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Articles 1 - 4 of 4
Full-Text Articles in Physical Sciences and Mathematics
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 …
A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson
A Macro-Level Order Metric For Self-Organizing Adaptive Systems, David W. King, Gilbert L. Peterson
Faculty Publications
Analyzing how agent interactions affect macro-level self-organized behaviors can yield a deeper understanding of how complex adaptive systems work. The dynamic nature of complex systems makes it difficult to determine if, or when, a system has reached a state of equilibrium or is about to undergo a major transition reflecting the appearance of self-organized states. Using the notion of local neighborhood entropy, this paper presents a metric for evaluating the macro-level order of a system. The metric is tested in two dissimilar complex adaptive systems with self-organizing properties: An autonomous swarm searching for multiple dynamic targets and Conway's Game of …
Behavior Flexibility For Autonomous Unmanned Aerial Systems, Taylor B. Bodin
Behavior Flexibility For Autonomous Unmanned Aerial Systems, Taylor B. Bodin
Theses and Dissertations
Autonomous unmanned aerial systems (UAS) could supplement and eventually subsume a substantial portion of the mission set currently executed by remote pilots, making UAS more robust, responsive, and numerous than permitted by teleoperation alone. Unfortunately, the development of robust autonomous systems is difficult, costly, and time-consuming. Furthermore, the resulting systems often make little reuse of proven software components and offer limited adaptability for new tasks. This work presents a development platform for UAS which promotes behavioral flexibility. The platform incorporates the Unified Behavior Framework (a modular, extensible autonomy framework), the Robotic Operating System (a RSF), and PX4 (an open- source …
Target Detection Using Convolutional Neural Networks, Robert P. Loibl
Target Detection Using Convolutional Neural Networks, Robert P. Loibl
Theses and Dissertations
This research explores the use of Convolutional Neural Networks (CNNs) to classify targets of interest within satellite imagery. Methods were specifically devised for the classification of airports within Landsat-8 scenes. A novel automated dataset generation technique was developed to create labeled datasets from satellite imagery using only coordinate metadata. Using this approach a very large dataset of over 132,000 labeled images was created without human input. This dataset was used to evaluate the effects of color and resolution on airport classification accuracy. Two experiments were run with the first experiment classifying large airports with 96.8% accuracy, and the second classifying …