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Articles 1 - 30 of 95
Full-Text Articles in Theory and Algorithms
Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson
Analyzing And Comparing Refinement Indicators For Rbf-Fd Adaptive Algorithms, Anders R. Johnson
Theses and Dissertations
Recent progress has been made in the development of collocation-based iterative algorithms that approximate solutions to PDEs. These algorithms rely on the ability to identify regions within a domain where a finer discretization is required. Such iterative algorithms are beneficial particularly when solution functions have highly localized behavior. This thesis proposes an indicator for node refinement that is constructed by approximating the forward error. This proposed indicator also helps to establish confidence in the accuracy of a given solution estimate. The proposed error estimator is theoretically examined and compared with contemporary refinement indicators. It is shown that an iterative algorithm, …
Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning, Charles Woodrum, Torrey J. Wagner, David E. Weeks
Improving 2–5 Qubit Quantum Phase Estimation Circuits Using Machine Learning, Charles Woodrum, Torrey J. Wagner, David E. Weeks
Faculty Publications
Quantum computing has the potential to solve problems that are currently intractable to classical computers with algorithms like Quantum Phase Estimation (QPE); however, noise significantly hinders the performance of today’s quantum computers. Machine learning has the potential to improve the performance of QPE algorithms, especially in the presence of noise. In this work, QPE circuits were simulated with varying levels of depolarizing noise to generate datasets of QPE output. In each case, the phase being estimated was generated with a phase gate, and each circuit modeled was defined by a randomly selected phase. The model accuracy, prediction speed, overfitting level …
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
The Impact Of Data Preparation And Model Complexity On The Natural Language Classification Of Chinese News Headlines, Torrey J. Wagner, Dennis Guhl, Brent T. Langhals
Faculty Publications
Given the emergence of China as a political and economic power in the 21st century, there is increased interest in analyzing Chinese news articles to better understand developing trends in China. Because of the volume of the material, automating the categorization of Chinese-language news articles by headline text or titles can be an effective way to sort the articles into categories for efficient review. A 383,000-headline dataset labeled with 15 categories from the Toutiao website was evaluated via natural language processing to predict topic categories. The influence of six data preparation variations on the predictive accuracy of four algorithms was …
Toward A Simulation Model Complexity Measure, J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill
Toward A Simulation Model Complexity Measure, J. Scott Thompson, Douglas D. Hodson, Michael R. Grimaila, Nicholas Hanlon, Richard Dill
Faculty Publications
Is it possible to develop a meaningful measure for the complexity of a simulation model? Algorithmic information theory provides concepts that have been applied in other areas of research for the practical measurement of object complexity. This article offers an overview of the complexity from a variety of perspectives and provides a body of knowledge with respect to the complexity of simulation models. The key terms model detail, resolution, and scope are defined. An important concept from algorithmic information theory, Kolmogorov complexity, and an application of this concept, normalized compression distance, are used to indicate the possibility of measuring changes …
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 …
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 …
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 …
Autonomous And Resilient Management Of All-Source Sensors For Navigation Integrity: A Comparison And Analysis, Niles A. Tate
Autonomous And Resilient Management Of All-Source Sensors For Navigation Integrity: A Comparison And Analysis, Niles A. Tate
Theses and Dissertations
When navigating using Global Navigation Satellite Systems (GNSS), multiple/redundant, synchronous pseudorange measurements are readily available. However, when navigating in a GNSS degraded and/or denied region, this is not guaranteed. In response to this challenge, the ANT Center developed a framework known as Autonomous and Resilient Management of All-source Sensors (ARMAS). The ARMAS framework is designed to be resilient towards data corruption caused from mismodeled, uncalibrated, and faulty sensors. This thesis further expands on this work by performing a comparison against a Residual-Based Receiver Autonomous Integrity Monitoring (RBRAIM) scheme using simulated and real flight data to evaluate each systems performance.
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Robust Error Estimation Based On Factor-Graph Models For Non-Line-Of-Sight Localization, O. Arda Vanli, Clark N. Taylor
Faculty Publications
This paper presents a method to estimate the covariances of the inputs in a factor-graph formulation for localization under non-line-of-sight conditions. A general solution based on covariance estimation and M-estimators in linear regression problems, is presented that is shown to give unbiased estimators of multiple variances and are robust against outliers. An iteratively re-weighted least squares algorithm is proposed to jointly compute the proposed variance estimators and the state estimates for the nonlinear factor graph optimization. The efficacy of the method is illustrated in a simulation study using a robot localization problem under various process and measurement models and measurement …
Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Effect Of Trigonometric Transformations On The Machine Learning Prediction And Quality Control Of Air Temperature, Andrea Fenoglio, Torrey J. Wagner, Paul Auclair, Brent T. Langhals
Faculty Publications
Conducting effective quality control of weather observations in real time is vital to the 14th Weather Squadron’s mission of providing authoritative climate data. This study explored automated quality control of weather observations by applying multiple machine learning techniques to 43,487 surface weather observations from 5 years of data at a single location. Temperature predictors were evaluated using recursive feature elimination on linear regression and XGBoost algorithms, as well as using a neural network hyperparameter sweep. Modeling was repeated after calculating trigonometric transforms of temporal variables to give the models insight into the diurnal heating cycle of the Earth. All models …
Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins
Rotating Scatter Mask For Directional Radiation Detection And Imaging, Darren Holland, Robert Olesen, Larry Burggraf, Buckley O'Day, James E. Bevins
AFIT Patents
A radiation imaging system images a distributed source of radiation from an unknown direction by rotating a scatter mask around a central axis. The scatter mask has a pixelated outer surface of tangentially oriented, flat geometric surfaces that are spherically varying in radial dimension that corresponds to a discrete amount of attenuation. Rotation position of the scatter mask is tracked as a function of time. Radiation counts from gamma and/or neutron radiation are received from at least one radiation detector that is positioned at or near the central axis. A rotation-angle dependent detector response curve (DRC) is generated based on …
Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis
Laser Illuminated Imaging: Beam And Scene Deconvolution Algorithm, Benjamin W. Davis
Theses and Dissertations
Laser illuminated imaging systems deal with several physical challenges that must be overcome to achieve high-resolution images of the target. Noise sources like background noise, photon counting noise, and laser speckle noise will all greatly affect the imaging systems ability to produce a high-resolution image. An even bigger challenge to laser illuminated imaging systems is atmospheric turbulence and the effect that it will have on the imaging system. The illuminating beam will experience tilt, causing the beam to wander off the center of the target during propagation. The light returning to the detector will similarly be affected by turbulence, and …
Amplitude Estimation For The Large Clutter Discrete Removal Algorithm, Hannah Gjermo Chomitz
Amplitude Estimation For The Large Clutter Discrete Removal Algorithm, Hannah Gjermo Chomitz
Theses and Dissertations
A large clutter discrete (LCD) is spectrally bright localized clutter that can cause a false alarm or missed target detection in space-time adaptive processing (STAP) radar data. For passive bistatic STAP, the four step LCD removal (LCDR) algorithm estimates the spatial/Doppler frequency and complex amplitude of the LCD and then removes it from the data. Once the LCD is removed from the data, homogeneous clutter suppression techniques can be used to process the data and search for targets. This research focuses on reducing the complexity of estimating the LCDs complex amplitude. This research proposes a method that directly solves for …
Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer
Electro-Optic Satellite Constellation Design Using Multi-Objective Genetic Algorithm, Yasin Tamer
Theses and Dissertations
Satellite constellation design is a complex, highly constrained, and multidisciplinary problem. Unless optimization tools are used, tradeoffs must be conducted at the subsystem level resulting in feasible, but not necessarily optimal, system designs. As satellite technology advances, new methods to optimize the system objectives are developed. This study is based on the development of a representative regional remote sensing constellation design. This thesis analyses the design process of an electrooptic satellite constellation with regional coverage considerations using system-level optimization tools. A multi objective genetic algorithm method is used to optimize the constellation design by utilizing MATLAB and STK integration. Cost, …
Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung
Improving Closely Spaced Dim Object Detection Through Improved Multiframe Blind Deconvolution, Ronald M. Aung
Theses and Dissertations
This dissertation focuses on improving the ability to detect dim stellar objects that are in close proximity to a bright one, through statistical image processing using short exposure images. The goal is to improve the space domain awareness capabilities with the existing infrastructure. Two new algorithms are developed. The first one is through the Neighborhood System Blind Deconvolution where the data functions are separated into the bright object, the neighborhood system, and the background functions. The second one is through the Dimension Reduction Blind Deconvolution, where the object function is represented by the product of two matrices. Both are designed …
Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri
Artificial Intelligence In Pursuit-Evasion Games, Specifically In The Scotland Yard Game, Arif M. Alamri
Theses and Dissertations
This research provides a heuristic algorithm for the detectives, who try to collectively capture a criminal known as Mr. X, in the Scotland Yard pursuer-evasion game. In Scotland Yard, a team of detectives attempts to converge on and capture a criminal known as Mr. X. The heuristic algorithm developed in this thesis is designed to emulate human strategies when playing the game. The algorithm uses the current state of the board at each time step, including the current positions of the detectives as well as the last known position of Mr. X. The heuristic algorithm then analyses all of the …
Comparison Of Visual Simultaneous Localization And Mapping Methods For Fixed-Wing Aircraft Using Slambench2, Patrick R. Latcham
Comparison Of Visual Simultaneous Localization And Mapping Methods For Fixed-Wing Aircraft Using Slambench2, Patrick R. Latcham
Theses and Dissertations
Visual Simultaneous Localization and Mapping (VSLAM) algorithms have evolved rapidly in the last few years, however there has been little research evaluating current algorithm's effectiveness and limitations when applied to tracking the position of a fixed-wing aerial vehicle. This research looks to evaluate current monocular VSLAM algorithms' performance on aerial vehicle datasets using the SLAMBench2 benchmarking suite. The algorithms tested are MonoSLAM, PTAM, OKVIS, LSDSLAM, ORB-SLAM2, and SVO, all of which are built into the SLAMBench2 software. The algorithms' performance is evaluated using simulated datasets generated in the AftrBurner Engine. The datasets were designed to test the quality of each …
Algorithm Selection Framework For Cyber Attack Detection, Marc W. Chalé, Nathaniel D. Bastian, Jeffery D. Weir
Algorithm Selection Framework For Cyber Attack Detection, Marc W. Chalé, Nathaniel D. Bastian, Jeffery D. Weir
Faculty Publications
The number of cyber threats against both wired and wireless computer systems and other components of the Internet of Things continues to increase annually. In this work, an algorithm selection framework is employed on the NSL-KDD data set and a novel paradigm of machine learning taxonomy is presented. The framework uses a combination of user input and meta-features to select the best algorithm to detect cyber attacks on a network. Performance is compared between a rule-of-thumb strategy and a meta-learning strategy. The framework removes the conjecture of the common trial-and-error algorithm selection method. The framework recommends five algorithms from the …
Meta Learning Recommendation System For Classification, Clarence O. Williams Iii
Meta Learning Recommendation System For Classification, Clarence O. Williams Iii
Theses and Dissertations
A data driven approach is an emerging paradigm for the handling of analytic problems. In this paradigm the mantra is to let the data speak freely. However, when using machine learning algorithms, the data does not naturally reveal the best or even a good approach for algorithm choice. One method to let the algorithm reveal itself is through the use of Meta Learning, which uses the features of a dataset to determine a useful model to represent the entire dataset. This research proposes an improvement on the meta-model recommendation system by adding classification problems to the candidate problem space with …
Global Gradient-Based Phase Unwrapping Algorithm For Increased Performance In Wavefront Sensing, Bryan R. Bartelt
Global Gradient-Based Phase Unwrapping Algorithm For Increased Performance In Wavefront Sensing, Bryan R. Bartelt
Theses and Dissertations
As the reliance on satellite data for military and commercial use increases, more effort must be exerted to protect our space-based assets. In order to help increase our space domain awareness (SDA), new approaches to ground-based space surveillance via wavefront sensing must be adopted. Improving phase-unwrapping algorithms in order to assist in phase retrieval methods is one way of increasing the performance in current adaptive optics (AO) systems. This thesis proposes a new phase-unwrapping algorithm that uses a global, gradient-based technique to more rapidly identify and correct for areas of phase wrapping during particular phase retrieval methods. This is beneficial …
Methodology For Comparison Of Algorithms For Real-World Multi-Objective Optimization Problems: Space Surveillance Network Design, Troy B. Dontigney
Methodology For Comparison Of Algorithms For Real-World Multi-Objective Optimization Problems: Space Surveillance Network Design, Troy B. Dontigney
Theses and Dissertations
Space Situational Awareness (SSA) is an activity vital to protecting national and commercial satellites from damage or destruction due to collisions. Recent research has demonstrated a methodology using evolutionary algorithms (EAs) which is intended to develop near-optimal Space Surveillance Network (SSN) architectures in the sense of low cost, low latency, and high resolution. That research is extended here by (1) developing and applying a methodology to compare the performance of two or more algorithms against this problem, and (2) analyzing the effects of using reduced data sets in those searches. Computational experiments are presented in which the performance of five …
The Effect Of Modeling Simultaneous Events On Simulation Results, John M. Carboni
The Effect Of Modeling Simultaneous Events On Simulation Results, John M. Carboni
Theses and Dissertations
This thesis explores the method that governs the prioritizing process for simultaneous events in relation to simulation results for discrete-event simulations. Specifically, it contrasts typical discrete-event simulation (DES) execution algorithms with how events are selected and ordered by the discrete-event system specification (DEVS) formalism. The motivation for this research stems from a desire to understand how the selection of events affects simulation output (i.e., response). As a particular use case, we briefly investigate the processing of simultaneous events by the Advanced Framework for Simulation, Integration and Modeling (AFSIM), a military discrete-event combat modeling and simulation package. To facilitate the building …
Performance Analysis Of Angle Of Arrival Algorithms Applied To Radiofrequency Interference Direction Finding, Taylor S. Barber
Performance Analysis Of Angle Of Arrival Algorithms Applied To Radiofrequency Interference Direction Finding, Taylor S. Barber
Theses and Dissertations
Radiofrequency (RF) interference threatens the functionality of systems that increasingly underpin the daily function of modern society. In recent years there have been multiple incidents of intentional RF spectrum denial using terrestrial interference sources. Because RF based systems are used in safety-of-life applications in both military and civilian contexts, there is need for systems that can quickly locate these interference sources. In order to meet this need, the Air Force Research Laboratory Weapons Directorate is sponsoring the following research to support systems that will be able to quickly geolocate RF interferers using passive angle-of-arrival estimation to triangulate interference sources. This …
A Generalized Phase Gradient Autofocus Algorithm, Aaron Evers
A Generalized Phase Gradient Autofocus Algorithm, Aaron Evers
Theses and Dissertations
The phase gradient autofocus (PGA) algorithm has seen widespread use and success within the synthetic aperture radar (SAR) imaging community. However, its use and success has largely been limited to collection geometries where either the polar format algorithm (PFA) or range migration algorithm is suitable for SAR image formation. In this work, a generalized phase gradient autofocus (GPGA) algorithm is developed which is applicable with both the PFA and backprojection algorithm (BPA), thereby directly supporting a wide range of collection geometries and SAR imaging modalities. The GPGA algorithm preserves the four crucial signal processing steps comprising the PGA algorithm, while …
Two-On-One Pursuit With A Non-Zero Capture Radius, Patrick J. Wasz
Two-On-One Pursuit With A Non-Zero Capture Radius, Patrick J. Wasz
Theses and Dissertations
In this paper, we revisit the "Two Cutters and Fugitive Ship" differential game that was addressed by Isaacs, but move away from point capture. We consider a two-on-one pursuit-evasion differential game with simple motion and pursuers endowed with circular capture sets of radius l > 0. The regions in the state space where only one pursuer effects the capture and the region in the state space where both pursuers cooperatively and isochronously capture the evader are characterized, thus solving the Game of Kind. Concerning the Game of Degree, the algorithm for the synthesis of the optimal state feedback strategies of the …
Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Sequence Pattern Mining With Variables, James S. Okolica, Gilbert L. Peterson, Robert F. Mills, Michael R. Grimaila
Faculty Publications
Sequence pattern mining (SPM) seeks to find multiple items that commonly occur together in a specific order. One common assumption is that all of the relevant differences between items are captured through creating distinct items, e.g., if color matters then the same item in two different colors would have two items created, one for each color. In some domains, that is unrealistic. This paper makes two contributions. The first extends SPM algorithms to allow item differentiation through attribute variables for domains with large numbers of items, e.g, by having one item with a variable with a color attribute rather than …
The Effectiveness Of Using Diversity To Select Multiple Classifier Systems With Varying Classification Thresholds, Harris K. Butler Iv, Mark A. Friend, Kenneth W. Bauer, Trevor J. Bihl
The Effectiveness Of Using Diversity To Select Multiple Classifier Systems With Varying Classification Thresholds, Harris K. Butler Iv, Mark A. Friend, Kenneth W. Bauer, Trevor J. Bihl
Faculty Publications
In classification applications, the goal of fusion techniques is to exploit complementary approaches and merge the information provided by these methods to provide a solution superior than any single method. Associated with choosing a methodology to fuse pattern recognition algorithms is the choice of algorithm or algorithms to fuse. Historically, classifier ensemble accuracy has been used to select which pattern recognition algorithms are included in a multiple classifier system. More recently, research has focused on creating and evaluating diversity metrics to more effectively select ensemble members. Using a wide range of classification data sets, methodologies, and fusion techniques, current diversity …
Cyber Anomaly Detection: Using Tabulated Vectors And Embedded Analytics For Efficient Data Mining, Robert J. Gutierrez, Kenneth W. Bauer, Bradley C. Boehmke, Cade M. Saie, Trevor J. Bihl
Cyber Anomaly Detection: Using Tabulated Vectors And Embedded Analytics For Efficient Data Mining, Robert J. Gutierrez, Kenneth W. Bauer, Bradley C. Boehmke, Cade M. Saie, Trevor J. Bihl
Faculty Publications
Firewalls, especially at large organizations, process high velocity internet traffic and flag suspicious events and activities. Flagged events can be benign, such as misconfigured routers, or malignant, such as a hacker trying to gain access to a specific computer. Confounding this is that flagged events are not always obvious in their danger and the high velocity nature of the problem. Current work in firewall log analysis is manual intensive and involves manpower hours to find events to investigate. This is predominantly achieved by manually sorting firewall and intrusion detection/prevention system log data. This work aims to improve the ability of …
Effects Of Dynamic Goals On Agent Performance, Nathan R. Ball
Effects Of Dynamic Goals On Agent Performance, Nathan R. Ball
Theses and Dissertations
Autonomous systems are increasingly being used for complex tasks in dynamic environments. Robust automation needs to be able to establish its current goal and determine when the goal has changed. In human-machine teams autonomous goal detection is an important component of maintaining shared situational awareness between both parties. This research investigates how different categories of goals affect autonomous change detection in a dynamic environment. In order to accomplish this goal, a set of autonomous agents were developed to perform within an environment with multiple possible goals. The agents perform the environmental task while monitoring for goal changes. The experiment tests …
Efficient Phase Retrieval For Off-Axis Point Spread Functions, Salome Esteban Carrasco
Efficient Phase Retrieval For Off-Axis Point Spread Functions, Salome Esteban Carrasco
Theses and Dissertations
A novel pairing of phase retrieval tools allows for efficient estimation of pupil phase in optical systems from images of point spread functions (PSFs). The phase retrieval algorithm uses correlation of modeled phase in the focal plane to decouple aberrations that are difficult to identify in complex PSFs. The use of a phase kernel that departs from the Fresnel approximation for off-axis PSFs is a more accurate representation of wavefront phase in finite conjugate imaging. The combination of the approximation and phase correlation algorithm can be more efficient and accurate than generic algorithms.