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Cited 9 time in webofscience Cited 10 time in scopus
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dc.contributor.authorJung, SK-
dc.contributor.authorLee, SB-
dc.date.accessioned2016-04-01T09:15:08Z-
dc.date.available2016-04-01T09:15:08Z-
dc.date.created2009-03-20-
dc.date.issued2003-11-05-
dc.identifier.issn0006-3592-
dc.identifier.other2003-OAK-0000010495-
dc.identifier.urihttps://oasis.postech.ac.kr/handle/2014.oak/29734-
dc.description.abstractLight intensity is a crucial factor that determines the growth of photosynthetic cells. This study analyzed the light distribution in a photobioreactor by processing images, captured with a digital camera, of a rectangular photobioreactor containing Synechococcus sp. PCC6801 as a model microorganism. The gray-scale images obtained clearly demonstrate the variation of the light-distribution profiles upon changing cell concentrations and external light intensity. Image-processing techniques were also used to predict the cell density in the photo bioreactor. By analyzing the digitized image data with a neural network model, we were able to predict the cell concentrations in the photobioreactor with a <5% error. (C) 2003 Wiley Periodicals, Inc.-
dc.description.statementofresponsibilityX-
dc.languageEnglish-
dc.publisherJOHN WILEY & SONS INC-
dc.relation.isPartOfBIOTECHNOLOGY AND BIOENGINEERING-
dc.subjectlight distribution-
dc.subjectimage analysis-
dc.subjectphotobioreactor-
dc.subjectneural networks-
dc.subjectSynechococcus sp PCC6801-
dc.subjectNEURAL NETWORKS-
dc.subjectGROWTH-
dc.subjectCULTURE-
dc.subjectKINETICS-
dc.subjectSYSTEM-
dc.subjectPLATES-
dc.subjectMODEL-
dc.titleImage analysis of light distribution in a photobioreactor-
dc.typeArticle-
dc.contributor.college경북씨그랜트센터-
dc.identifier.doi10.1002/BIT.10766-
dc.author.googleJung, SK-
dc.author.googleLee, SB-
dc.relation.volume84-
dc.relation.issue3-
dc.relation.startpage394-
dc.relation.lastpage397-
dc.contributor.id10105619-
dc.relation.journalBIOTECHNOLOGY AND BIOENGINEERING-
dc.relation.indexSCI급, SCOPUS 등재논문-
dc.relation.sciSCI-
dc.collections.nameJournal Papers-
dc.type.rimsART-
dc.identifier.bibliographicCitationBIOTECHNOLOGY AND BIOENGINEERING, v.84, no.3, pp.394 - 397-
dc.identifier.wosid000185642700015-
dc.date.tcdate2019-02-01-
dc.citation.endPage397-
dc.citation.number3-
dc.citation.startPage394-
dc.citation.titleBIOTECHNOLOGY AND BIOENGINEERING-
dc.citation.volume84-
dc.contributor.affiliatedAuthorLee, SB-
dc.identifier.scopusid2-s2.0-0141644307-
dc.description.journalClass1-
dc.description.journalClass1-
dc.description.wostc5-
dc.description.scptc5*
dc.date.scptcdate2018-05-121*
dc.type.docTypeArticle-
dc.subject.keywordPlusNEURAL-NETWORKS-
dc.subject.keywordPlusGROWTH-
dc.subject.keywordPlusKINETICS-
dc.subject.keywordPlusMODEL-
dc.subject.keywordAuthorlight distribution-
dc.subject.keywordAuthorimage analysis-
dc.subject.keywordAuthorphotobioreactor-
dc.subject.keywordAuthorneural networks-
dc.subject.keywordAuthorSynechococcus sp PCC6801-
dc.relation.journalWebOfScienceCategoryBiotechnology & Applied Microbiology-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaBiotechnology & Applied Microbiology-

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