DC Field | Value | Language |
---|---|---|
dc.contributor.author | Yang, TS | ko |
dc.contributor.author | Oh, Jun-Ho | ko |
dc.date.accessioned | 2013-03-04T12:56:03Z | - |
dc.date.available | 2013-03-04T12:56:03Z | - |
dc.date.created | 2012-02-06 | - |
dc.date.created | 2012-02-06 | - |
dc.date.issued | 2001-12 | - |
dc.identifier.citation | OPTICAL ENGINEERING, v.40, no.12, pp.2771 - 2779 | - |
dc.identifier.issn | 0091-3286 | - |
dc.identifier.uri | http://hdl.handle.net/10203/82705 | - |
dc.description.abstract | A method is proposed for the identification of primary aberrations with a single image of a lateral shearing interferogram of optical components, obtained from a monochromatic laser source, using a neural network. The neural network is used to illustrate the features of the sampled interferograms and identify the primary aberrations of the other interferograms by means of its excellence in interpolation and extrapolation. Analysis of the pattern is performed to find the proper features to teach the neural network in less time. Simulations are performed to verify the suggested method. (C) 2001 Society of Photo-Optical Instrumentation Engineers. | - |
dc.language | English | - |
dc.publisher | SPIE-INT SOCIETY OPTICAL ENGINEERING | - |
dc.title | Identification of primary aberrations on a lateral shearing interferogram of optical components using neural network | - |
dc.type | Article | - |
dc.identifier.wosid | 000173017300013 | - |
dc.identifier.scopusid | 2-s2.0-0035721299 | - |
dc.type.rims | ART | - |
dc.citation.volume | 40 | - |
dc.citation.issue | 12 | - |
dc.citation.beginningpage | 2771 | - |
dc.citation.endingpage | 2779 | - |
dc.citation.publicationname | OPTICAL ENGINEERING | - |
dc.identifier.doi | 10.1117/1.1418223 | - |
dc.contributor.localauthor | Oh, Jun-Ho | - |
dc.contributor.nonIdAuthor | Yang, TS | - |
dc.type.journalArticle | Article | - |
dc.subject.keywordAuthor | primary aberrations | - |
dc.subject.keywordAuthor | lateral shearing interferogram | - |
dc.subject.keywordAuthor | neural network | - |
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