• Journal of Intelligence and Information Systems
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• v.29 no.4
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• pp.229-256
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• 2023

The increase in telecommuting and household electricity demand due to the pandemic has led to significant changes in electricity demand patterns. This has led to difficulties in identifying KEPCO's PPA (power purchase agreements) and residential solar power generation and has added to the challenges of electricity demand forecasting and grid operation for power exchanges. Unlike other energy resources, electricity is difficult to store, so it is essential to maintain a balance between energy production and consumption. A shortage or overproduction of electricity can cause significant instability in the energy system, so it is necessary to manage the supply and demand of electricity effectively. Especially in the Fourth Industrial Revolution, the importance of data has increased, and problems such as large-scale fires and power outages can have a severe impact. Therefore, in the field of electricity, it is crucial to accurately predict the amount of power generation, such as renewable energy, along with the exact demand for electricity, for proper power generation management, which helps to reduce unnecessary power production and efficiently utilize energy resources. In this study, we reviewed the renewable energy generation forecasting system, its objectives, and practical applications to construct optimal aggregated power resources using data from 169 power plants provided by the Ministry of Trade, Industry, and Energy, developed an aggregation algorithm considering the settlement of the forecasting system, and applied it to the analytical logic to synthesize and interpret the results. This study developed an optimal aggregation algorithm and derived an aggregation configuration (Result_Number 546) that reached 80.66% of the maximum settlement amount and identified plants that increase the settlement amount (B1783, B1729, N6002, S5044, B1782, N6006) and plants that decrease the settlement amount (S5034, S5023, S5031) when aggregating plants. This study is significant as the first study to develop an optimal aggregation algorithm using aggregated power resources as a research unit, and we expect that the results of this study can be used to improve the stability of the power system and efficiently utilize energy resources.

• Journal of Service Research and Studies
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• v.8 no.2
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• pp.25-39
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• 2018

In the period of great change in human society, a change in educational services is also necessary. Thus, the current research investigates a new model of educational services to prepare people to be successful in an era of service and the fourth industrial revolution. We analyzed all the educational service models from the first educational institution, The Academy, founded by Plato to one of the most innovative institutions, Minerva schools. Then, we designed both an educational institution model and an educational service model that will cultivate and educate prospective students to be multidimensional to fit to the new upcoming eras. Since the era of service in the 21st century is also the era of job creation, we designed models focused on developing the broad knowledge and practical skills need to solve the most complex issues of our time. A new model was designed based on the results of the survey of existing major programs, analysis of the demands of the new generation, competency requirements, and etc. The newly designed conceptual model was improved from study focusing on tools to study that focuses on intrinsic discipline and competence, nurturing dream and imagination. In order to realize the new educational service, we developed technical conditions and a methodology for improving educational service performance. In the future, it is necessary to deepen the study and carry out research on implementing new educational service. In addition, an empirical study of the performance of the new model will also be needed.

• Korean Chemical Engineering Research
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• v.61 no.4
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• pp.496-504
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• 2023

Countries worldwide are striving to find new sources of sustainable energy without carbon emission due to the increasing impact of global warming. With the advancement of the fourth industrial revolution on a global scale, there has been a substantial rise in energy demand. Simultaneously, there is a growing emphasis on utilizing energy sources with minimal or zero carbon content to ensure a stable power supply while reducing greenhouse gas emissions. In this comprehensive overview, a comparative analysis of carbon reduction policies of government was conducted. Based on international carbon neutrality scenarios and the presence of remaining thermal power generation, it can be categorized into two types: "Rapid" and "Safety". For the domestic scenario, the projected power demand and current greenhouse gas emissions in alignment with "The 10th Basic Plan for Electricity Supply and Demand" was examined. Considering all these factors, an overview of the current status of carbon neutrality technologies by focusing on the energy sector, encompassing transitions, hydrogen, transportation and carbon capture, utilization, and storage (CCUS) was offered followed by summarization of key technological trends and government-driven policies. Furthermore, the central aspects of the domestic carbon reduction strategy were proposed by taking account of current mega trends in the energy sector which are highlighted in international scenario analyses.

• Journal of Christian Education in Korea
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• v.67
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• pp.11-44
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• 2021

In the early 21st century, the global COVID-19 pandemic, which has arisen during the development of the technological science of the Fourth Industrial Revolution, has been a great challenge in all fields including politics, economy, industry, education and religion in Korean society. To prevent the spread of the COVID-19 epidemic, the Korean government announced 'social distancing guidelines,' focused on the 'prohibition of three conditions'(crowd, closeness, airtight) for safety reasons. These quarantine guidelines made it more difficult for Korean churches and Sunday schools to operate. In general, looking at the statistical data of the major denominations of the Korean Church in the second half of the 20th century, shows that the Church has entered a period of stagnant or declining growth. Data also show that the number of students attending Sunday School is decreasing. The researcher identified four causes of the crisis faced by the Korean church and Korean Sunday school entering the 21st century. These trends are influenced by the tendencies of postmodernism, the deconstruction of modern universalism, the certainty and objectivity of knowledge, and the grand narrative and worldview of diffusion. Moreover, it is a phenomenon in which the young population decreases in contrast to the increasing elderly population in the age of population cliff in Korean society. Sunday Schools are also facing a crisis, as the youth population, who will become the future heroes of the Korean church, is declining. Finally, constraints of Church and Sunday school education activities are due to COVID-19 Pandemic. As analysis shows the loss of the Church's educational vision and a decrease in the passion for education. Accordingly, the researcher suggests four new strategies for the next generation of Korean Sunday schools, whose ranges from 200 members or less; this range covers the majority of Sunday School program run by churches in Korea. First, in the age of postmodernism, a time of uncertainty and relativism, Christian Societies requires teachers who are certain of absolute Christian truth and faith. Second, in an era of declining population cliffs for younger generations, a shift to a home-friendly Sunday school paradigm is needed. Third, during the COVID-19 pandemic, educational activities must appropriately utilize face-to-face and non-face-to-face communication. Finally, even in difficult times, Korean Sunday school should nevertheless remember the Lord's great commandment(Matthew 28:18-20) and restore the vision and passion of education to announce and teach the gospel. The researcher hopes that this study will provide small, positive steps in rebuilding Korean Sunday school educational activities for future generations in difficult times.

• Journal of Practical Engineering Education
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• v.15 no.1
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• pp.119-126
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• 2023

Today, in order to improve fuel efficiency and dynamic behavior of automobiles, an era of light weight and simplification of automobile parts is being formed. In order to simplify and design and manufacture the shape of the product, various components are integrated. For example, in order to commercialize three products into one product, product processing is occurring to a very narrow area. In the case of existing parts, precision die casting or casting production is used for processing convenience, and the multi-piece method requires a lot of processes and reduces the precision and strength of the parts. It is very advantageous to manufacture integrally to simplify the processing air and secure the strength of the parts, but if a deep and narrow pocket part needs to be processed, it cannot be processed with the equipment's own spindle. To solve a problem, research on cutting processing is being actively conducted, and multi-axis composite processing technology not only solves this problem. It has many advantages, such as being able to cut into composite shapes that have been difficult to flexibly cut through various processes with one machine tool so far. However, the reality is that expensive equipment increases manufacturing costs and lacks engineers who can operate the machine. In the five-axis cutting processing machine, when producing products with deep and narrow sections, the cycle time increases in product production due to the indirectness of tools, and many problems occur in processing. Therefore, dedicated machine tools and multi-axis composite machines should be used. Alternatively, an angle spindle may be used as a special tool capable of multi-axis composite machining of five or more axes in a three-axis machining center. Various and continuous studies are needed in areas such as processing vibration absorption, low heat generation and operational stability, excellent dimensional stability, and strength securing by using the angle spindle.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.1-32
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• 2018

In addition to stakeholders including managers, employees, creditors, and investors of bankrupt companies, corporate defaults have a ripple effect on the local and national economy. Before the Asian financial crisis, the Korean government only analyzed SMEs and tried to improve the forecasting power of a default prediction model, rather than developing various corporate default models. As a result, even large corporations called 'chaebol enterprises' become bankrupt. Even after that, the analysis of past corporate defaults has been focused on specific variables, and when the government restructured immediately after the global financial crisis, they only focused on certain main variables such as 'debt ratio'. A multifaceted study of corporate default prediction models is essential to ensure diverse interests, to avoid situations like the 'Lehman Brothers Case' of the global financial crisis, to avoid total collapse in a single moment. The key variables used in corporate defaults vary over time. This is confirmed by Beaver (1967, 1968) and Altman's (1968) analysis that Deakins'(1972) study shows that the major factors affecting corporate failure have changed. In Grice's (2001) study, the importance of predictive variables was also found through Zmijewski's (1984) and Ohlson's (1980) models. However, the studies that have been carried out in the past use static models. Most of them do not consider the changes that occur in the course of time. Therefore, in order to construct consistent prediction models, it is necessary to compensate the time-dependent bias by means of a time series analysis algorithm reflecting dynamic change. Based on the global financial crisis, which has had a significant impact on Korea, this study is conducted using 10 years of annual corporate data from 2000 to 2009. Data are divided into training data, validation data, and test data respectively, and are divided into 7, 2, and 1 years respectively. In order to construct a consistent bankruptcy model in the flow of time change, we first train a time series deep learning algorithm model using the data before the financial crisis (2000~2006). The parameter tuning of the existing model and the deep learning time series algorithm is conducted with validation data including the financial crisis period (2007~2008). As a result, we construct a model that shows similar pattern to the results of the learning data and shows excellent prediction power. After that, each bankruptcy prediction model is restructured by integrating the learning data and validation data again (2000 ~ 2008), applying the optimal parameters as in the previous validation. Finally, each corporate default prediction model is evaluated and compared using test data (2009) based on the trained models over nine years. Then, the usefulness of the corporate default prediction model based on the deep learning time series algorithm is proved. In addition, by adding the Lasso regression analysis to the existing methods (multiple discriminant analysis, logit model) which select the variables, it is proved that the deep learning time series algorithm model based on the three bundles of variables is useful for robust corporate default prediction. The definition of bankruptcy used is the same as that of Lee (2015). Independent variables include financial information such as financial ratios used in previous studies. Multivariate discriminant analysis, logit model, and Lasso regression model are used to select the optimal variable group. The influence of the Multivariate discriminant analysis model proposed by Altman (1968), the Logit model proposed by Ohlson (1980), the non-time series machine learning algorithms, and the deep learning time series algorithms are compared. In the case of corporate data, there are limitations of 'nonlinear variables', 'multi-collinearity' of variables, and 'lack of data'. While the logit model is nonlinear, the Lasso regression model solves the multi-collinearity problem, and the deep learning time series algorithm using the variable data generation method complements the lack of data. Big Data Technology, a leading technology in the future, is moving from simple human analysis, to automated AI analysis, and finally towards future intertwined AI applications. Although the study of the corporate default prediction model using the time series algorithm is still in its early stages, deep learning algorithm is much faster than regression analysis at corporate default prediction modeling. Also, it is more effective on prediction power. Through the Fourth Industrial Revolution, the current government and other overseas governments are working hard to integrate the system in everyday life of their nation and society. Yet the field of deep learning time series research for the financial industry is still insufficient. This is an initial study on deep learning time series algorithm analysis of corporate defaults. Therefore it is hoped that it will be used as a comparative analysis data for non-specialists who start a study combining financial data and deep learning time series algorithm.