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Articles 271 - 300 of 1292
Full-Text Articles in Operations Research, Systems Engineering and Industrial Engineering
Examining The Relationship Between An Aging Workforce And Logistics Performance Indicators, Daniel Parkhill
Examining The Relationship Between An Aging Workforce And Logistics Performance Indicators, Daniel Parkhill
Theses and Dissertations
In the United States, 10,000 baby boomers turn 65 every day. The subsequent generation born between 1965 and 1976 is significantly smaller, referred to as the baby bust. As a result, this causes a talent shortage as Baby Boomers retire, leaving a workforce gap which the subsequent generation is not large enough to fill. This issue also has been recognized as a potential problem in the logistics community of the United States Air Force. The purpose of this study is to examine the impact of workers age on key logistics performance indicators (LPIs) such as aircraft availability, product flow days, …
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 …
Air Force Specialty Code Assignment Optimization, Rebecca L. Reynolds
Air Force Specialty Code Assignment Optimization, Rebecca L. Reynolds
Theses and Dissertations
Each year, the Air Force Personnel Center determines which career field newly commissioned officers will serve under during their time in the Air Force. The career fields are assigned while considering five priorities, dictated by Headquarters Air Force, Manpower and Personnel: target number of cadets, education requirements, average cadet percentile, cadet source of commissioning, and cadet preference. A mixed-integer linear program with elasticized constraints is developed to generate cadet assignments according to these priorities. Each elasticized constraint carries an associated reward and penalty, which is used to dictate the importance of the constraint within the model. A subsequent analysis is …
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.
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
An Exploratory Analysis Of Time Series Econometric Data For Retention Forecasting Using Deep Learning, John C. O'Donnell
Theses and Dissertations
Officer retention in the Air Force has been researched many times in an attempt to better predict the personnel needs of the Air Force for the future. There has been previous work done in regards to specific AFSCs and how their retention compares to specific yet similar private sector jobs. This study considers different econometric time series statistics as a feature space and an average Air Force officer separation rate as the response variable for the multivariate time series analysis deep learning techniques. The econometric indicators used in this study are New Business Formations, New Durable Good Orders, and the …
Multiagent Routing Problem With Dynamic Target Arrivals Solved Via Approximate Dynamic Programming, Andrew E. Mogan
Multiagent Routing Problem With Dynamic Target Arrivals Solved Via Approximate Dynamic Programming, Andrew E. Mogan
Theses and Dissertations
This research formulates and solves the multiagent routing problem with dynamic target arrivals (MRP-DTA), a stochastic system wherein a team of autonomous unmanned aerial vehicles (AUAVs) executes a strike coordination and reconnaissance (SCAR) mission against a notional adversary. Dynamic target arrivals that occur during the mission present the team of AUAVs with a sequential decision-making process which we model via a Markov Decision Process (MDP). To combat the curse of dimensionality, we construct and implement a hybrid approximate dynamic programming (ADP) algorithmic framework that employs a parametric cost function approximation (CFA) which augments a direct lookahead (DLA) model via a …
A Proposed Mission Assurance Framework For University Cubesat Programs, Mitchell R. Thomas
A Proposed Mission Assurance Framework For University Cubesat Programs, Mitchell R. Thomas
Theses and Dissertations
The low cost, small form factor, and use of Commercial-Off-The-Shelf components make CubeSats an ideal platform for university use given their unique constraints. Since 2000, roughly 55% of CubeSats developed by universities fail to meet mission goals. Mission assurance, which is used to increase confidence in mission success, is based around traditional government programs, and does not provide an adequate road map for universities. Leaving them without specific guidance in tailoring mission assurance activities to operate within their constraints. This paper adapts these traditional practices to develop a baseline mission assurance framework for university CubeSat programs.
Analysis Of Container Shipments For Ustranscom, John N. Campos Y Campos
Analysis Of Container Shipments For Ustranscom, John N. Campos Y Campos
Theses and Dissertations
USTRANSCOM (United States Transportation Command) sends containers to various overseas destinations, mainly commercial container shipping companies. Delivering the containers to the destinations on time is important to support the United States (US) Forces deployed in foreign countries. This study analyzes container shipment records for two years from 2019 to 2021 and prepares insights for USTRANSCOM. This study utilizes descriptive statistics, data visualization, and one-way analysis of variance (ANOVA). According to the records, almost half of the containers (41.25%) were delivered late for one day or longer. The container with the longest delay took 408 days. Major reasons for delays included …
Analysis Of Twitter Networks To Aid Open Source Intelligence Capabilities: A Multilayer Network Approach, Austin P. Logan
Analysis Of Twitter Networks To Aid Open Source Intelligence Capabilities: A Multilayer Network Approach, Austin P. Logan
Theses and Dissertations
Open Source Intelligence using social media is a practice which gives military intelligence analysts a window into the thoughts and minds of an online population. Using Social Network Analysis, user interactions on Twitter will be modeled as a weighted and directed network. Topic modeling through Latent Dirichlet Allocation uncovers the topics of discussion in Tweets and is then integrated into a multi-layer network which allows users to be connected to the conversations with which they have participated. Influential users in this network as well as highly connected groups of individuals are then discovered to paint a picture for intelligence analysts …
The L1-Norm Regularized L1-Norm Best-Fit Line Problem And Applications, Xiao Ling
The L1-Norm Regularized L1-Norm Best-Fit Line Problem And Applications, Xiao Ling
Theses and Dissertations
The best-fit subspace or low-rank approximation of a data matrix revolves
around the norm approximation technique. l2-norm criterion is probably the most
widely used norm for fitting subspaces. As the computational power increases, the
l1-norm analogue has recently gained attention from the academic community. It is
widely agreed that the l1 norm is insensitive to outliers, compared to its l2 variant.
Because of the polyhedral structure interrelated with linear programming (LP),
the l0 norm is commonly relaxed into the l1-norm problem to induce sparsity in
models. In this work, we examine …
Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa
Energy Planning Model Design For Forecasting The Final Energy Consumption Using Artificial Neural Networks, Haidy Eissa
Theses and Dissertations
“Energy Trilemma” has recently received an increasing concern among policy makers. The trilemma conceptual framework is based on three main dimensions: environmental sustainability, energy equity, and energy security. Energy security reflects a nation’s capability to meet current and future energy demand. Rational energy planning is thus a fundamental aspect to articulate energy policies. The energy system is huge and complex, accordingly in order to guarantee the availability of energy supply, it is necessary to implement strategies on the consumption side. Energy modeling is a tool that helps policy makers and researchers understand the fluctuations in the energy system. Over the …
Effects Of The Inclusion Of Rice Hull Derived Bio-Oil On Wood Pellet Production, Tyler E. Lowe
Effects Of The Inclusion Of Rice Hull Derived Bio-Oil On Wood Pellet Production, Tyler E. Lowe
Theses and Dissertations
Wood pellet production has become an advancing industry for the sake of reducing greenhouse emissions into the atmosphere especially, in European Union countries. Researchers and industry executives seek new methods and materials to improve the pelletization process. Rice hulls or husks has the potential to aid in wood pelletization as they possess high calorific values. This study focuses on using rice hull derived bio-oil from pyrolysis, which will also decrease ash content, as an additive to aid in the wood pelletization process. Using two groups of rice hull derived bio-oil as an additive in wood pelletization: Group 1 uses heavy …
A Dual Perspective Towards Building Resilience In Manufacturing Organizations, Steven A. Fazio
A Dual Perspective Towards Building Resilience In Manufacturing Organizations, Steven A. Fazio
Theses and Dissertations
Modern manufacturing organizations exist in the most complex and competitive environment the world has ever known. This environment consists of demanding customers, enabling, but resource intensive Industry 4.0 technology, dynamic regulations, geopolitical perturbations, and innovative, ever-expanding global competition. Successful manufacturing organizations must excel in this environment while facing emergent disruptions generated as biproducts of complex man-made and natural systems. The research presented in this thesis provides a novel two-sided approach to the creation of resilience in the modern manufacturing organization. First, the systems engineering method is demonstrated as the qualitative framework for building literature-derived organizational resilience factors into organizational structures …
Testing Of Methods For Reducing Motivational Bias In Multi - Criteria Decision Analysis Problems, Chadwick Samuel Kerr
Testing Of Methods For Reducing Motivational Bias In Multi - Criteria Decision Analysis Problems, Chadwick Samuel Kerr
Theses and Dissertations
The idea of multi-criteria decision making has been around for quite a while. All judgement tasks are potential points of bias introduction. Each judgement task was assessed to identify common biases introduced through an extensive literature review for each task and bias. In several other studies, the distinction is made between cognitive and motivational biases. Cognitive biases are widely studied and well known with mitigations that have been validated. Motivational biases are judgements influenced by the decision maker’s desire for a specific outcome, also referred to as intentional bias, that are hard to correct and received very little testing and …
Leveraging Choice Modeling Technique For Enhancing The Cyber Resilience Of The Smart Grid, Kesava Karishma Devi Dadi
Leveraging Choice Modeling Technique For Enhancing The Cyber Resilience Of The Smart Grid, Kesava Karishma Devi Dadi
Theses and Dissertations
This research focuses on the cyber-attack of the smart grid and its retrieval to a normal state by estimating the smart grid's resilience. This study developed a theoretical model to estimate the resilience of the smart grid using choice modeling. A utility function is formulated based on various factors and sub-factors of resilience to estimate the resilience of the smart grid. Choice modeling is applied to estimate the model parameters in various fields such as marketing, energy, transportation, and health and to predict the outcome.
Deep Multi-Modal U-Net Fusion Methodology Of Infrared And Ultrasonic Images For Porosity Detection In Additive Manufacturing, Christian E. Zamiela
Deep Multi-Modal U-Net Fusion Methodology Of Infrared And Ultrasonic Images For Porosity Detection In Additive Manufacturing, Christian E. Zamiela
Theses and Dissertations
We developed a deep fusion methodology of non-destructive (NDT) in-situ infrared and ex- situ ultrasonic images for localization of porosity detection without compromising the integrity of printed components that aims to improve the Laser-based additive manufacturing (LBAM) process. A core challenge with LBAM is that lack of fusion between successive layers of printed metal can lead to porosity and abnormalities in the printed component. We developed a sensor fusion U-Net methodology that fills the gap in fusing in-situ thermal images with ex-situ ultrasonic images by employing a U-Net Convolutional Neural Network (CNN) for feature extraction and two-dimensional object localization. We …
The Development Of Authentic Virtual Reality Scenarios To Measure Individuals’ Level Of Systems Thinking Skills And Learning Abilities, Vidanelage L. Dayarathna
The Development Of Authentic Virtual Reality Scenarios To Measure Individuals’ Level Of Systems Thinking Skills And Learning Abilities, Vidanelage L. Dayarathna
Theses and Dissertations
This dissertation develops virtual reality modules to capture individuals’ learning abilities and systems thinking skills in dynamic environments. In the first chapter, an immersive queuing theory teaching module is developed using virtual reality technology. The objective of the study is to present systems engineering concepts in a more sophisticated environment and measure students learning abilities. Furthermore, the study explores the performance gaps between male and female students in manufacturing systems concepts. To investigate the gender biases toward the performance of developed VR module, three efficacy measures (simulation sickness questionnaire, systems usability scale, and presence questionnaire) and two effectiveness measures (NASA …
Using A Systemic Skills Model To Build An Effective 21st Century Workforce: Factors That Impact The Ability To Navigate Complex Systems, Morteza Nagahi
Using A Systemic Skills Model To Build An Effective 21st Century Workforce: Factors That Impact The Ability To Navigate Complex Systems, Morteza Nagahi
Theses and Dissertations
The growth of technology and the proliferation of information made modern complex systems more fragile and vulnerable. As a result, competitive advantage is no longer achieved exclusively through strategic planning but by developing an influential cadre of technical people who can efficiently manage and navigate modern complex systems. The dissertation aims to provide educators, practitioners, and organizations with a model that helps to measure individuals’ systems thinking skills, complex problem solving, personality traits, and the impacting demographic factors such as managerial and work experience, current occupation type, organizational ownership structure, and education level. The intent is to study how these …
Combat Assessment For The Simulation Of Warfare, Benjamin L. Finch
Combat Assessment For The Simulation Of Warfare, Benjamin L. Finch
Theses and Dissertations
Military assessment seeks to answer two primary questions: "are we creating the effects that we desire?" and "are we accomplishing tasks to standard?" This thesis steps through several desirable characteristics for a simulated combat assessment methodology. After developing a value hierarchy from these characteristics, this thesis provides and evaluates several candidate methodologies for use within a combat simulation. Each alternative's evaluation is informed by its application to a small combat simulation. Upon recommending the use of Linear Programming, we utilize value-focused thinking to modify this alternative. The thesis terminates with some conclusory thoughts and ideas for future research.
Using Custom Ner Models To Extract Dod Specific Entities From Contracts, Kayla P. Haberstich
Using Custom Ner Models To Extract Dod Specific Entities From Contracts, Kayla P. Haberstich
Theses and Dissertations
The Air Force Sustainment Center collected 3.7 million contracts onto the Air Force Research Laboratory’s high power computers. They are in the format of a .pdf or scanned document, making them unstructured data. The Data Analytics Resource Team extracted the documents into a textual format for use in further analysis. This thesis looks to extract four DOD specific entities (NSN, Part Number, CAGE Code, and Supplier Name) from the contracts using custom NER models. This newly extracted information will allow the Air Force to identify what parts are supplied by which vendors. This information along with historical CLIN pricing for …
Machine Learning Application For Mission Data Reprogramming, Paolo A. Bingham
Machine Learning Application For Mission Data Reprogramming, Paolo A. Bingham
Theses and Dissertations
Before entering a conflict or theater, USAF aircraft require updated mission data software reprogramming. Mission data controls all electronic warfare (EW) operations of the aircraft. EW operations include identifying and jamming radar operated systems, whether they are friendly or hostile. The process of reprogramming software is continuous and routinely updated for every EW system annually. On specific circumstances, the process can be expedited to months, but this puts a strain on the development team and shifts all attention to one specific mission data file. Unfortunately, a growing number of requests to upgrade mission data to a higher priority state, has …
Meta-Analysis Of Performance Characteristics Of Modern Database Schemas, Carter Grove
Meta-Analysis Of Performance Characteristics Of Modern Database Schemas, Carter Grove
Theses and Dissertations
Industry and academia alike are more commonly using databases as solutions to advanced and complex problems. Unfortunately, not all database schemas are created equal and can yield different advantages in different areas. To try to understand what database schema might be best suited for a user’s needs, we sought out to distinguish how databases are measured against each other, what their performance characteristics are, and what advantages each type of database inherently possesses. To allow for the ingestion of data across the five different categories of database schemas, we used a met-analysis of past literature and aggregated the data to …
Assessment Of Visual Field Performance Asymmetries While Utilizing Aircraft Attitude Symbology, George A. Reis
Assessment Of Visual Field Performance Asymmetries While Utilizing Aircraft Attitude Symbology, George A. Reis
Theses and Dissertations
Two experiments were conducted to examine visual performance asymmetries when perceiving complex, meaningful visual stimuli, such as the Arc Segment Attitude Reference (ASAR). The ASAR symbology represents an aircraft’s attitude. Experiment 1 examined participants’ performance while recalling and reporting various attitudes of ASAR symbology and a Gabor patch, which were briefly presented in the peripheral visual field. Performance was assessed for coordinate and categorical judgments at various display locations. The results were consistent with the horizontal-vertical anisotropy literature, which implies that performance would be better for stimuli placed on the horizontal meridian as compared to stimuli placed on the vertical …
The Effect Of Information Acquisition Automation And Workspace Design On Human Pattern Recognition For Information Fusion, Kellie L. Turner
The Effect Of Information Acquisition Automation And Workspace Design On Human Pattern Recognition For Information Fusion, Kellie L. Turner
Theses and Dissertations
Automation employed in information fusion systems is designed to help humans combine information derived from multiple sources to form a cohesive assessment of the situation. Research using the Levels of Automation model (Parasuraman, Wickens, and Sheridan, 2000) have produced conflicting results, which Patterson (2017) posited was because it focused solely on analytical processing while neglecting the effects of intuitive cognition. The present study examined how information acquisition automation affects the human’s ability to detect patterns in data needed to reach higher levels of information fusion. Results showed that when information acquisition was performed through manual operations, pattern recognition performance was …
Applications In Multi-Band Isolation Of Spectra With Data-Adaptive Sub-Banding (Midas): Using Multi-Criteria Decision Analysis To Optimize Midas-Based De-Noising Methods When Processing Infrasound And Other Signals Of Interest, Everett Raymond Coots
Theses and Dissertations
The ever-present challenge faced by the signal processing analyst is to get more from the available data, whether it be exploiting the same data in new ways to garner new information, or simply to increase the confidence in existing qualitative metrics. Traditional techniques include filtering (to improve the signal to noise ratio of detected signals or images or to isolate and possibly remove interfering signals), feature detection/extraction (identifying key characteristics within the signal) and signal decomposition (identification of dominant signals of interest relative to noise terms). Current research by our team began with an emphasis on the filtering of signals …
A Joint Soft Warping And Clustering Approach To Detecting Time Series Anomalies, Christopher John Schuchmann
A Joint Soft Warping And Clustering Approach To Detecting Time Series Anomalies, Christopher John Schuchmann
Theses and Dissertations
Unsupervised anomalous time series detection methods focus on identifying outliers without prior knowledge of the dataset. However, these methods often require multiple parameters to be optimized, with adequate performance tied to their careful tuning and prior domain knowledge. In this work, two methods are proposed for detecting outlier time series that adopt a joint clustering and alignment optimization to filter out the desired signals. The time series are globally clustered while simultaneously being aligned to other signals in their same cluster group. This alternating optimization employs time-warping similarity measures to help identify closely matching time series as well as the …
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 …
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 …
New Methods In Wavelet Analysis For Applications Of The Wavelet Transform, Jeffrey D. Williams
New Methods In Wavelet Analysis For Applications Of The Wavelet Transform, Jeffrey D. Williams
Theses and Dissertations
A commonality in the many applications and domains where signal processing (SP)is applied is the detection of events. Detection in SP requires the identification of the occurrence of an event, within a signal, and distinguishing the occurrence from no event. In a classical application of SP, seismologists seek to detect abnormalities in an electromagnetic (EM) signal to detect or not detect the occurrence of an earthquake, represented as an anomalous EM pulse. Since many signals are noisy, such as those produced by a seismograph, it can be challenging to distinguish a significant EM pulse from incident noise. In SP, smoothing …