• Management & Information Systems Review
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• v.35 no.1
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• pp.235-251
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• 2016

The main purposes of this study is to examine the effect of treatment setting on patient participation, and the moderating effect of negative emotion between treatment setting and patient participation. For the purposes of this study's goals, the 320 samples for this empirical study were collected from the general hospital and medical clinic outpatients in C and carried out hierarchial regression by SPSS 19.0. The results of this study are as follows. There is positive effect of cleanness of the treatment setting on behavior factor in patient participation. The higher aging of patient have influences on emotional factor in patient participation. There are positive effects of the third person in treatment settings on emotional and informational factors in patient participation. There is negative effect of negative emotion on emotional and informational factors in patient participation. There are negative effects of time pressure in treatment settings on emotional and informational factors in Patient Participation. On interaction effect, there are positive effects of cleanness in treatment setting and negative emotion on emotional and informational factors in patient participation. Implications for theoretical and practical patient participation are discussed.

• Science of Emotion and Sensibility
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• v.17 no.4
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• pp.41-50
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• 2014

This study examined the characteristics of brain's reaction pattern concerning the preference for product images and compared them with the result of subjects' intuitive evaluation of their preference for the same materials. Two tests were conducted; the first one that calculated comfort properties based on brainwave measurements in order to examine the impression given by the images of products presented separately, one-by-one, and the second one that had subjects evaluate the preference directly by comparing the images of all products simultaneously. The first test was titled 'Independent Cognitive Response' and the second test, 'Relative Cognitive Response', and their characteristics were as follows. In the 'Independent Cognitive Response', the state of emotion was expressed in absolute values based on the comparison with the information accumulated through the subject's own experience and the feeling expressed as 'pleasant' or 'unpleasant' persisted as absolute values. In the 'Relative Cognitive Response', the state of emotion relative to the information of other images compared in the stage of perceiving the images was expressed, and in this case, the state of emotion described as 'calm' or 'excited' acted as relative values.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.39C no.2
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• pp.122-129
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• 2014

In this study, emotion-classification gathered from users was performed, classification-experiments were then conducted using SVM(Support Vector Machine) and K-means algorithm. Total 15 numbers of channels; CP6, Cz, FC2, T7. PO4, AF3, CP1, CP2, C3, F3, FC6, C4, Oz, T8 and F8 among 32 members of the channels measured were adapted in Brain signals which indicated obvious the classification of emotions in previous researches. To extract emotion, watching DVD and IAPS(International Affective Picture System) which is a way to stimulate with photos were applied and SAM(Self-Assessment Manikin) was used in emotion-classification to users' emotional conditions. The collected users' Brain-wave signals gathered had been pre-processing using FIR filter and artifacts(eye-blink) were then deleted by ICA(independence component Analysis) using. The data pre-processing were conveyed into frequency analysis for feature extraction through FFT. At last, the experiment was conducted suing classification algorithm; Although, K-means extracted 70% of results, SVM showed better accuracy which extracted 71.85% of results. Then, the results of previous researches adapted SVM were comparatively analyzed.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.42 no.3 s.303
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• pp.29-38
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• 2005

Although people frequently rely on intuition in managing activities, they rarely use it in developing effective decision-making support systems. In this paper, we investigate and compare the correlations between such characteristics as cognition and emotion characteristics and judgmental time-series forecasting accuracy by using a self-organizing neural network, and eventually aim to help build efficient decision-making atmosphere. The neural network used in this paper employs a self-supervised adaptive algorithm, and the feature of which is that it inherently can use correlation between input vectors by exchanging information between neuron clusters in the self-organizing layer during the training. Our experiments showed that both cognition and emotion characteristics had correlations with judgmental time-series forecasting, and that cognition characteristics had larger correlation than emotion characteristics. We also found that conceptual style had larger correlation than behavioral and analytical styles, and displeasure-sleepiness style had larger correlation than pleasure-arousal style with the forecasting.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.8
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• pp.1064-1070
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• 2020

In Korea, video-based news broadcasters are primarily classified into terrestrial broadcasters, general programming cable broadcasters and YouTube broadcasters. Recently, news broadcasters get subjective while targeting the desired specific audience. This violates normative expectations of impartiality and neutrality on journalism from its audience. This phenomenon may have a negative impact on audience perceptions of issues. This study examined whether broadcast news reporting conveys emotions and if so, how news broadcasters differ according to emotion type. Emotion types were classified into neutrality, happiness, sadness and anger using a convolutional neural network which is a class of deep neural networks. Results showed that news anchors or reporters tend to express their emotions during TV broadcasts regardless of broadcast systems. This study provides the first quantative investigation of emotions in broadcasting news. In addition, this study is the first deep learning-based approach to emotion analysis of broadcasting news.

• Journal of Intelligence and Information Systems
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• v.23 no.1
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• pp.109-126
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• 2017

In the various kinds of intelligent services, many studies for detecting emotion are in progress. Particularly, studies on emotion recognition at the particular time have been conducted in order to provide personalized experiences to the audience in the field of exhibition though facial expressions change as time passes. So, the aim of this paper is to build a model to predict the audience's emotion from the changes of facial expressions while watching an exhibit. The proposed model is based on both sequential neural network and the Valence-Arousal model. To validate the usefulness of the proposed model, we performed an experiment to compare the proposed model with the standard neural-network-based model to compare their performance. The results confirmed that the proposed model considering time sequence had better prediction accuracy.

• The Journal of the Korea Contents Association
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• v.17 no.2
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• pp.439-448
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• 2017

Facial expression is important factor on social interaction. Facial muscle movement provides emotion information to develop social network. However, facial movement has less determined to recognize social emotion. This study is to analyze facial micro-movements and to recognize the social emotion such as intimacy, empathy, and valence. 76 university students were presented to the stimuli for social emotions and was measure their facial expression using camera. As a results, facial micro-movement. showed significant difference of social emotion. After extracting the movement amount of 3 unconscious muscles and 18 conscious muscles, Dominant Frequency band was confirmed. While muscle around the nose and cheek showed significant difference in the intimacy, one around mouth did in the empathy and one around jaw in the valence. The results proposed new facial movement to express social emotion in virtual avatars and to recognize social emotion.

• The Journal of Korean Institute for Practical Engineering Education
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• v.1 no.1
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• pp.91-98
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• 2009

In approach of human-robot interaction, it is importance task in future robot industry to make to robot recognize, express, coping the emotions. The purpose of this study was to examination the effects unconscious positive and negative emotion of information processing of motor program. 13 participants(male=11, female=2) viewed smile-face picture and angry-face picture priming at 10ms level, and then performanced button press, button press and one tennis ball hitting, and button press and two tennis ball hitting task. The results appeared that positive emotion triggered more fast RT than negative emotion in planning complex motor program. Possible explanations for the performance differences depended on emotion are discussed and future research directions were provided.

• Journal of Internet Computing and Services
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• v.9 no.5
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• pp.99-108
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• 2008

This paper proposes an image retrieval system which integrates metadata, contents, and emotions in textile images. First, the proposed system searches images using metadata. Among searched images, the system retrieves similar images based on color histogram, color sketch, and emotion histogram. To extract emotion features, this paper uses emotion colors which was proposed on 160 emotion words by H. Nagumo. To enhance the user's convenience, the proposed textile image retrieval system provides additional functions as like enlarging an image, viewing color histogram, viewing color sketch, and viewing repeated patterns.

• Journal of the Ergonomics Society of Korea
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• v.23 no.2
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• pp.37-54
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• 2004

Fuzzy theory provides an intelligence treatment model for judgement about information when it needs a solution or a decision making about vague problems. Therefore, fuzzy theory is used for appropriate evaluation and decision on obscure information as human's emotion in human factors, In previous study, fuzzy membership function is defined for judgement infOlmation as human's emotion then ultimate results are deducted through fuzzy inference model. This method uses general CWTent through literature review or max, min and average as representative statics value about considering variables. But, this method makes away with nonlinear's or inegular's factors of human sensibility. Accordingly, application of this method leads to considerable loss of information in the ultimate evaluation. For that reason, this method has a limitation in objective evaluation of human factors. So, this study focuses on development of fuzzy membership function, which evaluates human's emotion or feeling accurately and objectively. We used the regression analysis and reasoned a fuzzy membership function about the relation of the variables. Then we verified the adequacy with the reliability through the experiment after this.