these issues can enhance operations and reduce costs. The study also explores the impact of COVID-19 on care delivery.
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
A Random Forest-Based Q-Learning Algorithm: Toward Interpretable Artificial Intelligence, Victor R. Rae
Theses and Dissertations
A growing demand exists for interpretable artificial intelligence models, leading to extensive research efforts to enhance the explainability and transparency of policies generated by reinforcement learning (RL) methods. This research develops random forest-based RL algorithms as a logical progression in this academic pursuit. The algorithms are evaluated using three standard benchmark environments from OpenAI gym — CartPole, MountainCar, and LunarLander — and compared to implementations of the Deep Q-learning Network (DQN) and Double DQN (DDQN) algorithms for various metrics, including performance, robustness, efficiency, and interpretability. The random forest-based algorithms exhibit superior performance to both neural network-based algorithms in two out …
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham
Bayesian Augmentation Of Object Detection Algorithms To Enhance Object Classification Stability, Taylor D. Markham
Theses and Dissertations
Neural networks, despite their prowess in computer vision, often exhibit "flickering". Flickering occurs when networks fail to maintain consistent object representation across frames, leading to inaccurate and inconsistent output. This problem is particularly critical in mission-surety applications where reliable object recognition is crucial. This research presents a novel approach that combines existing object detection and tracking algorithms like YOLO and SORT with a Bayesian backend model. This Bayesian backend incorporates probabilistic reasoning to analyze the network's confidence in its predictions and infer the most likely object identity across multiple frames, effectively reducing flickering and enhancing robustness.
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance, Ebony N. Williams
Navigating Complex Environments: A Comparative Study Of Shortest Path Algorithms For Military A2ad And Civilian Obstacle Avoidance, Ebony N. Williams
Theses and Dissertations
This thesis investigates advanced navigation in complex environments for urban and military applications, focusing on overcoming obstacles through algorithms like Dijkstra's. It highlights the role of adaptable cost functions in customizing strategies for different scenarios. The research identifies effective algorithm-cost function combinations, improving route planning and safety in civilian and defense sectors. It advances pathfinding knowledge and sets the groundwork for future enhancements with Python simulations and AFSIM.
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton
Electric Vehicle Support Equipment Deployment At Military Installations: A Mixed-Integer Linear Programming Approach, Katelyn M. Barton
Theses and Dissertations
This research provides insights into a mixed integer linear programming model that finds the ideal number and type of Electric Vehicle Support Equipment (EVSE) required to meet U.S. military installations’ electric energy demands. Executive Order No. 14057 (2021) requires federal agencies to transition to electric non-tactical vehicles by 2035. This research determines minimum cost solutions to implement the transition incorporating real-world constraints, including the weekly vehicle mileage demand, EVSE cost, charging time, and EVSE capacity. This study contributes to the broader effort of the U.S. military to combat climate change and enhances the understanding of efficient EVSE deployment strategies in …
Advanced Intermediate Manufacturing (Aim) Supply Chain Concepts Leading To Reduced Lead Times And Improved Responsiveness, Eric S. Draudt
Advanced Intermediate Manufacturing (Aim) Supply Chain Concepts Leading To Reduced Lead Times And Improved Responsiveness, Eric S. Draudt
Theses and Dissertations
This thesis investigates the optimization of the supply chain for key aircraft components, focusing on the implementation of Advanced Intermediate Manufacturing (AIM) production facilities. Utilizing anyLogistix, the study compares the current supply chain model based on Supply Chain Operations Wing (SCOW) data with various AIM production facility configurations: single, dual, quadruple, and three utilization-driven models (high, medium, and low). The findings demonstrate that integrating AIM production facilities significantly reduces lead times, with even a single facility dramatically cutting down the lead time from over 800 days to approximately 104 days. The utilization models further provide insights into operational flexibility under …
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel
An Integer Programming Model To Optimize Us Army Deployment Cycle And Maximize Unit Availability, Grant R. Engel
Theses and Dissertations
The goal of this paper is to determine an optimal cycle length, in months, that minimizes costs and maximizes availability for deploying units in the United States (US) Army. The US Army must be cost efficient while maintaining the flexibility required to adapt to dynamic mission demand. The current practice is to deploy units for a length between the range of 6 to 12 months; however, this varies from unit to unit and the best policy is not clear. We address these issues by forming a mathematical programming model with unique characteristics that distinguish it from others of similar design. …
Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training, Colin W. Foley
Techniques For Addressing Extreme Class Imbalance For Artificial Neural Networks Training, Colin W. Foley
Theses and Dissertations
This research examined the class imbalance problem while training convolutional neural networks (CNN) by applying different techniques to combat this common issue. This research used a modified CIFAR-10 dataset along with a curated aerial image dataset. Methods covered included undersampling, oversampling, synthetic minority oversampling technique, Edited Nearest Neighbors and combinations of the aforementioned methods. This research found that undersampling methods tended to outperform oversampling methods. While undersampling methods showed a decrease in overall accuracy, the increase in minority class prediction performance was promising enough to warrant further investigation.