• The Journal of the Institute of Internet, Broadcasting and Communication
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• v.22 no.5
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• pp.29-35
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• 2022

This paper proposes a fuel cell education framework installed on a Metaverse environment, which is to reduce the burden of education costs and improve the effect of education or learning. This Meta-Fuel cell platform utilizes the Unity 3D Web and enables not only theoretical education but also hands-on training. The platform was designed and developed to accommodate a variety of unit education contents, such as ppt documents, videos, etc. The platform, therdore, integrates ppt and video demonstrations for theoretical education, as well as software content "STACK-Up" for hands-on training. Theoretical education section provides specialized liberal arts knowledge on hydrogen, including renewable energy, hydrogen economy, and fuel cells. The software "STACK-Up" provides a hands-on practice on assembling the stack parts. Stack is the very core component of fuel cells. The Meta-Fuelcell platform improves the limitations of face-to-face education. It provides educators with the opportunities of non-face-to-face education without restrictions such as educational place, time, and occupancy. On the other hand, learners can choose educational themes, order, etc. It provides educators and learners with interesting experiences to be active in the metaverse space. This platform is being applied experimentally to a education project which is to develop advanced manpower in the fuel cell industry. Its improvement is in progress.

• Journal of Korea Water Resources Association
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• v.55 no.10
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• pp.707-721
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• 2022

In this study, a state-of-the-art review on urban inundation simulation technology was presented summarizing major achievements and limitations, and future research recommendations and challenges. More than 160 papers published in major domestic academic journals since the 2000s were analyzed. After analyzing the core themes and contents of the papers, the status of technological development was reviewed according to simulation methodologies such as physically-based and data-driven approaches. In addition, research trends for application purposes and advances in overseas and related fields were analyzed. Since more than 60% of urban inundation research used Storm Water Management Model (SWMM), developing new modeling techniques for detailed physical processes of dual drainage was encouraged. Data-based approaches have become a new status quo in urban inundation modeling. However, given that hydrological extreme data is rare, balanced research development of data and physically-based approaches was recommended. Urban inundation analysis technology, actively combined with new technologies in other fields such as artificial intelligence, IoT, and metaverse, would require continuous support from society and holistic approaches to solve challenges from climate risk and reduce disaster damage.

• 한국노년학
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• v.41 no.4
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• pp.587-609
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• 2021

The purpose of this study was to investigate parenting experiences among grandmothers raising their grandchildren from grandmothers' perspective, and a variety of their physical health, psychological and social challenges they were facing in everyday life. In addition, this study explored new issues, changes, and difficulties grandparents and their grandchildren were going through during the COVID-19 pandemic. Seven grandmothers raising their grandchildren without their cohabiting parents participated in an in-depth interview, and the qualitative date were obtained using semi-structured questionnaires. Analyses identified 5 main categories: 1) my emotion, worries, and coping with parenting grandchildren, 2) difficulties and obstacles facing in real life of the parenting, 3) conflicts and coping with growing grandchildren who showed new characters, 4) relationships and emotions among grandparents, parents, and grandchildren, and 5) needs and desires toward social services and support. Sixteen themes and 60 sub-themes were also derived. The majority of grandmothers expressed diverse difficulties in their dail y lives including ambivalent emotions regarding grandchild-rearing(rewards and burden), economic hardships, physical health limitations, and a lack of communications with their grandchildren. Further, findings indicated profound generation conflicts which had been even deepened during school close period in COVID-19 pandemic and had been associated with increased hours of using internet and playing computer games. The top priority of the social service needs among interviewed grandmothers was learning support for their grandchildren. Emotional support and social support to cover their lack of family interactions, and financial support were the next of their desired social services. Implications to improve social services for grandparent-headed families are discussed.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.553-568
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• 2022

The Household Income and Expenditure Survey is a representative survey of Statistics Korea, which aims to measure and analyze national income and consumption levels and their changes by understanding the current state of household balances. Recently, the disconnection problem in these time series caused by the large-scale reorganization of the survey methods in 2017 and 2019 has become an issue. In this study, we model the characteristics of the time series in the Household Income and Expenditure Survey up to 2016, and use the modeling to compute forecasts for linking the expenditures in 2017 and 2018. In order to evenly reflect the characteristics across all expenditure item series and to reduce the impact of a specific forecast model, we synthesize a total of 8 models such as regression models, time series models, and machine learning techniques. In particular, the noteworthy aspect of this study is that it improves the forecast by using the optimal combination technique that can exactly reflect the hierarchical structure of the Household Income and Expenditure Survey without loss of information as in the top-down or bottom-up methods. As a result of applying the proposed method to forecast expenditure series from 2017 to 2019, it contributed to the recovery of time series linkage and improved the forecast. In addition, it was confirmed that the hierarchical time series forecasts by the optimal combination method make linkage results closer to the actual survey series.

• The Korean Journal of Applied Statistics
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• v.35 no.4
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• pp.457-468
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• 2022

In aged society, it is important to prevent older people from being disability needing long-term care. The purpose of this study is to develop a prediction model to discover high-risk groups who are likely to be beneficiaries of Long-Term Care Insurance. This study is a retrospective study using database of National Health Insurance Service (NHIS) collected in the past of the study subjects. The study subjects are 7,724,101, the population over 65 years of age registered for medical insurance. To develop the prediction model, we used logistic regression, decision tree, random forest, and multi-layer perceptron neural network. Finally, random forest was selected as the prediction model based on the performances of models obtained through internal and external validation. Random forest could predict about 90% of the older people in need of long-term care using DB without any information from the assessment of eligibility for long-term care. The findings might be useful in evidencebased health management for prevention services and can contribute to preemptively discovering those who need preventive services in older people.

• Journal of Advanced Navigation Technology
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• v.26 no.4
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• pp.211-218
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• 2022

Continuous wave (CW) radar has the advantage of reliability and accuracy compared to other sensors such as camera and lidar. In addition, binarized neural network (BNN) has a characteristic that dramatically reduces memory usage and complexity compared to other deep learning networks. Therefore, this paper proposes binarized neural network based human identification and motion classification system using CW radar. After receiving a signal from CW radar, a spectrogram is generated through a short-time Fourier transform (STFT). Based on this spectrogram, we propose an algorithm that detects whether a person approaches a radar. Also, we designed an optimized BNN model that can support the accuracy of 90.0% for human identification and 98.3% for motion classification. In order to accelerate BNN operation, we designed BNN hardware accelerator on field programmable gate array (FPGA). The accelerator was implemented with 1,030 logics, 836 registers, and 334.904 Kbit block memory, and it was confirmed that the real-time operation was possible with a total calculation time of 6 ms from inference to transferring result.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.81-93
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• 2022

The stock investing is one of the most popular investment techniques. However, since it is not easy to obtain a return through actual investment, various strategies have been devised and tried in the past to obtain an effective and stable return. Among them, the volatility breakout strategy identifies a strong uptrend that exceeds a certain level on a daily basis as a breakout signal, follows the uptrend, and quickly earns daily returns. It is one of the popular investment strategies that are widely used to realize profits. However, it is difficult to predict stock prices by understanding the price trend pattern of stocks. In this paper, we propose a method of buying and selling stocks by predicting the return in trading based on the volatility breakout strategy using a bi-directional long short-term memory deep neural network that can realize a return in a short period of time. As a result of the experiment assuming actual trading on the test data with the learned model, it can be seen that the results outperform both the return and stability compared to the existing closing price prediction model using the long-short-term memory deep neural network model.

• The Journal of the Korea Contents Association
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• v.22 no.1
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• pp.274-282
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• 2022

In order to secure pedestrians' right to walk, we have up-cycled second hand mobile phones to overcome limitations of the existing survey methods, analysis methods, and diagnosis to reduce pedestrian traffic accidents. Second hand mobile phones were up-cycled to produce mobile CCTVs and installed in areas where pedestrian deaths rate is high to secure image data sets for the period of more than 24 hours. It was analyzed by applying image visualization technology and clouding reporting technology, and more precise and accurate results were derived through modeling based on artificial intelligence learning and GIS-based diagnostic guidance. As a result, it was possible to analyze the risk factors and number of pedestrian safety, and even factors that were not known in the existing method could be derived. In addition, the traffic accident risk index was derived by converting data into one year to verify whether second hand mobile phone up-cycling mobile CCTV will be an objective tool for finding pedestrian risk factors. Up-cycling mobile CCTV of second hand mobile phones newly applied through research can be used as a new tool to find pedestrian risk factors, and it can be used as a service to protect the safety of the traffic vulnerable other than pedestrians.

• The Journal of the Korea Contents Association
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• v.22 no.3
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• pp.197-211
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• 2022

This study examines the structural relationship between parents' positive parenting attitudes, negative parenting attitudes, emotional problems, and academic helplessness. To this end, the data of 2,590 first-year middle school students in the Korean Children and Youth Panel Survey 2018 were used to understand the structural relationship between variables. For this study, the correlation between variables was examined with SPSS 21.0, and the structural relationship between variables was identified with AMOS 21.0. The research results are as follows. First, it was found that the positive parenting attitude and negative parenting attitude of parents had a significant effect on academic helplessness. Second, parents' positive and negative parenting attitudes had a significant effect on emotional problems. Third, emotional problems had a significant positive effect on academic ability. Fourth, emotional problems were partially mediated between parents' positive parenting attitudes, negative parenting attitudes, and academic helplessness. In other words, the emotions of adolescents affected by their parents' parenting attitudes affect their learning. Through this study, it is meaningful to confirm that emotional problems can be treated as factors that influence studies, not simply limited to factors influenced by other factors.

• KIPS Transactions on Software and Data Engineering
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• v.11 no.1
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• pp.1-10
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• 2022

Deep neural networks are widely used to solve various problems. In a fully connected neural network, the nonlinear activation function is a function that nonlinearly transforms the input value and outputs it. The nonlinear activation function plays an important role in solving the nonlinear problem, and various nonlinear activation functions have been studied. In this study, we propose a combined parametric activation function that can improve the performance of a fully connected neural network. Combined parametric activation functions can be created by simply adding parametric activation functions. The parametric activation function is a function that can be optimized in the direction of minimizing the loss function by applying a parameter that converts the scale and location of the activation function according to the input data. By combining the parametric activation functions, more diverse nonlinear intervals can be created, and the parameters of the parametric activation functions can be optimized in the direction of minimizing the loss function. The performance of the combined parametric activation function was tested through the MNIST classification problem and the Fashion MNIST classification problem, and as a result, it was confirmed that it has better performance than the existing nonlinear activation function and parametric activation function.