• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.36-42
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• 2021

With recent developments in the Fourth Industrial Revolution, the manufacturing industry has changed rapidly. Through key aspects of Fourth Industrial Revolution super-connections and super-intelligence, machine learning will be able to make fault predictions during the foam-making process. Polyol and isocyanate are components in polyurethane foam. There has been a lot of research that could affect the characteristics of the products, depending on the specific mixture ratio and temperature. Based on these characteristics, this study collects data from each factor during the foam-making process and applies them to machine learning in order to predict faults. The algorithms used in machine learning are the decision tree, kNN, and an ensemble algorithm, and these algorithms learn from 5,147 cases. Based on 1,000 pieces of data for validation, the learning results show up to 98.5% accuracy using the ensemble algorithm. Therefore, the results confirm the faults of currently produced parts by collecting real-time data from each factor during the foam-making process. Furthermore, control of each of the factors may improve the fault rate.

• The Journal of the Convergence on Culture Technology
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• v.7 no.3
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• pp.577-582
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• 2021

Recently, with the development of AR, VR, and smart device technologies, the demand for services based on non-face-to-face environments is also increasing in the fitness industry. The non-face-to-face online home training service has the advantage of not being limited by time and place compared to the existing offline service. However, there are disadvantages including the absence of exercise equipment, difficulty in measuring the amount of exercise and chekcing whether the user maintains an accurate exercise posture or not. In this study, we develop a standard exercise program that can compensate for these shortcomings and propose a new non-face-to-face home training application by using a deep learning-based body posture estimation image processing algorithm. This application allows the user to directly watch and follow the trainer of the standard exercise program video, correct the user's own posture, and perform an accurate exercise. Furthermore, if the results of this study are customized according to their purpose, it will be possible to apply them to performances, films, club activities, and conferences

• Environmental and Resource Economics Review
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• v.30 no.4
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• pp.607-625
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• 2021

The objective of this study is to estimate the relationship between CO₂ emissions and both nuclear power and renewable energy generation, and compare the cost efficiencies of nuclear power and renewable energy generation in reducing CO₂ emissions in Korea. The results show that nuclear power and renewable energy generation should be increased by 1.344% and 7.874% to reduce CO₂ emissions by 1%, respectively. Using the estimated coefficients and the levelized costs of electricity by source including the external costs, if the current amount of electricity generation is one megawatt-hour, the range of generation cost of nuclear power generation to reduce 1% CO₂ emissions is $0.72~$1.49 depending on the level of external costs. In the case of renewable energy generation, the generation cost to reduce 1% CO₂ emissions is $6.49. That is, to mitigate 1% of CO₂ emissions at the total electricity generation of 353 million MWh in 2020 in Korea, the total generation costs range for nuclear power is $254 million~$526 million for the nuclear power, and the cost for renewable energy is $2.289 billion for renewable energy. Hence, we can conclude that, in Korea, nuclear power generation is more cost-efficient than renewable energy generation in mitigating CO₂ emissions, even with the external costs of nuclear power generation.

• Journal of Life Science
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• v.32 no.7
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• pp.523-531
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• 2022

The purpose of this study was to evaluate the effect of antioxidant and anti-adipogenic activities in ethanol extracts from herb mixture (Ephedra sinica, Atractylodes lance, Gypsum fibrosum, and Theobroma cacao). DPPH, ABTS⁺ radical and xanthine oxidase scavenging activities were measured for antioxidant activity. Extracts of the herb mixture had 75.0, 100.8, and 79.5% scavenging activities at 1,000 ㎍/ml concentration, respectively. We investigated the inhibition of adipogenesis and adipocyte differentiation with an extract of an herb mixture in 3T3-L1 preadipocytes. An extract from the herb mixture at concentrations between 0 and 50 ㎍/ml did not affect 3T3-L1 cell viability. Treatment with herb mixture extracts of 25, 50, and 75 ㎍/ml in 3T3-L1 preadipocytes inhibited lipid accumulation in a dose-dependent manner. As a result of a Western blot experiment, it was shown that the herb mixture inhibited the differentiation transcription factors, PPARγ and C/EBPa, by 44.2 and 77.6%, respectively, at a concentration of 75 ㎍/ml in MDI-induced differentiated 3T3-L1 cells. As a result of RT-PCR, the gene expression of C/EBPa, SREBP-1c, and PPARγ was significantly inhibited by 43.4%, 59.6%, and 55.3%, respectively, at the concentration of 75 ㎍/ml of the herb mixture compared with the MDI-treated group. In addition, the expression of fatty acid synthase (FAS), a fatty acid synthesis regulator, was suppressed. These results can be applied to develop a functional food for anti-obesity with a herb mixture.

• The Journal of the Convergence on Culture Technology
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• v.7 no.4
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• pp.857-862
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• 2021

Recently, rapid climate change has had a significant impact on the bee ecosystem. The decrease in the number of bees and the change in the flowering period have a huge impact on the harvesting of beekeepers. Accordingly, attention is focused on smart beekeeping, which introduces IoT technology to beekeeping. According to the characteristics of beekeeping, it is impossible to continuously observe the beehive in the hive with the naked eye, and the condition of the hive is mostly dependent on knowledge from experience. Although a system that can measure partly through sensors such as temperature/humidity change inside the hive and measurement of the amount of CO2 is applied, there is no research on measuring the movement path and amount of movement of bees inside the beehive. Part of the migration of honeybees inside the hive can provide basic information to predict the most important cleavage time in beekeeping. In this study, we propose a device that detects the movement path of bees and measures and records data entering and exiting the hive in real time. The device proposed in this study was developed according to the honeycomb standard of the existing beehive so that beekeeping farms could use it. The development method used a photodetector that can detect the movement of bees to configure 16 movement paths and to detect the movement of bees in real time. If the measured honeybee movement status is utilized, the problem of directly observing the colony with the naked eye in order not to miss the swarming time can be solved.

• Journal of Digital Convergence
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• v.20 no.5
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• pp.559-567
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• 2022

The effect of smartphone usage motivation on application display advertising attitude and advertising avoidance was investigated. In addition, the mediating effect of advertising intrusion was confirmed. The total number of participants in the study was 309, and the data collection method used a survey method. Covariate structural analysis was conducted to investigate the causal relationship between smartphone usage motivation and advertising attitude and the mediating effect of perceived intrusion on advertising avoidance. There are five results. First, the motivation for using smartphones had a significant effect on the display advertising attitude of smartphone applications. Second, the display advertising attitude of the smartphone application had a significant effect on the advertising avoidance behavior. Third, the display advertising attitude of smart phone application had a significant effect on perceived advertising intrusion. Fourth, the perception of intrusiveness of display advertising in smartphone applications had a significant effect on advertising avoidance behavior. Finally, it was confirmed that the perceived ad intrusion has a partial mediating effect in the causal relationship of the smartphone application display advertising attitude to the ad avoidance behavior. The results of this study will contribute to suggesting strategies to reduce advertising avoidance behavior.

• Journal of Internet of Things and Convergence
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• v.8 no.5
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• pp.99-109
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• 2022

The purpose of this study is to verify the effect of experiential economy factors using metaverse on the intention to accept advertisements reflecting new technologies. The subjects of this study were those located in G Metropolitan City and J Province, and those who experienced metaverse. From August 1 to September 10, 2022, 150 people participated in the survey without face-to-face. Analysis methods were frequency analysis, factor analysis, reliability analysis, correlation analysis, multiple regression analysis, and three-step mediated regression analysis. The conclusion is as follows. First, the influence of metaverse experience on advertisement acceptance intention was shown in the order of relational experience, educational experience, and escapist experience. Second, it was found that the relational and educational experiences of metaverse partially mediate metaverse usefulness and affect the advertisement acceptance intention. Third, it was found that the relational and educational experiences of the metaverse partially mediate the metaverse presence and affect the advertisement acceptance intention. Also, it was found that the metaverse's escapist experience completely mediates the metaverse's presence and affects the advertisement acceptance intention. Fourth, it was found that the escapist experience of metaverse completely mediates the ease of use of metaverse and affects the advertisement acceptance intention. It is expected that this study will contribute to the construction of a new platform in the advertising market using various platforms of metaverse.

• Journal of Digital Convergence
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• v.19 no.5
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• pp.81-92
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• 2021

The purpose of this study is to present an approach to the "Digital Health Passport" (DHP), which will be the most important in the change of the travel industry among the sudden environmental changes brought about by COVID-19. To this end, this study reviewed a variety of empirical literature on DHP, and proposed a framework for DHP based on literature review. The framework is composed of travel intention, health information provision intention, and new technology acceptance/adoption of tourists. First, in terms of travel intention, providing information to DHP should not undermine the travel intention of the travelers. It should be possible to facilitate the travelers' enjoyment by using the information provided by the traveler. In addition, there is a need to assure that the data provided by travelers is managed in a reliable way. Second, it is necessary to understand why the travelers want to provide additional personal information (information disclosure), rather than seeing healthcare information only in terms of mandatory information provision. Finally, from the perspective of new technology, it is necessary to understand the intention of travelers to use/adopt DHP. The key implication of this work is that it proposed a DHP framework for realizing the travel bubble to predict and respond to foreign travelers' behaviors.

• Journal of the Society of Disaster Information
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• v.17 no.1
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• pp.165-175
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• 2021

Purpose: The purpose of this study is to quantitatively present the carbon dioxide(CO₂) emission change according to the application of autonomous driving technology at the network level for waste vehicles in the metropolitan area. Method: The target year was set to 2030, and the analysis method estimated the carbon dioxide (CO₂) emissions for each road link through user equilibrium assignment when unapplied scenario. The application scenario performed traffic assignment using route data on the premise that the group was running in accordance with the application of autonomous driving technology to waste vehicles. In addition, the other means estimated the carbon dioxide emissions through user balance allocation by reflecting the results of the waste vehicle allocation. Result: As a result of the analysis, carbon dioxide(CO₂) emissions were found to be reduced by about 56.9ton/day from the national network level, and the Seoul metropolitan area was analyzed to be reduced by about 54.7ton/day. Conclusion: This study quantitatively presented environmental impacts among various social effects that autonomous driving technology will bring, and in the future, development of various analytical methodologies and related studies should be continuously conducted.

• Journal of Digital Convergence
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• v.19 no.8
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• pp.319-325
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• 2021

With the advancement of wireless technology and the rapid growth of the infrastructure of mobile communication technology, systems applying AI-based platforms are drawing attention from users. In particular, the system that understands users' tastes and interests and recommends preferred items is applied to advanced e-commerce customized services and smart homes. However, there is a problem that these recommendation systems are difficult to reflect in real time the preferences of various users for tastes and interests. In this research, we propose a Fuzzy-AHP-based movies recommendation system using the Gated Recurrent Unit (GRU) language model to address a problem. In this system, we apply Fuzzy-AHP to reflect users' tastes or interests in real time. We also apply GRU language model-based models to analyze the public interest and the content of the film to recommend movies similar to the user's preferred factors. To validate the performance of this recommendation system, we measured the suitability of the learning model using scraping data used in the learning module, and measured the rate of learning performance by comparing the Long Short-Term Memory (LSTM) language model with the learning time per epoch. The results show that the average cross-validation index of the learning model in this work is suitable at 94.8% and that the learning performance rate outperforms the LSTM language model.