• Science of Emotion and Sensibility
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• v.11 no.1
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• pp.123-129
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• 2008

The purpose of this study is to analyse the effect of weaving preparatory process characteristics on the PET fabric sensibility through assessment of handle, garment formability and sewability for the enhancement of the physical property of the PET fabrics. For this purpose, eleven fabric specimens processed on the interlacing, pirn winder, 2-for-1 twister, weaving and dyeing and finishing processes were prepared and processing tension and interlacing intensity after each process were measured with various processing conditions.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.107-111
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• 2005

When a pedagogical agent system aims to provide students with interactive help, it needs to know what knowledge the student has and what goals the student is currently trying to achieve. That is, it must do both assessment and plan recognition. These modeling tasks involve a high level of uncertainty when students are allowed to follow various lines of reasoning and are not required to show all their reasoning explicitly. In this paper, the student model for interactive edutainment applications is proposed. This model is based on Bayesian Networks to expose constructs and parameters of rules and propositions pertaining to game and problem solving activities. This student model could be utilized as the emotion generation model for student and agent as well.

• Proceedings of the Korea Information Processing Society Conference
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• 2011.04a
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• pp.340-343
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• 2011

음악 감정 분류에 관한 기존의 연구들은 템포, 박자, 음정, 음표, 리듬 등과 같은 음악의 멜로디와 관련된 자질을 이용하여 음악 감정을 분류하였다. 그러나 노래(Song)와 같이 가사를 포함한 음악은 같은 스타일의 멜로디라도 가사의 내용에 따라 음악에 대하여 청자가 느끼는 감정이 크게 다르다. 본 논문에서는 감정 온톨로지를 활용하여 노래 가사를 감정에 따라 분류하는 방법에 대하여 제안한다. 기구축 된 감정 온톨로지를 바탕으로 네 가지 통사적 규칙을 적용하여 노래 가사로부터 감정 자질을 추출한다. 추출된 감정 자질을 이용하여 Naive Bayes, HMM, SVM과 같은 기계학습 기법을 이용하여 8개 감정 그룹에 대해 58.8%의 정확도를 보였다.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.05a
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• pp.493-495
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• 2023

Deep learning methods and attention mechanisms have been incorporated to improve facial emotion recognition, which has recently attracted much attention. The fusion approaches have improved accuracy by combining various types of information. This research proposes a fusion network with self-attention and local attention mechanisms. It uses a multi-layer perceptron network. The network extracts distinguishing characteristics from facial images using pre-trained models on RAF-DB dataset. We outperform the other fusion methods on RAD-DB dataset with impressive results.

• Proceedings of the Korea Information Processing Society Conference
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• 2023.11a
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• pp.1204-1205
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• 2023

본 논문은 소비자의 리뷰 데이터를 기반으로 한 새로운 감성 분석 방법을 제안한다. 긍정, 부정, 중립으로 분류하는 전통적 감성 분석방법은 텍스트에 나타난 감정의 섬세한 차이를 파악하기 어렵다. 이에 본 연구에서는 GPT 모델을 사용하여 텍스트에서 사용자의 감정을 8 가지의 카테고리로 세분화한다. 부정적 정서를 가진 리뷰에서 분노, 혐오, 실망과 같은 구체적인 감정들을 직관적으로 파악할 수 있었고, 감정의 강도까지 파악할 수 있었다. 제안된 방법을 통해 기업은 고객의 요구 사항을 정확하게 인지할 수 있으며, 고객 맞춤형 서비스 개선에 기여할 수 있다는 점이 기대된다.

• The Journal of the Korea Contents Association
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• v.7 no.5
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• pp.110-117
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• 2007

The recent developments of audio/video technology in the ubiquitous media have resulted in a variety of formal feature such as screen sizes for media and the new viewing environment can alter television viewers' experience of mediated communication. So the concern about formal feature of media itself is increasing in the recent media effects studies. From this point of view, this study is conducted to test the effect of the different monitor size of DMB Phone(2'1"), PMP(4'3") and PC monitor(19') on the viewers' memory and emotion of content by experimental research. The results showed that the effect by monitor size on the view's memory and emotion is significant. And watching moving image on a bigger monitor resulted in better memory of visual message elements contained in the content and more favorable emotion of the content.

• Science of Emotion and Sensibility
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• v.11 no.1
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• pp.11-21
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• 2008

This study was designed to examine the effect of depressive mood on identification of emotional facial expression. Participants were screened out of 305 college students on the basis of the BDI-II score. Students with BDI-II score higher than 14(upper 20%) were selected for the Depression Group and those with BDI-II score lower than 5(lower 20%) were selected for the Control Group. A final sample of 20 students in the Depression Group and 20 in the Control Group were presented with facial expression stimuli of an increasing degree of emotional intensity, slowly changing from a neutral to a full intensity of happy, sad, angry, or fearful expressions. The result showed that there was the significant interaction of Group by Emotion(esp. happy and sad) which suggested that depressive mood affects processing of emotional stimuli such as facial expressions. Implication of this result for mood-congruent information processing were discussed.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.4
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• pp.221-228
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• 2023

Physical and mental changes caused by emotions can affect various behaviors, such as driving or learning behavior. Therefore, recognizing these emotions is a very important task because it can be used in various industries, such as recognizing and controlling dangerous emotions while driving. In this paper, we attempted to solve the emotion recognition task by implementing a multimodal model that recognizes emotions using both audio and video data from different domains. After extracting voice from video data using RAVDESS data, features of voice data are extracted through a model using 2D-CNN. In addition, the video data features are extracted using a slowfast feature extractor. And the information contained in the audio and video data, which have different domains, are combined into one feature that contains all the information. Afterwards, emotion recognition is performed using the combined features. Lastly, we evaluate the conventional methods that how to combine results from models and how to vote two model's results and a method of unifying the domain through feature extraction, then combining the features and performing classification using a classifier.

• The Journal of the Acoustical Society of Korea
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• v.26 no.3E
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• pp.69-73
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• 2007

This paper presents an efficient algorithm to retrieve similar music files from a large archive of digital music database. Users are able to navigate and discover new music files which sound similar to a given query music file by searching for the archive. Since most of the methods for finding similar music files from a large database requires on computing the distance between a given query music file and every music file in the database, they are very time-consuming procedures. By measuring the acoustic distance between the pre-classified music files with the same type of emotion, the proposed method significantly speeds up the search process and increases the precision in comparison with the brute-force method.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.58-61
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• 2000

The purpose of this study is to investigate the characteristics of electroencephalogram (EEG) during emotional occurrence of likes and dislikes in humans subjected to visual stimuli. Fifteen healthy male subjects participated in the study. Portrayals of females and cars on a visual screen, one photographic display at a time. were used as the stimulus. The subjects, with their EEGs concomitantly monitored, were asked to record their likes and dislikes for each portrayal of a total of 50 sequential displays. The results indicated that beta ($\beta$)=wave was more prevalent with dislikes than likes, and the arousal level was higher when dislikes predominated over likes, implying that more cerebral information processing activity was involved in answering dislikes than likes. In the case of cars, the difference between likes and dislikes was focused within a frequency band of 15-20 Hz in the right cerebral hemisphere. Our findings suggest that the activity in the right brain predominates with increases in negative emotion.