• Journal of information and communication convergence engineering
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• 제21권1호
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• pp.68-74
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• 2023

This study examines the response of users' feelings from a gender perspective toward interactive video mobile learning (IVML). An IVML prototype was developed for the Android platform allowing users to install and make use of the app for m-learning purposes. This study aims to measure the level of feelings toward the IVML prototype and examine the differences in gender perspectives, identify the most responsive feelings between male, and female users as prominent feelings and measure the correlation between user-friendly feeling traits as an independent variable in accordance with gender attributes. The feelings response could then be extracted from the user experience, user interface, and human-computer interaction based on gender perspectives using the Kansei engineering approach as the measurement method. The statistical results demonstrated the different emotional reactions from a male and female perspective toward the IVML prototype may or may not have a correlation with the user-friendly trait, perhaps having a similar emotional response from one to another.

• Safety and Health at Work
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• 제11권1호
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• pp.88-96
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• 2020

Background: The current lack of the number of nurses and high nurse turnover rate leads to major problems for the health-care system in terms of cost, patient care ability, and quality of care. Theoretically, burnout may help link emotional labor with turnover intention. The purpose of this study was to investigate the mediating effect of burnout in the association between emotional labor and turnover intention in Korean clinical nurses. Methods: Using data collected from a sample of 606 nurses from six Korean hospitals, we conducted a multiple regression analysis to determine the relationships among clinical nurses' emotional labor, burnout, and turnover intention, looking at burnout as a mediator. Results: The results fully and partially support the mediating role of burnout in the relationship between the subfactors of emotional labor and turnover intention. In particular, burnout partially mediated the relationship between emotional disharmony and hurt, organizational surveillance and monitoring, and lack of a supportive and protective system in the organization. In addition, we found that burnout has a significant full mediation effect on the relationship between overload and conflicts in customer service and turnover intention. Although the mediating effect of burnout was significantly associated with the demands and regulation of emotions, no significant effects on turnover intention were found. Conclusion: To reduce nurses' turnover, we recommend developing strategies that target both burnout and emotional labor, given that burnout fully and partially mediated the effects of emotional labor on turnover intention, and emotional labor was directly associated with turnover intention.

• 대한간호학회지
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• 제47권6호
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• pp.770-780
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• 2017

Purpose: This study was conducted to investigate the influence of emotional labor, emotional dissonance, and burnout on nurse's turnover intention and examine the effect of job satisfaction on the relationships among emotional labor, emotional dissonance, burnout, and turnover intention. Methods: The sample consisted of 350 nurses recruited from 6 general hospitals in 2 cities in Korea. A multiple-group analysis was utilized. Data were analyzed using SPSS statistics 23 and AMOS 20. Results: In the path analysis, turnover intention was directly related to burnout in clinical nurses who had a high job satisfaction (${\beta}=.24$, p=.003), while it was indirectly related to emotional dissonance (${\beta}=.13$, p=.002). In the multiple-group path analysis, turnover intention was directly related to emotional dissonance (${\beta}=.18$, p=.033) and burnout (${\beta}=.26$, p=.002) for nurses with low job satisfaction. Conclusion: These results indicate that manuals and guidelines to alleviate the negative effects of emotional labor, emotional dissonance, and burnout, and to increase job satisfaction are strongly required to reduce turnover intention in nurses at the organizational level as well as at the individual level.

• 대한통합의학회지
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• 제11권4호
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• pp.115-124
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• 2023

Purpose : This study investigated the effects of nursing students' emotional intelligence and interpersonal competence on their caring efficacy. Methods : This study surveyed 217 junior and senior nursing students from City B in South Korea between June 1 and June 30, 2023. The SPSS 22.0 program was employed to analyze the collected data by computing the frequency, percentage, mean, and standard deviation, as well as by conducting t-test, ANOVA test, Scheffe's test, Pearson correlation coefficient, and a multivariate regression analysis. Results : The nursing students exhibited an average emotional intelligence of 5.31±.78, interpersonal competence of 3.47±.56, and caring efficacy of 4.02±.62. The students' emotional intelligence showed significant differences in terms of satisfaction with their major (p<.001), satisfaction with the clinical practice (p<.001), satisfaction with their relationship with clinical practice instructors (p=.001), and the standard of living (p=.021). Furthermore, a significant difference in interpersonal competence was observed in terms of the students' satisfaction with their major (p=.003), satisfaction with the clinical practice (p=.001), satisfaction with their relationship with clinical practice instructors (p=.002), and subjective mental health (p=.005). Meanwhile caring efficacy demonstrated a significant difference with regard to the grade level (p=.001), satisfaction with the major (p<.001), satisfaction with the clinical practice (p<.001), satisfaction with their relationship with clinical practice instructors (p=.007), subjective mental health (p<.001), and subjective physical health (p=.047). The factors that affected the caring efficacy included interpersonal competence (p=.002), grade level (p<.001), satisfaction with the major (p=.004), and emotional intelligence (p=.020), all of which together accounted for an explanatory power of 22.3 %. Conclusion : Based on the results of this study, it is evident that further research related to the emotional intelligence, interpersonal competence, and caring efficacy of nursing students must be encouraged in the future. Furthermore efforts should be made to develop appropriate programs aimed at enhancing nursing students' caring efficacy by accounting for their emotional intelligence and interpersonal competence.

• 한국컴퓨터정보학회논문지
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• 제14권1호
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• pp.127-134
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• 2009

최근 선진국 뿐 아니라 우리나라에서는 유비쿼터스 컴퓨팅 모델에 대해 많은 연구를 하고 있다. 유비쿼터스 컴퓨팅이란 언제 어디서나 사용하는 컴퓨팅 환경을 의미하는 말로, 다양한 종류의 컴퓨터가 사람, 사물, 환경 속에 내재되어 있고, 이들이 서로 연결되어 필요한 곳이라면 언제 어디서나 컴퓨팅을 할 수 있는 환경을 말한다. 본 연구에서는 사용자의 감성을 인식하기 위해서 얼굴 표정, 온도, 습도, 날씨, 조명등을 사용하여 온톨로지를 구축하였다. 온톨로지를 구축하기 위하여 OWL 언어를 사용하였고, 감성추론 엔진은 Jena(예나)를 사용한다. 본 연구에서 제시한 상황인식 서비스인프라(Context-Awareness Service Infra)의 구조는 기능에 따라 여러 개의 모듈로 나뉜다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제18권8호
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• pp.2119-2136
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• 2024

The rapid progress of social media allows more people to express their feelings and opinions online. Many data on social media contains people's emotional information, which can be used for people's psychological analysis and emotional calculation. This research is based on the simplified psychological scale algorithm of multi-theory integration. It aims to accurately analyze people's psychological emotion. According to the comparative analysis of algorithm performance, the results show that the highest recall rate of the algorithm in this study is 95%, while the highest recall rate of the item response theory algorithm and the social network analysis algorithm is 68% and 87%. The acceleration ratio and data volume of the research algorithm are analyzed. The results show that when 400,000 data are calculated in the Hadoop cluster and there are 8 nodes, the maximum acceleration ratio is 40%. When the data volume is 8GB, the maximum scale ratio of 8 nodes is 43%. Finally, we carried out an empirical analysis on the model that compute the population's psychological and emotional conditions. During the analysis, the psychological simplification scale algorithm was adopted and multiple theories were taken into account. Then, we collected negative comments and expressions about Japan's discharge of radioactive water in microblog and compared them with the trend derived by the model. The results were consistent. Therefore, this research model has achieved good results in the emotion classification of microblog comments.

• 인터넷정보학회논문지
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• 제12권6호
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• pp.83-94
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• 2011

감정 컴퓨팅의 대표적 연구 주제는 기계가 사람의 감정을 인식하여 이에 적절히 대응하는 것이다. 감정 인식 연구에서는 얼굴과 목소리 단서를 이용하여 감정을 포착하는데 집중했으며 최근에 와서 행동자세를 주요 수단으로 이용하고 있다. 본 연구의 목적은 감정 표현에서 중요한 역할을 담당하는 자세 특징을 포착하고 확인하여 감정을 판별하는 것이다. 이를 위해 먼저 자세포착시스템으로 다양한 감정 자세를 수집하여 감정별 특징을 공간적 특징으로 설명한다. 그리고 동작을 취하는 행위자가 의도하는 감정과 관찰자가 인지하는 감정 간에 통계적으로 의미 있는 상관관계가 있음을 표준통계기술을 통해 확인한다. 6가지 주요 감정을 판별하기 위해 판별 분석법을 이용하여 감정 자세 예측 모델을 구축하고 자세 특징을 측정한다. 제안 특징과 모델의 평가는 행위자-관찰자 감정 자세 집단의 상관관계를 이용하여 수행한다. 정량적 실험 결과는 제안된 자세 특징으로 감정을 잘 판별하며 감정 예측 모델이 잘 수행됨을 보여준다.

• International journal of advanced smart convergence
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• 제8권2호
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• pp.8-17
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• 2019

The main purpose of this study isto explore the potential of affective computing (AC) platforms in education through two phases ofresearch: Phase I - platform analysis and Phase II - classification of academic emotions. In Phase I, the results indicate that the existing affective analysis platforms can be largely classified into four types according to the emotion detecting methods: (a) facial expression-based platforms, (b) biometric-based platforms, (c) text/verbal tone-based platforms, and (c) mixed methods platforms. In Phase II, we conducted an in-depth analysis of the emotional experience that a learner encounters in online video-based learning in order to establish the basis for a new classification system of online learner's emotions. Overall, positive emotions were shown more frequently and longer than negative emotions. We categorized positive emotions into three groups based on the facial expression data: (a) confidence; (b) excitement, enjoyment, and pleasure; and (c) aspiration, enthusiasm, and expectation. The same method was used to categorize negative emotions into four groups: (a) fear and anxiety, (b) embarrassment and shame, (c) frustration and alienation, and (d) boredom. Drawn from the results, we proposed a new classification scheme that can be used to measure and analyze how learners in online learning environments experience various positive and negative emotions with the indicators of facial expressions.

• 인터넷정보학회논문지
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• 제23권2호
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• pp.45-52
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• 2022

With the development of artificial intelligence analysis methods, especially machine learning, various fields are widely expanding their application ranges. However, in the case of classical music, there still remain some difficulties in applying machine learning techniques. Genre classification or music recommendation systems generated by deep learning algorithms are actively used in general music, but not in classical music. In this paper, we attempted to classify opera among classical music. To this end, an experiment was conducted to determine which criteria are most suitable among, composer, period of composition, and emotional atmosphere, which are the basic features of music. To generate emotional labels, we adopted zero-shot classification with four basic emotions, 'happiness', 'sadness', 'anger', and 'fear.' After embedding the opera libretto with the doc2vec processing model, the optimal number of clusters is computed based on the result of the elbow method. Decided four centroids are then adopted in k-means clustering to classify unsupervised libretto datasets. We were able to get optimized clustering based on the result of adjusted rand index scores. With these results, we compared them with notated variables of music. As a result, it was confirmed that the four clusterings calculated by machine after training were most similar to the grouping result by period. Additionally, we were able to verify that the emotional similarity between composer and period did not appear significantly. At the end of the study, by knowing the period is the right criteria, we hope that it makes easier for music listeners to find music that suits their tastes.

• International Journal of Computer Science & Network Security
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• 제24권4호
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• pp.26-34
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• 2024

Most human emotions are conveyed through facial expressions, which represent the predominant source of emotional data. This research investigates the impact of crowds on human emotions by analysing facial expressions. It examines how crowd behaviour, face recognition technology, and deep learning algorithms contribute to understanding the emotional change according to different level of crowd. The study identifies common emotions expressed during congestion, differences between crowded and less crowded areas, changes in facial expressions over time. The findings can inform urban planning and crowd event management by providing insights for developing coping mechanisms for affected individuals. However, limitations and challenges in using reliable facial expression analysis are also discussed, including age and context-related differences.