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Results for “Feature Extraction”
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High-throughput feature extraction for measuring attributes of deforming open-cell foams
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YOLO11 to SAM2 pipeline for feature extraction from nuclear test films
The response to the effects of nuclear detonations is supported by models that describe the evolution of the nuclear fireball and cloud and the associated transport of active debris. Validation of those descriptions relies on data from the nuclear test operations. Video records of those events offer a rich source of information that was exploited to a limited extent in historic analyses. Computer vision and machine learning techniques are powerful tools that can be used to increase the number of measurements that can be obtained from those films. In this work, we apply computer vision techniques to automatically track the temporal evolution of the nuclear fireball. In particular, we apply You Only Look Once 11 (YOLO11) and Segment Anything Model 2 (SAM2) in combination with minimal human intervention to digitized versions of the original nuclear test films. As part of the proposed workflow, the YOLO11 model is applied to films to determine bounding boxes for the fireball within each frame. These are then used as inputs to SAM2, which uses image segmentation to determine the fireball boundaries and their temporal evolution. We assess the accuracy of our approach by using it to determine the energy released during the Trinity nuclear test and comparing the results with previous analyses based on manual measurements.
Feature extraction via similarity search: application to atom finding and denoising in electron and scanning probe microscopy imaging
Not Available
Nonlinear Feature Extraction and Energy Dissipation of Foam/Metal Interfaces
The physical mechanisms of energy dissipation in foam to metal interfaces must be understood in order to develop predictive models of systems with foam packaging common to many aerospace and aeronautical applications. Experimental data was obtained from hardware termed Ministack, which has large, unbonded interfaces held under compressive preload. This setup has a solid aluminum mass placed into two foam cups which are then inserted into an aluminum can and fastened with a known preload. Ministack was tested on a shaker using upward sine sweep base acceleration excitations to estimate the linearized natural frequency and energy dissipation of the first axial mode. The experimental system was disassembled and reassembled before each series of tests in order to observe the effects of the assembly to assembly variability on the dynamics. Additionally, Ministack was subjected to upward and downward sweeps to gain some understanding of the nonlinearities. Finally, Ministack was tested using a transient input, and the ring down was analyzed to find the effective stiffness and damping. There are some important findings in the measured data: there is significant assembly to assembly variability, the order in which the sine sweeps are performed influence s the dynamic response, and the system exhibits nontrivial damping and stiffness nonlinearities that must be accounted for in modeling efforts .
Feature Extraction and Porosity Assessment on Thick Stainless Steel 316 Samples with Embedded Slots.
Abstract not provided.
Feature Extraction and Porosity Assessment on Thick Stainless Steel 316 Test Samples with Embedded Slots.
Abstract not provided.
Feature Extraction and Porosity Assessment of Thick Additive Manufactured Stainless Steel 316 Using Enhanced Segmentation.
Abstract not provided.
A Comparison Study of Feature Extraction and Data Fusion Techniques for Improving Cyber-Physical Situational Awareness
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Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination.
Abstract not provided.
Supervised non-negative tensor factorization for automatic hyperspectral feature extraction and target discrimination.
Abstract not provided.
Unsupervised Machine Learning Methods for Feature Extraction
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Packet2Vec: Utlizing Word2Vec for Feature Extraction in Packet Data.
Abstract not provided.
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data.
Abstract not provided.
Packet2Vec: Utilizing Word2Vec for Feature Extraction in Packet Data.
Abstract not provided.
RF Feature Extraction using Wireless Protocol Informatics Pattern Matching and Natural Language Processing.
Abstract not provided.
Feature Extraction and Design from Gamma-ray Spectra for Radionuclide Identification
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Decentralizing Feature Extraction with Quantum Convolutional Neural Network for Automatic Speech Recognition
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