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

• Journal of Korean Tunnelling and Underground Space Association
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• v.23 no.6
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• pp.517-534
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• 2021

Hydrogen fuel is emerging as an new energy source to replace fossil fuels in that it can solve environmental pollution problems and reduce energy imbalance and cost. Since hydrogen is eco-friendly but highly explosive, there is a high concern about fire and explosion accidents of hydrogen fueled vehicles. In particular, in semi-enclosed spaces such as tunnels, the risk is predicted to increase. Therefore, this study was conducted on the applicability of the equivalent TNT model and the numerical analysis method to evaluate the hydrogen explosion pressure in the tunnel. In comparison and review of the explosion pressure of 6 equivalent TNT models and Weyandt's experimental results, the Henrych equation was found to be the closest with a deviation of 13.6%. As a result of examining the effect of hydrogen tank capacity (52, 72, 156 L) and tunnel cross-section (40.5, 54, 72, 95 m²) on the explosion pressure using numerical analysis, the explosion pressure wave in the tunnel initially it propagates in a hemispherical shape as in open space. Furthermore, when it passes the certain distance it is transformed a plane wave and propagates at a very gradual decay rate. The Henrych equation agrees well with the numerical analysis results in the section where the explosion pressure is rapidly decreasing, but it is significantly underestimated after the explosion pressure wave is transformed into a plane wave. In case of same hydrogen tank capacity, an explosion pressure decreases as the tunnel cross-sectional area increases, and in case of the same cross-sectional area, the explosion pressure increases by about 2.5 times if the hydrogen tank capacity increases from 52 L to 156 L. As a result of the evaluation of the limiting distance affecting the human body, when a 52 L hydrogen tank explodes, the limiting distance to death was estimated to be about 3 m, and the limiting distance to serious injury was estimated to be 28.5~35.8 m.

• Journal of Intelligence and Information Systems
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• v.28 no.4
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• pp.157-177
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• 2022

Investors are trading stocks by keeping a close watch on the order information submitted by domestic and foreign investors in real time through Limit Order Book information, so-called price current provided by securities firms. Will order information released in the Limit Order Book be useful in stock price prediction? This study analyzes whether it is significant as a predictor of future stock price up or down when order imbalances appear as investors' buying and selling orders are concentrated to one side during intra-day trading time. Using classification algorithms, this study improved the prediction accuracy of the order imbalance information on the short-term price up and down trend, that is the closing price up and down of the day. Day trading strategies are proposed using the predicted price trends of the classification algorithms and the trading performances are analyzed through empirical analysis. The 5-minute KOSPI200 Index Futures data were analyzed for 4,564 days from January 19, 2004 to June 30, 2022. The results of the empirical analysis are as follows. First, order imbalance information has a significant impact on the current stock prices. Second, the order imbalance information observed in the early morning has a significant forecasting power on the price trends from the early morning to the market closing time. Third, the Support Vector Machines algorithm showed the highest prediction accuracy on the day's closing price trends using the order imbalance information at 54.1%. Fourth, the order imbalance information measured at an early time of day had higher prediction accuracy than the order imbalance information measured at a later time of day. Fifth, the trading performances of the day trading strategies using the prediction results of the classification algorithms on the price up and down trends were higher than that of the benchmark trading strategy. Sixth, except for the K-Nearest Neighbor algorithm, all investment performances using the classification algorithms showed average higher total profits than that of the benchmark strategy. Seventh, the trading performances using the predictive results of the Logical Regression, Random Forest, Support Vector Machines, and XGBoost algorithms showed higher results than the benchmark strategy in the Sharpe Ratio, which evaluates both profitability and risk. This study has an academic difference from existing studies in that it documented the economic value of the total buy & sell order volume information among the Limit Order Book information. The empirical results of this study are also valuable to the market participants from a trading perspective. In future studies, it is necessary to improve the performance of the trading strategy using more accurate price prediction results by expanding to deep learning models which are actively being studied for predicting stock prices recently.

• Journal of the Korean Society of Marine Environment & Safety
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• v.29 no.7
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• pp.802-811
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• 2023

Marine spatial planning (MSP) is a crucial element for rational allocation and sustainable use of marine areas. Particularly, Fishing Activity Protected Areas constitute essential zones accounting for 45.6% designated for sustainable fishing activities. However, the current assessment of these zones does not adequately consider future demands and potential values, necessitating appropriate evaluation methods and predictive tools for long-term planning. In this study, we selected key fish species (Scomber japonicus, Trichiurus lepturus, Engraulis japonicus, and Larimichthys polyactis) within the Fishing Activity Protected Area to predict their distribution and compare it with the current designated zones for evaluating the ability of the prediction tool. Employing the Intergovernmental Panel on Climate Change (IPCC) 6th Assessment Report scenarios (SSP1-2.6 and SSP5-8.5), we used species distribution models (such as MaxEnt) to assess the movement and distribution changes of these species owing to future variations. The results indicated a 30-50% increase in the distribution area of S. japonicus, T. lepturus, and L. polyactis, whereas the distribution area of E. japonicus decreased by approximately 6-11%. Based on these results, a species richness map for the four key species was created. Within the marine spatial planning boundaries, the overlap between areas rated "high" in species richness and the Fishing Activity Protected Area was approximately 15%, increasing to 21% under the RCP 2.6 scenario and 34% under the RCP 8.5 scenario. These findings can serve as scientific evidence for future evaluations of use zones or changes in reserve areas. The current and predicted distributions of species owing to climate change can address the limitations of current use zone evaluations and contribute to the development of plans for sustainable and beneficial use of marine resources.

• Radiation Oncology Journal
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• v.15 no.4
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• pp.393-402
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• 1997

Purpose : When an x-ray beam of small field size is irradiated to target area containing an air cavity, such as larynx, the underdosing effect is observed in the region near the interfaces of air and soft tissue. With a larynx model, air cavity embedded in tissue-equivalent material, this study is intonded for examining Parameters, such as beam quality, field size, and cavity size, to affect the dose distribution near the air cavity. Materials and Methods : Three x-rar beams, 4-, 6- and 10-MV, were employed to Perform a measurement using a 2cm $(width){\times}L$ (length in cm, one side of x-ray field used 2cm (height) air cavity in the simulated larynx. A thin window parallel-plate chamber connected to an electrometer was used for a dosimetry system. A ratio of the dose at various distances from the cavity-tissue interface to the dose at the same points in a homogeneous Phantom (ebservedlexpected ratio, O/E) normalized buildup curves, and ratio of distal surface dose to dose at the maximum buildup depth were examined for various field sizes. Measurement for cavity size effect was performed by varying the height (Z) of the air cavity with the width kept constant for several field sizes. Results : No underdosing effect for 4-MV beam for fields larger than $5cm\times5cm$ was found For both 6- and 10-MV beams, the underdosing portion of the larynx at the distal surface was seen to occur for small fields, $4cm\times4cm\;and\;5cm\times5cm$. The underdosed tissue was increased in its volume with beam energy even for similar surface doses. The relative distal surface dose to maximum dose was changed to 0.99 from 0.95, 0.92, and 0.91 for 4-, 6-, and 10-MV, respectively, with increasing field size, $4cm\times4cm\;to\;8cm\times8cm$, For 6- and 10-MV beams, the dose at the surface of the cavity is measured less than the predicted by about two and three percent. respectively. but decrease was found for 4-MV beam for $5cm\times5cm$ field. For the $4cm\timesL\timesZ$ (height in cm). varying depth from 0.0 to 4.8cm, cavity, O/E> 1.0 was observed regardless of the cavity size for any field larger than about $8cm\times8cm$. Conclusion : The magnitude of underdosing depends on beam energy, field size. and cavity size for the larynx model. Based on the result of the study. caution must be used when a small field of a high quality x-ray beam is irradiated to regions including air cavities. and especially the region where the tumor extends to the surface. Low quality beam. such as. 4-MV x-ray, and larger fields can be used preferably to reduce the risk of underdosing, local failure. In the case of high quality beams such as 6- and 10-MV x-rays, however. an additional boost field is recommended to add for the compensation of the underdosing region when a typically used treatment field. $8cm\times8cm$, is employed.

• Tuberculosis and Respiratory Diseases
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• v.46 no.6
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• pp.795-802
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• 1999

Objective : Cardiopulmonary exercise test is a useful tool to evaluate the operative risk and to plan exercise treatment for the patients with chronic obstructive pulmonary disease(COPD). In cardiopulmonary exercise test, most of the measured parameters are recorded at the time of peak exercise, which are hard to attain in COPD patients. So we evaluated the usefulness of the parameter, breathing reserve index(BRI=minute ventilation [$V_E$]/maximal voluntary ventilation[MVV]) at the time of anaerobic threshold($BRI_{AT}$) for the differentiation of COPD patients with normal controls. Methods : Thirty-six COPD patients and forty-two healthy subjects underwent progressive, incremental exercise test with bicycle ergometer upto possible maximal exercise. All the parameters was measured by breath by breath method. Results : The maximal oxygen uptake in COPD patients (mean$\pm$SE) was $1061.2{\pm}65.6ml/min$ which was significantly lower than $2137.6{\pm}91.4ml/min$ of normal subjects(p<0.01). Percent predicted maximal oxygen uptake was 54.3% in COPD patients and 86.0% in normal subjects(p<0.01). Maximal exercise(respiratory quotient; $VCO_2/VO_2{\geq}1.09$) was accomplished in 7 of 36 COPD patients(19.4%) and in 18 of 42 normal subjects(42.9%). The $BRI_{AT}$ of COPD patients was higher($0.50{\pm}0.03$) than that of control subject($028{\pm}0.02$, p<0.01), reflecting early hyperventilation in COPD patient during exercise. The correlation between $BRI_{AT}$ and BRI at maximal exercise in COPD patients was good(r=0.9687, p<0.01). Conclusion : The $BRI_{AT}$ could be a useful parameter for the differentiation of COPD patients with normal controls in the submaximal cardiopulmonary exercise test.