• Journal of Distribution Science
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• v.19 no.2
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• pp.25-36
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• 2021

Purpose: This paper attempts to explore a Christian perspective on the process leading to growth after complex trauma caused by family violence experience. To achieve it, the article tackles the analysis of relationship between the inflictor father and victim, interpersonal relationship, and relationship with God in terms of growth after suffering from the trauma of family violence with a Christian perspective. Research design, data, and methodology: This study employed an in-depth interview as a methodology. Seven Christian adults who have experienced family violence in childhood are selected for the qualitative case study. 58 concepts, 24 low-level categories, and eight high-level categories are derived from each interview case. Results: The results of the case study show that the negative emotion caused by family violence during childhood is likely to lead to narcissistic rage. It is found that the reflection for posttraumatic growth starts with crying to God, simultaneously expressing pain and suffering. Conclusions: The interesting thing is that they are willing to forgive in the process of trauma therapy. It should be noted that the research results also demonstrate that relationship restoration entails the meaning reconstruction in the interpersonal relations.

• Archives of design research
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• v.17 no.4
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• pp.141-148
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• 2004

The purpose of this study is to define the emotion that expressed in step animation and to quantify the intuitional expression of emotion that related step for using extract, measure, analysis the stimulate element about step. The survey of relation with 27 word of emotional expressions and 36 moving pictures of step sample is used for method of this test. The emotional mental structure is transferred to 2 dimensional planes as applying the results of analysis of integrated data using Quantification Method 3, which the integrated data is composed two axial - confidential axial and stabling axial. Analysis of distribution of 2 dimensional diagram shows that the second of the plane and the third of the plane have much data. However, the first of the plane and the forth of the plane have a little data. Through this kind of analysis of graph, it is difficult to express a different emotion between unstable the timidity mind and stable feel the timidity mind using only step analysis. Six difference types about physical elements affecting to emotion are selected and analyzed such as the paces of step, the rate of step, the movement angle of pelvis, the swing range of arm, angle of backbone and the lean angle of body. The result is that the rate of stop and the lean angle of body are the major element that effects to emotional stimulate of stop. This thesis argues about methods transforming subjective expression to objective and quantitative expression with the state of delicate emotion of character apply to step animation naturally. Those data to apply to multi-contents in future are the main target in this study.

• Proceedings of the Korean DIstribution Association Conference
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• 2007.08a
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• pp.127-158
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• 2007

This study discusses salespersons' emotional intelligence, one of the key abilities necessary to meet customers' needs effectively, and express positive emotions in frequent interactions with customer. Emotional intelligence refers to self-controllability and social ability emphasizing pro-social aspect and understanding of others. This study investigates how salespersons' emotional intelligence affects adaptive selling and positive emotional expression during the process of interaction with customers, and how such adaptive selling and positive emotional expression affects the quality of service perceived by customers. The results show that greater salespersons' emotional intelligence results in better adaptive selling and positive emotional expression. Such adaptive selling and positive emotional expression had significant effects on the quality of service perceived by customers. These results are important in that they address emotional intelligence as salespersons' emotional ability, which has been overlooked as an antecedent variable for improving adaptive selling and display of positive emotion, consequently provide another factor to help salespersons improve their selling behavior.

• The Journal of Industrial Distribution & Business
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• v.12 no.6
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• pp.75-88
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• 2021

Purpose: This research aimed at exploring the functions of consumers' perceiving approach and avoidance roles and their feeling anger and disgust in the effect of the two types of self-discrepancy at social identity such as the ideal self-discrepancy and the ought self-discrepancy on within-self domain versus across-self domain consumption. Research design, data, and methodology - This study divided the self-discrepancy group into the ideal self-discrepancy and the ought self-discrepancy group as experimental groups for empirical study. Self-discrepancy type between-subjects design was used to develop two types of questionnaire according to the type of experimental groups. The platform, 'questionnaire stars' of 'WeChat' in China was used to collect 103 data from the ideal self-discrepancy group and 102 from the ought self-discrepancy group for empirical study. T-test and the structural equation model in Amos 21 were used to verify hypotheses developed through theoretical review. Results - First, ideal self-discrepancy positively affected the role-approaching goal and anger. Second, ought self-discrepancy positively affected the role-avoiding goal and disgust. Third, the role-approaching goal and anger positively influenced on the within- versus across- domain consumption. Fourth, the disgust negatively influenced on the within- versus across- domain consumption, however the role-avoiding goal did not influence on the consumption. Fifth, there was the mediation roles of anger (disgust) in the effects of ideal (ought) self-discrepancy on the consumption. Conclusions - When consumers feel anger at the ideal self- discrepancy induced by in-group, it is necessary for the marketers to promote their product brand used by the in-group. They should develop and advertise the messages priming the ideal self-discrepancy and the anger to increase the intent to purchase or use their product brand when the in-group members have used the brand by relating the brand to their social identity concerned with the ideal self-discrepancy. However, marketers should help consumers feel disgust by developing and advertising the messages expressing the ought self-discrepancy to lead the consumers to the place of purchasing or using their product brand when the members have used the brand based on keeping the consistence between the brand and other social identity not related to the ought self-discrepancy.

• KIPS Transactions on Computer and Communication Systems
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• v.12 no.8
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• pp.235-242
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• 2023

In recent years, as online counseling for infants and adolescents has increased, CNN-based deep learning models are widely used as assistance tools for emotion recognition. However, since most emotion recognition models are trained on mainly adult data, there are performance restrictions to apply the model to infants and adolescents. In this paper, in order to analyze the performance constraints, the characteristics of facial expressions for emotional recognition of infants and adolescents compared to adults are analyzed through LIME method, one of the XAI techniques. In addition, the experiments are performed on the male and female groups to analyze the characteristics of gender-specific facial expressions. As a result, we describe age-specific and gender-specific experimental results based on the data distribution of the pre-training dataset of CNN models and highlight the importance of balanced learning data.

• Journal of Korea Multimedia Society
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• v.12 no.8
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• pp.1155-1163
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• 2009

This paper proposes an automatic facial expression generation system of vector graphic character using gaussian process model. Proposed method extracts the main feature vectors from twenty-six facial data of character redefined based on Russell's internal emotion state. Also by using new gaussian process model, SGPLVM, we find low-dimensional feature data from extracted high-dimensional feature vectors, and learn probability distribution function (PDF). All parameters of PDF are estimated by maximization the likelihood of learned expression data, and these are used to select wanted facial expressions on two-dimensional space in real time. As a result of simulation, we confirm that proposed facial expression generation tool is working in the small facial expression datasets and can generate various facial expressions without prior knowledge about relation between facial expression and emotion.

• Journal of Distribution Research
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• v.15 no.2
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• pp.101-122
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• 2010

In this study, it is explored whether positive and negative emotions affected hedonic and utilitarian values experienced while shopping apparel in the Internet. In addition, the effect of hedonic and utilitarian shopping values on store approach behaviors was explored. For this study, Babin and Attaway's research model that was used for off-line shopping malls was adopted to investigate the relationships among research variables. Data obtained through experiments with 278 female college students were submitted for an analysis. Exploratory and confirmatory factor analysis and structural equation modeling with AMOS 6.0 were used to analyze data. Based on the model test, negative emotions negatively affected hedonic and utilitarian shopping value perception while positive emotions positively affected hedonic and utilitarian shopping value perception for the Internet apparel shopping site. Hedonic and utilitarian shopping values positively influenced attitude toward the Internet shopping site while only utilitarian shopping value affected revisiting Internet apparel shopping site. Managerial and academic implications were generated based on results.

• Science of Emotion and Sensibility
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• v.6 no.4
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• pp.25-32
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• 2003

This paper suggests the evaluation method of gait analysis in measurements obtained using the "Foot Scanner" and "Foot Analyzer" system. Previous examination method with the unaided eye on the sole of the foot and analysis method of pressure distribution in gait have been discussed by many researchers. Also they have concerned with pressure curve, COP(center of pressure) trace, and velocity in COP. However experiment results depend on test environment and conditions of subjects. Consequently we need to regard the special energy parameter for solving the problem. The kinetic energy and impulse parameter can be used as parameters of gait analysis. The results of this study confirmed the validity of presented of the parameters through the experiment with eight subjects.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.12C
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• pp.1200-1208
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• 2003

Affective computing plays an important role in intelligent Human Computer Interactions(HCI). To detect emotional events, it is desirable to construct a computing model for extracting emotion related features from video. In this paper, we propose a statistical model based on the probabilistic distribution of low level features in video shots. The proposed method extracts low level features from video shots and then from a GMM(Gaussian Mixture Model) for them to detect emotional shots. As low level features, we use color, camera motion and sequence of shot lengths. The features can be modeled as a GMM by using EM(Expectation Maximization) algorithm and the relations between time and emotions are estimated by MLE(Maximum Likelihood Estimation). Finally, the two statistical models are combined together using Bayesian framework to detect emotional events in video.

• Journal of Intelligence and Information Systems
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• v.27 no.3
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• pp.139-156
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• 2021

The biggest reason for using a deep learning model in image classification is that it is possible to consider the relationship between each region by extracting each region's features from the overall information of the image. However, the CNN model may not be suitable for emotional image data without the image's regional features. To solve the difficulty of classifying emotion images, many researchers each year propose a CNN-based architecture suitable for emotion images. Studies on the relationship between color and human emotion were also conducted, and results were derived that different emotions are induced according to color. In studies using deep learning, there have been studies that apply color information to image subtraction classification. The case where the image's color information is additionally used than the case where the classification model is trained with only the image improves the accuracy of classifying image emotions. This study proposes two ways to increase the accuracy by incorporating the result value after the model classifies an image's emotion. Both methods improve accuracy by modifying the result value based on statistics using the color of the picture. When performing the test by finding the two-color combinations most distributed for all training data, the two-color combinations most distributed for each test data image were found. The result values were corrected according to the color combination distribution. This method weights the result value obtained after the model classifies an image's emotion by creating an expression based on the log function and the exponential function. Emotion6, classified into six emotions, and Artphoto classified into eight categories were used for the image data. Densenet169, Mnasnet, Resnet101, Resnet152, and Vgg19 architectures were used for the CNN model, and the performance evaluation was compared before and after applying the two-stage learning to the CNN model. Inspired by color psychology, which deals with the relationship between colors and emotions, when creating a model that classifies an image's sentiment, we studied how to improve accuracy by modifying the result values based on color. Sixteen colors were used: red, orange, yellow, green, blue, indigo, purple, turquoise, pink, magenta, brown, gray, silver, gold, white, and black. It has meaning. Using Scikit-learn's Clustering, the seven colors that are primarily distributed in the image are checked. Then, the RGB coordinate values of the colors from the image are compared with the RGB coordinate values of the 16 colors presented in the above data. That is, it was converted to the closest color. Suppose three or more color combinations are selected. In that case, too many color combinations occur, resulting in a problem in which the distribution is scattered, so a situation fewer influences the result value. Therefore, to solve this problem, two-color combinations were found and weighted to the model. Before training, the most distributed color combinations were found for all training data images. The distribution of color combinations for each class was stored in a Python dictionary format to be used during testing. During the test, the two-color combinations that are most distributed for each test data image are found. After that, we checked how the color combinations were distributed in the training data and corrected the result. We devised several equations to weight the result value from the model based on the extracted color as described above. The data set was randomly divided by 80:20, and the model was verified using 20% of the data as a test set. After splitting the remaining 80% of the data into five divisions to perform 5-fold cross-validation, the model was trained five times using different verification datasets. Finally, the performance was checked using the test dataset that was previously separated. Adam was used as the activation function, and the learning rate was set to 0.01. The training was performed as much as 20 epochs, and if the validation loss value did not decrease during five epochs of learning, the experiment was stopped. Early tapping was set to load the model with the best validation loss value. The classification accuracy was better when the extracted information using color properties was used together than the case using only the CNN architecture.