Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods
Autor(a) principal: | |
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Data de Publicação: | 2015 |
Outros Autores: | , , , |
Tipo de documento: | Artigo |
Idioma: | eng |
Título da fonte: | Materials research (São Carlos. Online) |
Texto Completo: | http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392015000100127 |
Resumo: | Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research. |
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Materials research (São Carlos. Online) |
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Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methodspolymeric insulatorshydrophobicityimage processingcharacteristic parametersmultiboost decision treeHydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research.ABM, ABC, ABPol2015-02-01info:eu-repo/semantics/articleinfo:eu-repo/semantics/publishedVersiontext/htmlhttp://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392015000100127Materials Research v.18 n.1 2015reponame:Materials research (São Carlos. Online)instname:Universidade Federal de São Carlos (UFSCAR)instacron:ABM ABC ABPOL10.1590/1516-1439.286414info:eu-repo/semantics/openAccessDong,ZhengchengFang,YanjunWang,XianpeiZhao,YuWang,Quandeeng2015-04-10T00:00:00Zoai:scielo:S1516-14392015000100127Revistahttp://www.scielo.br/mrPUBhttps://old.scielo.br/oai/scielo-oai.phpdedz@power.ufscar.br1980-53731516-1439opendoar:2015-04-10T00:00Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR)false |
dc.title.none.fl_str_mv |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
title |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
spellingShingle |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods Dong,Zhengcheng polymeric insulators hydrophobicity image processing characteristic parameters multiboost decision tree |
title_short |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
title_full |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
title_fullStr |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
title_full_unstemmed |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
title_sort |
Hydrophobicity Classification of Polymeric Insulators Based on Embedded Methods |
author |
Dong,Zhengcheng |
author_facet |
Dong,Zhengcheng Fang,Yanjun Wang,Xianpei Zhao,Yu Wang,Quande |
author_role |
author |
author2 |
Fang,Yanjun Wang,Xianpei Zhao,Yu Wang,Quande |
author2_role |
author author author author |
dc.contributor.author.fl_str_mv |
Dong,Zhengcheng Fang,Yanjun Wang,Xianpei Zhao,Yu Wang,Quande |
dc.subject.por.fl_str_mv |
polymeric insulators hydrophobicity image processing characteristic parameters multiboost decision tree |
topic |
polymeric insulators hydrophobicity image processing characteristic parameters multiboost decision tree |
description |
Hydrophobicity is an important parameter to characterize electrical properties of insulated materials. Therefore, it is an urgent task to develop on-line instruments to identify the hydrophobicity of insulated material's surface conveniently, quickly and accurately. For this purpose, a novel evaluation system with image processing and decision tree is proposed which is based on embedded platform. For obtaining satisfactory results, we first propose a mixed image segmentation method to overcome the complex conditions outside, concerning non-controlled illumination, nonstandard surfaces and unfixed shooting angle. Then we adopt four new characteristic parameters to describe the image of each sample. Finally, a classification method based on MultiBoost decision tree is conducted which synthesizes the merits of both AdaBoost and Wagging algorithm. Results indicate the procedures can be applied in the DSP (Digital Signal Processor) platform perfectly and better results can be obtained than those did in our previous study or that of some other research. |
publishDate |
2015 |
dc.date.none.fl_str_mv |
2015-02-01 |
dc.type.driver.fl_str_mv |
info:eu-repo/semantics/article |
dc.type.status.fl_str_mv |
info:eu-repo/semantics/publishedVersion |
format |
article |
status_str |
publishedVersion |
dc.identifier.uri.fl_str_mv |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392015000100127 |
url |
http://old.scielo.br/scielo.php?script=sci_arttext&pid=S1516-14392015000100127 |
dc.language.iso.fl_str_mv |
eng |
language |
eng |
dc.relation.none.fl_str_mv |
10.1590/1516-1439.286414 |
dc.rights.driver.fl_str_mv |
info:eu-repo/semantics/openAccess |
eu_rights_str_mv |
openAccess |
dc.format.none.fl_str_mv |
text/html |
dc.publisher.none.fl_str_mv |
ABM, ABC, ABPol |
publisher.none.fl_str_mv |
ABM, ABC, ABPol |
dc.source.none.fl_str_mv |
Materials Research v.18 n.1 2015 reponame:Materials research (São Carlos. Online) instname:Universidade Federal de São Carlos (UFSCAR) instacron:ABM ABC ABPOL |
instname_str |
Universidade Federal de São Carlos (UFSCAR) |
instacron_str |
ABM ABC ABPOL |
institution |
ABM ABC ABPOL |
reponame_str |
Materials research (São Carlos. Online) |
collection |
Materials research (São Carlos. Online) |
repository.name.fl_str_mv |
Materials research (São Carlos. Online) - Universidade Federal de São Carlos (UFSCAR) |
repository.mail.fl_str_mv |
dedz@power.ufscar.br |
_version_ |
1754212665391054848 |