• Korean Journal of Human Ecology
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• v.23 no.4
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• pp.613-631
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• 2014

Personality education activities based on the roots of empathy program were designed and practiced in this study to investigate their effects on young children's empathic ability and emotional intelligence. The subjects of this research were 60 five years old of 2 classes in 'W' kindergarten in 'I' city, Jeonra Buk province. We randomly assigned 30 children of one class to the experimental group and 30 children of the other class to the controlled group. The personality education activities based on the roots of empathy program was by the researcher. The results of this study are summarized as follows. First, the personality education activities based on the roots of empathy program improved children's empathic ability. And that effects are shown in all sub-areas of empathic ability(sorrow/burden/joy/fear). Second, the personality education activities based on the roots of empathy program improved children's emotional intelligence. And that effects are shown in all sub-areas of emotional intelligence(recognition and expression of emotion/promotion of thinking by emotion/application of emotional knowledge/emotional reflective control).

• Journal of the Korea Society of Computer and Information
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• v.27 no.4
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• pp.89-97
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• 2022

This research intends the provide the influence of emotional intelligence and resilience on burnout of University sports athletes. For this purpose, purposive sampling method was used. The subject of this study were 257 University sports athletes from Korea. Data were collected through the self-administrated questionaire which were used by preceding study. The frequency analysis, factor analysis, correlation analysis and multiple regression analysis were used to solve the research problems in this study. The conclusion were drawn as follows. First the emotion intelligence influenced to resilience significantly. Second, the emotion intelligence influenced to burnout partially. Third, the resilience influenced to burnout partially.

• Safety and Health at Work
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• v.13 no.4
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• pp.448-455
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• 2022

Background: While the organizational factors that account for firefighters' burnout have been extensively explored, the individual factors related to how they regulate interpersonal conflicts and emotions remain to be investigated. Previous research has demonstrated the association between emotional intelligence and conflict management styles and burnout, but no study has looked at the interrelationships among these factors in high-risk sectors. The present exploratory study aimed to fill this research gap by investigating the relationships between conflict management style, emotional intelligence and burnout in a sample of firefighters. Methods: A cross-sectional study was conducted with 240 French firefighters. Measures comprised validated scales of conflict management styles, emotional intelligence and burnout. Results: Results showed that the integrating conflict style reduced burnout. They also revealed the effects of emotion regulation on burnout, whereby the awareness and management of one's own emotions reduced burnout. Moreover, awareness of one's own emotions moderated the relationship between integrating conflict resolution style and burnout, whereby the effect of integrating style on reduced burnout was higher when awareness of one's own emotions was high. Conclusion: These results reveal that strategies used by firefighters to regulate their emotions in order to meet the emotional demands specific to their job are important for reducing the emotional exhaustion component of burnout. Training programs for conflict and emotion management are needed to preserve the mental health of firefighters and ensure the safety of interventions.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.8
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• pp.410-416
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• 2018

The purpose of this study was to investigate the factors affecting the exhaustion of emotional intelligence of clinical dental hygienist and to improve the quality of customer service and effective management of clinical dental hygienist. The results of the study were as follows: 394 clinical dental hygienists working in Jeollabukdo from February 6 to May 31, 2017. Clinical dental hygienist's emotional intelligence showed a significant negative correlation with burnout(r=-.623, p<.001). Self emotion appraisal, others' emotion appraisal, and use of emotion were found to be related factors to exhaustion of clinical dental hygienist..Based on the results of this study, it would be necessary to develop programs to improve the emotional intelligence of clinical dental hygienists, to reduce burnout and to improve the work efficiency of clinical dental hygienists.

• 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.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.16 no.2
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• pp.713-725
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• 2022

Mostly, artificial intelligence does not show any definite change in emotions. For this reason, it is hard to demonstrate empathy in communication with humans. If frequency modification is applied to neutral emotions, or if a different emotional frequency is added to them, it is possible to develop artificial intelligence with emotions. This study proposes the emotion conversion using the Generative Adversarial Network (GAN) based voice frequency synthesis. The proposed method extracts a frequency from speech data of twenty-four actors and actresses. In other words, it extracts voice features of their different emotions, preserves linguistic features, and converts emotions only. After that, it generates a frequency in variational auto-encoding Wasserstein generative adversarial network (VAW-GAN) in order to make prosody and preserve linguistic information. That makes it possible to learn speech features in parallel. Finally, it corrects a frequency by employing Amplitude Scaling. With the use of the spectral conversion of logarithmic scale, it is converted into a frequency in consideration of human hearing features. Accordingly, the proposed technique provides the emotion conversion of speeches in order to express emotions in line with artificially generated voices or speeches.

• Science of Emotion and Sensibility
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• v.15 no.2
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• pp.183-192
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• 2012

This study aims at expressing the correlation of 'creativity' and 'emotional intelligence' in cartoon expression learning through literary research and correlation analysis. Analyses were made on each sub-factor for the self emotional intelligence evaluation and the creativity evaluation made by experts through cartoon expressions by elementary school students, who are the learners. Studies on preceding research showed that creativity and emotional intelligence had a correlation and that it is common preception that higher creativity is equivalent to higher emotional intelligence. However, results of correlation analysis in this study showed that while there is a relation between creativity evaluation and emotional intelligence in cartoon expression learning, not all factors were correlated. Furthermore, the results of emotional evaluation of the upper and lower group learners did not show similar results in the creativity evaluation. Through this study, it can be said that for emotional intelligence and creativity factors, finding the appropriate emotional intelligence development method would be the way to enhance creativity. Therefore, in order to develop creativity through cartoon expression learning, systematic research should be performed for extracting the relative emotional intelligence factors.

• Journal of Korean Elementary Science Education
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• v.33 no.1
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• pp.82-94
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• 2014

The purpose of this study is to develop teaching program which utilizes emotional intelligence elements as a measure to stimulate the motive and scientific attitude of learners and examine the effect of its application. The target unit for this study is 'world of plants' in the fourth grade of elementary school, and the teaching program is composed of 3 stages including I(encounter with myself), S(encounter with science), and U(encounter with friends). The teaching program is organized in the way to reflect 5 emotional intelligence elements including self-awareness, self-regulation, self-motivation, sympathy, and personal relations properly according to each stage of teaching program. The result of applying this program into actual classrooms is as follows. First, it is proven that the teaching program actually helps improving the motive of learners to study science. The emotional intelligence takes a role of positive motive for thinking, and the learners monitor their emotion and behavior patterns by using a mirror notebook to reduce their anxiety about science. Second, it is proven that the teaching program changes the science related attitude of learners positively. The emotional intelligence elements help the learners to create friendly feeling toward science subject and have a friendly attitude toward science and a sense of expectancy to science class. Third, it is proven that the teaching program contributes to the improvement of learners' science study achievement. The emotional intelligence takes an important role in improving the learners' science study achievement through the role of adjusting and controlling the recognition capability. However, emphasizing the emotional intelligence excessively also has a risk to break the balance between emotion and recognition, so it is considered that the balanced approach should be applied.

• KIPS Transactions on Software and Data Engineering
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• v.7 no.6
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• pp.239-248
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• 2018

Many new technologies are studied with the arrival of the 4th industrial revolution. In particular, emotional intelligence is one of the popular issues. Researchers are focused on emotional analysis studies for music services, based on artificial intelligence and pattern recognition. However, they do not consider how we recommend proper music according to the specific emotion of the user. This is the practical issue for music-related IoT applications. Thus, in this paper, we propose an probability-based music emotion classification technique that makes it possible to classify music with high precision based on the range of emotion, when developing music related services. For user emotion recognition, one of the popular emotional model, Russell model, is referenced. For the features of music, the average amplitude, peak-average, the number of wavelength, average wavelength, and beats per minute were extracted. Multiple regressions were derived using regression analysis based on the collected data, and probability-based emotion classification was carried out. In our 2 different experiments, the emotion matching rate shows 70.94% and 86.21% by the proposed technique, and 66.83% and 76.85% by the survey participants. From the experiment, the proposed technique generates improved results for music classification.