Detailed Information

Cited 1 time in webofscience Cited 1 time in scopus
Metadata Downloads

An approach of the diffraction loss prediction using artificial neural network in hilly mountainous terrain

Full metadata record
DC Field Value Language
dc.contributor.authorLee, Changwon-
dc.contributor.authorPark, Sungkwon-
dc.date.accessioned2021-08-02T14:27:58Z-
dc.date.available2021-08-02T14:27:58Z-
dc.date.issued2017-11-
dc.identifier.issn0895-2477-
dc.identifier.issn1098-2760-
dc.identifier.urihttps://scholarworks.bwise.kr/hanyang/handle/2021.sw.hanyang/18648-
dc.description.abstractAn artificial neural network based diffraction model for hilly mountainous terrain is proposed. The proposed diffraction model is applied to predict the path in the case of non-line-of-sight. The input parameters of the proposed model are presented and their performance in the path loss prediction is compared with the results from traditional empirical diffraction models using measurement data taken in mountain areas.-
dc.format.extent6-
dc.language영어-
dc.language.isoENG-
dc.publisherJohn Wiley & Sons Inc.-
dc.titleAn approach of the diffraction loss prediction using artificial neural network in hilly mountainous terrain-
dc.typeArticle-
dc.publisher.location미국-
dc.identifier.doi10.1002/mop.30852-
dc.identifier.scopusid2-s2.0-85028040952-
dc.identifier.wosid000408332500044-
dc.identifier.bibliographicCitationMicrowave and Optical Technology Letters, v.59, no.11, pp 2917 - 2922-
dc.citation.titleMicrowave and Optical Technology Letters-
dc.citation.volume59-
dc.citation.number11-
dc.citation.startPage2917-
dc.citation.endPage2922-
dc.type.docTypeArticle-
dc.description.isOpenAccessN-
dc.description.journalRegisteredClassscie-
dc.description.journalRegisteredClassscopus-
dc.relation.journalResearchAreaEngineering-
dc.relation.journalResearchAreaOptics-
dc.relation.journalWebOfScienceCategoryEngineering, Electrical & Electronic-
dc.relation.journalWebOfScienceCategoryOptics-
dc.subject.keywordPlusPROPAGATION-
dc.subject.keywordAuthordiffraction loss-
dc.subject.keywordAuthormultiple edge diffraction-
dc.subject.keywordAuthorneural network-
dc.subject.keywordAuthorpath loss prediction-
dc.identifier.urlhttps://onlinelibrary.wiley.com/doi/10.1002/mop.30852-
Files in This Item
Go to Link
Appears in
Collections
서울 공과대학 > 서울 융합전자공학부 > 1. Journal Articles

qrcode

Items in ScholarWorks are protected by copyright, with all rights reserved, unless otherwise indicated.

Altmetrics

Total Views & Downloads

BROWSE