• KIPS Transactions on Computer and Communication Systems
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• v.10 no.6
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• pp.181-190
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• 2021

Sports activities conducted by multiple people are difficult to proceed in a situation where a widespread epidemic such as COVID-19 is spreading, and this causes a lack of physical activity in modern people. This problem can be overcome by using online exercise contents, but it is difficult to check detailed postures such as during face-to-face exercise. In this study, we present a model that detects posture and tracks movement using IT system for better non-face-to-face exercise content management. The proposed motion tracking model defines a body model with reference to motion analysis methods widely used in physical education and defines posture and movement accordingly. Using the proposed model, it is possible to recognize and analyze movements used in exercise, know the number of specific movements in the exercise program, and detect whether or not the exercise program is performed. In order to verify the validity of the proposed model, we implemented motion tracking and exercise program tracking programs using Azure Kinect DK, a markerless motion capture device. If the proposed motion tracking model is improved and the performance of the motion capture system is improved, more detailed motion analysis is possible and the number of types of motions can be increased.

• The Journal of the Korea Contents Association
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• v.21 no.5
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• pp.607-626
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• 2021

The purpose of this study is to verify the effect of leader's managerial coaching behavior on employees' innovative behavior and to confirm the mediating effect of entrepreneurship and the moderating effect of LMX(Leader-Member Exchange). Hypotheses were established based on prior research and variety of theories including broaden-build theory and self-efficacy theory. The survey was accessed via the online, 279 employees over 20's or older, who have worked in various domestic organizations were participated. SPSS 25.0 and AMOS 25.0 were used to verify the reliability and validity of the collected data, and the hypothesis was analyzed by SPSS process macro 3.0. The study found that leader's managerial coaching behavior has positive effects on both employees' entrepreneurship and innovative behavior and that entrepreneurship has mediating effect between leader's managerial coaching behavior and an employees' innovative behavior. The results of this study suggested leader's managerial coaching behavior is a prominent factor in facilitating innovative behavior among employees. Implications include an organizational requirement to develop systems for initiating effective managerial coaching behavior in leadership, and for improvement of both entrepreneurship and LMX among employees.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.697-705
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• 2021

Currently, roadway operation agencies provide interior zones of tunnels with emergency information including crash, fire, and vehicles' stop, through state-of-the-art technologies such as variable message signs and radio-based broadcast systems. However, when coping with an emergency in tunnel interior zones, such information could be too late for drivers to access. A radio-based emergency alert system at the access zones of freeway tunnels, on the other hand,could be a good alternative for solving this problem. Therefore, the objective of this study is to assess user acceptability of such an alternative system. To carry out this study, an online survey was conducted on 762 drivers, and the survey results were analyzed using a structural equation modeling to identify factors affecting acceptability of the proposed system. As a result, driver characteristics such as age group, driving frequency, and driving career, utilization of conventional traffic information, and usefulness of conventional traffic information have a positive impact on acceptability. It is expected that the findings of the study will be a basis to effectively address and deploy a new emergency alert system at the access zones of freeway tunnels.

• The Journal of Society for e-Business Studies
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• v.27 no.1
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• pp.15-28
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• 2022

Due to recent expansion of online market such as clothing, utilizing customer review has become a major marketing measure. User review has been used as a tool of analyzing sentiment of customers. Sentiment analysis can be largely classified with machine learning-based and lexicon-based method. Machine learning-based method is a learning classification model referring review and labels. As research of sentiment analysis has been developed, multi-modal models learned by images and video data in reviews has been studied. Characteristics of words in reviews are differentiated depending on products' and customers' categories. In this paper, sentiment is analyzed via considering review data and metadata of products and users. Gated Recurrent Unit (GRU), Long Short-Term Memory (LSTM), Self Attention-based Multi-head Attention models and Bidirectional Encoder Representation from Transformer (BERT) are used in this study. Same Multi-Layer Perceptron (MLP) model is used upon every products information. This paper suggests a multi-modal sentiment analysis model that simultaneously considers user reviews and product meta-information.

• Journal of Industrial Convergence
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• v.20 no.6
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• pp.83-92
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• 2022

This study is to investigate the factors affecting post-traumatic stress disorder (PTSD) symptoms among hospital nurses during the COVID-19 pandemic in Korea. Cross-sectional, descriptive design is used in this study. Data collection was completed through an online self-administered survey from December 2020 to January 2021 among 180 registered nurses dealing with COVID-19 patients at hospitals. This survey includes socio-demographic questions, including a 22-item PTSD questionnaire, a 14-item type D personality questionnaire, a 25-item resilience questionnaire, and a 23-item Social Support Scale questionnaire. 56.1% of the subjects in this study were at risk of PTSD. In the high-risk group for PTSD, resilience and social support were lower than those in the low-risk group for PTSD. But there was no statistically significant difference in both variables (resilience t=0.21, p=.836, social support t=1.07, p=.287). However, education (OR = 2.23, p= .041) and type D personality (OR = 3.67, p < .001) were significant factors for PTSD symptoms. The results of the study can be utilized to recognize PTSD in nurses by identifying factors influencing PTSD during epidemics such as COVID-19, and to apply management systems such as psychological programs to help overcome them.

• 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.

• Journal of Korea Entertainment Industry Association
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• v.14 no.7
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• pp.397-408
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• 2020

This study conducted a focus group interview with 15 college freshman from J college to find out their experiences in non-face-to-face lectures with COVID-19. The contents of the interview were recorded and conducted, and the meaning was analyzed according to the focus group interview procedure through repeated listening. Components were 'Operation of non-face-to-face lectures in unprepared situations', 'Loss of orientation in lectures and departure from learning', 'One way listening', 'The convenience of taking a lectures'. The experience of 'Operating non-face-to-face lectures in unprepared situations' included the start of mixed non-face-to-face lectures, cumbersome and inconvenient online systems, and the demand for tuition refunds. The experience of 'Loss of orientation in lectures and departure from learning' has experienced difficulty in concentrating on lectures, Deficiency in the degree of recognition of learning content, and burden of assignments and exams. The experience of 'One way listening' has experienced lack of interaction between professors and learners and non reflection of liveliness in the field. Finally, participants experienced satisfaction with being able to lectures and repeat lectures at anytime and anywhere they wanted with the convenience of taking lectures. Based on this study, participants called for improvements in the quality lecture contents and interaction between professors and learners, and it is thought that universities will need administrative and financial support and education design and system construction to construct high-quality lecture contents.

• The Korean Journal of Archival Studies
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• no.72
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• pp.165-203
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• 2022

Many people create records in digital space, and the amount of digital records left after individual dies has increased. The digital record left by the deceased is different from the record heritage that has physical substances. In many cases, the records of the deceased not just belong to the deceased, and many deceased did not explicitly disclose their online accounts and method of dispose of digital records during their lifetime, so this problem may lead to problems of inheritance to the bereaved family. In addition, digital records may be neglected or deleted after a person's death due to software problems, specific platform's terms of use, account deletion by bereaved family, etc. This leads to the problem that daily records, which are important clues to the social aspects at the time, are easily lost. Several studies have revealed that individuals are interested in preserving their digital records, but do not know how to do it, so they are benign neglect. For this reason, it is necessary to pay attention to personal digital records and personal digital legacy, and to prepare related policies and plans. Accordingly, this study analyzes problems related to the management of digital records after an individual's death, related to laws and systems, the status and policies of platforms and industries, the status of personal record management, etc. Various solutions were suggested, such as a need for enactment for digital personal record management act, platform's explicit policy for individual's post-mortem records, digital records management plan for archival institutions, individual's a preemptive management plan for his/her own records, and a method for writing a will related to digital account information.

• 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.

• Journal of Internet Computing and Services
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• v.23 no.4
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• pp.21-33
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• 2022

Recently, the recommender system has been widely used in various fields such as movies, music, online shopping, and social media, and in the meantime, the recommender model has been developed from correlation analysis through the Apriori model, which can be said to be the first-generation model in the recommender system field. In 2005, many models have been proposed, including deep learning-based models, which are receiving a lot of attention within the recommender model. The recommender model can be classified into a collaborative filtering method, a content-based method, and a hybrid method that uses these two methods integrally. However, these basic methods are gradually losing their status as methodologies in the field as they fail to adapt to internal and external changing factors such as the rapidly changing user-item interaction and the development of big data. On the other hand, the importance of deep learning methodologies in recommender systems is increasing because of its advantages such as nonlinear transformation, representation learning, sequence modeling, and flexibility. In this paper, among deep learning methodologies, RNN, CNN, and GAN-based models suitable for sequential modeling that can accurately and flexibly analyze user-item interactions are classified, compared, and analyzed.