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dc.contributor.author용환승*
dc.date.accessioned2017-12-28T16:30:33Z-
dc.date.available2017-12-28T16:30:33Z-
dc.date.issued2018*
dc.identifier.isbn9789811065194*
dc.identifier.issn1876-1100*
dc.identifier.otherOAK-21716*
dc.identifier.urihttps://dspace.ewha.ac.kr/handle/2015.oak/239560-
dc.description.abstractIn this paper, we have reported an effective model for familiarity analysis in indoor environments based on proximity and direction. We employ the positioning data of users; thus, we avoid recording the action or any conversation pertaining to the users. We use the beacon signal to find a user’s location and choose a subgroup, which is a temporary group obtained using the location of the users. The proposed method analyzes the familiarity using two different methods. The proximity-based method is used to calculate the familiarity based on the time for which the user has stayed in the subgroup. The direction-based method is used to calculate the familiarity based on the direction of each user in the subgroup. This study addressed situations arising in an event or a group activity in indoors to analyze the degree of familiarity by determining the location of a user. © 2018, Springer Nature Singapore Pte Ltd.*
dc.languageEnglish*
dc.publisherSpringer Verlag*
dc.subjectBluetooth low-energy beacon*
dc.subjectFamiliarity analysis*
dc.subjectIndoor positioning*
dc.subjectSubgroup analysis*
dc.titleProximity and Direction-Based Subgroup Familiarity-Analysis Model*
dc.typeConference Paper*
dc.relation.volume461*
dc.relation.indexSCOPUS*
dc.relation.startpage309*
dc.relation.lastpage318*
dc.relation.journaltitleLecture Notes in Electrical Engineering*
dc.identifier.doi10.1007/978-981-10-6520-0_34*
dc.identifier.scopusid2-s2.0-85032501550*
dc.author.googleChoi J.-I.*
dc.author.googleYong H.-S.*
dc.contributor.scopusid용환승(7101899751)*
dc.date.modifydate20240322133226*
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