• Journal of the Korean Society for information Management
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• v.38 no.4
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• pp.231-250
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• 2021

Despite the increasing number of international students in Korea, there is a lack of research on the health information-seeking behavior of international students. This study examined the health information search behavior of international students in Korea through a questionnaires and in-depth interviews adopting Critical Incident Technique. Most frequent health information needs that the participants experienced were related to Covid-19 and locating doctors/hospitals. The difficulties in seeking health information were language barriers, lack of knowledge of the Korean medical system, insufficient or overflowing information on the Internet. However, despite the language barrier, international students mainly used Korean sources (friends/family, websites, social media) for searching health information. In order to search health information on Korean websites, they used Google Translator or got help from bilingual friends/family members. The participants who have lived in Korea for a shorter period of time or who have lower Korean language proficiency tend to obtain health information through the community on social networks; whereas the longer the period of residence in Korea and the better the Korean language proficiency, the more likely to use websites. Only 28% of the participants gave positive answers to the question asking their confidence in finding the health information they needed. It is discussed how to help international students find accurate and credible health information.

• Investigative Magnetic Resonance Imaging
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• v.25 no.4
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• pp.313-322
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• 2021

Purpose: To find diagnostic image features, to compare diagnostic performance of multiphase CT versus gadoxetic acid disodium-enhanced MRI (GAD-MRI), and to evaluate the impact of analyzing Liver Imaging Reporting and Data System (LI-RADS) imaging features, for distinguishing combined hepatocellular-cholangiocarcinoma (CHC) from hepatocellular carcinoma (HCC). Materials and Methods: Ninety-six patients with pathologically proven CHC (n = 48) or HCC (n = 48), diagnosed June 2008 to May 2018 were retrospectively analyzed in random order by three radiologists with different experience levels. In the first analysis, the readers independently determined the probability of CHC based on their own knowledge and experiences. In the second analysis, they evaluated imaging features defined in LI-RADS 2018. Area under the curve (AUC) values for CHC diagnosis were compared between CT and MRI, and between the first and second analyses. Interobserver agreement was assessed using Cohen's weighted κ values. Results: Targetoid LR-M image features showed better specificities and positive predictive values (PPV) than the others. Among them, rim arterial phase hyperenhancement had the highest specificity and PPV. Average sensitivity, specificity, and AUC values were higher for MRI than for CT in both the first (P = 0.008, 0.005, 0.002, respectively) and second (P = 0.017, 0.026, 0.036) analyses. Interobserver agreements were higher for MRI in both analyses (κ = 0.307 for CT, κ = 0.332 for MRI in the first analysis; κ = 0.467 for CT, κ = 0.531 for MRI in the second analysis), with greater agreement in the second analysis for both CT (P = 0.001) and MRI (P < 0.001). Conclusion: Rim arterial phase hyperenhancement on GAD-MRI can be a good indicator suggesting CHC more than HCC. GAD-MRI may provide greater accuracy than CT for distinguishing CHC from HCC. Interobserver agreement can be improved for both CT and MRI by analyzing LI-RADS imaging features.

• Journal of Korean Tunnelling and Underground Space Association
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• v.24 no.4
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• pp.341-353
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• 2022

Pipelines are buried in urban area, and the position (depth and orientation) of buried pipeline should be clearly identified before ground excavation. Although various geophysical methods can be used to detect the buried pipeline, it is not easy to identify the exact information of pipeline due to heterogeneous ground condition. Among various non-destructive geo-exploration methods, ground penetration radar (GPR) can explore the ground subsurface rapidly with relatively low cost compared to other exploration methods. However, the exploration data obtained from GPR requires considerable experiences because interpretation is not intuitive. Recently, researches on automated detection technology for GPR data using deep learning have been conducted. However, the lack of GPR data which is essential for training makes it difficult to build up the reliable detection model. To overcome this problem, we conducted a preliminary study to improve the performance of the detection model using finite difference time domain (FDTD)-based numerical analysis. Firstly, numerical analysis was performed with homogeneous soil media having single permittivity. In case of heterogeneous ground, numerical analysis was performed considering the ground heterogeneity using fractal technique. Secondly, deep learning was carried out using convolutional neural network. Detection Model-A is trained with data set obtained from homogeneous ground. And, detection Model-B is trained with data set obtained from homogeneous ground and heterogeneous ground. As a result, it is found that the detection Model-B which is trained including heterogeneous ground shows better performance than detection Model-A. It indicates the ground heterogeneity should be considered to increase the performance of automated detection model for GPR exploration.

• Journal of Korea Entertainment Industry Association
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• v.15 no.1
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• pp.27-38
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• 2021

Non-Contact society has arrived due to social distinctions by COVID 19 pandemic. The arrival of the era of non-contact is having a profound impact on educational activities as well as on our social and economic lives. In response to the pandemic situation universities and all other educational institutions have implemented non-contact online classes. In particular arts physical educations and other practical classes are experiencing many difficulties due to the limited environment caused by social distancing from COVID 19 pandemic. Vocal classes are undergoing a transition mainly from 1:1 individual face-to-face lessons or group teaching methods to the non-contact era of online teaching or lesson methods. It is necessary to look at the direction of non-face-to-face practical classes in preparation for accelerated educational innovation. Edu-tech, which innovates technology in the wake of the age of non-contact after COVID 19 pandemic is expected to begin in earnest at school sites in Korea which have remained in the traditional way of education. The purpose of this study is to effectively non-contact vocal instructional methods by cogitating the current state of higher practical education and vocal classes in Korea. In addition, This study conducted two components of satisfied instructions such as 'Priorlearning of monitoring of recorded singing', and 'Immediate analyzing of various vocal contents and supplementary lessons of music theory' with a research on the peos and cons of non-face-to-face vocal class. Over a period of time, The effective non-contact of vocal instructional methods is in need to supplement non-face-to-face vocal class problems and further research and system construction with non-face-to-face vocal class's pros and cons to construct high-quality lecture contents is warranted.

• The Journal of the Convergence on Culture Technology
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• v.8 no.2
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• pp.367-372
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• 2022

With the recent advent of the New Normal era, realistic technologies and non-contact technologies are receiving social attention. However, the hair styling field focuses on the direction of the hair itself, individual movements, and modeling, focusing on hair simulation. In order to create an improved practice environment and demand of the times, this study proposed a non-contact hair styling VR system. In the theoretical review, we studied the existing cases of hair cut research. Existing haircut-related research tend to be mainly focused on force-based feedback. Research on the interactive haircut work in the virtual environment as addressed in this paper has not been done yet. VR controllers capable of finger tracking the movements necessary for beauty enable selection, cutting, and rotation of beauty tools, and built a non-contact collaboration environment. As a result, we conducted two experiments for interactive hair cutting in VR. First, it is a haircut operation for synchronization using finger tracking and holding hook animation. We made position correction for accurate motion. Second, it is a real-time interactive cutting operation in a multi-user virtual collaboration environment. This made it possible for instructors and learners to communicate with each other through VR HMD built-in microphones and Photon Voice in non-contact situations.

• KIPS Transactions on Computer and Communication Systems
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• v.11 no.10
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• pp.373-380
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• 2022

This paper presents a deep learning based group synchronization that supports networked immersive interactions between remote users. The goal of group synchronization is to enable all participants to synchronously interact with others for increasing user presence Most previous methods focus on NTP-based clock synchronization to enhance time accuracy. Moving average filters are used to control media playout time on the synchronization server. As an example, the exponentially weighted moving average(EWMA) would be able to track and estimate accurate playout time if the changes in input data are not significant. However it needs more time to be stable for any given change over time due to codec and system loads or fluctuations in network status. To tackle this problem, this work proposes the Deep Group Synchronization(DeepGroupSync), a group synchronization based on deep learning that models important features from the data. This model consists of two Gated Recurrent Unit(GRU) layers and one fully-connected layer, which predicts an optimal playout time by utilizing the sequential playout delays. The experiments are conducted with an existing method that uses the EWMA and the proposed method that uses the DeepGroupSync. The results show that the proposed method are more robust against unpredictable or rapid network condition changes than the existing method.

• Journal of Intelligence and Information Systems
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• v.28 no.3
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• pp.161-183
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• 2022

Opinion polls have become a powerful means for election campaigns and one of the most important subjects in the media in that they predict the actual election results and influence people's voting behavior. However, the more active the polls, the more often they fail to properly reflect the voters' minds in measuring the effectiveness of election campaigns, such as repeatedly conducting polls on the likelihood of winning or support rather than verifying the pledges and policies of candidates. Even if the poor predictions of the election results of the polls have undermined the authority of the press, people cannot easily let go of their interest in polls because there is no clear alternative to answer the instinctive question of which candidate will ultimately win. In this regard, we attempt to retrospectively grasp public opinion on the 20th presidential election by applying the 'YouTube Analysis' function of Sometrend, which provides an environment for discovering insights through online big data. Through this study, it is confirmed that a result close to the actual public opinion (or opinion poll results) can be easily derived with simple YouTube data results, and a high-performance public opinion prediction model can be built.

• Education of Primary School Mathematics
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• v.25 no.4
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• pp.411-431
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• 2022

In this paper, implications for future curriculum compilation were sought by analyzing the process and results of compiling books for elementary school mathematics textbooks government published according to the 2015 revised curriculum. The 2015 revised elementary mathematics textbooks government published was operated with a systematic compilation system so that academia and school field experts across the country could demonstrate their expertise. As improvements in content, the unit and time to strengthen basic computational skills were increased, and the mathematical concept and principle introduction method and algorithm presentation method were improved, and the internal connection between contents was strengthened. The learning period was adjusted, such as moving and arranging contents that are difficult for students to understand to the upper semester or the upper grade. In the 1st and 2nd graders, the amount of reading was drastically reduced to suit the students' level of Korean, and sentences and vocabulary were improved, and instructions were briefly revised. As for editing and design improvements, illustrations of each unit's introduction and contextual pictures were presented in detail, and the characters in the textbook were consistently presented across all grades, giving children characters a role to actively participate in learning in the textbook. In the process of compiling, the media, the National Assembly, and civic groups raised opinions that sentences and vocabulary in first-year textbooks are more difficult than students' level of Hangeul education, that reducing textbooks makes it difficult for students to understand. Accordingly, efforts to improve textbook compilation and the results were viewed. Through the overall analysis as above, for future compilation of state-authored textbooks and certified textbooks, a plan to improve textbook compilation for students and teachers and a plan to operate compilation was proposed.

• Journal of Broadcast Engineering
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• v.26 no.4
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• pp.429-440
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• 2021

With the recent development of deep convolutional neural network learning, deep learning techniques applied to single image super-resolution are showing good results. One of the existing deep learning-based super-resolution techniques is RDN(Residual Dense Network), in which the initial feature information is transmitted to the last layer using residual dense blocks, and subsequent layers are restored using input information of previous layers. However, if all hierarchical features are connected and learned and a large number of residual dense blocks are stacked, despite good performance, a large number of parameters and huge computational load are needed, so it takes a lot of time to learn a network and a slow processing speed, and it is not applicable to a mobile system. In this paper, we use the residual dense structure, which is a continuous memory structure that reuses previous information, and the residual dense channel attention block using the channel attention method that determines the importance according to the feature map of the image. We propose a method that can increase the depth to obtain a large receptive field and maintain a concise model at the same time. As a result of the experiment, the proposed network obtained PSNR as low as 0.205dB on average at 4× magnification compared to RDN, but about 1.8 times faster processing speed, about 10 times less number of parameters and about 1.74 times less computation.

• Korean Association of Arts Management
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• no.53
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• pp.101-125
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• 2020

This paper presents the problems and improvement methods in estimating demand and benefit, which have been controversial in the feasibility study of building cultural facilities. Although there are justifications for supplying cultural facilities by expanding leisure time and increasing income, the economic burden from the insolvent operation after construction is high. Feasibility studies can prevent these problems in advance. In order to estimate the demand for cultural facilities, similar facilities were selected and the gravity model was used to estimate the demand. In the future, it is necessary to prepare the criteria for setting the reference facility to increase the accuracy of the demand estimation. In addition, in the case of cultural facilities constructed through feasibility study, it is necessary to induce and enforce the disclosure of operational data and information, and to establish a database so that it can be used as a reference facility for demand estimation in future feasibility study on cultural facility. Accurate benefit estimation requires multiple CVM surveys. In addition to the current CVM survey, this paper suggest that supplementary online non-face-to-face surveys is considered. Furthermore, this research suggests that the use of video media for explanation of alternative materials for cultural facilities to be constructed because the WTP may be excessive due to lack of alternatives for survey respondents in the current CVM survey.