Scaling and enhancement of non-thermal line emission on z to hν ∼ 22 kev
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: 2014DOI: 10.1109/FUZZ-IEEE.2014.6891592
Keyword(s): data handling,n fuzzy set theory,n granular computing,n pattern classification,n classification schemes,n clustering methods,n granular classifiers,n granular data,n granular prototypes,n information granules,n pattern recognition pursuits,n publicly available data,n synthetic data,n Abstracts,n Clustering algorithms,n Iris,n Prototypes,n Support vector machine classification,n Testing,n Training,n Fuzzy C-Means,n Granular Computing
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Scaling and enhancement of non-thermal line emission on z to hν ∼ 22 kev
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| contributor author | Ampleford, D.J. | |
| contributor author | Hansen, S.B. | |
| contributor author | Jennings, C.A. | |
| contributor author | Jones, B. | |
| contributor author | Webb, T.C. | |
| contributor author | Harper-Slaboszewicz, V. | |
| contributor author | Cuneo, M.E. | |
| contributor author | Rochau, G.A. | |
| contributor author | Coverdale, C.A. | |
| contributor author | Harvey-Thompson, A.J. | |
| contributor author | Sinars, D.B. | |
| contributor author | Moore, J.K. | |
| contributor author | Flanagan, T.M. | |
| contributor author | Ouart, N. | |
| contributor author | Dasgupta, A. | |
| contributor author | Giuliani, J. | |
| contributor author | Velikovich, A.L. | |
| contributor author | Apruzese, J.P. | |
| contributor author | Chittenden, J.P. | |
| contributor author | Niasse, N. | |
| contributor author | Appelbe, B. | |
| date accessioned | 2020-03-12T22:30:04Z | |
| date available | 2020-03-12T22:30:04Z | |
| date issued | 2014 | |
| identifier other | 7012503.pdf | |
| identifier uri | https://libsearch.um.ac.ir:443/fum/handle/fum/1085151 | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | Scaling and enhancement of non-thermal line emission on z to hν ∼ 22 kev | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8222529 | |
| subject keywords | data handling | |
| subject keywords | n fuzzy set theory | |
| subject keywords | n granular computing | |
| subject keywords | n pattern classification | |
| subject keywords | n classification schemes | |
| subject keywords | n clustering methods | |
| subject keywords | n granular classifiers | |
| subject keywords | n granular data | |
| subject keywords | n granular prototypes | |
| subject keywords | n information granules | |
| subject keywords | n pattern recognition pursuits | |
| subject keywords | n publicly available data | |
| subject keywords | n synthetic data | |
| subject keywords | n Abstracts | |
| subject keywords | n Clustering algorithms | |
| subject keywords | n Iris | |
| subject keywords | n Prototypes | |
| subject keywords | n Support vector machine classification | |
| subject keywords | n Testing | |
| subject keywords | n Training | |
| subject keywords | n Fuzzy C-Means | |
| subject keywords | n Granular Computing | |
| identifier doi | 10.1109/FUZZ-IEEE.2014.6891592 | |
| journal title | lasma Sciences (ICOPS) held with 2014 IEEE International Conference on High-Power Particle Beams (BE | |
| filesize | 159701 | |
| citations | 0 |