Advanced gas chemistry model for gasses disturbed by an intense electron beam
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: 2014DOI: 10.1109/FUZZ-IEEE.2014.6891620
Keyword(s): feature extraction,n fuzzy set theory,n genetic algorithms,n geophysical image processing,n hyperspectral imaging,n image classification,n image resolution,n image segmentation,n iterative methods,n land cover,n pattern clustering,n remote sensing,n shape recognition,n support vector machines,n unsupervised learning,n execution time reduction,n fitness component,n flexible object shape extraction,n fuzzy membership map,n genetic algorithm-
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Advanced gas chemistry model for gasses disturbed by an intense electron beam
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| contributor author | Angus, J.R. | |
| contributor author | Richardson, A.S. | |
| contributor author | Schumer, J.W. | |
| contributor author | Swanekamp, S.B. | |
| contributor author | Mosher, D. | |
| contributor author | Ottinger, P.F. | |
| date accessioned | 2020-03-12T22:30:07Z | |
| date available | 2020-03-12T22:30:07Z | |
| date issued | 2014 | |
| identifier other | 7012531.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1085180 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Advanced gas chemistry model for gasses disturbed by an intense electron beam | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8222570 | |
| subject keywords | feature extraction | |
| subject keywords | n fuzzy set theory | |
| subject keywords | n genetic algorithms | |
| subject keywords | n geophysical image processing | |
| subject keywords | n hyperspectral imaging | |
| subject keywords | n image classification | |
| subject keywords | n image resolution | |
| subject keywords | n image segmentation | |
| subject keywords | n iterative methods | |
| subject keywords | n land cover | |
| subject keywords | n pattern clustering | |
| subject keywords | n remote sensing | |
| subject keywords | n shape recognition | |
| subject keywords | n support vector machines | |
| subject keywords | n unsupervised learning | |
| subject keywords | n execution time reduction | |
| subject keywords | n fitness component | |
| subject keywords | n flexible object shape extraction | |
| subject keywords | n fuzzy membership map | |
| subject keywords | n genetic algorithm- | |
| identifier doi | 10.1109/FUZZ-IEEE.2014.6891620 | |
| journal title | lasma Sciences (ICOPS) held with 2014 IEEE International Conference on High-Power Particle Beams (BE | |
| filesize | 119323 | |
| citations | 0 |