• Journal of Digital Convergence
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• v.20 no.2
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• pp.511-523
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• 2022

This study was conducted using non-face-to-face care technology for the elderly with mild dementia and the physically weak living in the community, as various methods of care for the elderly have been raised due to the prolonged COVID-19. The purpose of this study is a similar experimental study before and after the inequality control group to compare cognitive function, daily living activities, and the degree of depression by applying an AI robot integrated management program using. The data was collected from June 4 to September 17, 2021, and the survey results of 17 people in the experimental group and 18 in the control group were analyzed using the SPSS 25.0 program. As a result of the study, the experimental group was significant in language function, activities of daily living, and depression. In particular, the results showed a decrease in moderate to severe depression and mild depression. Cognitive function was significant with long-term care grade and daily living activity with family living together. Therefore, if such non-face-to-face care technology is introduced to the elderly care field in the 'With Corona era', it is thought that it will contribute to cognitive function training and depression reduction of the elderly.

• Journal of the Korean Applied Science and Technology
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• v.40 no.1
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• pp.113-122
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• 2023

This study attempted to investigate the effects of experience of non-contact lectures on learning flow against college students majoring in cosmetology and provide basic data to beauty education industry in the with-COVID-19 era. For this, a self-administered questionnaire survey was performed against 300 college students majoring in cosmetology from June 7 to 21, 2022. A total of 286 copies were collected and used for final analysis. The collected data were analyzed by frequency analysis, factor analysis, exploratory factor analysis, descriptive statistics, correlation analysis and multiple regression analysis, using SPSS 21.0, and the results found the followings: According to analysis of non-contact lecture experience factors, two course satisfaction factors were obtained. In learning flow, learning pleasure and learning flow were found. Specifically, class activities had a positive influence on 'learning pleasure (𝛽=.279, p<.007)' and 'learning flow (𝛽=.221, p<.031)' with statistical significance (p<.05). In addition, course satisfaction revealed a positive effect on 'learning flow (𝛽=.223, p<.041)' with statistical significance (p<.05). The above results confirm that experience of non-contact lectures affects learning flow. Therefore, it is anticipated that there would be more efforts to seek an efficient non-contact learning plan in this new era.

• Korean Journal of Legislative Studies
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• v.26 no.3
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• pp.125-159
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• 2020

Amid global pandemic of covid-19, Korean government's response has drawn wide attention among social scientists as well as medical studies. The role of Korean state and civil society has attracted particular attention among others. Yet, this paper criticizes extant studies on Korean case which focus on the extensive intervention of the strong state and subjective attitude of Korean citizens in coping with covid-19. The concept of the strong state lacks social scientific specification and subjective citizens do not match with Korean realities. This article argues that Korean state's capacity in collecting and mobilizing digital data may offer better understanding for the successful responses to the pandemic. First, Korean state is the ultimate coordinator in collecting, analyzing and applying big data about the expansion of covid-19 with its huge network of dataveillance. Also, such role has been largely based upon relevant legal framework and well prepared manuals and cooperation with civic actors and companies. In other words, Korean digital dataveillance had demonstrated its transparency and cooperative governance. Second, such dataveillance capacity has deep roots in the long-term development of Korean state's big data management. Korean state has evolved about thirty years while enhancing digital data network within governments, companies and private sectors. Third, the relationship between Korean state's dataveillance and civil society can be characterized as a state centered push model. This model demonstrates highly effective governmental responses to covid-19 crisis but fall short of building social consensus in balancing individual freedom, human rights and effective containment policies. It means communitarian solidarity among citizens has not been a major factor in Korea's successful response yet.

• Journal of the Korean Society of Safety
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• v.31 no.5
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• pp.22-27
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• 2016

The Electrostatic Charge Prevention Technology is a core factor that highly influences the yield of Ultra High Resolution Flat Panel Display and high-integrated semiconductor manufacturing processes. The corona or x-ray ionizations are commonly used in order to eliminate static charges during manufacturing processes. To develop such a revolutionary x-ray ionizer that is free of x-ray radiation and has function to control the volume of ion formation simultaneously is a goal of this research and it absolutely overcomes the current risks of x-ray ionization. Under the International Commission on Radiological Protection, it must have a leakage radiation level that should be lower than a recommended level that is $1{\mu}Sv/hour$. In this research, the new generation of x-ray ionizer can easily control both the volume of ion formation and the leakage radiation level at the same time. In the research, the test constraints were set and the descriptions are as below; First, In order not to leak x-ray radiation while testing, the shielding box was fully installed around the test equipment area. Second, Implement the metallic Ring Electrode along a tube window and applied zero to ${\pm}8kV$ with respect to manage the positive and negative ions formation. Lastly, the ion duty ratio was able to be controlled in different test set-ups along with a free x-ray leakage through the metallic Ring Electrode. In the result of experiment, the maximum x-ray radiation leakage was $0.2{\mu}Sv/h$. These outcome is lower than the ICRP 103 recommended value, which is $1{\mu}Sv/h$. When applying voltage to the metallic ring electrode, the positive decay time was 2.18s at the distance of 300 mm and its slope was 0.272. In addition, the negative decay time was 2.1s at the distance of 300 mm and its slope was 0.262. At the distance of 200 mm, the positive decay time was 2.29s and its slope was 0.286. The negative decay time was 2.35s and its slope was 0.293. At the distance of 100 mm, the positive decay time was 2.71s and its slope was 0.338. The negative decay time was 3.07s and its slope was 0.383. According to these research, the observation was shown that these new concept of ionizer is able to minimize the leakage radiation level and to control the positive and negative ion duty ratio while ionization.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.1
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• pp.114-123
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• 2018

This paper concerns a design and learning method of softmax function neural networks based on K-means clustering. The partial discharge data Information is preliminarily processed through simulation using an Epoxy Mica Coupling sensor and an internal Phase Resolved Partial Discharge Analysis algorithm. The obtained information is processed according to the characteristics of the pattern using a Motor Insulation Monitoring System program. At this time, the processed data are total 4 types that void discharge, corona discharge, surface discharge and slot discharge. The partial discharge data with high dimensional input variables are secondarily processed by principal component analysis method and reduced with keeping the characteristics of pattern as low dimensional input variables. And therefore, the pattern classifier processing speed exhibits improved effects. In addition, in the process of extracting the partial discharge data through the MIMS program, the magnitude of amplitude is divided into the maximum value and the average value, and two pattern characteristics are set and compared and analyzed. In the first half of the proposed partial discharge pattern classifier, the input and hidden layers are classified by using the K-means clustering method and the output of the hidden layer is obtained. In the latter part, the cross entropy error function is used for parameter learning between the hidden layer and the output layer. The final output layer is output as a normalized probability value between 0 and 1 using the softmax function. The advantage of using the softmax function is that it allows access and application of multiple class problems and stochastic interpretation. First of all, there is an advantage that one output value affects the remaining output value and its accompanying learning is accelerated. Also, to solve the overfitting problem, L2-normalization is applied. To prove the superiority of the proposed pattern classifier, we compare and analyze the classification rate with conventional radial basis function neural networks.

• Journal of the Korea Convergence Society
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• v.11 no.7
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• pp.19-24
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• 2020

WHO declared a global pandemic on March 11th for Corona 19. However, there is a situation where you have to go to building for face-to-face education or seminars for economic and social activities. The first check method of COVID-19 infection is to measure body temperature, so the primary entrance and exit is blocked for near-field body temperature measurement. However, since it is troublesome to check directly, thermal camera is installed at the entrance of the building, and body temperature is measured indirectly using the infrared camera to control access. In case of middle and high schools, universities, and lifelong education center, we need a system that is possible to interoperate with attendance checks and automatically recognizes whether to wear masks and can authenticate students. We proposed the system that is to confirm whether to wear a mask with a camera that is embedded in a smart mirror, and that authenticates the user through voice recognition of the user who wants to enter the building by using voice recognition technology and determines whether to enter them or not. The proposed system can check attendance if it is linked with near-field temperature measurement and attendance check APP of student's smart phone.

• Journal of IKEEE
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• v.24 no.2
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• pp.638-648
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• 2020

Currently, the method of user-follwoing in intelligent cooperative robots usually based in vision system and using Lidar is common and have excellent performance. But in the closed space of Corona 19, which spread worldwide in 2020, robots for cooperation with medical staff were insignificant. This is because Medical staff are all wearing protective clothing to prevent virus infection, which is not easy to apply with existing research techniques. Therefore, in order to solve these problems in this paper, the ultrasonic sensor is separated from the transmitting and receiving parts, and based on this, this paper propose that estimating the user's position and can actively follow and cooperate with people. However, the ultrasonic sensors were partially applied by improving the Median filter in order to reduce the error caused by the short circuit in communication between hard reflection and the number of light reflections, and the operation technology was improved by applying the curvature trajectory for smooth operation in a small area. Median filter reduced the error of degree and distance by 70%, vehicle running stability was verified through the training course such as 'S' and '8' in the result.

• Journal of Industrial Convergence
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• v.19 no.1
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• pp.17-26
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• 2021

This study was conducted to do in-depth exploration of coroperate employees' learning experiences for workplace under COVID-19. We collected data through depth interviews from August 10th to November 30th, 2020 with five employees of S Energy company, and a qualitative case study was conducted using Ricci's 3-step analysis procedure. As a result, In the process of adapting from the landing of COVID-19, "The Story of a Distant Country," "No More Safe Zones," "Exploring effective responses created by a Sense of Crisis," and "Learning the changed way of work for adaptation." appeared. In addition, It has been found that the experience of learning in the work environment of the With Corona era includes "learning experience for survival from a sense of crisis", "learning experience for adapting to Untact culture", "learning experience through SNS communication", and "competitive learning experience for performance creation". In conclusion, employees have adapted to changes in the workplace environment through various learning experiences, which can enhance workers' ability to cope with crisis situations and can be used as basic data for an effective learning. In the future, we suggested follow-up researches of corporate employees in various fields.

• Korean Journal of Cognitive Science
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• v.33 no.3
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• pp.135-154
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• 2022

The purpose of this study was to confirm that the property generalization to social categories with low coherence is stronger when stress due to COVID-19 is perceived as high, compared to when stress is perceived as low. To this end, this study selected categories with high coherence(nun, soldier, flight attendant) and categories with low coherence(wedding planner, interpreter, florist), and recruited 336 participants to perform a category-based inductive generalization task(inferring how many properties repeatedly observed by some category members would appear across all category members), and measured their perceived COVID-19 stress. As a result, this study showed that when the cohesion of social categories is high, the effect of property generalization is stronger than when it is low, and the effect of property generalization is stronger in those who perceive stress due to Corona 19 higher than those who perceive it as low. In addition, this study confirmed that people who perceive COVID-19 stress strongly tend to generalize strongly to properties that are repeatedly observed in the low coherence category. This study is important in that it shows that there is a cognitive mechanism that is at the root of the phenomenon that stereotypes and prejudices deepen and discriminatory behaviors increase after the outbreak of COVID-19, such as COVID-19 stress and the resulting increase in attribute generalization tendency.

• 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.