• Journal of the Institute of Electronics Engineers of Korea CI
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• v.45 no.5
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• pp.140-147
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• 2008

This paper proposes Story Generation system based on Emotional Agent. In the proposed system, Emotional Agent is used as Actor whereas Story Generation produces goal and detailed plans to achieve goal. The storyline is constructed when the goal oriented plan is processed. The proposed system is effective and realistic since it employs human-like Emotional Agent as a main character in generating story.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.3 no.4
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• pp.23-27
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• 2010

This paper performs emotion classification test to find out the best parameter of electroencyphalogram(EEG) signal. Linear predictor coefficients, band cross-correlation coefficients of fast Fourier transform(FFT) and autoregressive model spectra are used as the parameters of 10-channel EEG signal. A multi-layer neural network is used as the pattern classifier. Four emotions for relaxation, joy, sadness, irritation are induced by four university students of an acting circle. Electrode positions are Fp1, Fp2, F3, F4, T3, T4, P3, P4, O1, O2. As a result, the Linear predictor coefficients showed the best performance.

• Journal of KIISE:Software and Applications
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• v.37 no.10
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• pp.779-787
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• 2010

Since robots have been used widely recently, research about human-robot communication is in process actively. Typically, natural language processing or gesture generation have been applied to human-robot interaction. However, existing methods for communication among robot and human have their limits in performing only static communication, thus the method for more natural and realistic interaction is required. In this paper, an emotional gesture based dialogue management system is proposed for sophisticated human-robot communication. The proposed system performs communication by using the Bayesian networks and pattern matching, and generates emotional gestures of robots in real-time while the user communicates with the robot. Through emotional gestures robot can communicate the user more efficiently also realistically. We used behavior networks as the gesture generation method to deal with dialogue situations which change dynamically. Finally, we designed a usability test to confirm the usefulness of the proposed system by comparing with the existing dialogue system.

• Journal of KIISE:Software and Applications
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• v.36 no.12
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• pp.986-995
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• 2009

Color affects people in their various combinations of hue, saturation and value. On the other hand, people may feel different emotion from the same color. If we can introduce these characteristics of color and people's emotion about color to emotion-based digital technologies and their contents, we can effectively draw users' interest and immersion to the contents. In this paper, we will show how people feel about color and present a method of image coloring that reflects the user's preference. First, we define basic templates that reflect the relationship between color and emotion, and then perform an image coloring. To reflect user's preference, we compute weights for hue, saturation and value through the experiments on each subject's preference about hue, saturation and value. The image coloring for each subject's taste will be drawn by updating the weights of hue, saturation and value. Through the results of experiments and surveys, we found that people were more satisfied with the transformation of the templates which reflected user's preference than the one that did not.

• Journal of KIISE
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• v.42 no.4
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• pp.512-521
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• 2015

Internet users purchase goods on the Internet and express their positive or negative emotions of the goods in product reviews. Analysis of the product reviews become critical data to both potential consumers and to the decision making of enterprises. Therefore, the importance of opinion mining techniques which derive opinions by analyzing meaningful data from large numbers of Internet reviews. Existing studies were mostly based on comments written in English, yet analysis in Korean has not actively been done. Unlike English, Korean has characteristics of complex adjectives and suffixes. Existing studies did not consider the characteristics of the Internet language. This study proposes an emotional classification method which increases the accuracy of emotional classification by analyzing the characteristics of the Internet language connoting feelings. We can classify positive and negative comments about products automatically using the Internet emoticon. Also we can check the validity of the proposed algorithm through the result of high precision, recall and coverage for the evaluation of this method.

• 한국체육학회지인문사회과학편
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• v.58 no.4
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• pp.447-459
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• 2019

This study was conducted to investigate the differences between emotional responses and neurotransmitters in moderate-intensity continuous exercise (MICE) and high-intensity interval exercise (HIIE) in 30 low-active women. Both groups performed a designed acute treadmill exercise and repeated the same exercise three times at intervals of one week. MICE performed a 25-minute continuous exercise at 90% VT(ventilation threshold) after a 5-minute warm-up session at 50% VT and then cooled down for 5 minutes at 50% VT level. The HIIE was repeated 6 times for 2 minutes at 115% VT level, and the intermediate active recovery was repeated 4 times for 2 minutes at 85% VT level. The results of the statistical analysis are as follows. MICE was showed positive effect for feeling scale and PACES after exercise in the first experiment, but negative effect in the third experiment. Conversely, HIIE was showed negative effect for feeling scale and PACES after exercise in the first experiment, but positive effect in the third experiment. Neurotransmitters were significantly increased in all three groups after 10 minutes of exercise compared to before exercise. In summary, HIIE exercise may be a strategy to increase exercise compliance for low-active women.

• Culinary science and hospitality research
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• v.18 no.3
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• pp.149-162
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• 2012

This study is intended to investigate the effects of customers' emotional response upon behavioral intention with taking customers who visited Korean restaurants into account. Luxury Korean restaurants where we apply to this study are selected by the food service industry association of Busan-metro city among the model restaurants. For this aim, designing a research model and drawing a hypothesis were carried out with a basis of advanced research. In data processing, SPSS win package 15.0 statistical program was used to verify the hypothesis through frequency analysis, reliability analysis, factor analysis, and regression analysis. The result of the analysis on the effects of physical environment factor upon domination among emotional response could be summed up as follows. Employee and geniality factors turned out to exercise a significant impact on domination, convenience and cleanliness turned out not to. The result of the analysis on the effects of physical environment factor upon arousal among emotional responses could be summed up as follows. Cleanliness and geniality factors turned out to exercise a significant impact on evocation, convenience while employee factor turned out not to. As a result of the analysis on the effects of emotional response upon behavioral intention, domination and evocation factors among emotional responses had a statistically significant impact upon behavioral intention. From the findings of the study, it became evident that dining-out customers might regard luxury Korean restaurants as a space for experiencing various emotions by physical environment rather than for dining and meeting for friendship. To induce emotional response is therefore expected to help a company to promote the practical improvement in achievement over various aspects such as behavioral intention.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.5
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• pp.177-189
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• 2017

The aim of this study was to understand and describe the awareness of the emotional labor of nursing college students in a graduation year. The participants were eleven students in nursing colleges. The data were collected from September 5, 2016 to November 25 through in-depth interviews until it was saturated. All interviews were recorded and transcribed as they were spoken. Colaizzi's phenomenological method was used for data analysis. In this study, twenty-one themes, ten theme clusters and five categories were generated. The five categories consisted of 'Confused by irrational circumstances,' 'Skepticism on nursing occupation,' 'Empathy for the nurse's difficult situation,' 'Learning nurses' words and behavior', and 'Preparing for the future.' These results will contribute to the qualitative improvement of nursing practice education by providing the grounds for an effective educational strategy development that manages the emotional labor of Nursing students from clinical practice. In-depth studies on the experience of nursing students' emotional labor and studies on various factors affecting the awareness of emotional labor in nursing students and problems will be required.

• Journal of KIISE:Software and Applications
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• v.37 no.12
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• pp.946-950
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• 2010

Many sentiment categorization systems based on machine learning methods use morphological analyzers in order to extract linguistic features from sentences. However, the morphological analyzers do not generally perform well in a customer review domain because online customer reviews include many spacing errors and spelling errors. These low performances of the underlying systems lead to performance decreases of the sentiment categorization systems. To resolve this problem, we propose a feature extraction method based on simple longest matching of Eojeol (a Korean spacing unit) and phoneme patterns. The two kinds of patterns are automatically constructed from a large amount of POS (part-of-speech) tagged corpus. Eojeol patterns consist of Eojeols including content words such as nouns and verbs. Phoneme patterns consist of leading consonant and vowel pairs of predicate words such as verbs and adjectives because spelling errors seldom occur in leading consonants and vowels. To evaluate the proposed method, we implemented a sentiment categorization system using a SVM (Support Vector Machine) as a machine learner. In the experiment with Korean customer reviews, the sentiment categorization system using the proposed method outperformed that using a morphological analyzer as a feature extractor.

• Journal of KIISE
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• v.42 no.11
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• pp.1459-1466
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• 2015

Lockscreen is one of the most frequently encountered interfaces by smartphone users. Although users perform unlocking actions every day, there are no benefits in using lockscreens apart from security and authentication purposes. In this paper, we replace the traditional lockscreen with an application that analyzes facial expressions in order to collect facial expression data and provide real-time feedback to users. To evaluate this concept, we have implemented Quantified Lockscreen application, supporting the following contributions of this paper: 1) an unobtrusive interface for collecting facial expression data and evaluating emotional patterns, 2) an improvement in accuracy of facial expression detection through a personalized machine learning process, and 3) an enhancement of the validity of emotion data through bidirectional, multi-channel and multi-input methodology.