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Articles 1 - 5 of 5
Full-Text Articles in Physical Sciences and Mathematics
Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter
Multiple Pursuer Multiple Evader Differential Games, Eloy Garcia, David Casbeer, Alexander Von Moll, Meir Pachter
Faculty Publications
In this paper an N-pursuer vs. M-evader team conflict is studied. The differential game of border defense is addressed and we focus on the game of degree in the region of the state space where the pursuers are able to win. This work extends classical differential game theory to simultaneously address weapon assignments and multi-player pursuit-evasion scenarios. Saddle-point strategies that provide guaranteed performance for each team regardless of the actual strategies implemented by the opponent are devised. The players' optimal strategies require the co-design of cooperative optimal assignments and optimal guidance laws. A representative measure of performance is proposed and …
Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson
Improving Optimization Of Convolutional Neural Networks Through Parameter Fine-Tuning, Nicholas C. Becherer, John M. Pecarina, Scott L. Nykl, Kenneth M. Hopkinson
Faculty Publications
In recent years, convolutional neural networks have achieved state-of-the-art performance in a number of computer vision problems such as image classification. Prior research has shown that a transfer learning technique known as parameter fine-tuning wherein a network is pre-trained on a different dataset can boost the performance of these networks. However, the topic of identifying the best source dataset and learning strategy for a given target domain is largely unexplored. Thus, this research presents and evaluates various transfer learning methods for fine-grained image classification as well as the effect on ensemble networks. The results clearly demonstrate the effectiveness of parameter …
Near Earth Space Object Detection Using Parallax As Multi-Hypothesis Test Criterion, Joseph C. Tompkins, Stephen C. Cain, David J. Becker
Near Earth Space Object Detection Using Parallax As Multi-Hypothesis Test Criterion, Joseph C. Tompkins, Stephen C. Cain, David J. Becker
Faculty Publications
The US Strategic Command (USSTRATCOM) operated Space Surveillance Network (SSN) is tasked with Space Situational Awareness (SSA) for the U.S. military. This system is made up of Electro-Optic sensors, such as the Ground-based Electro-Optical Deep Space Surveillance (GEODSS) and RADAR based sensors, such as the Space Fence Gaps. They remain in the tracking of Resident Space Objects (RSO’s) in Geosynchronous Orbits (GEO), due to limitations of SST and GEODSS system implementation. This research explores a reliable, ground-based technique used to quickly determine an RSO’s altitude from a single or limited set of observations. Implementation of such sensors into the SSN …
Developmental Test And Requirements Best Practices Of Successful Information Systems Efforts Using Agile Methods, Jeremy D. Kramer, Torrey J. Wagner
Developmental Test And Requirements Best Practices Of Successful Information Systems Efforts Using Agile Methods, Jeremy D. Kramer, Torrey J. Wagner
Faculty Publications
This article provides insights into the current state of developmental testing (DT) and requirements management in Department of Defense information systems employing Agile development. The authors describe the study methodology and provide an overview of Agile development and testing. Insights are described for requirements, detailed planning, test execution, and reporting. This work articulates best practices related to DT and requirements management strategies for programs employing modernized Software Development Life Cycle practices.
Improved N-Dimensional Data Visualization From Hyper-Radial Values, Todd J. Paciencia, Trevor J. Bihl, Kenneth W. Bauer
Improved N-Dimensional Data Visualization From Hyper-Radial Values, Todd J. Paciencia, Trevor J. Bihl, Kenneth W. Bauer
Faculty Publications
Higher-dimensional data, which is becoming common in many disciplines due to big data problems, are inherently difficult to visualize in a meaningful way. While many visualization methods exist, they are often difficult to interpret, involve multiple plots and overlaid points, or require simultaneous interpretations. This research adapts and extends hyper-radial visualization, a technique used to visualize Pareto fronts in multi-objective optimizations, to become an n-dimensional visualization tool. Hyper-radial visualization is seen to offer many advantages by presenting a low-dimensionality representation of data through easily understood calculations. First, hyper-radial visualization is extended for use with general multivariate data. Second, a method …