Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | 전한종 | - |
dc.date.accessioned | 2021-09-28T02:38:48Z | - |
dc.date.available | 2021-09-28T02:38:48Z | - |
dc.date.issued | 2020-04 | - |
dc.identifier.citation | 대한건축학회논문집 계획계, v. 36, no. 4, page. 41-49 | en_US |
dc.identifier.issn | 1226-9093 | - |
dc.identifier.issn | 2384-177X | - |
dc.identifier.uri | http://koreascience.or.kr/article/JAKO202013461498769.page | - |
dc.identifier.uri | https://www.dbpia.co.kr/journal/articleDetail?nodeId=NODE09329857 | - |
dc.identifier.uri | https://repository.hanyang.ac.kr/handle/20.500.11754/165354 | - |
dc.description.abstract | The purpose of this study was to investigate the use of the Deep Neural Networks(DNN) model to classify user's emotions, in particular Electroencephalography(EEG) toward Virtual-Reality(VR) based 3D design alternatives. Four different types of VR Space were constructed to measure a user's emotion and EEG was measured for each stimulus. In addition to the quantitative evaluation based on EEG data, a questionnaire was conducted to qualitatively check whether there is a difference between VR stimuli. As a result, there is a significant difference between plan types according to the normalized ranking method. Therefore, the value of the subjective questionnaire was used as labeling data and collected EEG data was used for a feature value in the DNN model. Google TensorFlow was used to build and train the model. The accuracy of the developed model was 98.9%, which is higher than in previous studies. This indicates that there is a possibility of VR and Fast Fourier Transform(FFT) processing would affect the accuracy of the model, which means that it is possible to classify a user's emotions toward VR based 3D design alternatives by measuring the EEG with this model. | en_US |
dc.description.sponsorship | 이 논문은 정부(과학기술정보통신부)의 재원으로 한국연구재단의 지원을 받아 수행된 연구임.과제번호:NRF-2019R1A2C1088896 | en_US |
dc.language.iso | ko_KR | en_US |
dc.publisher | 대한건축학회 | en_US |
dc.subject | 가상현실 | en_US |
dc.subject | 감정 | en_US |
dc.subject | 뇌파 | en_US |
dc.subject | 딥러닝 | en_US |
dc.subject | Virtual Reality(VR) | en_US |
dc.subject | Emotion | en_US |
dc.subject | Electroencephalography(EEG) | en_US |
dc.subject | Fast Fourier Transform(FFT) | en_US |
dc.subject | Deep Learning | en_US |
dc.title | 가상현실 기반 3차원 공간에 대한 감정분류 딥러닝 모델 | en_US |
dc.title.alternative | Emotion Classification DNN Model for Virtual Reality based 3D Space | en_US |
dc.type | Article | en_US |
dc.relation.no | 4 | - |
dc.relation.volume | 36 | - |
dc.identifier.doi | 10.5659/JAIK_PD.2020.36.4.41 | - |
dc.relation.page | 41-49 | - |
dc.relation.journal | 대한건축학회논문집 계획계 | - |
dc.contributor.googleauthor | 명지연 | - |
dc.contributor.googleauthor | 전한종 | - |
dc.contributor.googleauthor | Myung, Jee-Yeon | - |
dc.contributor.googleauthor | Jun, Han-Jong | - |
dc.relation.code | 2020040673 | - |
dc.sector.campus | S | - |
dc.sector.daehak | COLLEGE OF ENGINEERING[S] | - |
dc.sector.department | DEPARTMENT OF ARCHITECTURE | - |
dc.identifier.pid | hanjong | - |
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