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Physical Sciences and Mathematics Commons™
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Full-Text Articles in Physical Sciences and Mathematics
Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson
Machine Learning Nuclear Detonation Features, Daniel T. Schmitt, Gilbert L. Peterson
Faculty Publications
Nuclear explosion yield estimation equations based on a 3D model of the explosion volume will have a lower uncertainty than radius based estimation. To accurately collect data for a volume model of atmospheric explosions requires building a 3D representation from 2D images. The majority of 3D reconstruction algorithms use the SIFT (scale-invariant feature transform) feature detection algorithm which works best on feature-rich objects with continuous angular collections. These assumptions are different from the archive of nuclear explosions that have only 3 points of view. This paper reduces 300 dimensions derived from an image based on Fourier analysis and five edge …
Timing Mark Detection On Nuclear Detonation Video, Daniel T. Schmitt, Gilbert L. Peterson
Timing Mark Detection On Nuclear Detonation Video, Daniel T. Schmitt, Gilbert L. Peterson
Faculty Publications
During the 1950s and 1960s the United States conducted and filmed over 200 atmospheric nuclear tests establishing the foundations of atmospheric nuclear detonation behavior. Each explosion was documented with about 20 videos from three or four points of view. Synthesizing the videos into a 3D video will improve yield estimates and reduce error factors. The videos were captured at a nominal 2500 frames per second, but range from 2300-3100 frames per second during operation. In order to combine them into one 3D video, individual video frames need to be correlated in time with each other. When the videos were captured …