• Information Systems Review
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• v.22 no.4
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• pp.185-203
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• 2020

With the advent of 4th industrial revolution, the manufacturing industry is converging with ICT and changing into the era of smart manufacturing. In the smart factory, all machines and facilities are connected based on ICT, and thus security should be further strengthened as it is exposed to complex security threats that were not previously recognized. To reduce the risk of security incidents and successfully implement smart factories, it is necessary to identify key security factors to be applied, taking into account the characteristics of the industrial environment of smart factories utilizing ICT. In this study, we propose a 'hierarchical classification model of security factors in smart factory' that includes terminal, network, platform/service categories and analyze the importance of security factors to be applied when developing smart factories. We conducted an assessment of importance of security factors to the groups of smart factories and security experts. In this study, the relative importance of security factors of smart factory was derived by using AHP technique, and the priority among the security factors is presented. Based on the results of this research, it contributes to building the smart factory more securely and establishing information security required in the era of smart manufacturing.

• Journal of Internet of Things and Convergence
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• v.5 no.1
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• pp.13-28
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• 2019

The purpose of this study is to identify degree of the personality type, resilience, self-efficacy, and workplace bullying Experience among nurses at General Hospital, to ascertain the correlation between them and to ascertain factors that may affect workplace bullying. This study collected data from October 22, 2018 to October 26, 2018 through structured questionnaires from 260 nurses working at General Hospital in Seoul City, and the collected data were analyzed by descriptive statistics, t-test, ANOVA, Pearson's correlation coefficients, and multiple regression analysis using SPSS / WIN 21.0. Analysis of differences in general characteristics showed that workplace bullying was significantly different according to witness of bullying, workplace satisfaction. Resilience was significantly different according to marital status, religion, final education, and workplace satisfaction. And self-efficacy was found to be different according to respondents' marital status, religion. The relationship between DISC personality type, workplace bullying, resilience and self-efficacy showed that workplace bullying had a negative correlation with resilience, and resilience showed a positive correlation with self-efficacy. As a result of the multiple regression analysis in order to identify the factors that, workplace satisfaction, witness of workplace bullying and the resilience was the factor affecting workplace bullying; workplace bullying was decreased as the resilience was higher.

• Journal of Internet of Things and Convergence
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• v.6 no.2
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• pp.57-64
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• 2020

The purpose of this study was to investigate the smartphone addition, which effects sleep, scholastic achievement and mental health in nursing students. The study subjects consisted of 185 nursing students in grade 2nd, 3rd who completed a informed consent. The date were collected from 13th of May to 3rd of June 2019. Data were analyzed descriptive statistics, Independent t-test, one-way ANOVA, Pearson correlation and stepwise multiple regression using by IBM SPSS/WIN 24.0. The score for Smart Phone addition was 36.99 out of 60. The more addicted a smartphone is, the more likely shows lower score of sleep (r=.19, p=.009), mental health (r=.34, p<.001) was found to be negative. The key variable for effecting nursing students' mental health was smartphone addiction, quality of sleep and time, level of grade. Among them, smartphone addiction (β=.27) was found to have the highest influence for all. Explanation that by influencing factors was 20.4%. Therefore, for the mental health of nursing students, various programs that can reduce smartphone addiction should be applied and sleep management should be done.

• Journal of Internet of Things and Convergence
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• v.6 no.4
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• pp.27-32
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• 2020

Along with the aging of the population, the number of dementia patients is increasing, and the social and economic burden is also increasing. Currently, the effective way to manage dementia patients is to identify patients with dementia early. However, in rural and island areas where medical staff are scarce, there is a problem that it is difficult to visit a hospital and get an early examination. Therefore, we propose a remote early detection system for dementia to solve the problems. The remote dementia early diagnosis system is a system that allows a patient to receive examination and treatment from a remote dementia expert using remote medical technology based on real-time image communication. The remote early diagnosis system for dementia consists of a local client system used by medical staff at health centers in the island, an image server that transmits, stores and manages images, and an expert client used by remote dementia experts. The local client subsystem satisfies the current medical law's remote collaboration by allowing the patient to use it with the health center's medical staff. In addition, expert clients are used by dementia experts, and can store/manage patient information, analyze patient history information, and predict the degree of dementia progression in the future.

• Journal of Internet of Things and Convergence
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• v.8 no.6
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• pp.99-113
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• 2022

This study is descriptive research to investigate the effects of COVID-19 stress, interpersonal (caregiver-patient) stress, and emotional labor on burnout in emergency room (ER) nurses during the COVID-19 pandemic. The data collection of this study was conducted from December 9 to 23, 2021 with ER nurses working at five tertiary general hospitals and general hospitals of Medical Center H. The data was collected with a questionnaire using tools measuring the subjects' general & job-related characteristics, COVID-19 stress, interpersonal(caregiver-patient) stress, emotional labor and burnout. The collected data was analyzed using the SPSS/WIN 25.0 statistical program for frequency analysis, descriptive statistical analysis, independent sample t-test, one-way ANOVA, Scheffé test, correlation analysis, and multiple regression analysis. The average score of COVID-19 stress in ER nurses was 3.64, interpersonal(caregiver-patient) stress 4.35, emotional labor 3.38, and burnout 3.44. As a result of analyzing differences according to general & job-related characteristics, burnout showed a significant difference according to gender, marital status, total clinical experience, and working organization. And burnout showed a significant positive correlation with COVID-19 stress, interpersonal stress and emotional labor. As a result of multiple linear regression analysis, regional emergency medical centers and local emergency medical centers among the work organization types, interpersonal stress, COVID-19 stress, and gender and the explanatory power was 28.6%. Through these results, we intend to provide basic data for the development of an intervention program to prevent burnout of emergency room nurses and improve nursing performance at the time of a new infectious disease pandemic.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.11-16
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• 2022

As an interest in the development of artificial intelligence(AI) technology in the water supply sector increases, we have developed an AI algorithm that can predict improvement decision-making ratings through repetitive learning using the data of pipe condition evaluation results, and present the most reliable prediction model through a verification process. We have developed the algorithm that can predict pipe ratings by pre-processing 12 indirect evaluation items based on the 2020 Han River Basin's basic plan and applying the AI algorithm to update weighting factors through backpropagation. This method ensured that the concordance rate between the direct evaluation result value and the calculated result value through repetitive learning and verification was more than 90%. As a result of the algorithm accuracy verification process, it was confirmed that all water pipe type data were evenly distributed, and the more learning data, the higher prediction accuracy. If data from all across the country is collected, the reliability of the prediction technique for pipe ratings using AI algorithm will be improved, and therefore, it is expected that the AI algorithm will play a role in supporting decision-making in the objective evaluation of the condition of aging pipes.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.85-91
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• 2022

In the IOT social environment, which emphasizes the convergence of science and technology and emotion, this study aims to explore the experience and meaning of the experience while participating in the design convergence class based on the operation of the Learning community of Chinese graduate students majoring in design. To this end, an implementation study was conducted by visualizing various research problems using a design-based inquiry method for 12 graduate students attending T University. The design-oriented convergence class was conducted in a small group with content that was deepened into a visual strategy by the expression technique given by the external environment and the learner's own inner motivation. In order to express the perspective and intention of the research problem in the research, the convergence design research expressed using various visual strategies such as metaphorical use and analysis of visual data in the research process and sensory approach to the research problem was presented in a form that expresses the creative thinking process. As a way of exploration, the teaching method of presenting results based on various experiences suggests changes in new teaching formats, practical knowledge sharing by instructors, and community participation by learning participants.

• Journal of Internet of Things and Convergence
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• v.8 no.4
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• pp.65-76
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• 2022

The purpose of this study aimed to identify the level of knowledge & preventive health behaviors related to COVID-19, self-efficacy, anxiety, and perceived stress of students at a nursing college and to investigate the correlation between them. The data were collected from 133 students at a nursing college in Seoul, Korea, from April 15, 2022 to May 10, 2022 through a Google online questionnaire. The data were analyzed using SPSS/WIN 25.0 to perform descriptive statistics, t-test, one-way ANOVA, Pearson's Correlation Coefficients. As a result of analyzing the difference according to general characteristics, preventive health behavior showed a significant difference according to the necessity of COVID-19 infection control education, and self-efficacy showed a difference according to the subjective health status. The degree of instability of the subjects was shown to have significant differences according to grade and subjective health status, and perceived stress showed significant differences according to subjective health status. The result of analyzing the correlation between preventive health behavior and knowledge, self-efficacy, anxiety, and perceived stress showed that there was no significant correlation, but self-efficacy had a significant negative correlation with anxiety and perceived stress, and anxiety had a significant positive correlation with perceived stress. The results of this study will be used as basic data for education programs and countermeasures to prevent COVID-19 infections.

• Journal of Internet of Things and Convergence
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• v.9 no.1
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• pp.89-95
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• 2023

In January 2007, the Remains Excavation and Investigation Team of the Ministry of National Defense was established, and full-scale excavation of remains was promoted. Currently, the scope of the excavation is being expanded to Baekma Hill within the DMZ, where fierce battles were fought during the Korean War. Now, many remains and remains are being excavated in Baekma hill. Most are in damaged condition. Therefore, in this study, the original form of the excavated remains was restored using 3D scanning and 3D modeling. This digital restoration method can be an alternative to compensate for the disadvantages of the manual method. Currently, various digital restorations using 3D technology are active in the field of cultural heritage. Digitally restored materials can be used as basic data for digital heritage. Based on this, various contents related to excavation of remains and patriots and veterans can be developed. Furthermore, if digital human restoration is made based on the excavated remains, it will be possible to reproduce the appearance of the dead.

• Journal of Internet of Things and Convergence
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• v.9 no.6
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• pp.63-73
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• 2023

The purpose of this study is to identify the level of learning flow, learning satisfaction, academic self-efficacy, academic achievement, and academic stress of nursing students who experienced non-face-to-face online lectures, and to investigate the correlation between variables and the factors affecting academic stress. The data of this study was collected from 143 students at a nursing college in Seoul, through a Google online questionnaire from September 1, 2023 to September 25, 2023, and descriptive statistics, Student's t-test, analysis of variance, Pearson's Correlation, and linear multiple regression were conducted using SPSS Statistics 25.0. Following an analysis of the difference according to general characteristics, academic stress showed significant difference according to Motivation for applying to department(F=4.465, p=.005) and Major satisfaction(F=36.499, p=.000) of the subjects. The result of analyzing the correlation academic stress was negatively correlated with learning flow (r=-.464, p<.010), academic self-efficacy (r=-.522, p<.010), and academic achievement (r=-.379, p<.010), but learning satisfaction was not correlated with academic stress. Variables affecting academic stress were major satisfaction (𝛽=.367, p<.01), learning flow (𝛽=-.186, p<.05), and academic self-efficacy (𝛽=-.241, p<.05), and the explanatory power for academic stress was 40%. The results of this study can be used as basic data for intervention programs for relieving academic stress of nursing students.