• Journal of Digital Convergence
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• v.12 no.4
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• pp.157-170
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• 2014

Professional coffee shops are trying to increase customers' satisfaction and to invite more customers by providing the differentiated services. The existing researches show that the effects which the physical environment in shops has on customers' satisfaction and word of mouth intention are appealing to people's attention. In comprehensively examining the studies related to the physical environment, they can be summarized into two main perspectives, that is, the direct effect that the physical environment has on customers' satisfaction, quality perception, and other customers' responses (purchase desire, revisit intention, etc.) and the indirect effect that the physical environment has on customers' responses by means of customers' emotion or value perception. This research established 4 hypotheses by sampling 321 customers of those who have visited professional coffee shops, and empirically analyzed them. The empirical analysis carried out the structure analysis of covariance by using SPSS 17.0 statistics package and AMOS 17.0. As a result of the hypothesis qualification, the other hypotheses excluding one little hypothesis were adopted. The one refused hypothesis is that the only symbolism of the environmental elements in shops doesn't influence the customers' emotion positively (+). This is considered as a very unexpected result, and yet many customers who visit coffee shops express the symbols of professional coffee shops using the expressions such as 'bean coffee shop' or 'star coffee shop', but these expressions seem not to influence customers' mind positively in practice.

• The Journal of the Korea institute of electronic communication sciences
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• v.17 no.6
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• pp.1137-1144
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• 2022

In this study, an artificial intelligence(AI) was developed to help with facial expression practice in order to express emotions. The developed AI used multimodal inputs consisting of sentences and facial images for deep neural networks (DNNs). The DNNs calculated similarities between the emotions predicted by the sentences and the emotions predicted by facial images. The user practiced facial expressions based on the situation given by sentences, and the AI provided the user with numerical feedback based on the similarity between the emotion predicted by sentence and the emotion predicted by facial expression. ResNet34 structure was trained on FER2013 public data to predict emotions from facial images. To predict emotions in sentences, KoBERT model was trained in transfer learning manner using the conversational speech dataset for emotion classification opened to the public by AIHub. The DNN that predicts emotions from the facial images demonstrated 65% accuracy, which is comparable to human emotional classification ability. The DNN that predicts emotions from the sentences achieved 90% accuracy. The performance of the developed AI was evaluated through experiments with changing facial expressions in which an ordinary person was participated.

• International Journal of Advanced Culture Technology
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• v.5 no.4
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• pp.1-9
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• 2017

This study of Cho Ji - Hoon's Poem "Falling Flowers" was attempted to find the mechanism of poetic healing and utilize it for literary therapy. In this study, I examined how Cho Ji-Hoon's poem "Falling Flowers" encoded crying. Especially, we focused on the organic relationship of each layer represented by poem and put emotional codes on the layer of functor and argument. The results are as follow. It represents the Separation Layer of 1-3strophes, 4-6strophes constitute the Time Layer, and 7-9strophes the Sadness Layer. This poem proceeds the encoding of the sentence in which the crying of cuckoo in the 1-3strophes transforms into the crying of the poetic narrator in the last 9strophe. The relation of emotional layers in this poem is in the same function relations as "(1-3strophes) ${\subset}$ (4-6strophes) ${\subset}$ (7-9strophes)". Since these functional relations consist of the encoding of sadness, encrypts emotion signals of sadness as "U+U+U" becomes "UUU". 1-3strophes' U is the cry of the cuckoo, and U of the 4-6strophes is blood cry. Therefore, "UUU" is the blood cry of poetic narrator. This Cho Ji-Hoon's poem has a Han(恨) at its base. So, as Cho Ji-Hoon's poem "Falling Flowers" is uttered, the poetic mechanism of U, the code of sadness, is amplified. Then we get caught up in the emotions we want to cry. The poetic catharsis of "crying" is providing the effect of literary therapy. In the future, it will be possible to develop a more effective literary therapy technique by developing a literary therapy program like this poetic structure.

• Journal of Sasang Constitutional Medicine
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• v.12 no.2
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• pp.17-33
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• 2000

Purpose of this study is to investigate the correlation between Sung-Jung' concept of Sasang Constitutional Medicine and Brain. So, After studying the meaning of Sung-Jung' concept of Sasang Constitutional Medicine, I made a comparative study through the structure, function, development of Brain. The conclusions were as follows. 1. Human's brain acts a rational, control his actions. and It manage human body's physiology and pathology. and It perceive his surroundings, express his emotion through comprehension, synthesis, judgement about information from various fields. and It's abnormality bring about a spiritual, bodily injury. Therefore, human's brain have many correlation with Sung-Jung' concept of Sasang Constitutional Medicine. 2. Neocortex' function have many correlation with Sung' concept of Hearing-Sight-Smell-Taste (聽視嗅味=sensation=a highly mental capacity) through Ear-Eye-Nose-Mouse(耳目鼻?). 3. Limbic-system'function have many correlation with Jung' concept of Sorrow-Anger-Pleasure-Joy(哀怒喜榮=emotion) through Lung-Spleen-Liver-Kidney(肺脾肝腎) 4. Brain-stem' function have many correlation with vitalistic concept through Qui of Sorrow - Anger - Pleasure - Joy(哀怒喜樂之氣)' rise and fall. 5. Relation of emotions and diseases through Limbic system and Autonomic nervous system have many correlation with relation of Sung-Jung and diseases of Sasang Constitutional Medicine 6. Left-hemisphere' function that has superior power of verbal, analysis, logicality, consideration have many correlation with tendency of Soeumin and Taeumin. and Right-hemisphere' function that has superior power of emotion, non-verbal, imagination, spatial perception have many correlation with tendency of Soyangin and Taeyangin.

• Science of Emotion and Sensibility
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• v.6 no.2
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• pp.37-47
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• 2003

The objectives of this study were to find the relationship between the olfactory sense such as perfume and sense of sight and how it affects the sensibility based on the fact that ,senses are compound feeling from detective parts of human body. First, to build the emotional rate stale we selected the 19 pairs of adjective from the previous studies that overlaps smell, color, and clothes and added one pairs of related clause. Then each pair was divided into 7 level of emotional stage. In the experiment by the selected 15 man and woman from the visual design major student of Hong-ick Univ each student was given floral, jasmin, lavender, papaya and asked them to pick one color from I.R.I Hue & Tone chart for each smell. Then, analyzed the emotional rating of selected color for given smell. The emotional structure of smell and color consists five parts; 'Esthetics', 'Romance', 'Character', 'Intensity', 'Nature', There were significant differences in frequencies of selected colors for each given smell and gender difference also affected the color selected. Average value of emotional rating scale of smell and color was ,similar to the results from previous studies on relationship between smell and emotion.

• Journal of the Korea Society of Computer and Information
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• v.15 no.10
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• pp.105-112
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• 2010

In this paper, the bio-signals, such as EEG, ECG were measured with a sensor and their characters were drawn out and analyzed. With results from the analysis, four emotion of rest, concentration, tension and depression were inferred. In order to assess one's emotion, the characteristic vectors were drawn out by applying various ways, including the frequency analysis of the bio-signals like the measured EEG and HRV. RBFN, a neural network of the complex structure of unsupervised and supervised learning, was applied to classify and infer the deducted information. Through experiments, the system suggested in this thesis showed better capability to classify and infer than other systems using a different neural network. As follow-up research tasks, the recognizance rate of the measured bio-signals should be improved. Also, the technology which can be applied to the wired or wireless sensor measuring the bio-signals more easily and to wearable computing should be developed.

• Journal of Broadcast Engineering
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• v.25 no.5
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• pp.742-749
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• 2020

This paper proposes a method for recognizing both phoneme and emotion using a common neural network and a multi-task training method for the common neural network. The common neural network performs the same function for both recognition tasks, which corresponds to the structure of multi-information recognition of human using a single auditory system. The multi-task training conducts a feature modeling that is commonly applicable to multiple information and provides generalized training, which enables to improve the performance by reducing an overfitting occurred in the conventional individual training for each information. A method for increasing phoneme recognition performance is also proposed that applies weight to the phoneme in the multi-task training. When using the same feature vector and neural network, it is confirmed that the proposed common neural network with multi-task training provides higher performance than the individual one trained for each task.

• The Journal of the Acoustical Society of Korea
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• v.25 no.6
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• pp.269-276
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• 2006

In this paper, we propose an emotion recognition system that can discriminate human emotional state into neutral or anger from the speech captured by a cellular-phone in real time. In general. the speech through the mobile network contains environment noise and network noise, thus it can causes serious System performance degradation due to the distortion in emotional features of the query speech. In order to minimize the effect of these noise and so improve the system performance, we adopt a simple MA (Moving Average) filter which has relatively simple structure and low computational complexity, to alleviate the distortion in the emotional feature vector. Then a SFS (Sequential Forward Selection) feature optimization method is implemented to further improve and stabilize the system performance. Two pattern recognition method such as k-NN and SVM is compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance such as 86.5%. so that it will be very useful in application areas such as customer call-center.

• Journal of Fashion Business
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• v.14 no.5
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• pp.49-65
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• 2010

The purpose of this paper is to derive the categories of negative e-WOM (electronic word of mouth) via consumer review. Disclosing the details of negative e-WOM based consumer reviews has never been done before. For this reason, a content analysis was adopted to provide knowledge and understanding of the phenomenon. This paper analyzes the content of 630 consumer reviews posted on the open market internet site, www.auction.co.kr. The analysis was conducted from October 20th, 2008 to March 10th, 2009. The results indicated that the negative e-WOM based consumer reviews can be divided into two categories: the cognitive evaluation and the expression of consumer's emotion. The category of cognitive evaluation is consisted of negative e-WOM of product, negative e-WOM of service, and warning about the risk of purchasing products. The category of expressing consumers' emotion are composed of venting customers' dissatisfaction and passive response of dissatisfaction. Investigating the details of negative e-WOM has a number of implications. Most importantly, the results revealed multidimensional structure of negative e-WOM. This understanding of negative e-WOM communication allows marketers to improve products and services that better meet customers' current and future needs.

• International Commerce and Information Review
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• v.12 no.3
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• pp.267-288
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• 2010

The collapse of housing market bubble in United States had a considerable impact on the world economy. The collapse of housing market bubble in United States generates the global financial crisis at the worldwide level. The housing market bubble question the efficacy of fundamental proposition of orthodox economics based on the rationality of human choice. According to neuroeconomics and neuroscience, emotion and affect play the important role in purchasing the commodity in retails market. On the basis of the neuroeconomics, this study focuses on the role of affect and emotion in the purchase of subprime loan in United States. Robert Schiller suggests that one of the causes of housing market bubble is the contagion effect of human belief in the society. The structure of this paper is as follows. First, this study investigates the mechanism of human brain and role of various neurons to influence the human behavior in the purchase of house in United States, such as Dophamine neuron and mirroring neuron. Second, this study shows the possibility that the mirror neuron might explain the contagion of human belief in housing transaction market. It can be a seed of housing market bubble. Furthermore, this study show the implications of neuroeconomics is suggestive to the negotiation process in commercial policy in United States.