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Articles 1 - 21 of 21
Full-Text Articles in Physical Sciences and Mathematics
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 …
Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby
Improving Country Conflict And Peace Modeling: Datasets, Imputations, And Hierarchical Clustering, Benjamin D. Leiby
Theses and Dissertations
Many disparate datasets exist that provide country attributes covering political, economic, and social aspects. Unfortunately, this data often does not include all countries nor is the data complete for those countries included, as measured by the dataset’s missingness. This research addresses these dataset shortfalls in predicting country instability by considering country attributes in all aspects as well as in greater thresholds of missingness. First, a structured summary of past research is presented framed by a developed casual taxonomy and functional ontology. Additionally, a novel imputation technique for very large datasets is presented to account for moderate missingness in the expanded …
Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando
Modern Approaches And Theoretical Extensions To The Multivariate Kolmogorov Smirnov Test, Gonzalo Hernando
Theses and Dissertations
Most statistical tests are fully developed for univariate data, but when inference is required for multivariate data, univariate tests risk information loss and interpretability. This research 1) derives and extends the multivariate Komolgorov Smirnov test for 2 and into m-dimensions, 2) derives small sample critical values for the KS test that are not reliant on sample size simulations or correlation between variables, 3) extends large sample estimations and current KS implementations, and 4) provides sample size and power calculations in order to enable experimental design with respect to testing for differences in distributions. Through extensive simulation, we demonstrate that our …
Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman
Innovative Heuristics To Improve The Latent Dirichlet Allocation Methodology For Textual Analysis And A New Modernized Topic Modeling Approach, Jamie T. Zimmerman
Theses and Dissertations
Natural Language Processing is a complex method of data mining the vast trove of documents created and made available every day. Topic modeling seeks to identify the topics within textual corpora with limited human input into the process to speed analysis. Current topic modeling techniques used in Natural Language Processing have limitations in the pre-processing steps. This dissertation studies topic modeling techniques, those limitations in the pre-processing, and introduces new algorithms to gain improvements from existing topic modeling techniques while being competitive with computational complexity. This research introduces four contributions to the field of Natural Language Processing and topic modeling. …
Generalized Robust Feature Selection, Bradford L. Lott
Generalized Robust Feature Selection, Bradford L. Lott
Theses and Dissertations
Feature selection may be summarized as identifying salient features to a given response. Understanding which features affect the response enables, in the future, only collecting consequential data; hence, the feature selection algorithm may lead to saving effort spent collecting data, storage resources, as well as computational resources for making predictions. We propose a generalized approach to select the salient features of data sets. Our approach may also be applied to unsupervised datasets to understand which data streams provide unique information. We contend our approach identifies salient features robust to the sub-sequent predictive model applied. The proposed algorithm considers all provided …
Constructing Prediction Intervals With Neural Networks: An Empirical Evaluation Of Bootstrapping And Conformal Inference Methods, Alexander N. Contarino
Constructing Prediction Intervals With Neural Networks: An Empirical Evaluation Of Bootstrapping And Conformal Inference Methods, Alexander N. Contarino
Theses and Dissertations
Artificial neural networks (ANNs) are popular tools for accomplishing many machine learning tasks, including predicting continuous outcomes. However, the general lack of confidence measures provided with ANN predictions limit their applicability, especially in military settings where accuracy is paramount. Supplementing point predictions with prediction intervals (PIs) is common for other learning algorithms, but the complex structure and training of ANNs renders constructing PIs difficult. This work provides the network design choices and inferential methods for creating better performing PIs with ANNs to enable their adaptation for military use. A two-step experiment is executed across 11 datasets, including an imaged-based dataset. …
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Leveraging Machine Learning For Large Scale Analysis Of Publicly-Available Data For Gnss Interference Events, David K. Stamper
Theses and Dissertations
This research documents architecture and implementation of an enhanced interference detection and classification analysis system, using both a database and storage solution utilizing machine learning algorithms to detect changes in Carrier-to-Noise strength over multiple GNSS sites. The system uses publicly-available government supported receivers to detect interference, and built using FOSS packaged as a programming library through Python. Two algorithms are discussed in terms of enhancing interference detection using both non-machine learning and machine learning approaches. Two algorithms are also discussed which are used for classification of events. In addition, an approach to Large Scale data analytics is demonstrated via a …
Telemetry Data Mining For Unmanned Aircraft Systems, Li Yu
Telemetry Data Mining For Unmanned Aircraft Systems, Li Yu
Theses and Dissertations
With ever more data becoming available to the US Air Force, it is vital to develop effective methods to leverage this strategic asset. Machine learning (ML) techniques present a means of meeting this challenge, as these tools have demonstrated successful use in commercial applications. For this research, three ML methods were applied to a unmanned aircraft system (UAS) telemetry dataset with the aim of extracting useful insight related to phases of flight. It was shown that ML provides an advantage in exploratory data analysis and as well as classification of phases. Neural network models demonstrated the best performance with over …
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 …
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 …
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 …
Effects Of Data Replication On Data Exfiltration In Mobile Ad Hoc Networks Utilizing Reactive Protocols, Corey T. Willinger
Effects Of Data Replication On Data Exfiltration In Mobile Ad Hoc Networks Utilizing Reactive Protocols, Corey T. Willinger
Theses and Dissertations
A swarm of autonomous UAVs can provide a significant amount of ISR data where current UAV assets may not be feasible or practical. As such, the availability of the data the resides in the swarm is a topic that will benefit from further investigation. This thesis examines the impact of le replication and swarm characteristics such as node mobility, swarm size, and churn rate on data availability utilizing reactive protocols. This document examines the most prominent factors affecting the networking of nodes in a MANET. Factors include network routing protocols and peer-to-peer le protocols. It compares and contrasts several open …
The Affect Of Varying Arousal Methods Upon Vigilance And Error Detection In An Automated Command And Control Environment, Brent T. Langhals
The Affect Of Varying Arousal Methods Upon Vigilance And Error Detection In An Automated Command And Control Environment, Brent T. Langhals
Theses and Dissertations
This study focused on improving vigilance performance through developing methods to arouse subjects to the possibility of errors in a data manipulation information warfare attack. The study suggests that by continuously applying arousal stimuli, subjects would retain initially high vigilance levels thereby avoiding the vigilance decrement phenomenon and improving error detection. The research focused on which methods were the most effective as well the impact of age upon the arousability of the subjects. Further the implications of vigilance and vigilance decrement for correct detections as well as productivity were explored. The study used a simulation experiment to provide a vigilance …
Extracting Mission Semantics From Unmanned Aerial Vehicle Telemetry And Flight Plans, Walter T. Berridge
Extracting Mission Semantics From Unmanned Aerial Vehicle Telemetry And Flight Plans, Walter T. Berridge
Theses and Dissertations
With the acceptance of Unmanned Aerial Vehicles (UAVs) as a primary platform within the Department of Defense (DOD) for gathering intelligence data, the amount of video information being recorded, analyzed, and archived continues to grow. Mechanisms for quickly locating and retrieving video segments of interest amongst the many hours of recorded video are required to accommodate the rapid turnaround expected in today's wartime planning environments. This research demonstrates that text-based data accompanying UAV video yields sufficient information to identify and create data items that can be indexed to provide for rapid identification and retrieval of video segments of interest. Four …
Evolving Compact Decision Rule Sets, Robert E. Marmelstein
Evolving Compact Decision Rule Sets, Robert E. Marmelstein
Theses and Dissertations
While data mining technology holds the promise of automatically extracting useful patterns (such as decision rules) from data, this potential has yet to be realized. One of the major technical impediments is that the current generation of data mining tools produce decision rule sets that are very accurate, but extremely complex and difficult to interpret. As a result, there is a clear need for methods that yield decision rule sets that are both accurate and compact. The development of the Genetic Rule and Classifier Construction Environment (GRaCCE) is proposed as an alternative to existing decision rule induction (DRI) algorithms. GRaCCE …
An Examination Of Multi-Tier Designs For Legacy Data Access, Michael L. Acker
An Examination Of Multi-Tier Designs For Legacy Data Access, Michael L. Acker
Theses and Dissertations
This work examines the application of Java and the Common Object Request Broker Architecture (CORBA) to support access to remote databases via the Internet. The research applies these software technologies to assist an Air Force distance learning provider in improving the capabilities of its World Wide Web-based correspondence system. An analysis of the distance learning provider's operation revealed a strong dependency on a non-collocated legacy relational database. This dependency limits the distance learning provider's future web-based capabilities. A recommendation to improve operation by data replication is proposed, and the implementation details are provided for two alternative test systems that support …
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
A Neural Network Approach To The Prediction And Confidence Assignation Of Nonlinear Time Series Classifications, Erin S. Heim
Theses and Dissertations
This thesis uses multiple layer perceptrons (MLP) neural networks and Kohonen clustering networks to predict and assign confidence to nonlinear time series classifications. The nonlinear time series used for analysis is the Standard and Poor's 100 (S&P 100) index. The target prediction is classification of the daily index change. Financial indicators were evaluated to determine the most useful combination of features for input into the networks. After evaluation it was determined that net changes in the index over time and three short-term indicators result in better accuracy. A back-propagation trained MLP neural network was then trained with these features to …
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
A Comparison Of Loose And Tight Gps/Ins Integration Using Real Ins And Gps Data, Warren H. Nuibe
Theses and Dissertations
An extended Kalman filter (EKE) is used to combine the information obtained from a Global Positioning System (GPS) receiver and an Inertial Navigation System (INS) to provide a navigation solution. This research compares the results of a tightly-coupled GPS/INS integrated system with a loosely-coupled integrated system, using real world data. A fair comparison is accomplished by using the same sets of data, and keeping the integration structures as close as possible. Both integrations are feedforward and have the same error states in the navigation Kalman filters. Differences between the two, such as navigation solutions and tuning values, are shown in …
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Acquiring Consistent Knowledge For Bayesian Forests, Darwyn O. Banks
Theses and Dissertations
This thesis develops a methodology and a tool for knowledge acquisition with the new probabilistic knowledge representation-the Bayesian Forest. It establishes the structure of the Knowledge Acquisition and Maintenance module of the Probabilities. Expert Systems, Knowledge and Inference (PESKI) architecture. The tool, MACK, is designed to be used directly by the domain expert(s) rather than by knowledge engineer(s), and thus supports automated knowledge acquisition. This research determines and implements the constraints necessary to ensure the consistency of Bayesian Forest knowledge bases as data is both acquired and subsequently maintained. The impact to the PESKI architecture of time-dependent information and default …
The Application Of A Readiness-Based Sparing Model To Foreign Military Sales, Karen M. Klinger
The Application Of A Readiness-Based Sparing Model To Foreign Military Sales, Karen M. Klinger
Theses and Dissertations
Current Foreign Military Sales FMS models provide stock levels that result in a very low system availability or a funding requirement that exceeds the overall budget. The purpose of this research was to determine if an inventory model exists that can be used in FMS reparable sparing to provide a more efficient and economical inventory purchase. The Aircraft Sustainability Model ASM is such a model, providing the most aircraft availability possible from a given inventory investment by computing the optimal number of spare parts to buy for each item. FMS data was obtained from two sources - the International Data …
Data Reduction With Least Squares Differential Correction Using Equinoctial Elements, Michael S. Wasson
Data Reduction With Least Squares Differential Correction Using Equinoctial Elements, Michael S. Wasson
Theses and Dissertations
This study investigates earth satellite orbit estimation on a track of range, azimuth, and elevation data from a single tracking station. The estimation routine is a least squares batch filter based solely on two-body orbital motion. Using equinoctial elements for the reference orbit avoids the numerical difficulties of the classical elements at eccentricities near zero and inclinations near zero or 90 degrees. Orbits for Mir, DMSP, Explorer, Cosmos, and GPS are investigated. The goal of this study is to reduce orbit information from observations (range, azimuth, and elevation) to an element set and a covariance matrix without considering perturbation effects. …