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PIR 센서와 딥러닝 알고리즘을 활용한 사용자 이동 방향 인식

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dc.contributor.author우지영-
dc.contributor.author윤재석-
dc.date.accessioned2021-08-11T11:23:25Z-
dc.date.available2021-08-11T11:23:25Z-
dc.date.created2021-06-17-
dc.date.issued2019-
dc.identifier.issn1598-849X-
dc.identifier.urihttps://scholarworks.bwise.kr/sch/handle/2021.sw.sch/5236-
dc.description.abstractIn this paper, we propose a method to recognize the moving direction in the indoor environment by using the sensing system equipped with passive infrared (PIR) sensors and a deep learning algorithm. A PIR sensor generates a signal that can be distinguished according to the direction of movement of the user. A sensing system with four PIR sensors deployed by 45° increments is developed and installed in the ceiling of the room. The PIR sensor signals from 6 users with 10-time experiments for 8 directions were collected. We extracted the raw data sets and performed experiments varying the number of sensors fed into the deep learning algorithm. The proposed sensing system using deep learning algorithm can recognize the users’ moving direction by 99.2 %. In addition, with only one PIR senor, the recognition accuracy reaches 98.4%.-
dc.language한국어-
dc.language.isoko-
dc.publisher한국컴퓨터정보학회-
dc.titlePIR 센서와 딥러닝 알고리즘을 활용한 사용자 이동 방향 인식-
dc.title.alternativeDetection of Moving Direction using PIR Sensors and Deep Learning Algorithm-
dc.typeArticle-
dc.contributor.affiliatedAuthor우지영-
dc.contributor.affiliatedAuthor윤재석-
dc.identifier.doi10.9708/jksci.2019.24.03.011-
dc.identifier.bibliographicCitation한국컴퓨터정보학회논문지, v.24, no.3, pp.11 - 17-
dc.relation.isPartOf한국컴퓨터정보학회논문지-
dc.citation.title한국컴퓨터정보학회논문지-
dc.citation.volume24-
dc.citation.number3-
dc.citation.startPage11-
dc.citation.endPage17-
dc.type.rimsART-
dc.identifier.kciidART002448726-
dc.description.journalClass2-
dc.description.journalRegisteredClasskci-
dc.subject.keywordAuthorPassive infrared-
dc.subject.keywordAuthormovement direction detection-
dc.subject.keywordAuthordeep learning-
dc.subject.keywordAuthormachine learning-
dc.subject.keywordAuthorconvolutional neural network-
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