• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.199-202
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• 2001

We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper, we proposed a vertical slicing model with an emotion layer in it. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

• Proceedings of the IEEK Conference
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• 2001.06e
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• pp.227-230
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• 2001

We, Human beings, use both powers of reason and emotion simultaneously, which surely help us to obtain flexible adaptability against the dynamic environment. We assert that this principle can be applied into the general system. That is, it would be possible to improve the adaptability by covering a digital oriented information processing system with an analog oriented emotion layer. In this paper. we proposed a vertical slicing model with an emotion layer in it. And we showed that the emotion-based control allows us to improve the adaptability of a system at least under some conditions.

• Journal of the Ergonomics Society of Korea
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• v.31 no.6
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• pp.757-764
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• 2012

Objective: The aim of this study is to find the optimal values of sliding factors which influence the mechanical emotion of users when they use sliding type mobile phones. Background: There are various researches that study the emotion of using mobile phones. They focus the correlation between emotion words and design factors and use the commercial products by the subjects in the experiment. However, it has a limit in finding the optimal point of emotional factors because we can search the restricted values in the mass production of the products. Therefore, we will find the optimal points by realizing the full range of the user's mechanical emotion. Method: First, we need to get the detailed factors which can describe the mechanical emotion in sliding up and down the mobile phone. Next, we find the control factors by considering the correlation between the factors of the sliding emotion and the possibility of quantitative design. To find the optimal points on the control factors, we make a sliding evaluation system which can help users feel the sliding mechanical emotion by realizing control factors. Finally, we find the optimal points by doing the experiment the system being used. Results: The critical values of the factors which are the main variables of this study are Open Max Force and Dead point Ratio. The optimal point of the Open Max Force is 200~250g/f, and the Dead point Ratio is 40~50%. Conclusion: In this study we develop the sliding evaluation system to realize the control factors of the sliding type phone and find the optimal values of the critical factors. Application: The results can be used as the criteria for designing sliding type phone.

• Journal of Institute of Control, Robotics and Systems
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• v.10 no.9
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• pp.763-770
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• 2004

The important issue of emotion recognition from speech is a feature extracting and pattern classification. Features should involve essential information for classifying the emotions. Feature selection is needed to decompose the components of speech and analyze the relation between features and emotions. Specially, a pitch of speech components includes much information for emotion. Accordingly, this paper searches the relation of emotion to features such as the sound loudness, pitch, etc. and classifies the emotions by using the statistic of the collecting data. This paper deals with the method of recognizing emotion from the sound. The most important emotional component of sound is a tone. Also, the inference ability of a brain takes part in the emotion recognition. This paper finds empirically the emotional components from the speech and experiment on the emotion recognition. This paper also proposes the recognition method using these emotional components and the transition probability.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2012.05a
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• pp.130-131
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• 2012

• Journal of Korea Game Society
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• v.17 no.3
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• pp.45-52
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• 2017

This paper proposes a new strategy of emotion-based music visualization. Emotional LED lighting control system is suggested to help audiences enhance the musical experience. In the system, emotion in music is recognized by a proposed algorithm using a dimensional approach. The algorithm used a method of music emotion variation detection to overcome some weaknesses of Thayer's model in detecting emotion in a one-second music segment. In addition, IRI color model is combined with Thayer's model to determine LED light colors corresponding to 36 different music emotions. They are represented on LED lighting control system through colors and animations. The accuracy of music emotion visualization achieved to over 60%.

• Journal of Distribution Science
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• v.10 no.10
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• pp.17-24
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• 2012

Purpose - This study aimed to (a) investigate the effects of emotional intelligence on customer orientation, (b) examine the impact of customer orientation on customer relationship management (CRM) performance (including CRM-related variables such as 'relationship commitment,' 'image of corporation,' and 'customer loyalty'), and (c) identify the conceptual framework of emotional intelligence. Research design, data, and methodology - The data were collected using a questionnaire given to a sample of employees of luxury hotels in the metropolitan area. To test the hypotheses, AMOS were conducted for the 271 respondents of the sample using the SPSS Win 17.0 software. The concept of emotional intelligence (EI) has been on the radar of many leaders and managers over the past few decades. Emotional intelligence is generally accepted to be a combination of emotional and interpersonal competencies that influence behavior, thoughts, and interactions with others. Emotional intelligence consists of four factors: understanding the self's emotion, understanding other people's emotions, emotion utilization, and emotion control. Understanding the self's emotion means to understand of my own emotions. Understanding other people's emotions is to understand of the emotions of the people around me and to know how my friends feel based on their behavior. The concept of emotion utilization means to set goals for myself and then try to achieve them, encouraging myself to do my best. The concept of emotion control means I can control my temper, handle difficult situations rationally, and calm down quickly when I am very angry. Results - As a result of the analysis, three factors (understanding the self's emotion, understanding of other people's emotions, and emotion utilization) were shown to have a significant effect on customer orientation. Emotion control had an insignificant effect on customer orientation. Only emotion control makes it difficult to solve customers' problems because it is a passive behavior. In order to solve the customers' problems, hotel employees have to show a positive attitude. Second, customer orientation had a significant effect on customer relationship management performance (customer relationship commitment, corporate image, and customer loyalty). In other words, customer orientation increases commitment to customer relationships. For example, employees who have a customer-orientated perspective provide good service to their customers, while employees who don't have a customer-orientated perspective can't satisfy their customers. Customer orientation can also generate a good image among customers, because they evaluate the image of a hotel through the behavior of hotel employees. So it is very important for employees to show excellent customer orientation. Conclusions - It is very important for hotel CEOs to manage their employees' emotional intelligence. In order to increase their employees' emotional intelligence abilities, CEOs have to manage the overall corporate culture and reward programs to achieve what they want. This is because the system can lead to a customer-orientated mind-set and CRM performance among employees. As a result, the hotel CEO has to pay attention to the emotional intelligence of employees to achieve strong CRM performance. The sentence as originally written was a bit unclear. If this edit does not retain your intended meaning please consider: "Only emotion control does not have a significant impact on customer orientation, and therefore on the ability of an employee to solve customer problems, because it is a passive behavior." Please use the version of the sentence that best captures your original meaning.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.4
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• pp.371-376
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• 2007

This paper presents an emotion recognition and its expression system of an intelligent robot like a home robot or a service robot. Emotion recognition method in the robot is used by a facial image. We use a motion and a position of many facial features. apply a tracking algorithm to recognize a moving user in the mobile robot and eliminate a skin color of a hand and a background without a facial region by using the facial region detecting algorithm in objecting user image. After normalizer operations are the image enlarge or reduction by distance of the detecting facial region and the image revolution transformation by an angel of a face, the mobile robot can object the facial image of a fixing size. And materialize a multi feature selection algorithm to enable robot to recognize an emotion of user. In this paper, used a multi layer perceptron of Artificial Neural Network(ANN) as a pattern recognition art, and a Back Propagation(BP) algorithm as a learning algorithm. Emotion of user that robot recognized is expressed as a graphic LCD. At this time, change two coordinates as the number of times of emotion expressed in ANN, and change a parameter of facial elements(eyes, eyebrows, mouth) as the change of two coordinates. By materializing the system, expressed the complex emotion of human as the avatar of LCD.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.3
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• pp.284-288
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• 2006

In this paper, we apply several pattern recognition algorithms to emotion recognition system with speech signal and compare the results. Firstly, we need emotional speech databases. Also, speech features for emotion recognition is determined on the database analysis step. Secondly, recognition algorithms are applied to these speech features. The algorithms we try are artificial neural network, Bayesian learning, Principal Component Analysis, LBG algorithm. Thereafter, the performance gap of these methods is presented on the experiment result section. Truly, emotion recognition technique is not mature. That is, the emotion feature selection, relevant classification method selection, all these problems are disputable. So, we wish this paper to be a reference for the disputes.

• Journal of Institute of Control, Robotics and Systems
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• v.12 no.7
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• pp.666-670
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• 2006

This paper presents the novel feature selection method for Emotion Recognition, which may include a lot of original features. Specially, the emotion recognition in this paper treated speech signal with emotion. The feature selection has some benefits on the pattern recognition performance and 'the curse of dimension'. Thus, We implemented a simulator called 'IFS' and those result was applied to a emotion recognition system(ERS), which was also implemented for this research. Our novel feature selection method was basically affected by Reinforcement Learning and since it needs responses from human user, it is called 'Interactive feature Selection'. From performing the IFS, we could get 3 best features and applied to ERS. Comparing those results with randomly selected feature set, The 3 best features were better than the randomly selected feature set.