• International Journal of Advanced Culture Technology
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• 제7권2호
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• pp.188-194
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• 2019

Always, emotion is mutant. This is principle of literary treatment. In the literature, sadness is not sadness, and 'loving emotion' is not 'loving emotion.' Despite loving of our, loving is sadness. Also loving is to cry. This crying becomes love. This study is to show the mutant emotion which is to be able to code Deep Learning AI. We explored the Sijo "Streams that cried last night", because this Sijo was useful to study mutant emotion. The result was that this Sijo was coding the mutant emotion. Almost continuously, the sadness codes were spawning and concentrating. So this Sijo was making the emotion of love with the sadness. If this study is continued, It is believed that our lives will be much happier. And the method of literary therapy will be able to more upgrade.

• 아동학회지
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• 제23권1호
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• pp.37-54
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• 2002

The purpose of study was to examine child emotion regulation as a function of child's sex, temperament, mother's emotion regulation, and mother's parenting. The subjects were 386 mothers of fifth or sixth graders in Seoul and Kyonggi province. The data were gathered through questionnaires developed for the current study. The major findings were as follows: 1) There were significant sex differences in some categories of child's emotion regulation. 2) The more active the child's temperament, the child showed more negative emotion regulation(venting, aggressive expression, avoidance). 3) Mother's negative emotion regulation was significantly related to the child's negative emotion regulation. 4) There were significant correlations between mother's parenting and child's emotion regulation. That is, mother's positive parenting was related to child's positive emotion regulation. 5) The relationship between mother's emotion regulation and child's emotion regulation was mediated by mothers' parenting.

• International Journal of Fuzzy Logic and Intelligent Systems
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• 제8권2호
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• pp.105-110
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• 2008

Human being recognizes emotion fusing information of the other speech signal, expression, gesture and bio-signal. Computer needs technologies that being recognized as human do using combined information. In this paper, we recognized five emotions (normal, happiness, anger, surprise, sadness) through speech signal and facial image, and we propose to method that fusing into emotion for emotion recognition result is applying to multimodal method. Speech signal and facial image does emotion recognition using Principal Component Analysis (PCA) method. And multimodal is fusing into emotion result applying fuzzy membership function. With our experiments, our average emotion recognition rate was 63% by using speech signals, and was 53.4% by using facial images. That is, we know that speech signal offers a better emotion recognition rate than the facial image. We proposed decision fusion method using S-type membership function to heighten the emotion recognition rate. Result of emotion recognition through proposed method, average recognized rate is 70.4%. We could know that decision fusion method offers a better emotion recognition rate than the facial image or speech signal.

• 영어어문교육
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• 제17권4호
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• pp.193-206
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• 2011

The purpose of the study is to examine the use of emotion vocabulary by Korean English learners. Three basic emotion fields, pleasure, anger, and fear were selected to elicit the participants' responses. L1 English speakers' data was also collected for comparison. The major results are as follows. First, English learners responded with various inappropriate verb forms like I feel~, I am~ while the majority of English native speaking teachers responded with subjunctive forms like I would feel~. In addition, L2 English learners used mostly simple and coordination sentences. Second, the lexical richness, measured through type/token ratio, was higher in English L1 data than in English L2 data. The proportion of emotion lemmas reflects the lexical richness or the diversity of the emotion words. Lastly, L2 English learners' responses focused on a few typical adjectives like happy, angry and scared. This structural and semantic distinctiveness of Korean English learners' emotion words was discussed from pedagogical perspectives.

• 한국생활과학회지
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• 제19권2호
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• pp.285-294
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• 2010

This study examined the relationship between children's school adjustment and their emotion regulation. The subjects were 122 1st grade students selected from one elementary school in Incheon. Teachers rated each child using the Emotion Regulation Scale (Lee, 1997) and School Adjustment Scale (Chi & Jung, 2006). The data were analyzed by using descriptive statistics, t-test, correlation analysis, and stepwise regressions. The children's emotion regulation and school adjustment were differed by sex of the child. The girls were assessed to be better adapted in emotion regulation and school adjustment than the boys. The children's emotion regulation was positively related to the children's school adjustment. In addition, the children's emotion regulation predicted how well they would adjust to school life.

• 디지털융복합연구
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• 제10권2호
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• pp.287-292
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• 2012

본 논문에서는 모바일환경에서의 사용자 감정인지를 통한 개인화 서비스 지원에 필요한 위치기반 센싱 데이터의 전처리 기법과 사용자 감정 데이터의 구축 및 전처리를 위한 V-A 감정 모델에서의 감정 데이터 전처리 기법에 대하여 연구한다. 이를 위하여 그래뉼러 컨텍스트 트리 및 스트링 매칭 기반의 감정 패턴 매칭 기법을 사용한다. 또한 상황 인지를 통한 개인화 서비스를 위해 확률 기반 추론을 이용한 상황 인식 및 개인화 서비스 추천 기법에 대하여 연구한다.

• 로봇학회논문지
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• 제2권2호
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• pp.119-128
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• 2007

Emotion interaction between human and robot is an important element for natural interaction especially for service robot. We propose a hybrid emotion generation architecture and detailed design of reactive process in the architecture based on insight about human emotion system. Reactive emotion generation is to increase task performance and believability of the service robot. Experiment result shows that it seems possible for the reactive process to function for those purposes, and reciprocal interaction between different layers is important for proper functioning of robot's emotion generation system.

• 감성과학
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• 제15권1호
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• pp.133-140
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• 2012

본 연구에서는 개인 맞춤형 감성인식이 가능한 실시간 개인화 프로세스를 개발하였다. 설계된 개인화 프로세스는 실시간 생체신호 중립밴드 알고리즘과 사용자의 주관적인 감성응답을 실시간으로 입력받아 개인 감성룰베이스를 업데이트하는 과정을 포함한다. 실시간 감성인식을 위해 PPG(Photoplethysmography), GSR(Galvanic skin reflex), SKT(Skin Temperature)를 자율신경계 신호를 측정하고 사용하였다. 개인화 프로세스를 적용한 감성인식과 적용하지 않은 감성인식의 일치도 평가는 대학생 10명(남 5명, 여 5명, 22.1세${\pm}$2.2세)의 피실험자를 대상으로 실시하였다. 45장의 이미지를 무작위로 제시하였으며, 5회 반복 측정하였다. 개인화 프로세스를 적용시켰을 때 약 71.67%의 일치도를 보였으며, 적용시키지 않았을 때보다 약 5배 이상 높은 일치도의 차이를 보였다. 본 연구에서는 개인화 프로세스가 실시간 개인 맞춤 감성인식에 매우 유용함을 보였다. 추후 개인화 프로세스는 다양한 감성 애플리케이션이나 서비스에서 감성인식 만족도를 높일 수 있을 것으로 판단된다.

• 가정과삶의질연구
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• 제27권2호
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• pp.1-9
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• 2009

The purpose of this study was to investigate the influence of demographical variables(gender and age) and alienation on adolescent's negative emotion-induced eating motivation. The subjects were 389 middle and high school students who lived in Keoungbok. The data were analyzed by t-test, one-way ANOVA and stepwise multiple regression(using SPSS 12.1). Major findings were as follows: 1) There was difference in adolescent's negative emotion-induced eating motivation according to gender, age, and level of alienation. Adolescent's negative emotion-induced eating motivation tended to be higher when adolescent were girl students, or high school students or in higher level of alienation. 2) Alienation was the stronger predictor of adolescent's negative emotion-induced eating motivation than gender and age. 3) There was difference in boy adolescent's negative emotion-induced eating motivation according to age and level of alienation. But there was difference in girl adolescent's negative emotion-induced eating motivation only according to level of alienation. 4) For boy, alienation was the stronger predictor of adolescent's negative emotion-induced eating motivation than age. But for girl alienation was only significant predictor of adolescent's negative emotion-induced eating motivation.

• 대한인간공학회지
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• 제31권2호
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• pp.271-279
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• 2012

Objective: This study classified three different emotional states(boredom, pain, and surprise) using physiological signals. Background: Emotion recognition studies have tried to recognize human emotion by using physiological signals. It is important for emotion recognition to apply on human-computer interaction system for emotion detection. Method: 122 college students participated in this experiment. Three different emotional stimuli were presented to participants and physiological signals, i.e., EDA(Electrodermal Activity), SKT(Skin Temperature), PPG(Photoplethysmogram), and ECG (Electrocardiogram) were measured for 1 minute as baseline and for 1~1.5 minutes during emotional state. The obtained signals were analyzed for 30 seconds from the baseline and the emotional state and 27 features were extracted from these signals. Statistical analysis for emotion classification were done by DFA(discriminant function analysis) (SPSS 15.0) by using the difference values subtracting baseline values from the emotional state. Results: The result showed that physiological responses during emotional states were significantly differed as compared to during baseline. Also, an accuracy rate of emotion classification was 84.7%. Conclusion: Our study have identified that emotions were classified by various physiological signals. However, future study is needed to obtain additional signals from other modalities such as facial expression, face temperature, or voice to improve classification rate and to examine the stability and reliability of this result compare with accuracy of emotion classification using other algorithms. Application: This could help emotion recognition studies lead to better chance to recognize various human emotions by using physiological signals as well as is able to be applied on human-computer interaction system for emotion recognition. Also, it can be useful in developing an emotion theory, or profiling emotion-specific physiological responses as well as establishing the basis for emotion recognition system in human-computer interaction.