• Journal of the Korea Convergence Society
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• v.8 no.10
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• pp.167-172
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• 2017

The purpose of this study is to analyze the emotional codes of Sijo, which has been acknowledged to have excellent therapeutic function, to activate the contents of the therapy of humanities. Sijo as a function of healing forms emotional codes of therapy, which is the total of emotions, through the fusion of emotions formed during the process of appreciation of various works. This process enables the literary therapeutic activities to proceed physiologically in the human body. Just as machine learning is self-learning by cognitive functions, the coding process for encoding and re-encoding at all times operates on collections of numerous neurons in the human system. In such a process, it is predicted that amino acids are synthesized in the human body by collective encoding of emotion codes. These amino acids regulate the signaling system of the human body. In the future, if the study on the healing process as such at the contact point of humanities and human physiology proceeds, it is expected that a program of higher quality humanistic therapy will be activated.

• Journal of Institute of Control, Robotics and Systems
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• v.22 no.3
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• pp.241-246
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• 2016

Nowadays many people have an interest in facial expression and the behavior of people. These are human-robot interaction (HRI) researchers utilize digital image processing, pattern recognition and machine learning for their studies. Facial feature point detector algorithms are very important for face recognition, gaze tracking, expression, and emotion recognition. In this paper, a cascade facial feature point detector is used for finding facial feature points such as the eyes, nose and mouth. However, the detector has difficulty extracting the feature points from several images, because images have different conditions such as size, color, brightness, etc. Therefore, in this paper, we propose an algorithm using a modified cascade facial feature point detector using a convolutional neural network. The structure of the convolution neural network is based on LeNet-5 of Yann LeCun. For input data of the convolutional neural network, outputs from a cascade facial feature point detector that have color and gray images were used. The images were resized to $32{\times}32$. In addition, the gray images were made into the YUV format. The gray and color images are the basis for the convolution neural network. Then, we classified about 1,200 testing images that show subjects. This research found that the proposed method is more accurate than a cascade facial feature point detector, because the algorithm provides modified results from the cascade facial feature point detector.

• Journal of the Korean Society of Clothing and Textiles
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• v.39 no.2
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• pp.233-246
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• 2015

Focus on Digital Fashion Image, the conceptual framework for the thesis is established from Virtuality in Digital Art. Formative characteristics and aesthetic characteristics were studied by classifying the Digital Fashion Image applied and expressed by digital media and technology. A detective research method was used for a case study. A literature study for case-by-case data was analyzed with focus on the works expressing fashion that utilized digital media and technology since the 2000s. Through this study, the Digital revolution has created the socio-cultural impact of a Virtual representation to implement technology and fashion culture that finds ways to take advantage of the image shown in a Digital Fashion Media by understanding Virtuality. The results are as follows. First, it was a re-formation of the fashion culture through the experience of virtuality with mental zone parameters between the media 'Mediation Code'. Reflect the reality of the virtual environment as represented by a cultural image of fashion brands and fashion that reset the team relationship and formed a Homo Ludens cultural code. Second, 'Interactive Exchange' acts on the exchange interaction between the method of digital technology, the human and the machine as well as the technical interoperability of network elements and techniques. This exchange is applied to fashion images that express emotion. Forming personalized fashion items and the user interactively storage that expresses the interactive exchange to forward the identity of the emotional fashion by a change in the message delivery system fashion. Third, the emphasis on intuitive artistic expression 'Synesthesia Immersion' induces a sense of immersion and excitement through the fusion of the interconnected. Enhance a visual image in fashion sensory representation and maximize a tactile and visual virtual reality involvement.

• The Journal of the Korea Contents Association
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• v.15 no.2
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• pp.74-85
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• 2015

The positive effect of percussion instruments can induce increases in self-esteem and decreases in depression in the elderly. Based on this, the content for a percussion instrument robot that the elderly can use to play music is developed. The elements of the interaction between the elderly and the robot through the robot content are extracted. Music that arouses positive memories in the elderly is selected as part of the music therapy robot content in order to relieve depression, and a scoring system for playing music is constructed. In addition, the interaction components of the robot's facial expressions, which stimulate emotions and sensitivity in the elderly, are also designed. These components enable the elderly to take an active part in using the instrument to change the robot's facial expressions, which have three degrees of emotion: neutral-happy, happy, and very happy. The robot is not only a music game machine: it also maximizes the relief of depression in the elderly through interactions with the robot that allow the elderly person to listen to what the robot plays and through the elderly person becoming involved and playing music along with the robot.

• Journal of the Architectural Institute of Korea Planning & Design
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• v.34 no.12
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• pp.85-94
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• 2018

The purpose of this paper was to propose a model that recognizes potential users' emotional response toward design by classifying Electroencephalography(EEG). Studies in neuroscience and psychology have made an effort to recognize subjects' emotional response by analyzing EEG data. And this approach has been adopted in design since it is critical to monitor users' subjective response in the preface of design. Moreover, the building design process cannot be reversed after construction, recognizing clients' affection toward design alternatives plays important role. An experiment was conducted to record subjects' EEG data while they view their most/least liked images of small-house designs selected by them among the eight given images. After the recording, a subjective questionnaire, PANAS, was distributed to the subjects in order to describe their own affection score in quantitative way. Google TensorFlow was used to build and train the model. Dataset for model training and testing consist of feature columns for recorded EEG data and labels for the questionnaire results. After training and testing, the measured accuracy of the model was 0.975 which was higher than the other machine learning based classification methods. The proposed model may suggest one quantitative way of evaluating design alternatives. In addition, this method may support designer while designing the facilities for people like disabled or children who are not able to express their own feelings toward alternatives.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.46 no.1
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• pp.32-41
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• 2023

Recently, many studies are being conducted to extract emotion from text and verify its information power in the field of finance, along with the recent development of big data analysis technology. A number of prior studies use pre-defined sentiment dictionaries or machine learning methods to extract sentiment from the financial documents. However, both methods have the disadvantage of being labor-intensive and subjective because it requires a manual sentiment learning process. In this study, we developed a financial sentiment dictionary that automatically extracts sentiment from the body text of analyst reports by using modified Bayes rule and verified the performance of the model through a binary classification model which predicts actual stock price movements. As a result of the prediction, it was found that the proposed financial dictionary from this research has about 4% better predictive power for actual stock price movements than the representative Loughran and McDonald's (2011) financial dictionary. The sentiment extraction method proposed in this study enables efficient and objective judgment because it automatically learns the sentiment of words using both the change in target price and the cumulative abnormal returns. In addition, the dictionary can be easily updated by re-calculating conditional probabilities. The results of this study are expected to be readily expandable and applicable not only to analyst reports, but also to financial field texts such as performance reports, IR reports, press articles, and social media.

• Science of Emotion and Sensibility
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• v.13 no.1
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• pp.47-60
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• 2010

Most of the researches about classification usually have used kNN(k-Nearest Neighbor), SVM(Support Vector Machine), which are known as learn-based model, and Bayesian classifier, NNA(Neural Network Algorithm), which are known as statistics-based methods. However, there are some limitations of space and time when classifying so many web pages in recent internet. Moreover, most studies of classification are using uni-gram feature representation which is not good to represent real meaning of words. In case of Korean web page classification, there are some problems because of korean words property that the words have multiple meanings(polysemy). For these reasons, LSA(Latent Semantic Analysis) is proposed to classify well in these environment(large data set and words' polysemy). LSA uses SVD(Singular Value Decomposition) which decomposes the original term-document matrix to three different matrices and reduces their dimension. From this SVD's work, it is possible to create new low-level semantic space for representing vectors, which can make classification efficient and analyze latent meaning of words or document(or web pages). Although LSA is good at classification, it has some drawbacks in classification. As SVD reduces dimensions of matrix and creates new semantic space, it doesn't consider which dimensions discriminate vectors well but it does consider which dimensions represent vectors well. It is a reason why LSA doesn't improve performance of classification as expectation. In this paper, we propose new LSA which selects optimal dimensions to discriminate and represent vectors well as minimizing drawbacks and improving performance. This method that we propose shows better and more stable performance than other LSAs' in low-dimension space. In addition, we derive more improvement in classification as creating and selecting features by reducing stopwords and weighting specific values to them statistically.

• Science of Emotion and Sensibility
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• v.16 no.1
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• pp.107-116
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• 2013

In the implementation of a P300 speller, rows and columns paradigm (RCP) is most commonly used. However, the RCP remains subject to adjacency-distraction error and double-flash problems. This study suggests a novel P300 speller stimuli presentation-the sub-block paradigm (SBP) that is likely to solve the problems effectively. Fifteen subjects participated in this experiment where both SBP and RCP were used to implement the P300 speller. Electroencephalography (EEG) activity was recorded from Fz, Cz, Pz, Oz, P3, P4, PO7, and PO8. Each paradigm consisted of a training phase to train a classifier and a testing phase to evaluate the speller. Eighteen characters were used for the target stimuli in the training phase. Additionally, 5 subjects were required to spell 50 characters and the rest of the subjects were to spell 25 characters in the testing phase. Classification accuracy results show that average accuracy was significantly higher in SBP as of 83.73% than that of RCP as of 66.40%. Grand mean event-related potentials (ERPs) at Pz show that positive peak amplitude for the target stimuli was greater in SBP compared to that of RCP. It was found that subjects tended to attend more to the characters in SBP. According to the participants' ratings on how comfortable they were with using each type of paradigm on 7-point Likert scale, most subjects responded 'very difficult' in RCP while responding 'medium' and 'easy' in SBP. The result showed that SBP was felt more comfortable than RCP by the subjects. In sum, the SBP was more correct in P300 speller performance as well as more convenient for users than the RCP. The actual limitations in the study were discussed in the last part of this paper.

• Science of Emotion and Sensibility
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• v.21 no.1
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• pp.59-70
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• 2018

With the breakthrough of speech recognition technology, conversational agents have become pervasive through smartphones and smart speakers. The recognition accuracy of speech recognition technology has developed to the level of human beings, but it still shows limitations on understanding the underlying meaning or intention of words, or understanding long conversation. Accordingly, the users experience various errors when interacting with the conversational agents, which may negatively affect the user experience. In addition, in the case of smart speakers with a voice as the main interface, the lack of feedback on system and transparency was reported as the main issue when the users using. Therefore, there is a strong need for research on how users can better understand the capability of the conversational agents and mitigate negative emotions in error situations. In this study, we applied social strategies, "forewarning" and "apology", to conversational agent and investigated how these strategies affect users' perceptions of the agent in breakdown situations. For the study, we created a series of demo videos of a user interacting with a conversational agent. After watching the demo videos, the participants were asked to evaluate how they liked and trusted the agent through an online survey. A total of 104 respondents were analyzed and found to be contrary to our expectation based on the literature study. The result showed that forewarning gave a negative impression to the user, especially the reliability of the agent. Also, apology in a breakdown situation did not affect the users' perceptions. In the following in-depth interviews, participants explained that they perceived the smart speaker as a machine rather than a human-like object, and for this reason, the social strategies did not work. These results show that the social strategies should be applied according to the perceptions that user has toward agents.

• The Journal of Korean Physical Therapy
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• v.16 no.4
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• pp.90-114
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• 2004

This study was conducted to examine the influence and effect of muscle enforcement program on Activity of daily living(ADL) improvement and posture balance of the old, and to provide more effective muscle enforcement program and educational data. The muscle enforcement exercise program was performed on the old(institution, 16 men, 10 women) for 8 weeks from April 22, 2002 through June 17,2002. Programed Exercise 1 - Exercise 10 were practised 8 times per program for 3 days a week. The load of exercise was increased per two weeks. The methods of measurement were questionnaire, Indiana 47903(action-response analysis machine) and Sample exercise protocol for KAT 2000(balance training device). SAS/PC statistic analysis was used for data analysis. T-test was used for analysis of change before and after exercise in this study. The summary and conclusions are as follows. 1. On subjectively recognized health states, the healthy were $42.3\%$. On the satisfaction with health states, the satisfied were $50.0\%$. On the factors of effects on daily-life behavior performance, the group who had troubles was $50\%$ and the group who was so and so was $34.6\%$ compared with the old of the same age. On prospect about health states in the future, the group who would be better was $38.\%$. On effective methods for problem solving, exercise was $42.3\%.\;88.5\%$ of respondents answered the need of health care. The participation intention in health program was $92.3\%$. 2. On the change of psychological emotion and behavior aspects, the group who had repeated complaints or anxieties and reduced activities or interests was effective(P<0.01). 3. On the improvement effects of IADL difficulties, the group who had difficulties in doing daily-life indoors was improved effectively compared with before and after exercise(P<0.01). On medication management, the effects of improvement after exercise were high compared with before exercise(P<0.01), the effects of improvement was high on the whole. 4. On the effects of ADL function improvement, putting on upper clothing and lower clothing was improved effectively(P<0.05), toilet use and individual sanitation was improved effectively(P<0.05). 5. On the effects of action-response, the results of 8weeks regular exercise program were not different significantly compared with before and after exercise. The behavior quickness of the old by muscle enforcement program was not increased. This means that the old needs much time for exercise sense training because of the regression of cognition sense. 6. In the effect of posture balance, the whole grades were effective from 1272.69 before excercise to 476.92 after exercise(P<0.01). Especially right balance 657.65 was lowered to 208.57 after exercise most effectively(P<0.01). Rear balance 776.34 before exercise was lowered to 136.65 after exercise. The results of measurement were significant(P<0.05).