• Journal of The Korean Society of Integrative Medicine
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• v.4 no.1
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• pp.65-72
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• 2016

Purpose : This study was carried out to find out the relationship between emotion intelligence, learning flow and career stress of nursing students and influence factors for career stress. Methods : This study targeted 197 university students in their freshman-senior year attending College of Nursing located in P Metropolitan City. For collected data, real numbers and percentage, mean and standard deviation and multiple regression analysis were carried out by using PASW 21.0 program and the correlation between emotion intelligence, learning flow and career stress was analyzed with Pearson's correlation coefficients. Results : Emotional self-awareness(M=3.80, SD =.71), clear goals(M=3.39, SD=.90) and school environment stress(M=2.97, SD=.96) were found to be high in the degree of emotion intelligence, learning flow and career stress of the subjects. The relationship between emotion intelligence and learning flow showed a positive correlation(r=.489, p<.01) in the correlation between emotion intelligence, learning flow, career stress and emotion intelligence showed a negative correlation with career stress(r=-.204, p<.01). Emotion intelligence and learning flow show that career stress is predicted significantly (${\beta}$ =-.15, p < .01) and explained a career stress variate as 18%(F = 24.5, p < .01). Conclusion : Emotion intelligence of nursing students was found to be very influential on the degree of learning flow or career stress. Based on the results of this study, replication studies on emotion intelligence and career stress are needed and the development of intervention programs to increase emotion intelligence is needed.

• Journal of the Korean Society of Clothing and Textiles
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• v.26 no.5
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• pp.663-674
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• 2002

The objectives of this study were 1) to ascertain whether there was a difference in product performance (expressive or instrumental), which consumer recognized after using, related to consumer involvement toward clothing, 2) to clarify the effect of product performance on consumption emotion(positive or negative), 3) to investigate the effect of consumption emotion on satisfaction, and 4) to find out whether product performance had a direct effect on satisfaction toward product. The study was conducted in three steps. Through the two steps, measurement instruments were developed. At the last step, judgement sampling method were utilized to collect the data and subjects were 614 university students. Confirmatory factor analysis and structural equation model analysis were used to analyze the data. The results were as follows: 1) Consumer involvement had an effect on product performances but it was related to the expressive product performance more than to the instrumental product performance. 2) Product performance had positive influence on positive consumption emotion, while it had negative influence on negative consumption emotion. The results revealed that there were significant relationships between product performance and consumption emotion. 3) Positive consumption emotion had a positive effect on consumer satisfaction, on the other hand negative consumption emotion had a negative effect on consumer satisfaction. 4) Although the direct effects of product performances on satisfaction were larger than the indirect effects, product performance was greatly influential in consumption emotion and consumption emotion was strongly related to consumer satisfaction. Therefore, consumption emotion is an important determinant variable in the process of consumer satisfaction.

• Korean Journal of Human Ecology
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• v.23 no.2
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• pp.193-204
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• 2014

The aims of this study were to examine whether the preschool children's temperament predicted their emotion regulation in different situations. The participants in this study were 148 3-and 5-year-old children recruited from five child-care centers located in a middle-income region of Seoul. Statistical methods used for the data analysis were the frequencies, means, standard deviations, Pearson correlation, multiple regression. The major findings were as follows. The children's temperament contributed to individual differences in emotion regulation. The 3-year-olds' negative affectivity and surgency significantly predicted their mothers' ratings of the children's emotion regulation in everyday situations. The 5-year-olds' effortful control significantly predicted their teachers' ratings of the children's emotion regulation in everyday situations. In addition, the 5-year-olds' negative affectivity, effortful control, and surgency predicted the mothers' ratings of their children's emotion regulation. These results suggest that children's temperament are contributors to the development of their emotion regulation.

• Journal of Korea Multimedia Society
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• v.18 no.3
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• pp.376-386
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• 2015

Since affective computing has been introduced in 90s, affect recognition technology has achieved substantial progress recently. However, the application of user emotion recognition into software user interface is in its early stages. In this paper, we describe a new approach for developing mobile user interface which could react differently depending on user emotion states. First, an emotion reaction model is presented which determines user interface reactions for each emotional state. We introduce a pair of mappings from user states to different user interface versions. The reacting versions are implemented by a set of variations for a view. Further, we present an authoring framework to help developers/designers to create emotion-aware reactions based on the proposed emotion reaction model. The authoring framework is necessary to alleviate the burden of creating and handling multi versions for views at the development process. A prototype implementation is presented as an extension of the existing authoring tool DAT4UX. Moreover, a proof-of-concept application featuring an emotion-aware interface is developed using the tool.

• International Journal of Contents
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• v.18 no.3
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• pp.11-20
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• 2022

Human Emotion Recognition is an exciting topic that has been attracting many researchers for a lengthy time. In recent years, there has been an increasing interest in exploiting contextual information on emotion recognition. Some previous explorations in psychology show that emotional perception is impacted by facial expressions, as well as contextual information from the scene, such as human activities, interactions, and body poses. Those explorations initialize a trend in computer vision in exploring the critical role of contexts, by considering them as modalities to infer predicted emotion along with facial expressions. However, the contextual information has not been fully exploited. The scene emotion created by the surrounding environment, can shape how people perceive emotion. Besides, additive fusion in multimodal training fashion is not practical, because the contributions of each modality are not equal to the final prediction. The purpose of this paper was to contribute to this growing area of research, by exploring the effectiveness of the emotional scene gist in the input image, to infer the emotional state of the primary target. The emotional scene gist includes emotion, emotional feelings, and actions or events that directly trigger emotional reactions in the input image. We also present an attention-based fusion network, to combine multimodal features based on their impacts on the target emotional state. We demonstrate the effectiveness of the method, through a significant improvement on the EMOTIC dataset.

• The Journal of the Acoustical Society of Korea
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• v.25 no.8
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• pp.375-382
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• 2006

This paper is about how to identify emotional information and how to detect a specific emotion from speech signals. For emotion identification and detection task. we use long-term acoustic feature parameters and select the optimal Parameters using the feature selection technique based on F-score. We transform the conventional SVM into probabilistic output SVM for our emotion identification and detection system. In this paper we propose three approximation methods for log-likelihoods in a hypothesis test and compare the performance of those three methods. Experimental results using the SUSAS database showed the effectiveness of both feature selection and Probabilistic output SVM in the emotion identification task. The proposed methods could detect anger emotion with 91.3% correctness.

• Journal of the Korean Applied Science and Technology
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• v.41 no.3
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• pp.657-665
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• 2024

The purpose of this review is to provide a comprehensive analysis of the intricate relationships between cognitive control, depression, and emotion regulation. Cognitive control, encompassing processes such as attentional control, inhibitory control, and cognitive flexibility, plays a central role in regulating thoughts, behaviors, and emotions in alignment with internal goals and external demands. Depression, characterized by persistent feelings of sadness, hopelessness, and cognitive impairments, is associated with deficits in cognitive control processes. Emotion regulation strategies, such as cognitive reappraisal and expressive suppression, enable individuals to modulate emotional experiences and responses. The bidirectional relationships between cognitive control, depression, and emotion regulation underscore the complexity of cognitive and emotional processes in depression. Understanding these relationships is crucial for developing targeted interventions aimed at promoting cognitive and emotional well-being and preventing depression onset and recurrence. Moreover, recognizing the roles of cognitive control and emotion regulation in depression holds promise for informing clinical practice and enhancing therapeutic interventions. This review highlights the importance of considering cognitive control and emotion regulation in the assessment and treatment of depression and provides insights for future research and clinical practice.

• Korean Journal of Child Studies
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• v.30 no.1
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• pp.43-57
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• 2009

This study examined relationships between caregivers' attachment, young children's emotion regulation, and aggression. Subjects were 110 institutionalized and 105 home-reared children (M=3.8 and 4.2 years, respectively). Data were collected from children's caregivers via questionnaires and analyzed by ANOVA, correlations, and hierarchical regressions. Results showed that (1) caregivers' attachment and the emotion regulation and aggression of institutionalized children were significantly different from those of home-reared children; (2) caregiver's attachment was positively related to emotion regulation for both institutionalized and home-reared children; (3) for institutional children, negative relationships between attachment and aggression was found for girls; negative relationships between emotion regulation and aggression was found for boys. These findings underscore negative effects of institutionalized care on children's socio-emotional behaviors.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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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.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.