• Journal of Industrial Convergence
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• v.21 no.1
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• pp.175-185
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• 2023

Recently, novel viral infections such as COVID-19 have spread and pose a serious public health problem. In particular, these diseases have a fatal effect on the elderly, threatening life and causing serious social and economic losses. Accordingly, applications such as telemedicine, healthcare, and disease prevention using the Internet of Things (IoT) and artificial intelligence (AI) have been introduced in many industries to improve disease detection, monitoring, and quarantine performance. However, since existing technologies are not applied quickly and comprehensively to the sudden emergence of infectious diseases, they have not been able to prevent large-scale infection and the nationwide spread of infectious diseases in society. Therefore, in this paper, we try to predict the spread of infection by collecting various infection information with regional limitations through a virus disease information collector and performing AI analysis and severity matching through an AI broker. Finally, through the Korea Centers for Disease Control and Prevention, danger alerts are issued to the elderly, messages are sent to block the spread, and information on evacuation from infected areas is quickly provided. A realistic elderly support system compares the location information of the elderly with the information of the infected area and provides an intuitive danger area (infected area) avoidance function with an augmented reality-based smartphone application. When the elderly visit an infected area is confirmed, quarantine management services are provided automatically. In the future, the proposed system can be used as a method of preventing a crushing accident due to sudden crowd concentration in advance by identifying the location-based user density.

• Journal of the Korean Society of Marine Environment & Safety
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• v.28 no.7
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• pp.1120-1128
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• 2022

In the event of a marine accident, the longer the exposure time to the sea increases, the faster the chance of survival decreases. However, because the search area of the sea is extremely wide compared to that of land, marine object detection technology based on the sensor mounted on a satellite or an aircraft must be applied rather than ship for an efficient search. The purpose of this study was to rapidly detect an object in the ocean using a hyperspectral image sensor mounted on an aircraft. The image captured by this sensor has a spatial resolution of 8,241 × 1,024, and is a large-capacity data comprising 127 spectra and a resolution of 0.7 m per pixel. In this study, a marine object detection model was developed that combines a seawater identification algorithm using DBSCAN and a density-based land removal algorithm to rapidly analyze large data. When the developed detection model was applied to the hyperspectral image, the performance of analyzing a sea area of about 5 km² within 100 s was confirmed. In addition, to evaluate the detection accuracy of the developed model, hyperspectral images of the Mokpo, Gunsan, and Yeosu regions were taken using an aircraft. As a result, ships in the experimental image could be detected with an accuracy of 90 %. The technology developed in this study is expected to be utilized as important information to support the search and rescue activities of small ships and human life.

• KIPS Transactions on Software and Data Engineering
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• v.12 no.2
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• pp.59-76
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• 2023

Since the COVID-19 era, the rise in apartment prices has been unconventional. In this uncertain real estate market, price prediction research is very important. In this paper, a model is created to predict the actual transaction price of future apartments after building a vast data set of 870,000 from 2015 to 2020 through data collection and crawling on various real estate sites and collecting as many variables as possible. This study first solved the multicollinearity problem by removing and combining variables. After that, a total of five variable selection algorithms were used to extract meaningful independent variables, such as Forward Selection, Backward Elimination, Stepwise Selection, L1 Regulation, and Principal Component Analysis(PCA). In addition, a total of four machine learning and deep learning algorithms were used for deep neural network(DNN), XGBoost, CatBoost, and Linear Regression to learn the model after hyperparameter optimization and compare predictive power between models. In the additional experiment, the experiment was conducted while changing the number of nodes and layers of the DNN to find the most appropriate number of nodes and layers. In conclusion, as a model with the best performance, the actual transaction price of apartments in 2021 was predicted and compared with the actual data in 2021. Through this, I am confident that machine learning and deep learning will help investors make the right decisions when purchasing homes in various economic situations.

• Journal of Korean Library and Information Science Society
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• v.53 no.4
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• pp.143-162
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• 2022

Altmetrics is an alternative impact evaluation index that evaluates the social interest in the research performance of individuals or institutions in universities, research institutions, and research fund support institutions. This study empirically analyzed what kind of attention a papers of domestic library and information science researchers published in an international academic journal was receiving in the international community using Altmetric explorer. As a result of the analysis, 230 papers were tracked. The average Altmetric Attention Score (AAS) was 6.63, but there were 2 papers that received overwhelming attention (over 170 points) as they were mentioned in news report and Twitter. Second, there was a tendency for high AAS to appear in cases where a domestic researcher participated as a co-author and the main author belonged to an overseas institution, and in the case where the research funds were supported by foreign government agencies. In addition to the field of the library information science or information system, the papers classified as the field of public health service and education showed high AAS, and it was confirmed that these papers were published in the journals of various fields such as life science. Finally, it was confirmed that there was a weak correlation of r =0.25 between the AAS and the number of citations of the analyzed paper, but a strong correlation of r =0.68 between the number of Mendeley readers and the number of citations.

• Journal of Adhesion and Interface
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• v.24 no.1
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• pp.17-25
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• 2023

In this study, waste polyethylene terephthalate (PET) was recycled to produce a support and then polyetherimide (PEI) was used for environmentally friendly organic solvent nanofiltration. The prepared composite membrane was first prepared by electrospinning a PET support, then casted on the support using PEI having excellent solvent resistance, and organic solvent nanoparticles using a Non-solvent Induced Phase Separation (NIPS) method. A filtration membrane was prepared. First, the fiber diameter and tensile strength of the PET scaffold prepared prior to membrane fabrication were identified through morphology analysis, and the optimal scaffold for the organic solvent nanofiltration membrane was identified. Afterward, the PET/PEI composite membrane prepared was checked for the DEA removal rate of Congo red having a molecular weight of 697 g/mol in ethanol to understand the performance as an organic solvent nanofiltration membrane according to the concentration of PEI. Finally, the removal rate of Congo red was 90% or more.

• The Korean Journal of Coaching Psychology
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• v.7 no.1
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• pp.1-31
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• 2023

The purpose of this study is to verify the mediating effect of adaptive cognitive emotion regulation and the moderating effect of supervisor's emotional regulation in the relationship between the emotion recognition and negative feedback acceptance of employees. The data was collected from 273 non-managerial workers in various domestic companies. Confirmatory factor analysis was conducted with AMOS 22 to verify the reliability and validity of the measurement tool, and the mediating and moderating effects were examined using SPSS Process Macro to verify the hypothesis. The results of this study are summarized as follows. First, emotion recognition of employees indirectly affects the acceptance of negative feedback through adaptive cognitive emotional regulation. Second, the effect of emotion recognition on negative feedback acceptance is moderated by supervisor's emotion regulation. Specifically, it was confirmed that when the supervisor's emotional control is low, the relationship between emotional recognition and negative feedback acceptance becomes stronger. Based on the results of the study, it was confirmed that the level of awareness of oneself and others' emotions was psychological process of accepting performance-related feedback, and the importance of supervisor's emotional regulation in positively accepting negative feedback. Finally, based on the research results, the academic significance of this study, implications in coaching practice, limitations, and future research were discussed.

• Journal of Korea Port Economic Association
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• v.38 no.4
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• pp.45-64
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• 2022

Due to the recent and rapid globalization, logistics outsourcing has expanded globally and is seen as a means of creating a robust logistics system. However, many businesses continue to have difficulties with their logistics outsourcing contracts, which compels them to reinstate the logistics function for internal management. This study aims to investigate how organizational capabilities of logistics service providers (LSPs), notably flexibility, integration, innovation, and technological capabilities, impact on the logistics outsourcing success in Ugandan food processing firms. Using a structured questionnaire survey, cross-sectional data collected from 211 food processing firms in Kampala - Uganda were analyzed by partial least squares-structural equation modeling (PLS-SEM) using SmartPLS 3.3.7 software to examine the theorized relationships. The study findings revealed that whereas the technological and innovation capabilities positively and significantly influence logistics outsourcing success, the effects of flexibility and integration capabilities were insignificant. Additionally, the importance-performance map analysis (IPMA) reveals that the technological capability is a priority capability, followed by the innovation capability if logistics outsourcing success is to be achieved. Conversely, flexibility and integration capabilities are of low priority.

• Korean Journal of Culture and Social Issue
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• v.25 no.4
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• pp.249-277
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• 2019

This study examined the involuntary retirement of executives in conglomerates based on Williams' need-threat temporal model of ostracism(2009), which explains the psychology of individuals facing social exclusion in the stages of reflexive, reflective and resignation. In-depth interviews were conducted on 15 retirees from conglomerates, and their contents were used for phenomenological method of analysis. As a result, in the reflexive stage of need-threat temporal model of ostracism, they experienced cognitive panic and emotional panic immediately following retirement. Due to an unexpected shocking experience of retirement notice, they experienced cognitive numbness first, and repressed the following negative feelings cognitively to hide them. In the reflective stage, retirees dream of 'a complete restoration to their best performance in the past', but as such expectation fails, they don't adjust to the reality more due to 'unrealistic thought', 'self-deception', and 'shift responsibility'. In resignation stage, a long-term failure to satisfy the desire led them to experience a sense of defeat and helplessness. Such results were reviewed and compared to Williams' need-threat temporal model of ostracism, and the implications of such result on the nation, companies and retirees in terms of the response to retirement.

• Journal of Intelligence and Information Systems
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• v.29 no.3
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• pp.19-36
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• 2023

As advances in information and communication technology have made it easier for anyone to produce and disseminate information, a new problem has emerged: fake news, which is false information intentionally shared to mislead people. Initially spread mainly through text, fake news has gradually evolved and is now distributed in multimedia formats. Since its founding in 2005, YouTube has become the world's leading video platform and is used by most people worldwide. However, it has also become a primary source of fake news, causing social problems. Various researchers have been working on detecting fake news on YouTube. There are content-based and background information-based approaches to fake news detection. Still, content-based approaches are dominant when looking at conventional fake news research and YouTube fake news detection research. This study proposes a fake news detection method based on background information rather than content-based fake news detection. In detail, we suggest detecting fake news by utilizing related video information from YouTube. Specifically, the method detects fake news through CNN, a deep learning network, from the vectorized information obtained from related videos and the original video using Doc2vec, an embedding technique. The empirical analysis shows that the proposed method has better prediction performance than the existing content-based approach to detecting fake news on YouTube. The proposed method in this study contributes to making our society safer and more reliable by preventing the spread of fake news on YouTube, which is highly contagious.

• Journal of the Korean Applied Science and Technology
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• v.40 no.3
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• pp.521-533
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• 2023

The purpose of this study was to investigate the effect on the clinical competence by the face-to-face, non-face-to-face clinical practice stress and the stress coping. A survey was conducted among nursing students of university in Seoul and Chungcheong City from June 10 to July 10, 2021. 201 copies were included in the final data analysis, Pearson's correlation coefficient and hierarchical regressions was used. As a result, in the first stage, nursing students grades, major satisfaction, and face-to-face practice satisfaction explained 19.4% of their clinical performance ability, in the second step, stress coping was added to increase explanatory power by 19.6% allowing a total of 39.0% of randomness to be explained. Therefore, this study could be used as a basic data for the counseling, development, and education programs for stress coping to increase clinical competence.