• 감성과학
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• 제15권2호
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• pp.283-296
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• 2012

본 연구는 SNS 사용자 간의 관계 유형을 감성공유관계와 정보공유관계로 분류하여 두 관계에 따른 사회감성모델을 구축하는 것이 목적이다. 이를 위해 먼저, 기존 문헌에 근거하여 92개의 감성을 수집하여 적합성 검증을 수행한 후 연구목적에 부합하는 26개의 사회감성을 추출하였다. 추출된 사회감성은 관계 유형에 따른 요인분석을 통해 12개의 대표 사회감성과 13개의 대표 사회감성을 도출하였다. 도출된 대표 사회감성은 다차원척도분석을 통하여 2차원의 공간에 매핑하여 사회감성 모델을 도출하였다. 도출된 사회감성 모델은 구조방정식모형 분석을 통해 통계적으로 유의하지 않은 요소들을 제거하였다. 타당성 검증 결과 적합도 지수를 통해 감성공유관계의 사회감성 모델(CFI:.887, TLI:.885, RMSEA:.094)과 정보공유관계의 사회감성 모델(CFI:.917, TLI:.900, RMSEA:.050)의 적합도가 나타났다. 본 연구의 결과로 사용자 관계 유형에 따라 상이한 사회감성 모델이 검증되었다. 본 연구를 통해 제시된 사회감성 모델은 SNS상에서 인간의 감성을 측정하는데 필요한 평가 자료로 활용될 수 있을 뿐만 아니라 향후 사회감성의 발전 방향을 제시할 수 있는 자료로 활용될 수 있을 것이라 사료된다.

• 제어로봇시스템학회:학술대회논문집
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• 제어로봇시스템학회 2005년도 ICCAS
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• pp.2373-2378
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• 2005

An emotion detection algorithm using frontal facial image is presented in this paper. The algorithm is composed of three main stages: image processing stage and facial feature extraction stage, and emotion detection stage. In image processing stage, the face region and facial component is extracted by using fuzzy color filter, virtual face model, and histogram analysis method. The features for emotion detection are extracted from facial component in facial feature extraction stage. In emotion detection stage, the fuzzy classifier is adopted to recognize emotion from extracted features. It is shown by experiment results that the proposed algorithm can detect emotion well.

• 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.

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2011년도 추계학술대회
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• pp.19-20
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• 2011

• 한국감성과학회:학술대회논문집
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• 한국감성과학회 2012년도 춘계학술대회
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• pp.11-12
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• 2012

• 제어로봇시스템학회논문지
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• 제12권3호
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 2006년도 춘계학술대회 학술발표 논문집 제16권 제1호
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• pp.347-350
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• 한국의류학회지
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• 제27권3_4호
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• pp.418-428
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• 2003

The purpose of this study was to provide the guidance in more objective and proper clothing design reflecting today's consumers' modes in value consumption by identifying the meaning structure and relationship between fashion sensibility and emotion. The stimulus was 54 photos of contemporary costume which represented the Izard' DES. The questionnaire consisted of hi-polar 25 pairs adjective scale of fashion sensibility and the 18 noun scale of emotion was distributed to 970 male and female living in Pusan area. The data were analyzed by Factor analysis, Correlation analysis and Regression analysis using the statistical SPSS package. The major finding of this research were as follows.1. Fashion sensibilities consist of estheticism, maturity, character and feminity to represent 57.17% total varlarlce. 2. Emotions consist of negative emotion, distress afraid, arousal, shame and enjoyment to represent 70.84% total variance. 3. For the relation between fashion sensibility and emotion, they showed significant relationship in most of factors.

• Safety and Health at Work
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• 제14권3호
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• pp.309-316
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• 2023

Background: Job performance is known as an essential reflection of nursing quality. Colleague solidarity, positive emotion, and turnover intention play effective roles in a clinical working environment, but their impacts on job performance are unclear. Investigating the association between nurses' colleague solidarity and job performance may be valuable, both directly and through the mediating roles of positive emotion and turnover intention. Methods: In this cross-sectional study, a total of 324 Chinese nurses were recruited by convenience sampling method from July 2016 to January 2017. Descriptive analysis, Spearman's correlation analysis, and the structural equation model were applied for analysis by SPSS 26.0 and AMOS 24.0. Results: A total of 49.69% of participants were under 30 years old, and 90.12% of participants were female. Colleague solidarity and positive emotion were positively connected with job performance. The results indicated the mediating effects of positive emotion and turnover intention in this relationship, respectively, as well as the chain mediating effect of positive emotion and turnover intention. Conclusions: In conclusion, dynamic and multiple supportive strategies are needed for nurse managers to ameliorate nursing job performance by improving colleague solidarity and positive emotion and decreasing turnover intention based on the job demand-resource model.

• 아동학회지
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• 제25권6호
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• pp.355-370
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• 2004

This study was to examine the relationship between temperament and children's emotion regulation with negative emotion. Following are the purposes of this research. First, children's temperament and negative emotion, and emotion regulation was found based on children's sex and age difference. Second, if there is a relationship between children's temperament and children's negative emotion and emotion regulation. The subjects of this study were 213 students who were in 8, 10, 12 ages of elementary school in Mokpo. The scales used in this study was Buss & Plomin(1975)'s EAS(Emotionality, Activity, Sociability, Impulsivity) and Brand & Halpern(1998)'s ERACH(Emotion Response and Coping Interview). The data analysis was made by SPSS PC+, and average, two-way ANOVA, Scheffe test, correlation and were employed to test the research questions. As a result of this research a difference in age was found, and a difference in sex was not found. Children's temperament was in positive relation with anger and emotion approach. This research will assist to make the foundation of counseling psychology and developmental psychology.