• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.11
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• pp.435-440
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• 2018

This study was conducted to compare the effectiveness of the newly developed electrode pad of ECG with that of a conventional method. To accomplish this, participants who performed both methods on a 46 year old male model were queried and their satisfaction, time, and accuracy were measured by a specialist from 01/06/2018 to 15/06/2018. In the conventional method, a newly developed single pad employing a 12-lead ECG and SIM-Tree was employed. There were 104 total participants in this study (44% medical members). Evaluation of the total procedure time revealed that SIM (mean 65.39 seconds) was more rapid than C (mean 94.38 seconds) (p<0.05). When we evaluated the response after all process, satisfaction with SIM (mean 97.69 seconds) was greater than that with C (mean 68.5 seconds) (p<0.05). Moreover, the intra-class correlation coefficient (ICC) was 0.959 and accuracy was very high (p<0.05). In conclusion, the SIM-Tree was very effective based on procedure time, satisfaction and accuracy when compared with conventional methods.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.1
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• pp.184-188
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• 2019

The purpose of this paper is to investigate the effectiveness of Google Classroom (GC) and to examine the satisfaction of professors using GC as an online environment at a Mongolian University. Fourteen professors designed the lecture model and provided lessons using GC at D University for four weeks. GC provides new learning opportunities that are more efficient than face-to-face learning, because it can overcome the limitations of time and space. The results of the survey conducted with the professors who participated in the class to explore the effectiveness of GC show that the system provides: cooperation: 100% (strongly agree=7, Agree=7), personal learning opportunity: 100% (strongly agree=10, Agree=4), ease in learning: 100% (strongly agree=11, Agree=3), suitability: 100% (strongly agree=8, Agree=6), feedback opportunities: 100% (strongly agree=7, Agree=7), connection: 100% (strongly agree=7, Agree=7), accessibility: 100% (strongly agree=7, Agree=7), learning effectiveness: 100% (strongly agree=9, Agree=5), paperless experience: 100% (strongly agree=8, Agree=6). The professors who attended the class reacted positively to the use of GC, proving that the application of GC at this Mongolian University was appropriate and efficient. The use of GC is expected to help educational institutions strengthen and improve online learning, especially by breaking from traditional learning, and opening new paths for professors and students in Mongolia.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.3
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• pp.438-446
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• 2019

This study examined the effects of non - personality behavior of supervisors on the job exhaustion and organizational effectiveness of nurses. A survey in the form of a questionnaire was completed by 250 nurses working at three general hospitals located in Seoul and Gyeonggi from September 20 to October 31, 2014. The collected data were analyzed by frequency analysis, factor analysis, correlation, and linear regression analysis using the SPSS program. The results of the analysis were as follows. First, the effects of the non-personality behavior of supervisors on the job exhaustion of nurses were statistically significant. Second, the effects of non-personality behavior of supervisors on the organizational effectiveness of nurses were statistically significant in job satisfaction, organizational commitment, and organizational citizenship behavior. Third, the effects of job exhaustion on organizational effectiveness of nurses was statistically significant for job satisfaction, job exhaustion, and organizational citizenship behavior. Because the non-personality behavior of a supervisor can have a negative effect on the exhaustion of a nurse's job and the organizational effectiveness, there should be a wide range of human resources and effective task allocations in a hospital to reduce job burnout and increase job satisfaction. In addition, it is necessary for the boss to develop desirable leadership education, appropriate modeling, and reduce their negative influence in the workplace.

• KIPS Transactions on Computer and Communication Systems
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• v.9 no.12
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• pp.291-306
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• 2020

Nowadays, Data-Network-AI (DNA)-based intelligent services and applications have become a reality to provide a new dimension of services that improve the quality of life and productivity of businesses. Artificial intelligence (AI) can enhance the value of IoT data (data collected by IoT devices). The internet of things (IoT) promotes the learning and intelligence capability of AI. To extract insights from massive volume IoT data in real-time using deep learning, processing capability needs to happen in the IoT end devices where data is generated. However, deep learning requires a significant number of computational resources that may not be available at the IoT end devices. Such problems have been addressed by transporting bulks of data from the IoT end devices to the cloud datacenters for processing. But transferring IoT big data to the cloud incurs prohibitively high transmission delay and privacy issues which are a major concern. Edge computing, where distributed computing nodes are placed close to the IoT end devices, is a viable solution to meet the high computation and low-latency requirements and to preserve the privacy of users. This paper provides a comprehensive review of the current state of leveraging deep learning within edge computing to unleash the potential of IoT big data generated from IoT end devices. We believe that the revision will have a contribution to the development of DNA-based intelligent services and applications. It describes the different distributed training and inference architectures of deep learning models across multiple nodes of the edge computing platform. It also provides the different privacy-preserving approaches of deep learning on the edge computing environment and the various application domains where deep learning on the network edge can be useful. Finally, it discusses open issues and challenges leveraging deep learning within edge computing.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.3
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• pp.181-189
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• 2021

This study uses a structural equation model for the turnover intention of female nurses performing shift work at a tertiary hospital. Data collection was conducted from November 11 to December 20, 2015, and 283 samples were included in the final analysis. As a result, 12 of the 19 hypotheses of the final model were supported. It was confirmed that external employment opportunities, nursing professional value, nursing organizational culture, job stress, job satisfaction, organizational commitment, and burn out accounted for 47.8% of the turnover intention. Burn out(+) and organizational commitment(-)had direct effects on turnover intentions, and nursing professional, relationship-oriented nursing organizational culture, and job stress showed indirect effects. Therefore, in order to reduce the turnover intention of female nurses working in shifts at tertiary hospitals, it is necessary to prevent burnout, increase organizational commitment, and job satisfaction. For this purpose, measures to strengthen relation-oriented nursing organizational culture and nursing professional intuition are required. In terms of hospital manpower management, the institutional arrangements of hospitals that enable flexible working hours adjustment, mutually respectful relationship-centered organizational culture, education, and policy support to reinforce nursing professional intuition, and the institutional system of hospitals to work with pride should be implemented.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.21 no.5
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• pp.117-132
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• 2022

The purpose of this study is to analyze factors that affect road users' acceptance of fully autonomous vehicles (level 4 or higher). A survey was done with drivers of general cars and pedestrians who share roads with fully autonomous vehicles. Five acceptability factors were selected: trust towards technology, compatibility, policy, perceived safety, and perceived usefulness. The effect on behavioral intention was analyzed using structural equation modeling (SEM). The perceived safety and trust towards technology were found to be very important in the acceptance of fully autonomous vehicles, regardless of the respondent, and policy was not influential. Compatibility and perceived usefulness were particularly influential factors for drivers. In order to improve the acceptance by road users, securing technical completeness of fully autonomous vehicles is important. Certification and evaluation of the safe driving ability of fully autonomous vehicles should be thoroughly performed, and based on the results, it is necessary to improve the perception by road users. It is necessary to positively recognize fully autonomous vehicles through education and publicity for road users and to support their smooth interaction.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.97-109
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• 2022

This study analyzed successful cases of using metaverse in a reality where interest in metaverse is increasing. The use of Metaverse is mainly used in companies to explore its industrial potential or government agencies strive for policy support, but the possibility of application in educational institutions has another meaning. We tried to find the success factors and future implications by analyzing actual cases of using metaverse at university entrance ceremonies. As a result of analyzing the case of S University in Asan, Chungcheongnam-do's metaverse entrance ceremony, it was determined that the university's first metaverse entrance ceremony could be counted as a very meaningful success story. Specifically, on the technical level, it stood out that the existing metaverse technology and the new technology for the event were properly harmonized. At the organizational level, it is meaningful that the internal organization's resources were efficiently utilized based on previous experiences. On the environmental level, the COVID19 environment and the MZ generation. It was analyzed that the social change of going to college contributed to the planning and success of the metaverse entrance ceremony. As a result, it is judged that the successful use of the resources possessed by a clear goal is the success factor of the metaverse entrance ceremony.

• The Journal of the Korea Contents Association
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• v.22 no.5
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• pp.56-66
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• 2022

Due to the recent increase in information encountered on social media, algorithm-based recommendation formats selectively provide information based on user information, which often causes a filter bubble effect by an Echo Chamber. Eco-chamber refers to a phenomenon in which beliefs are amplified or strengthened by communication only in an enclosed system, and filter bubbles refer to a phenomenon in which information providers provide customized information according to users' interests, and users encounter only filtered information. The purpose of this study is to propose a method of efficiently selecting information as a way to improve the filter bubble phenomenon by such an echo chamber. The research progress method analyzed recommended algorithms used on YouTube, Facebook and Amazon. In this study, humanities solutions such as training critical thinking skills of social media users and strengthening objective ethical standards according to self-preservation laws, and technical solutions of model-based cooperative filtering or cross-recommendation methods were presented. As a result, recommended algorithms should continue to supplement technology and develop new techniques, and humanities should make efforts to overcome cognitive dissonance and prevent users from falling into confirmation bias through critical thinking training and political communication education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.141-156
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• 2023

The advancement of Information and Communication Technology (ICT), along with the expansion of government and private investment in startup discovery and funding, has led to the emergence of startups seeking to generate outstanding results based on innovative ideas. As successful startups serve as role models, the number of aspiring entrepreneurs preparing to launch their own startups continues to increase. However, unlike entrepreneurs who challenge themselves with serial entrepreneurship after experiencing success, early-stage startups face various challenges such as team building, technology development, and fundraising. Accelerators play a dual role of mentor and investor by providing education, mentoring, consulting, network connection, and initial investment activities to help startups overcome various challenges they face and facilitate their growth. This study investigated whether there is a correlation between the characteristics of startups and their entrepreneurial performance, and analyzed whether accelerators mediate the relationship between startup characteristics and entrepreneurial performance. A total of 11 hypotheses were proposed, and a survey was conducted on 302 startup founders and employees located across the country, including the metropolitan area, for empirical research. SPSS 23.0 and Amos 23.0 were used for statistical analysis. Through this study, it was found that factors such as innovation, organizational culture, financial characteristics, and learning orientation among the characteristics of startups, rather than having a direct impact on entrepreneurial performance, are linked to entrepreneurial performance through the role of accelerators. By analyzing the impact factors of startup characteristics on entrepreneurial performance, this study presents research on the role of accelerators and provides institutional improvements. It is expected to contribute to the expansion of investment and differentiated acceleration programs, enabling startups to seize the market and grow stably in the market.

• Journal of Internet Computing and Services
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• v.25 no.2
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• pp.79-92
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• 2024

ChatGPT, as a representative chatbot leveraging generative artificial intelligence technology, is used valuable not only in scientific and technological domains but also across diverse sectors such as society, economy, industry, and culture. This study conducts an explorative analysis of user sentiments and needs for ChatGPT by examining global social media discourse on Reddit. We collected 10,796 comments on Reddit from December 2022 to August 2023 and then employed keyword analysis, sentiment analysis, and need-mining-based topic modeling to derive insights. The analysis reveals several key findings. The most frequently mentioned term in ChatGPT-related comments is "time," indicative of users' emphasis on prompt responses, time efficiency, and enhanced productivity. Users express sentiments of trust and anticipation in ChatGPT, yet simultaneously articulate concerns and frustrations regarding its societal impact, including fears and anger. In addition, the topic modeling analysis identifies 14 topics, shedding light on potential user needs. Notably, users exhibit a keen interest in the educational applications of ChatGPT and its societal implications. Moreover, our investigation uncovers various user-driven topics related to ChatGPT, encompassing language models, jobs, information retrieval, healthcare applications, services, gaming, regulations, energy, and ethical concerns. In conclusion, this analysis provides insights into user perspectives, emphasizing the significance of understanding and addressing user needs. The identified application directions offer valuable guidance for enhancing existing products and services or planning the development of new service platforms.