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Operations Research, Systems Engineering and Industrial Engineering
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Articles 1 - 30 of 127
Full-Text Articles in Physical Sciences and Mathematics
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Bayesian Recurrent Neural Networks For Real Time Object Detection, Stephen Z. Kimatian
Theses and Dissertations
Neural networks have become increasingly popular in real time object detection algorithms. A major concern with these algorithms is their ability to quantify their own uncertainty, leading to many high profile failures. This research proposes three novel real time detection algorithms. The first of leveraging Bayesian convolutional neural layers producing a predictive distribution, the second leveraging predictions from previous frames, and the third model combining these two techniques together. These augmentations seek to mitigate the calibration problem of modern detection algorithms. These three models are compared to the state of the art YOLO architecture; with the strongest contending model achieving …
Uncertainty Quantification In Federated Learning For Persistent Post-Traumatic Headache, Byungmoo Brian Kim
Uncertainty Quantification In Federated Learning For Persistent Post-Traumatic Headache, Byungmoo Brian Kim
Theses and Dissertations
A post-traumatic headache (PTH), resulting from a mild traumatic brain injury (mTBI), potentially develops into persistent post-traumatic headache (PPTH). Although no known cure for PPTH exists, research has shown that receiving treatment at earlier stages of PTH lowers the risk of patients developing PPTH. Previous studies have shown machine learning (ML) models capable of predicting a patient’s PTH progression, but none have considered the issue of protecting patient privacy. Due to patient privacy, ML models only have access to data within the institution. Federated learning (FL) harnesses data from separate institutions without sacrificing patient privacy as institutions can run ML …
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Simulation And Analysis Of Dynamic Threat Avoidance Routing In An Anti-Access Area Denial (A2ad) Environment, Dante C. Reid
Theses and Dissertations
This research modeled and analyzed the effectiveness of different routing algorithms for penetration assets in an A2AD environment. AFSIM was used with different configurations of SAMs locations and numbers to compare the performance of AFSIM’s internal zone and shrink algorithm routers with a Dijkstra algorithm router. Route performance was analyzed through computational and operational metrics, including computational complexity, run-time, mission survivability, and simulation duration. This research also analyzed the impact of the penetration asset’s ingress altitude on those factors. Additionally, an excursion was conducted to analyze the Dijkstra algorithm router’s grid density holding altitude constant to understand its impact on …
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Automated Registration Of Titanium Metal Imaging Of Aircraft Components Using Deep Learning Techniques, Nathan A. Johnston
Theses and Dissertations
Studies have shown a connection between early catastrophic engine failures with microtexture regions (MTRs) of a specific size and orientation on the titanium metal engine components. The MTRs can be identified through the use of Electron Backscatter Diffraction (EBSD) however doing so is costly and requires destruction of the metal component being tested. A new methodology of characterizing MTRs is needed to properly evaluate the reliability of engine components on live aircraft. The Air Force Research Lab Materials Directorate (AFRL/RX) proposed a solution of supplementing EBSD with two non-destructive modalities, Eddy Current Testing (ECT) and Scanning Acoustic Microscopy (SAM). Doing …
Probability Of Agreement As A Simulation Validation Methodology, Matthew C. Ledwith
Probability Of Agreement As A Simulation Validation Methodology, Matthew C. Ledwith
Theses and Dissertations
Determining whether a simulation model is operationally valid requires the rigorous assessment of agreement between observed functional responses of the simulation model and the corresponding real world system or process of interest. This research seeks to extend and formulate the probability of agreement approach to the operational validation of simulation models. The first paper provides a methodological approach and an initial demonstration which leverages bootstrapping to overcome situations where one’s ability to collect real-world data is limited. The second paper extends the probability of agreement approach to account for second-order heteroscedastic variability structures and establishes a weighted probability of agreement …
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Classification And Analysis Of Twitter Bot And Troll Accounts, Callan P. Mccormick
Theses and Dissertations
This research trains, tests, and analyzes bot and troll classification models using publicly available, open source datasets. Specifically, it applies decision tree, random forest, feed forward neural networks, and long-short term memory neural networks with hyperparameters tuned via designed experiment to five labeled bot datasets created between 2011 and 2020 and one dataset labeling state-sponsored disinformation accounts or trolls. The first three models utilize account profile features, while the last model applies natural language processing techniques, specifically GloVe embedding, to analyze a user’s Tweet history. Results indicate that the random forest model outperforms the other three models with an average …
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Analysis And Optimization Of Contract Data Schema, Franklin Sun
Theses and Dissertations
agement, development, and growth of U.S Air Force assets demand extensive organizational communication and structuring. These interactions yield substantial amounts of contracting and administrative information. Over 4 million such contracts as a means towards obtaining valuable insights on Department of Defense resource usage. This set of contracting data is largely not optimized for backend service in an analytics environment. To this end, the following research evaluates the efficiency and performance of various data structuring methods. Evaluated designs include a baseline unstructured schema, a Data Mart schema, and a snowflake schema. Overall design success metrics include ease of use by end …
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Hierarchical Federated Learning On Healthcare Data: An Application To Parkinson's Disease, Brandon J. Harvill
Theses and Dissertations
Federated learning (FL) is a budding machine learning (ML) technique that seeks to keep sensitive data private, while overcoming the difficulties of Big Data. Specifically, FL trains machine learning models over a distributed network of devices, while keeping the data local to each device. We apply FL to a Parkinson’s Disease (PD) telemonitoring dataset where physiological data is gathered from various modalities to determine the PD severity level in patients. We seek to optimally combine the information across multiple modalities to assess the accuracy of our FL approach, and compare to traditional ”centralized” statistical and deep learning models.
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Air Force Cadet To Career Field Matching Problem, Ian P. Macdonald
Theses and Dissertations
This research examines the Cadet to Air Force Specialty Code (AFSC) Matching Problem (CAMP). Currently, the matching problem occurs annually at the Air Force Personnel Center (AFPC) using an integer program and value focused thinking approach. This paper presents a novel method to match cadets with AFSCs using a generalized structure of the Hospitals Residents problem with special emphasis on lower quotas. This paper also examines the United States Army Matching problem and compares it to the techniques and constraints applied to solve the CAMP. The research culminates in the presentation of three algorithms created to solve the CAMP and …
Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell
Approximate Dynamic Programming For An Unmanned Aerial Vehicle Routing Problem With Obstacles And Stochastic Target Arrivals, Kassie M. Gurnell
Theses and Dissertations
The United States Air Force is investing in artificial intelligence (AI) to speed analysis in efforts to modernize the use of autonomous unmanned combat aerial vehicles (AUCAVs) in strike coordination and reconnaissance (SCAR) missions. This research examines an AUCAVs ability to execute target strikes and provide reconnaissance in a SCAR mission. An orienteering problem is formulated as anMarkov decision process (MDP) model wherein a single AUCAV must optimize its target route to aid in eliminating time-sensitive targets and collect imagery of requested named areas of interest while evading surface-to-air missile (SAM) battery threats imposed as obstacles. The AUCAV adjusts its …
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 …
Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice
Team Air Combat Using Model-Based Reinforcement Learning, David A. Mottice
Theses and Dissertations
We formulate the first generalized air combat maneuvering problem (ACMP), called the MvN ACMP, wherein M friendly AUCAVs engage against N enemy AUCAVs, developing a Markov decision process (MDP) model to control the team of M Blue AUCAVs. The MDP model leverages a 5-degree-of-freedom aircraft state transition model and formulates a directed energy weapon capability. Instead, a model-based reinforcement learning approach is adopted wherein an approximate policy iteration algorithmic strategy is implemented to attain high-quality approximate policies relative to a high performing benchmark policy. The ADP algorithm utilizes a multi-layer neural network for the value function approximation regression mechanism. One-versus-one …
Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis
Predicting Tf33-Pw-100a Engine Failures Due To Oil Issues Using Survival Analyses, Anna M. Davis
Theses and Dissertations
In 2007, the Office of the Assistant Secretary of Defense for Sustainment pushed for the need to transition to a Condition Based Maintenance Plus (CBM ) initiative for weapon systems in the U.S. Department of Defense. The CBM initiative can help increase aircraft availability (AA) for the United States Air Force. There are many reasons where AA can be affected but one such issue is engine availability primarily due to oil issues. Within the CBM perspective, this study examines the risk of a jet engine failure due to an oil issue and attempts to predict an engines time until next …
Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich
Identifying Characteristics For Success Of Robotic Process Automations, Charles M. Unkrich
Theses and Dissertations
In the pursuit of digital transformation, the Air Force creates digital airmen. Digital airmen are robotic process automations designed to eliminate the repetitive high-volume low-cognitive tasks that absorb so much of our Airmen's time. The automation product results in more time to focus on tasks that machines cannot sufficiently perform data analytics and improving the Air Force's informed decision-making. This research investigates the assessment of potential automation cases to ensure that we choose viable tasks for automation and applies multivariate analysis to determine which factors indicate successful projects. The data is insufficient to provide significant insights.
Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner
Analysis Of Generalized Artificial Intelligence Potential Through Reinforcement And Deep Reinforcement Learning Approaches, Jonathan Turner
Theses and Dissertations
Artificial Intelligence is the next competitive domain; the first nation to develop human level artificial intelligence will have an impact similar to the development of the atomic bomb. To maintain the security of the United States and her people, the Department of Defense has funded research into the development of artificial intelligence and its applications. This research uses reinforcement learning and deep reinforcement learning methods as proxies for current and future artificial intelligence agents and to assess potential issues in development. Agent performance were compared across two games and one excursion: Cargo Loading, Tower of Hanoi, and Knapsack Problem, respectively. …
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.
Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins
Simulating Autonomous Cruise Missile Swarm Behaviors In An Anti-Access Area Denial (A2ad) Environment, Kyle W. Goggins
Theses and Dissertations
The increasingly sophisticated anti-access area denial (A2AD) threat imposed by the modern integrated air defense system (IADS), coupled with the decreasingly potent advantage provided by high-end stealth platforms, has prompted Air Force senior leaders to invest in radically changing the nature of air power for the year 2030 and beyond. A prominent element of this new vision is weapon swarming, which aims to address this challenge by overwhelming the IADS with huge numbers of low-cost, attritable aerial assets emboldened by autonomous capabilities. This research proposes a framework for classifying the different levels of autonomous capability along three independent dimensions—namely ability …
Comparison Of Lightning Warning Radii Distributions, Michael M. Maestas
Comparison Of Lightning Warning Radii Distributions, Michael M. Maestas
Theses and Dissertations
Previous research investigating lightning warning radii about the Cape Canaveral space launch facilities have focused on reducing these radii from either 5 nautical miles (NM) to 4 NM or from 6 NM to 5 NM depending on the structures being protected. Some of these findings have suggested the possibility of both a seasonal difference (warm versus cold) and lightning detection events (cloud-to-ground lightning (CG) or total lightning (TL)) impacting these radii and associated risk levels. Utilizing the 2017-2020 data provided by the 45th Weather Squadron at Patrick Space Force Base via the Mesoscale Eastern Range Lightning Information System (MERLIN), this …
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 …
Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price
Applying Models Of Circadian Stimulus To Explore Ideal Lighting Configurations, Alexander J. Price
Theses and Dissertations
Increased levels of time are spent indoors, decreasing human interaction with nature and degrading photoentrainment, the synchronization of circadian rhythms with daylight variation. Military imagery analysts, among other professionals, are required to work in low light level environments to limit power consumption or increase contrast on display screens to improve detail detection. Insufficient exposure to light in these environments results in inadequate photoentrainment which is associated with degraded alertness and negative health effects. Recent research has shown that both the illuminance (i.e., perceived intensity) and wavelength of light affect photoentrainment. Simultaneously, modern lighting technologies have improved our ability to construct …
Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood
Enterprise Resource Allocation For Intruder Detection And Interception, Adam B. Haywood
Theses and Dissertations
This research considers the problem of an intruder attempting to traverse a defender's territory in which the defender locates and employs disparate sets of resources to lower the probability of a successful intrusion. The research is conducted in the form of three related research components. The first component examines the problem in which the defender subdivides their territory into spatial stages and knows the plan of intrusion. Alternative resource-probability modeling techniques as well as variable bounding techniques are examined to improve the convergence of global solvers for this nonlinear, nonconvex optimization problem. The second component studies a similar problem but …
Deep Learning For Weather Clustering And Forecasting, Nathaniel R. Beveridge
Deep Learning For Weather Clustering And Forecasting, Nathaniel R. Beveridge
Theses and Dissertations
Clustering weather data is a valuable endeavor in multiple respects. The results can be used in various ways within a larger weather prediction framework or could simply serve as an analytical tool for characterizing climatic differences of a particular region of interest. This research proposes a methodology for clustering geographic locations based on the similarity in shape of their temperature time series over a long time horizon of approximately 11 months. To this end an emerging and powerful class of clustering techniques that leverages deep learning, called deep representation clustering (DRC), are utilized. Moreover, a time series specific DRC algorithm …
Wavelet Methods For Very-Short Term Forecasting Of Functional Time Series, Jared K. Nystrom
Wavelet Methods For Very-Short Term Forecasting Of Functional Time Series, Jared K. Nystrom
Theses and Dissertations
Space launch operations at Kennedy Space Center and Cape Canaveral Space Force Station (KSC/CCSFS) are complicated by unique requirements for near-real time determination of risk from lightning. Lightning forecast weather sensor networks produce data that are noisy, high volume, and high frequency time series for which traditional forecasting methods are often ill-suited. Current approaches result in significant residual uncertainties and consequentially may result in forecasting operational policies that are excessively conservative or inefficient. This work proposes a new methodology of wavelet-enabled semiparametric modeling to develop accurate and timely forecasts robust against chaotic functional data. Wavelets methods are first used to …
Statistically Defensible Wind Tunnel Models, Timothy A. Roche
Statistically Defensible Wind Tunnel Models, Timothy A. Roche
Theses and Dissertations
Wind tunnels are used to test scale-model air frames in order to collect aerodynamic data. The Subsonic Aerodynamic Research Laboratory (SARL) Wind Tunnel is a low speed wind tunnel located at Wright-Patterson Air Force Base. The SARL Wind Tunnel team approached AFIT for assistance in creating statistically defensible models for the conditions inside the wind tunnel. During a wind tunnel test, pressure sensors cannot be placed at the test model. Instead, pressure is measured by a pitot probe permanently mounted in the corner of the test chamber. The pressure at the model location is predicted from the measurements taken by …
An Examination Into Retention Behavior Of Air Force Female Officers, Jessica M. Astudillo
An Examination Into Retention Behavior Of Air Force Female Officers, Jessica M. Astudillo
Theses and Dissertations
Female retention rates in the US military have been considerably lower than that of their male counterparts for numerous years. In the Air Force, women represent 14 percent of officer ranks from O-5 level and above. Comparatively, the overall rate of women officers in service is 20 percent. Understanding the negative factors associated with the attrition rate of this group can help the Air Force leverage positive change. It may also influence adjustments that will increase the number of women serving, and improve diversity throughout both the officer and enlisted ranks. In this study, logistic regression and survival analysis are …
Contract Information Extraction Using Machine Learning, Zachary E. Butcher
Contract Information Extraction Using Machine Learning, Zachary E. Butcher
Theses and Dissertations
The Air Force Sustainment Center assisted by the Data Analytics Resource Team and the Defense Logistics Agency collected four million contracts onto one of the Air Force Research Laboratory’s high power computers. This thesis focuses on the effort to determine if parts are available through those contracts. Some information is extracted using machine learning in combination with natural language processing. Where machine learning methods are unsuccessful or inappropriate, text mining techniques, such as pattern recognition and rules, are used. Upon completion, the information is combined into a Gantt chart for quick evaluation. Only 21% of the contracts have their information …
Ccsfs/Ksc Total Lightning Warning Radii Optimization For Merlin Using Preexisting Lightning Areas, Kimberly G. Holland
Ccsfs/Ksc Total Lightning Warning Radii Optimization For Merlin Using Preexisting Lightning Areas, Kimberly G. Holland
Theses and Dissertations
The purpose of this research is to optimize lightning warning radii specifications for the 45th Space Wing (45 SW), thus reducing the number of unnecessary warnings that delay ground processing needed for space launch execution at Kennedy Space Center and Cape Canaveral Space Force Station. This thesis sought to answer two key research questions addressing: 1) What radius reduction effectively balances both safety and operations and do reduction recommendations from previous research align with results from the new detection system? 2) What insights can be gained from comparing measurement results for seasonal lightning events as well as lightning types? This …
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Ground Weather Radar Signal Characterization Through Application Of Convolutional Neural Networks, Stephen M. Lee
Theses and Dissertations
The 45th Weather Squadron supports the space launch efforts out of the Kennedy Space Center and Cape Canaveral Air Force Station for the Department of Defense, NASA, and commercial customers through weather assessments. Their assessment of the Lightning Launch Commit Criteria (LLCC) for avoidance of natural and rocket triggered lightning to launch vehicles is critical in approving space shuttle and rocket launches. The LLCC includes standards for cloud formations, which requires proper cloud identification and characterization methods. Accurate reflectivity measurements for ground weather radar are important to meet the LLCC for rocket triggered lightning. Current linear interpolation methods for ground …
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Algorithm Selection Framework: A Holistic Approach To The Algorithm Selection Problem, Marc W. Chalé
Theses and Dissertations
A holistic approach to the algorithm selection problem is presented. The “algorithm selection framework" uses a combination of user input and meta-data to streamline the algorithm selection for any data analysis task. The framework removes the conjecture of the common trial and error strategy and generates a preference ranked list of recommended analysis techniques. The framework is performed on nine analysis problems. Each of the recommended analysis techniques are implemented on the corresponding data sets. Algorithm performance is assessed using the primary metric of recall and the secondary metric of run time. In six of the problems, the recall of …
An Analysis Of Learning Curve Theory & Diminishing Rates Of Learning, Dakotah W. Hogan
An Analysis Of Learning Curve Theory & Diminishing Rates Of Learning, Dakotah W. Hogan
Theses and Dissertations
Traditional learning curve theory assumes a constant learning rate regardless of the number of units produced; however, a collection of theoretical and empirical evidence indicates that learning rates decrease as more units are produced in some cases. These diminishing learning rates cause traditional learning curves to underestimate required resources, potentially resulting in cost overruns. A diminishing learning rate model, Boones Learning Curve (2018), was recently developed to model this phenomenon. This research confirmed that Boones Learning Curve is more accurate in modeling observed learning curves using production data of 169 Department of Defense end-items. However, further empirical analysis revealed deficiencies …