• Journal of the Computational Structural Engineering Institute of Korea
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• v.31 no.6
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• pp.353-358
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• 2018

In this study, it is describe to an optimization analysis process for the weight reduction of the voltage converter in the electric vehicle charging systems. The optimization design is a technique that finds the optimal material distribution under a given material quantity constraint by combining the design sensitivity with the material properties and the mathematical optimization. Among the topology optimization, a lightweight design is performed by a solid isotropic material with penalization with simple formula and well-convergence. The lightweight design consists of three steps. As a first step, a finite element model for the basic design of the on-board voltage converter was constructed and static analysis was performed on the load. In the second step, the optimum shape is obtained for the lightweight by performing the topology optimization using the solid isotropic material with penalization applying the stiffness coefficient of the isotropic material to the static analysis result. As a final step, impact analysis was performed by applying a half-sinusoidal pulse shape impact load which satisfies the impact test standard of the vehicle-mounted part with respect to the optimum shape. In the topology optimization, the design domain was defined as the mounting bracket area, and the design technology was finally achieved by optimizing the mounting bracket to achieve a weight reduction of 20% over the basic design.

• Journal of Korean Society of Disaster and Security
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• v.11 no.2
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• pp.37-43
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• 2018

The storm surge is caused by an typhoons and it is not easy to predict the location, strength, route of the storm. Therefore, research using a scenario for storms occurrence has been conducted. In Korea, hazard maps for various scenarios were produced using the storm surge numerical simulation. Such a method has a disadvantage in that it is difficult to predict when other scenario occurs, and it is difficult to cope with in real time because the simulation time is long. In order to compensate for this, we developed a method to predict the storm surge damage by using research database. The risk grade prediction for the storm surge was performed predominantly in the study area of the East coast. In order to estimate the equation, COMSOL developed by COMSOL AB Corporation was utilized. Using some assumptions and limitations, the form of the basic equation was derived. the constants and coefficients in the equation were estimated by the trial and error method. Compared with the results, the spatial distribution of risk grade was similar except for the upper part of the map. In the case of the upper part of the map, it was shown that the resistance coefficient, k was calculated due to absence of elevation data. The SIND model is a method for real-time disaster prediction model and it is expected that it will be able to respond quickly to disasters caused by abnormal weather.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.12
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• pp.872-877
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• 2018

STEAM is an acronym for Science, Technology, Engineering, Arts, and Mathematics. It is considered important to equip students with a creative thinking ability and the core competences required in future society, helping them devise new ideas emerging from branches of study. This study is about the convergence of instructional design in private finance for the life sciences, which aims to foster talent through problem-based learning (PBL). Skills like collaboration, creativity, critical thinking, and problem solving are part of any STEAM PBL, and are needed for students to be effective. STEAM projects give students a chance to problem-solve in unique ways, because they are forced to use a variety of methods to solve problems that pop up during these types of activities. The results of this study are as follows. First is the structured process of convergence lessons. Second is the convergence lesson process. Third is the development of problems in the introduction of private finance and the life sciences for a convergence lesson at Dornod University. Learning motivation shows the following results: understanding of learning content (66.6%), effectiveness (63.3%), self-directed learning (59.9%), motivation (63.2%), and confidence (63.3%). To make an effective model, studies applying this instructional design are to be implemented.

• Journal of the Korean GEO-environmental Society
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• v.12 no.4
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• pp.25-31
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• 2011

In this study, the experimental design methodology was applied to optimize 1,4-dioxane treatment in E-beam process. Main factor was mathematically described as a function of parameters 1,4-dioxane removal efficiencies(%), TOC removal efficiencies(%) modeled by the use of the central composite design(CCD) method among the response surface methodology(RSM). Concentration of 1,4-dioxane is designated as "$x_1$" and Irradiation intensity is designated as "$x_2$". The regression equation in coded unit between the 1,4-dioxane concentration and removal efficiencies(%) was $y=71.00-10.85x_1+20.67x_2+{1.53x_1}^2-{7.92x_2}^2-1.23x_1x_2$. The regression equation in coded unit between the 1,4-dioxane concentration and TOC removal efficiencies(%) was $y=44.48-13.25x_1+9.54x_2+{5.43x_1}^2-{1.35x_2}^2+4.45x_1x_2$. The model predictions agreed well with the experimentally observed results $R^2$(Adj) over 90%. Toxicity test using algae Pseudokirchneriella Subcapitata showed that the inhibition was reduced according to increasing an E-beam irradiation.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.2
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• pp.487-495
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• 2021

The voltages mainly used in ships are 450 [V], 6.6 [kV], and 11 [kV], and an earthed system is applied to ensure the stability of the power distribution system. In general, low-voltage ships using 450 [V] apply an unearthed system, while high-voltage ships using 6.6 [kV] or 11 [kV] use a high-resistance earthed system. When an earth fault occurs in a ship's power distribution system, the voltage of the healthy phase increases to the line-to-line voltage or higher, which causes an excessive impact on the insulation of the cable. Thus, analyzing this behavior is very important. In this paper, we investigate the characteristics of the line-to-earth voltage variation according to earth faults and a recognition procedure of a faulty phase using the symmetrical coordinate method for a high-resistance earthed system and unearthed system. A mathematical model of the line-to-earth voltage was derived through the symmetric coordinate method, and the ship voltage for simulations was selected as 6.6 [kV] and 450 [V]. A MATLAB simulation proved that this method can determine the highest increase of the line-to-earth voltage, which leads by 120° on the faulty phase, and it accurately judges the faulty phase in both earthed systems.

• Journal of The Korean Association of Information Education
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• v.24 no.5
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• pp.495-509
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• 2020

This study is a theoretical research to explore ways to effectively learn AI in the age of intelligent information driven by artificial intelligence (hereinafter referred to as AI). The emphasis is on presenting a teaching method to make AI education accessible not only to students majoring in mathematics, statistics, or computer science, but also to other majors such as humanities and social sciences and the general public. Given the need for 'Explainable AI(XAI: eXplainable AI)' and 'the importance of storytelling for a sensible and intelligent machine(AI)' by Patrick Winston at the MIT AI Institute [33], we can find the significance of research on AI storytelling learning model. To this end, we discuss the possibility through a pilot study targeting general students of an university in Daegu. First, we introduce the AI storytelling(AI+ST) learning method[30], and review the educational goals, the system of contents, the learning methodology and the use of new AI tools in the method. Then, the results of the learners are compared and analyzed, focusing on research questions: 1) Can the AI+ST learning method complement algorithm-driven or developer-centered learning methods? 2) Whether the AI+ST learning method is effective for students and thus help them to develop their AI comprehension, interest and application skills.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.40 no.4
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• pp.325-332
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• 2022

Determination of VLBI IVP (Very Long Baseline Interferometry Invariant Point) position with high accuracy is required to compute local tie vectors between the space geodetic techniques. In general, reflective targets are attached on VLBI antenna and slant distances, horizontal and vertical angles are measured from the pillars. Then, adjustment computation is performed by using the mathematical model which connects measurements and unknown parameters. This indicates that the accuracy of the estimated solutions is affected by the accuracy of the measurements. One of issues in local tie surveying, however, is that the reflective targets are not in favorable condition, that is, the reflective sheet target cannot be perfectly aligned to the instrument perpendicularly. Deviation from the line of sight of an instrument may cause different type of measurement errors. This inherent limitation may lead to incorrect stochastic modeling for the measurements in adjustment computation procedures. In this study, error characteristics by measurement types and pillars are analyzed, respectively. The analysis on the studentized residuals is performed after adjustment computation. The normality of the residuals is tested and then equal variance test between the measurement types are performed. The results show that there are differences in variance according to the measurement types. Differences in variance between distances and angle measurements are observed when F-test is performed for the measurements from each pillar. Therefore, more detailed stochastic modeling is required for optimal solutions, especially in local tie survey.

• Korean Journal of Comparative Education
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• v.20 no.2
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• pp.95-128
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• 2010

This article presents a one and half-year process of Lesson Study conducted at a K-8 school in an urban district in the eastern U.S. Lesson Study, a Japanese form of professional development that centers on collaborative study of live classroom lessons, has spread rapidly in the U.S. since 1999 and has been argued as a promising alternative model for teacher-led school reform through professional development. The Lesson Study group described here was composed of five teachers, one administrator, and one instructional improvement coordinator belonging to the participant school and two instructional super-intendants from the school district. Data was collected from October 2007 to February 2009 and a qualitative case study method was employed for this study. Drawing a case of Lesson Study, this article intended to show how Lesson Study group members participated in planning, teaching, observing, discussing, and improving lessons collaboratively for student learning by enhancing teacher professional competence so that find directions for future implementation in Korea. This article investigates (1) process of Lesson Study, (2) issues Lesson Study group members mainly dealt with, and (3) changes have taken place in Lesson Study as it is conducted over time. (4) Finally, this article concludes with challenges to adopting Lesson Study successfully in Korea.

• Economic and Environmental Geology
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• v.56 no.5
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• pp.533-545
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• 2023

In this study, an investigation was conducted on the features, events, and processes (FEP) that could impact the long-term safety of the natural barriers constituting high-level radioactive waste geological repositories. The FEP list was developed utilizing the IFEP list 3.0 provided by the Nuclear Energy Agency (NEA) as foundational data, supplemented by geological investigations and research findings from leading countries in this field. A total of 49 FEPs related to the performance of the natural barrier were identified. For each FEP, detailed definitions, classifications, impacts on long-term safety, significance in domestic conditions, and feasibility of quantification were provided. Moreover, based on the compiled FEP list, three scenarios that could affect the long-term safety of the disposal facility were developed. Geological factors affecting the performance of the natural barrier in each scenario were selected and their relationships were visualized. The constructed FEP list and the visualization of interrelated factors in various scenarios are anticipated to provide essential information for selecting and organizing factors that must be considered in the development of mathematical models for quantitatively evaluating the long-term safety of deep geological repositories. In addition, these findings could be effectively utilized in establishing criteria related to the key performance of natural barriers for the confirmation of repository sites.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.1
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• pp.229-249
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• 2022

This paper investigates machine learning models for predicting the designation of administrative issues in the KOSDAQ market through various techniques. When a company in the Korean stock market is designated as administrative issue, the market recognizes the event itself as negative information, causing losses to the company and investors. The purpose of this study is to evaluate alternative methods for developing a artificial intelligence service to examine a possibility to the designation of administrative issues early through the financial ratio of companies and to help investors manage portfolio risks. In this study, the independent variables used 21 financial ratios representing profitability, stability, activity, and growth. From 2011 to 2020, when K-IFRS was applied, financial data of companies in administrative issues and non-administrative issues stocks are sampled. Logistic regression analysis, decision tree, support vector machine, random forest, and LightGBM are used to predict the designation of administrative issues. According to the results of analysis, LightGBM with 82.73% classification accuracy is the best prediction model, and the prediction model with the lowest classification accuracy is a decision tree with 71.94% accuracy. As a result of checking the top three variables of the importance of variables in the decision tree-based learning model, the financial variables common in each model are ROE(Net profit) and Capital stock turnover ratio, which are relatively important variables in designating administrative issues. In general, it is confirmed that the learning model using the ensemble had higher predictive performance than the single learning model.