• Journal of the Korea Academia-Industrial cooperation Society
• /
• v.20 no.5
• /
• pp.627-635
• /
• 2019

A programmable drone is a drone developed not only to experience the basic principles of flight but also to control drones through Arduino-based programming. Due to the nature of the training drones, the main users are students who are inexperienced in controlling the drones, which often cause frequent collisions with external objects, resulting in high damage to the drones' frame. In this study, the structural stability of the drone was evaluated by means of a structural dynamics based collision simulation for educational drone frame. Collision simulations were performed on three cases according to the impact angle of $0^{\circ}$, $+15^{\circ}$ and $-15^{\circ}$, using an analytical model with approximately 240,000 tetrahedron elements. Using ANSYS LS-DYNA, which provides excellent functions for the simulation of the dynamic behavior of three-dimensional structures, the stress distribution and strain generated on the drone upper, the drone lower, and the ring assembly were analyzed when the drones collided against the wall at a rate of 4 m/s. Safety factors resulting from the equivalent stress and the yield strain were calculated in the range of 0.72 to 2.64 and 1.72 to 26.67, respectively. To ensure structural stability for areas where stress exceeds yield strain and ultimate strain according to material properties, the design reinforcement is presented.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2023.05a
• /
• pp.31-31
• /
• 2023

During December 2022, the northeast monsoon, which dominates the south and the Gulf of Thailand, had significant rainfall that impacted the lower southern region, causing flash floods, landslides, blustery winds, and the river exceeding its bank. The Golok River, located in Narathiwat, divides the border between Thailand and Malaysia was also affected by rainfall. In flood management, instruments for measuring precipitation and water level have become important for assessing and forecasting the trend of situations and areas of risk. However, such regions are international borders, so the installed measuring telemetry system cannot measure the rainfall and water level of the entire area. This study aims to predict 72 hours of water level and evaluate the situation as information to support the government in making water management decisions, publicizing them to relevant agencies, and warning citizens during crisis events. This research is applied to machine learning (ML) for water level prediction of the Golok River, Lan Tu Bridge area, Sungai Golok Subdistrict, Su-ngai Golok District, Narathiwat Province, which is one of the major monitored rivers. The eXtreme Gradient Boosting (XGBoost) algorithm, a tree-based ensemble machine learning algorithm, was exploited to predict hourly water levels through the R programming language. Model training and testing were carried out utilizing observed hourly rainfall from the STH010 station and hourly water level data from the X.119A station between 2020 and 2022 as main prediction inputs. Furthermore, this model applies hourly spatial rainfall forecasting data from Weather Research and Forecasting and Regional Ocean Model System models (WRF-ROMs) provided by Hydro-Informatics Institute (HII) as input, allowing the model to predict the hourly water level in the Golok River. The evaluation of the predicted performances using the statistical performance metrics, delivering an R-square of 0.96 can validate the results as robust forecasting outcomes. The result shows that the predicted water level at the X.119A telemetry station (Golok River) is in a steady decline, which relates to the input data of predicted 72-hour rainfall from WRF-ROMs having decreased. In short, the relationship between input and result can be used to evaluate flood situations. Here, the data is contributed to the Operational support to the Special Water Resources Management Operation Center in Southern Thailand for flood preparedness and response to make intelligent decisions on water management during crisis occurrences, as well as to be prepared and prevent loss and harm to citizens.

• Journal of Gifted/Talented Education
• /
• v.25 no.2
• /
• pp.339-358
• /
• 2015

This study was to examine perceptions of information technology(IT) competencies among gifted and non-gifted students(i.e., information science high school students and technical high school students). Of the 370 high school students surveyed from 3 high schools(i.e., gifted academy, information science high school, and technical high school) in three metropolitan cities, Korea, 351 students completed and returned the questionnaires yielding a total response rate of 94.86%. High school students recognized the IT professional competence as being most important when recruiting IT employees. And they considered that practice-oriented education was the most importantly needed to improve their IT skills. In addition, the most important sub-factors of IT core competencies among gifted academy students and information science high school students were basic software skills. Also Technical high school students responded that the main network and security capabilities were the most importantly needed to do so. Finally, the most appropriate training courses for enhancing IT competencies were recognized differently among gifted and non-gifted students. Gifted academy students responded that the 'algorithm' was the mostly needed for enhancing IT competencies, whereas information science high school students responded that 'data structures' and 'computer architecture' were mostly needed to do. For technical high school students, they responded that a 'programming language' course was the most needed to do so. Results are discussed in relations to IT corporate and school settings.

• Journal of Intelligence and Information Systems
• /
• v.27 no.1
• /
• pp.65-82
• /
• 2021

Stock market investors are generally split into foreign investors, institutional investors, and individual investors. Compared to individual investor groups, professional investor groups such as foreign investors have an advantage in information and financial power and, as a result, foreign investors are known to show good investment performance among market participants. The purpose of this study is to propose an investment strategy that combines investor-specific transaction information and machine learning, and to analyze the portfolio investment performance of the proposed model using actual stock price and investor-specific transaction data. The Korea Exchange offers daily information on the volume of purchase and sale of each investor to securities firms. We developed a data collection program in C# programming language using an API provided by Daishin Securities Cybosplus, and collected 151 out of 200 KOSPI stocks with daily opening price, closing price and investor-specific net purchase data from January 2, 2007 to July 31, 2017. The self-organizing map model is an artificial neural network that performs clustering by unsupervised learning and has been introduced by Teuvo Kohonen since 1984. We implement competition among intra-surface artificial neurons, and all connections are non-recursive artificial neural networks that go from bottom to top. It can also be expanded to multiple layers, although many fault layers are commonly used. Linear functions are used by active functions of artificial nerve cells, and learning rules use Instar rules as well as general competitive learning. The core of the backpropagation model is the model that performs classification by supervised learning as an artificial neural network. We grouped and transformed investor-specific transaction volume data to learn backpropagation models through the self-organizing map model of artificial neural networks. As a result of the estimation of verification data through training, the portfolios were rebalanced monthly. For performance analysis, a passive portfolio was designated and the KOSPI 200 and KOSPI index returns for proxies on market returns were also obtained. Performance analysis was conducted using the equally-weighted portfolio return, compound interest rate, annual return, Maximum Draw Down, standard deviation, and Sharpe Ratio. Buy and hold returns of the top 10 market capitalization stocks are designated as a benchmark. Buy and hold strategy is the best strategy under the efficient market hypothesis. The prediction rate of learning data using backpropagation model was significantly high at 96.61%, while the prediction rate of verification data was also relatively high in the results of the 57.1% verification data. The performance evaluation of self-organizing map grouping can be determined as a result of a backpropagation model. This is because if the grouping results of the self-organizing map model had been poor, the learning results of the backpropagation model would have been poor. In this way, the performance assessment of machine learning is judged to be better learned than previous studies. Our portfolio doubled the return on the benchmark and performed better than the market returns on the KOSPI and KOSPI 200 indexes. In contrast to the benchmark, the MDD and standard deviation for portfolio risk indicators also showed better results. The Sharpe Ratio performed higher than benchmarks and stock market indexes. Through this, we presented the direction of portfolio composition program using machine learning and investor-specific transaction information and showed that it can be used to develop programs for real stock investment. The return is the result of monthly portfolio composition and asset rebalancing to the same proportion. Better outcomes are predicted when forming a monthly portfolio if the system is enforced by rebalancing the suggested stocks continuously without selling and re-buying it. Therefore, real transactions appear to be relevant.