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긍정, 부정 감정 유발 시각자극에 의한 감마-대역 신경동기화 증가
여동훈,최정우,김경환,Yeo, Donghoon,Choi, Jeong Woo,Kim, Kyung Hwan 대한의용생체공학회 2018 의공학회지 Vol.39 No.2
It is known that gamma-band activity (GBA) and phase synchrony (GBPS) are induced by emotional visual stimuli. However, the characteristics of GBA and GBPS according to different emotional states have not been identified. The purpose of this study is to investigate the changes in gamma-band neuronal synchronization induced by positive and negative emotional visual stimuli using electroencephalograms (EEGs). Thirteen healthy male subjects have participated in the experiment. The induced spectral power in gamma-band was the highest for negative stimuli, and the lowest for neutral stimuli in 300-2,000 ms after the stimulus onset. The inter-regional phase synchronization in gamma-band was increased in 500-2,000 ms, mainly between the bilateral frontal regions and the parieto-occipital regions. Larger number of significant connections were found by negative stimuli compared to positive ones. Judging from temporal and spatial characteristics of the gamma-band activity and phase synchrony increases, the results may imply that affective visual stimuli cause stronger memory encoding than non-emotional stimuli, and this effect is more significant for negative emotional stimuli than positive ones.
딥러닝 기반 안면 상태 감지 모델을 통한 학습자 강의 집중도 분석 시스템
여동훈,라이언,황병일,김동주,황도경 대한전자공학회 2023 전자공학회논문지 Vol.60 No.1
This paper proposes a learner concentration analysis system through deep learning-based facial state detection models and algorithms in a face-to-face and non-face-to-face mixed educational environment. Previously, learners' concentration was analyzed using the facial emotion analysis model, posture detection using the image segmentation model, and eye tracking and brain wave. The proposed method detects drowsiness and abnormal behavior through eye blink and head position states for the learner's concentration analysis. We propose a deep learning model-based concentration analysis algorithm that yields concentration through quantitative values, not random confidence values inferred by the model. The algorithm performs drowsiness recognition through eye blink detection and abnormal behavior recognition through head pose detection based on the learner's facial state collected by the camera. It calculates the concentration state in a certain period based on the recognition data. We used face landmarks and object detectors to detect facial conditions and constructed the system by adopting more advantageous object detectors by comparing the accuracy and operating speed. In order to verify the system, learner concentration analysis system calculated through scenarios of 20 and 100 students in non-face-to-face and face-to-face situations, and showed accuracies of 90% and 93%.
Mg2SiO4/Glass Composite계 세라믹스를 이용한 음이온 발생용 후막형 클러스터
여동훈,신효순,홍연우 한국전기전자재료학회 2010 전기전자재료학회논문지 Vol.23 No.2
The eco-friendly technologies have been extended as matter of international concern due to various diseases and syndromes according to an environmental pollution. In this study, we have manufactured a ceramic cluster with thick film type for anion generation equipment which is maximized anion but minimized ozone contents generated. To develop the formulation of ceramic cluster, we conducted the Mg2SiO4 powders doped with 10 vol% glass frits as Na-Zn-B-O system and sintered at 1050℃ for 2 hours in air for starting materials and investigated the matching properties between the Ag-Pd electrode and the starting materials. The sintered sample for the composition of cluster has 6.7 of dielectric constant and 32 kV/mm of withstand voltage. The yield of anions was measured according to an electrode pattering, discharge gap between electrode, and thickness of electrode protective layer in the cluster of thick film type. We have manufactured the ceramic clusters with optimized thick film structure that have an anion over a hundred particles and the ozone of 0.6 ppb generated.