• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.4
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• pp.562-567
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• 2009

As a key mechanism of the human emotion interaction, Facial Expression is a powerful tools in HRI(Human Robot Interface) such as Human Computer Interface. By using a facial expression, we can bring out various reaction correspond to emotional state of user in HCI(Human Computer Interaction). Also it can infer that suitable services to supply user from service agents such as intelligent robot. In this article, We addresses the issue of expressive face modeling using an advanced active appearance model for facial emotion recognition. We consider the six universal emotional categories that are defined by Ekman. In human face, emotions are most widely represented with eyes and mouth expression. If we want to recognize the human's emotion from this facial image, we need to extract feature points such as Action Unit(AU) of Ekman. Active Appearance Model (AAM) is one of the commonly used methods for facial feature extraction and it can be applied to construct AU. Regarding the traditional AAM depends on the setting of the initial parameters of the model and this paper introduces a facial emotion recognizing method based on which is combined Advanced AAM with Bayesian Network. Firstly, we obtain the reconstructive parameters of the new gray-scale image by sample-based learning and use them to reconstruct the shape and texture of the new image and calculate the initial parameters of the AAM by the reconstructed facial model. Then reduce the distance error between the model and the target contour by adjusting the parameters of the model. Finally get the model which is matched with the facial feature outline after several iterations and use them to recognize the facial emotion by using Bayesian Network.

• Journal of Service Research and Studies
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• v.6 no.1
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• pp.29-50
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• 2016

The purpose of this study is to identify the motivation of using and choosing factors in credit cards. These results indicate that considerate of other people factor may emphasize the convenience and safety in using credit cards, while the leisure factor may prefer the real-worth factor as they know that the more they use, the more benefits they would get. As the personality factor and the fashion factor, we can pay much attention to the advertisements, promotions, card designs, the colors, the characters and the social positions of the card holders and that considerate other people factor may like the low annual fees and the accumulating points, buying items on the installments without paying additional interest, which had offered them cash service and card loan. The authors of this paper has suggested an information by which credit card companies can establish the strategies according to the lifestyle of the elderly in silver business market.

• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.9 no.4
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• pp.17-24
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• 2009

This paper deals with the cancellation performance of loop back interference signal in the case of multihop relay of 16-QAM received signal at the USN radio network. For this, it is necessary to the exchange of information with long distance located station by means of the relay function between the node in the USN environment. In the relay node, the loop-back interference signal which the retransmitting signal is feedback to the receiver side due to the antenna of transmitter and receiver are co-used or very colsely located or using the nonlinear device. Due to this signal, the performance of USN system are degraded which are using the limited resource of frequency and power. For improve this, it is necessary to applying the adaptive signal processing algorithm in order to cancellating the unwanted loop-back interference signal at the frontend of receiver in relaying node, we can get the better system and multi hop performance. In the adaptive signal processing, we considered the 16-QAM signal which has a good spectral efficiency, firstly, than, the QR-Array RLS algorithm was used that has a fairly good convergence property and the solving the finite length problem in the H/W implementation. Finaly, we confirmed that the good elimination performanc was confirmed by computer simulation in the learing cuved and received signal constellation compared to the conventional RLS.

• Journal of the Korea Convergence Society
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• v.11 no.3
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• pp.53-59
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• 2020

Initial response is important in marine oil spills, such as the Hebei Spirit oil spill, but it is very difficult to predict the movement of oil out of the ocean, where there are many variables. In order to solve this problem, the forecasting of oil spill has been carried out by expanding the particle prediction, which is an existing study that studies the movement of floats on the sea using the data of the float. In the ocean data format HDF5, the current and wind velocity data at a specific location were extracted using bilinear interpolation, and then the movement of numerous points was predicted by particles and the results were visualized using polygons and heat maps. In addition, we propose a spill oil particle matching algorithm to compensate for the lack of data and the difference between the spilled oil and movement. The spilled oil particle matching algorithm is an algorithm that tracks the movement of particles by granulating the appearance of surface oil spilled oil. The problem was segmented using principal component analysis and matched using genetic algorithm to the point where the variance of travel distance of effluent oil is minimized. As a result of verifying the effluent oil visualization data, it was confirmed that the particle matching algorithm using principal component analysis and genetic algorithm showed the best performance, and the mean data error was 3.2%.

• The Journal of Korea Institute of Information, Electronics, and Communication Technology
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• v.13 no.3
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• pp.197-205
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• 2020

Korean language has the characteristics that the pronunciation of phoneme units such as vowels and consonants are fixed and the pronunciation associated with a notation does not change, so that foreign learners can approach rather easily Korean language. However, when one pronounces words, phrases, or sentences, the pronunciation changes in a manner of a wide variation and complexity at the boundaries of syllables, and the association of notation and pronunciation does not hold any more. Consequently, it is very difficult for foreign learners to study Korean standard pronunciations. Despite these difficulties, it is believed that systematic analysis of pronunciation errors for Korean words is possible according to the advantageous observations that the relationship between Korean notations and pronunciations can be described as a set of firm rules without exceptions unlike other languages including English. In this paper, we propose a visualization framework which shows the differences between standard pronunciations and erratic ones as quantitative measures on the computer screen. Previous researches only show color representation and 3D graphics of speech properties, or an animated view of changing shapes of lips and mouth cavity. Moreover, the features used in the analysis are only point data such as the average of a speech range. In this study, we propose a method which can directly use the time-series data instead of using summary or distorted data. This was realized by using the deep learning-based technique which combines Self-organizing map, variational autoencoder model, and Markov model, and we achieved a superior performance enhancement compared to the method using the point-based data.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.4
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• pp.2060-2077
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• 2019

Recently, mobile healthcare services have attracted significant attention because of the emerging development and supply of diverse wearable devices. Smartwatches and health bands are the most common type of mobile-based wearable devices and their market size is increasing considerably. However, simple value comparisons based on accumulated data have revealed certain problems, such as the standardized nature of health management and the lack of personalized health management service models. The convergence of information technology (IT) and biotechnology (BT) has shifted the medical paradigm from continuous health management and disease prevention to the development of a system that can be used to provide ground-based medical services regardless of the user's location. Moreover, the IT-BT convergence has necessitated the development of lifestyle improvement models and services that utilize big data analysis and machine learning to provide mobile healthcare-based personal health management and disease prevention information. Users' health data, which are specific as they change over time, are collected by different means according to the users' lifestyle and surrounding circumstances. In this paper, we propose a prediction model of user physical activity that uses data characteristics-based long short-term memory (DC-LSTM) recurrent neural networks (RNNs). To provide personalized services, the characteristics and surrounding circumstances of data collectable from mobile host devices were considered in the selection of variables for the model. The data characteristics considered were ease of collection, which represents whether or not variables are collectable, and frequency of occurrence, which represents whether or not changes made to input values constitute significant variables in terms of activity. The variables selected for providing personalized services were activity, weather, temperature, mean daily temperature, humidity, UV, fine dust, asthma and lung disease probability index, skin disease probability index, cadence, travel distance, mean heart rate, and sleep hours. The selected variables were classified according to the data characteristics. To predict activity, an LSTM RNN was built that uses the classified variables as input data and learns the dynamic characteristics of time series data. LSTM RNNs resolve the vanishing gradient problem that occurs in existing RNNs. They are classified into three different types according to data characteristics and constructed through connections among the LSTMs. The constructed neural network learns training data and predicts user activity. To evaluate the proposed model, the root mean square error (RMSE) was used in the performance evaluation of the user physical activity prediction method for which an autoregressive integrated moving average (ARIMA) model, a convolutional neural network (CNN), and an RNN were used. The results show that the proposed DC-LSTM RNN method yields an excellent mean RMSE value of 0.616. The proposed method is used for predicting significant activity considering the surrounding circumstances and user status utilizing the existing standardized activity prediction services. It can also be used to predict user physical activity and provide personalized healthcare based on the data collectable from mobile host devices.

• Journal of the Korean earth science society
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• v.40 no.3
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• pp.300-312
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• 2019

The purpose of this study is to develop an astronomical observing education program that enables high school students to inquire the distance of astronomical bodies based on the research methods (observing open clusters and exploring collected big data) using small telescopes and DSLR cameras. After analyzing the 2015 revised science curriculum, we developed science club activity materials and teacher-student learning contents suitable for high school earth science education. A panel of six teachers and researchers of earth science education and astronomy, participated in developing the educational materials. The validity of the program was verified through establishing the agreement among the panels after in-depth discussions and clarifications. The program, developed with 10 lessons in total, showed high satisfactory content validity (CVI, .89) and conformity of school class (Likert's 5 point scales, 4.17). The feedback of the panels and the Delphi analysis continued to improve the quality of the program. The pilot testing result with high school students (N=9) showed that the students' satisfaction rate was high as 4.48. Using the astronomical observational education program of this study is expected to contribute in improving the convergence educational activity, interest, curiosity, and inquiry ability of students in the universe and the astronomical bodies.

• Journal of Music and Human Behavior
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• v.18 no.1
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• pp.1-27
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• 2021

The purpose of this study was to investigate how primary caregivers of children with developmental disabilities aged 6 to 18 years perceived on tele-music programs in which their children participated during COVID-19. A total of 83 caregivers who voluntarily agreed to participate in this study responded to a survey either on-line or in person and 67 questionnaires were included in the final analysis after deleting 16 incomplete responses. The results showed that tele-music programs were rated somewhat suitable for distance learning but that there was still a need for adult assistance to help children with developmental disabilities participate in the program. When comparing the perceptions of caregivers who participated in remote general education versus who participated in tele-music program, significantly higher level of program engagement and positive responses from a child were perceived with tele-music program. The caregivers who participated in tele-music program showed significantly greater willingness to participate in tele-music therapy in the future than those who did not. The findings of this study presents information on how tele-music therapy has been implemented to children with disabilities and what can be considered for the the development of a tele-music therapy program.

• Journal of the Korea Society of Computer and Information
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• v.27 no.3
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• pp.53-61
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• 2022

Recent years have drawn a great attention to generation MZ and Metaverse, due to 4th industrial revolution and the development of digital environment that blurs the boundary between reality and virtual reality. Generation MZ approaches the information very differently from the existing generations and uses distinguished communication methods. In terms of learning, they have different motivations, types, skills and build relationships differently. Meanwhile, Metaverse is drawing a great attention as a teaching method that fits traits of gen MZ. Thus, the current research aimed to investigate how to increase the use of Metaverse in Educational Technology. Specifically, this research examined the antecedents of popularity of Gather Town, a platform of Metaverse. Big data of news articles have been collected and analyzed using the Bigkinds system provided by Korea Press Foundation. The analysis revealed, first, a rapid increasing trend of media exposure of Gather Town since July 2021. This suggests a greater utilization of Gather Town in the field of education after the COVID-19 pandemic. Second, Word Association Analysis and Word Cloud Analysis showed high weights on education related words such as 'remote', 'university', and 'freshman', while words like 'Metaverse', 'Metaverse platform', 'Covid19', and 'Avatar' were also emphasized. Third, Network Analysis extracted 'COVID19', 'Avatar', 'University student', 'career', 'YouTube' as keywords. The findings also suggest potential value of Gather Town as an educational tool under COVID19 pandemic. Therefore, this research will contribute to the application and utilization of Gather Town in the field of education.

• Journal of Korea Entertainment Industry Association
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• v.15 no.4
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• pp.267-277
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• 2021

Both teachers and learners should participate in non-face-to-face class due to COVID-19. The non-face-to-face class has brought about many problems, where they made adequate preparations for such abrupt situation. This study attempted to understand and improve problems occurring during non-face-to-face midi class. The findings are as follows: First, there were differences in equipment available to contact and non-face-to-face class. Such a problem could be improved by using Reaper, DAW which can be installed and freely utilized without any functional limits, regardless of the types of operating systems. Second, latency could not be reduced, when the screen share function of Zoom was used, since it was impossible to select audio interface's drivers in DAW. This problem was improved by again receiving audio output as input and sending it, from the perspectives of teachers. In addition, learners who used the operating system of Windows and have no audio interfaces usually suffer from latency during practices. The latency can be reduced by installing Asio4all. Third, image degradation and screen disconnection phenomena occurred due to the lack of resource. Two computers were connected by using a capture board and the screen disconnection phenomena could be improved by distributing resources and maintaining high-resolution. The system for allowing non-face-to-face midi class could be successfully established, as one more computer was connected by using Vienna Ensemble Pro and more plug-ins were used by securing additional resources. Consequently, the problems of non-face-to-face midi class could be understood and improved.