FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy
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Year
: 2014DOI: 10.1109/ICITCS.2014.7021751
Keyword(s): Accuracy,Biological neural networks,Classification algorithms,Feature extraction,Noise,Principal component analysis,Ultra wideband radar
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FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy
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| contributor author | Blue, J. , Roussy, A. , Pinaton, J. | |
| date accessioned | 2020-03-12T20:01:47Z | |
| date available | 2020-03-12T20:01:47Z | |
| date issued | 2014 | |
| identifier other | 6846984.pdf | |
| identifier uri | http://libsearch.um.ac.ir:80/fum/handle/fum/999143?locale-attribute=en | |
| format | general | |
| language | English | |
| publisher | IEEE | |
| title | FDC R2R variation monitoring for sensor level diagnosis in tool condition hierarchy | |
| type | Conference Paper | |
| contenttype | Metadata Only | |
| identifier padid | 8119439 | |
| subject keywords | Accuracy | |
| subject keywords | Biological neural networks | |
| subject keywords | Classification algorithms | |
| subject keywords | Feature extraction | |
| subject keywords | Noise | |
| subject keywords | Principal component analysis | |
| subject keywords | Ultra wideband radar | |
| identifier doi | 10.1109/ICITCS.2014.7021751 | |
| journal title | dvanced Semiconductor Manufacturing Conference (ASMC), 2014 25th Annual SEMI | |
| filesize | 695966 | |
| citations | 0 |