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| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | SUH, YOUNG JOO | - |
| dc.contributor.author | kim, dongdeok | - |
| dc.date.accessioned | 2023-03-06T00:20:33Z | - |
| dc.date.available | 2023-03-06T00:20:33Z | - |
| dc.date.created | 2023-03-05 | - |
| dc.date.issued | 2023-04 | - |
| dc.identifier.issn | 0916-8532 | - |
| dc.identifier.uri | https://oasis.postech.ac.kr/handle/2014.oak/116815 | - |
| dc.description.abstract | We propose GConvLoc, a WiFi fingerprinting-based in-door localization method utilizing graph convolutional networks. Using the graph structure, we can consider the fingerprint data of the reference points and their location labels in addition to the fingerprint data of the test point at inference time. Experimental results show that GConvLoc outperforms baseline methods that do not utilize graphs. | - |
| dc.language | English | - |
| dc.publisher | Oxford University Press | - |
| dc.relation.isPartOf | IEICE Transactions on Information and Systems | - |
| dc.title | GConvLoc: WiFi Fingerprinting-Based Indoor Localization using Graph Convolutional Networks | - |
| dc.type | Article | - |
| dc.identifier.doi | 10.1587/transinf.2022EDL8081 | - |
| dc.type.rims | ART | - |
| dc.identifier.bibliographicCitation | IEICE Transactions on Information and Systems, v.E106D, no.4, pp.570 - 574 | - |
| dc.identifier.wosid | 000964298300016 | - |
| dc.citation.endPage | 574 | - |
| dc.citation.number | 4 | - |
| dc.citation.startPage | 570 | - |
| dc.citation.title | IEICE Transactions on Information and Systems | - |
| dc.citation.volume | E106D | - |
| dc.contributor.affiliatedAuthor | SUH, YOUNG JOO | - |
| dc.identifier.scopusid | 2-s2.0-85153938653 | - |
| dc.description.journalClass | 1 | - |
| dc.description.journalClass | 1 | - |
| dc.description.isOpenAccess | N | - |
| dc.type.docType | Article | - |
| dc.subject.keywordAuthor | key indoor localization | - |
| dc.subject.keywordAuthor | indoor positioning | - |
| dc.subject.keywordAuthor | WiFi fingerprinting | - |
| dc.subject.keywordAuthor | deep learning | - |
| dc.subject.keywordAuthor | graph convolutional network | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Information Systems | - |
| dc.relation.journalWebOfScienceCategory | Computer Science, Software Engineering | - |
| dc.description.journalRegisteredClass | scie | - |
| dc.description.journalRegisteredClass | scopus | - |
| dc.relation.journalResearchArea | Computer Science | - |
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