• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.6
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• pp.453-459
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• 2021

The system marginal price of electricity is the amount paid to all the generating units, which is an important decision-making factor for the construction and maintenance of an electrical power unit. In this paper, we suggest a long-term forecasting model for calculating the system marginal price based on prices of natural gas and oil. As most variables used in the analysis are nonstationary time series, the long run relationship among the variables should be examined by cointegration tests. The forecasting model is similar to an error correction model which consists of a long run cointegrating equation and another equation for short run dynamics. To mitigate the robustness issue arising from the relatively small data sample, this study employs various testing and estimating methods. Compared to previous studies, this paper considers multiple fuel prices in the forecasting model of system marginal price, and provides greater emphasis on the robustness of analysis. As none of the cointegrating relations associated with system marginal price, natural gas price and oil price are excluded, three error correction models are estimated. Considering the root mean squared error and mean absolute error, the model based on the cointegrating relation between system marginal price and natural gas price performs best in the out-of-sample forecast.

• The Journal of Korean Philosophical History
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• no.25
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• pp.7-40
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• 2009

The aim of this paper is to establish the theoretical foundation on "the integrative study of the character education for the promotion of social and emotional competencies of children.". Based on the social and emotional learning(SEL), this paper is tried to find out the effective ways to develop children's good character. According to SEL, social and emotional competence is the ability to understand, manage, and express the social and emotional aspects of one's life in ways that enable the successful management of life tasks such as learning, forming relationships, solving everyday problems, and adapting to the complex demands of growth and development. And it is also the process of acquiring and effectively applying the knowledge, attitudes, and skills necessary to recognize and manage emotions. Five key competencies such as self-awareness, social awareness, responsible decision making, self-management, relationship skills are taught, practiced, and reinforced through SEL programming. Both the social and emotional learning movement and the character education share in common the idea that much of human character can be modified for the better through learning. While character educators engage in developing civic virtue and moral character in our youth for more compassionate and responsible society, SEL educators engage in educating for a safe, secure, caring society. To effectively teach social and emotional competencies, the teachers themselves must embrace a teaching and learning philosophy that models the attitudes, feelings, and behaviors we aim to teach.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.67-75
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• 2021

The structure of universities needs to be adjusted and reformed to cope with the decrease in admission resources and the quality of education due to the low birth rate and aging population. Such a policy is receiving much attention. To analyze the relative efficiency of private universities in Korea from the perspective of resource and performance, this study evaluated the efficiency of private university operation by applying a DEA(Data Envelopment Analysis) technique. The DEA measurements were compared with the diagnosis results of the department of education (Government) in 2018. The input and output variables used in the research analysis were utilized by the university's notification materials (public disclosure information). An analysis of the operational efficiency showed that 48% (12 universities) of the 25 DMUs (Decision Making Unit) were efficient for DEA-BCC models and that some of the capacity-building universities were operating efficiently. In addition, the DEA analysis found ways to improve inefficient groups through DEA-Additive results. This paper can be meaningful because it confirmed the relative efficiency of private universities and suggested improvement directions through the DEA method, which is characterized by the simultaneous consideration of various input and output factors. This will help apply the limited resources related to the input and output elements of each university.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.307-332
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• 2022

One of the most intensively conducted research areas in business application study is a bankruptcy prediction model, a representative classification problem related to loan lending, investment decision making, and profitability to financial institutions. Many research demonstrated outstanding performance for bankruptcy prediction models using artificial intelligence techniques. However, since most machine learning algorithms are "black-box," AI has been identified as a prominent research topic for providing users with an explanation. Although there are many different approaches for explanations, this study focuses on explaining a bankruptcy prediction model using a counterfactual example. Users can obtain desired output from the model by using a counterfactual-based explanation, which provides an alternative case. This study introduces a counterfactual generation technique based on a genetic algorithm (GA) that leverages both domain knowledge (i.e., causal feasibility) and feature importance from a black-box model along with other critical counterfactual variables, including proximity, distribution, and sparsity. The proposed method was evaluated quantitatively and qualitatively to measure the quality and the validity.

• Journal of the Society of Disaster Information
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• v.18 no.1
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• pp.146-153
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• 2022

Purpose: In the case of domestic port facilities, port structures that have been in use for a long time have many problems in terms of safety performance and functionality due to the enlargement of ships, increased frequency of use, and the effects of natural disasters due to climate change. A big data analysis method was studied to develop an approximate model that can predict the aging pattern of a port facility based on the maintenance history data of the port facility. Method: In this study, member-level maintenance history data for caisson-type quay walls were collected, defined as big data, and based on the data, a predictive approximation model was derived to estimate the aging pattern and deterioration of the facility at the project level. A state-based aging pattern prediction model generated through Gaussian process (GP) and linear interpolation (SLPT) techniques was proposed, and models suitable for big data utilization were compared and proposed through validation. Result: As a result of examining the suitability of the proposed method, the SLPT method has RMSE of 0.9215 and 0.0648, and the predictive model applied with the SLPT method is considered suitable. Conclusion: Through this study, it is expected that the study of predicting performance degradation of big data-based facilities will become an important system in decision-making regarding maintenance.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.25 no.6
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• pp.51-64
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• 2022

Along with the urbanization, the risk of urban flooding due to climate change is increasing. Flood regulation, one of the ecosystem services, is implemented in the different level of function of flood risk mitigation by the type of ecosystem such as forests, arable land, wetlands etc. Land use changes due to development pressures have become an important factor in increasing the vulnerability by flash flood. This study has conducted evaluating the urban flood regulation service using InVEST UFRM(Urban Flood Risk Model). As a result of the simulation, the potential water retention by ecosystem type in the event of a flash flood according to RCP 4.5(10 year frequency) scenario was 1,569,611 tons in urbanized/dried areas, 907,706 tons in agricultural areas, 1,496,105 tons in forested areas, 831,705 tons in grasslands, 1,021,742 tons in wetlands, and 206,709 tons in bare areas, the water bodies was estimated to be 38,087 tons. In the case of more severe 100-year rainfall, 1,808,376 tons in urbanized/dried areas, 1,172,505 tons in agricultural areas, 2,076,019 tons in forests, 1,021,742 tons in grasslands, 47,603 tons in wetlands, 238,363 tons in bare lands, and 52,985 tons in water bodies. The potential economic damage from flood runoff(100 years frequency) is 122,512,524 thousand won in residential areas, 512,382,410 thousand won in commercial areas, 50,414,646 thousand won in industrial areas, 2,927,508 thousand won in Infrastructure(road), 8,907 thousand won in agriculture, Total of assuming a runoff of 50 mm(100 year frequency) was estimated at 688,245,997 thousand won. In a conclusion. these results provided an overview of ecosystem functions and services in terms of flood control, and indirectly demonstrated the possibility of using the model as a tool for policy decision-making. Nevertheless, in future research, related issues such as application of models according to various spatial scales, verification of difference in result values due to differences in spatial resolution, improvement of CN(Curved Number) suitable for the research site conditions based on actual data, and development of flood damage factors suitable for domestic condition for the calculation of economic loss.

• Journal of Intelligence and Information Systems
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• v.28 no.2
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• pp.171-189
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• 2022

Online reviews play an essential role in the consumer's purchasing decision-making process, and thus, providing helpful and reliable reviews is essential to consumers. Previous online review helpfulness prediction studies mainly predicted review helpfulness based on the consistency of text and rating information of online reviews. However, there is a limitation in that representation capacity or review text and rating interaction. We propose a CNN-RHP model that effectively learns the interaction between review text and rating information to improve the limitations of previous studies. Multi-channel CNNs were applied to extract the semantic representation of the review text. We also converted rating into independent high-dimensional embedding vectors representing the same dimension as the text vector. The consistency between the review text and the rating information is learned based on element-wise operations between the review text and the star rating vector. To evaluate the performance of the proposed CNN-RHP model in this study, we used online reviews collected from Amazom.com. Experimental results show that the CNN-RHP model indicates excellent performance compared to several benchmark models. The results of this study can provide practical implications when providing services related to review helpfulness on online e-commerce platforms.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.2
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• pp.249-256
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• 2022

Various studies are being actively conducted to show that the real-time visualization technology that combines BIM (Building Information Modeling) and AR (Augmented Reality) helps to increase construction management decision-making and processing efficiency. However, when large-capacity BIM data is projected into AR, there are various limitations such as data transmission and connection problems and the image cut-off issue. To improve the high efficiency of visualizing, a mesh optimization algorithm based on the k-nearest neighbors (KNN) classification framework to reconstruct BIM data is proposed in place of existing mesh optimization methods that are complicated and cannot adequately handle meshes with numerous boundaries of the 3D models. In the proposed algorithm, our target BIM model is optimized with the Unity C# code based on triangle centroid concepts and classified using the KNN. As a result, the algorithm can check the number of mesh vertices and triangles before and after optimization of the entire model and each structure. In addition, it is able to optimize the mesh vertices of the original model by approximately 56 % and the triangles by about 42 %. Moreover, compared to the original model, the optimized model shows no visual differences in the model elements and information, meaning that high-performance visualization can be expected when using AR devices.

• Korean small business review
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• v.41 no.1
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• pp.29-51
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• 2019

This study investigates how SMEs' (small and medium-sized enterprises) financing strategies affect firm valuation. Given that information asymmetry is engaged in firm valuation in the stock market, investors interpret the meanings of debt financing depending on how SMEs construct the portfolio of financing strategies (retained earnings vs debt financing), thereby making investment decision. Specifically, given that SMEs' debt financing has two meanings in the market signals, called "benefit" and "cost", this study postulates that firm valuation will be differently made by investors, depending on how they interpret the meanings of debt financing under choice competition between retained earnings and debt financing. In this study, we argue that under choice competition, as a SME's debt proportion increases, the "cost" signal outweighes the "benefit" signal, thereby decreasing firm valuation. Moreover, the effect of such signal can be contingent on the SME's characteristics-firm visibility. These ideas are examined using 363 U.S. SMEs ranging from 1971 to 2010. The fixed-effects models estimating Tobin's q show that under choice competition, a SME's debt proportion has a negative impact on firm valuation and that the firm's high visibility mitigates the effect of "cost" signal. In conclusion, this study sheds new light on how investors' interpretations of SMEs' financing strategies affect firm valuation.

• Journal of Intelligence and Information Systems
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• v.29 no.2
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• pp.85-100
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• 2023

Corporate ESG performance (environmental, social, and corporate governance) reflecting a company's strategic sustainability has emerged as one of the main factors in today's investment decisions. The traditional ESG performance rating process is largely performed in a qualitative and subjective manner based on the institution-specific criteria, entailing limitations in reliability, predictability, and timeliness when making investment decisions. This study attempted to predict the corporate ESG rating through automated machine learning based on quantitative and disclosed corporate financial information. Using 12 types (21,360 cases) of market-disclosed financial information and 1,780 ESG measures available through the Korea Institute of Corporate Governance and Sustainability during 2019 to 2021, we suggested a deep neural network prediction model. Our model yielded about 86% of accurate classification performance in predicting ESG rating, showing better performance than other comparative models. This study contributed the literature in a way that the model achieved relatively accurate ESG rating predictions through an automated process using quantitative and publicly available corporate financial information. In terms of practical implications, the general investors can benefit from the prediction accuracy and time efficiency of our proposed model with nominal cost. In addition, this study can be expanded by accumulating more Korean and international data and by developing a more robust and complex model in the future.