• Journal of Science Education
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• v.45 no.2
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• pp.156-171
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• 2021

This study aims at investigating the characteristics of trends of future education over time though the literature review and examining the accuracy of the framework for forecasting future education proposed by the previous studies by comparing the outcomes between the literature review and media articles. Thus, this study collects the articles dealing with future education searched from the Web of Science and categorized them into four periods during the new millennium. The new articles from media were selected to find out the present of education so that we can figure out the appropriateness of the proposed framework to predict the future of education. Research findings reveal that gradual tendencies of topics could not be found except teacher education and they are diverse from characteristics of agents (students and teachers) to the curriculum and pedagogical strategies. On the other hand, the results of analysis on the media articles focuses more on the projects launched by the government and the immediate responses to the COVID-19, as well as educational technologies related to big data and artificial intelligence. It is surprising that only a few key words are occupied in the latest articles from the literature review and many of them have not been discussed before. This indicates that the predictive framework is not effective to establish the long-term plan for education due to the uncertainty of educational environment, and thus this study will give some implications for developing the model to forecast the future of education.

• Ecology and Resilient Infrastructure
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• v.9 no.4
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• pp.218-227
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• 2022

This study attempted classification through deep learning-based image training for land cover classification in river spaces which is one of the important data for efficient river management. For this purpose, land cover classification analysis with the RGB image of the target section based on the category classification index of major land cover map was conducted by using the learning outcomes from the result of labeling. In addition, land cover classification of the river spaces was performed by unsupervised and supervised classification from Sentinel-2 satellite images provided in an open format, and this was compared with the results of deep learning-based image classification. As a result of the analysis, it showed more accurate prediction results compared to unsupervised classification results, and it presented significantly improved classification results in the case of high-resolution images. The result of this study showed the possibility of classifying water areas and wetlands in the river spaces, and if additional research is performed in the future, the deep learning based image train method for the land cover classification could be used for river management.

• Journal of Korea Water Resources Association
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• v.41 no.2
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• pp.149-162
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• 2008

The Soil and Water Assessment Tool (SWAT) developed by the USDA-Agricultural Research Service for the prediction of land management impact on water, sediment, and agricultural chemical yields in a large-scale basin was applied to Daecheong Reservoir basin to estimate the amount of soil losses from different land uses. The research outcomes provide important indications for reservoir managers and policy makers to search alternative watershed management practices for the mitigation of reservoir turbidity flow problems. After calibrations of key model parameters, SWAT showed fairly good performance by adequately simulating observed annual runoff components and replicating the monthly flow regimes in the basin. The specific soil losses from agricultural farm field, forest, urban area, and paddy field were 33.1, $2.3{\sim}5.4$ depending on the tree types, 1.0, and 0.1 tons/ha/yr, respectively in 2004. It was noticed that about 55.3% of the total annual soil loss is caused by agricultural activities although agricultural land occupies only 10% in the basin. Although the soil erosion assessment approach adopted in this study has some extent of uncertainties due to the lack of detailed information on crop types and management activities, the results at least imply that soil erosion control practices for the vulnerable agricultural farm lands can be one of the most effective alternatives to reduce the impact of turbidity flow in the river basin system.

• Journal of the Korean Society of Marine Environment & Safety
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• v.24 no.7
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• pp.930-938
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• 2018

Underwater noise radiated from submarines is directly related to the probability of being detected by the sonar of an enemy vessel. Therefore, minimizing the noise of a submarine is essential for improving survival outcomes. For modern submarines, as the speed and size of a submarine increase and noise reduction technology is developed, interest in flow noise around the hull has been increasing. In this study, a noise analysis technique was developed to predict flow noise generated around a submarine shape considering the free surface effect. When a submarine is operated near a free surface, turbulence-induced noise due to the turbulence of the flow and bubble noise from breaking waves arise. First, to analyze the flow around a submarine, VOF-based incompressible two-phase flow analysis was performed to derive flow field data and the shape of the free surface around the submarine. Turbulence-induced noise was analyzed by applying permeable FW-H, which is an acoustic analogy technique. Bubble noise was derived through a noise model for breaking waves based on the turbulent kinetic energy distribution results obtained from the CFD results. The analysis method developed was verified by comparison with experimental results for a submarine model measured in a Large Cavitation Tunnel (LCT).

• Current Optics and Photonics
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• v.1 no.2
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• pp.107-112
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• 2017

An adaptive optics system can be simulated or analyzed to predict its closed-loop performance. However, this type of prediction based on various assumptions can occasionally produce outcomes which are far from actual experience. Thus, every adaptive optics system is desired to be tested in a closed loop on an optical test bench before its application to a telescope. In the close-loop test bench, we need an atmospheric simulator that simulates atmospheric disturbances, mostly in phase, in terms of spatial and temporal behavior. We report the development of an atmospheric turbulence simulator consisting of two point sources, a commercially available deformable mirror with a $12{\times}12$ actuator array, and two random phase plates. The simulator generates an atmospherically distorted single or binary star with varying stellar magnitudes and angular separations. We conduct a simulation of a binary star by optically combining two point sources mounted on independent precision stages. The light intensity of each source (an LED with a pin hole) is adjustable to the corresponding stellar magnitude, while its angular separation is precisely adjusted by moving the corresponding stage. First, the atmospheric phase disturbance at a single instance, i.e., a phase screen, is generated via a computer simulation based on the thin-layer Kolmogorov atmospheric model and its temporal evolution is predicted based on the frozen flow hypothesis. The deformable mirror is then continuously best-fitted to the time-sequenced phase screens based on the least square method. Similarly, we also implement another simulation by rotating two random phase plates which were manufactured to have atmospheric-disturbance-like residual aberrations. This later method is limited in its ability to simulate atmospheric disturbances, but it is easy and inexpensive to implement. With these two methods, individually or in unison, we can simulate typical atmospheric disturbances observed at the Bohyun Observatory in South Korea, which corresponds to an area from 7 to 15 cm with regard to the Fried parameter at a telescope pupil plane of 500 nm.

• The Korean Journal of Applied Statistics
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• v.31 no.6
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• pp.677-692
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• 2018

The total fertility rate of Korea was 1.05 in 2017, showing a return to the 1.08 level in the year 2005. 1.05 is a very low fertility level that is far from replacement level fertility or safety zone 1.5. The number may indicate a low fertility trap. It is therefore important to predict fertility than at any other time. In the meantime, we have predicted the age-specific fertility rate and total fertility rate by various statistical methods. When the data trend is disconnected or fluctuating, it applied a nonparametric method applying the smoothness and weight. In addition, the Bayesian method of using the pre-distribution of fertility rates in advanced countries with reference to the three-stage transition phenomenon have been applied. This paper examines which method is reasonable in terms of precision and feasibility by applying estimation, forecasting, and comparing the results of the recent variability of the Korean fertility rate with parametric, non-parametric and Bayesian methods. The results of the analysis showed that the total fertility rate was in the order of KOSTAT's total fertility rate, Bayesian, parametric and non-parametric method outcomes. Given the level of TFR 1.05 in 2017, the predicted total fertility rate derived from the parametric and nonparametric models is most reasonable. In addition, if a fertility rate data is highly complete and a quality is good, the parametric model approach is superior to other methods in terms of parameter estimation, calculation efficiency and goodness-of-fit.

• Korean Journal of Clinical Laboratory Science
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• v.50 no.2
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• pp.126-135
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• 2018

This study examined whether the difference in the middle cerebral artery (MCA) velocities can predict the prognosis of stroke and whether the prognostic impact differs among stroke subtypes. Transient ischemic attack (TIA) or acute ischemic stroke patients, who underwent a routine evaluation and transcranial Doppler (TCD), were included in this study. The MCA asymmetry index was calculated using the relative percentage difference in the mean flow velocity (MFV) between the left and right MCA: (|RMCA MFV-LMCA MFV|/mean MCA MFV)${\times}100$. The stroke subtypes were determined using the TOAST classification. Poor functional outcomes were defined as a mRS score ${\geq}3$ at 3 months after the onset of stroke. A total of 988 patients were included, of whom 157 (15.9%) had a poor functional outcome. Multivariable analysis showed that only the MCA asymmetry index was independently associated with a poor functional outcome. ROC curve analysis showed that adding the MCA asymmetry index to the prediction model improved the discrimination of a poor functional outcome from acute ischemic stroke (from 88.6% [95% CI, 85.2~91.9] to 89.2% [95% CI, 85.9~92.5]). The MCA asymmetry index has an independent prognostic value for predicting a poor short-term functional outcome after an acute cerebral infarction. Therefore, TCD may be useful for predicting a poor functional outcome in patients with acute ischemic stroke.

• Journal of Digital Convergence
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• v.19 no.4
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• pp.467-475
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• 2021

Purpose: When a patient with acute deterioration occurs in a ward, the decision to transfer to intensive care unit (ICU) is critical to improve the patient's outcomes. However, when available ICU resources limited, it is difficult to determine which of the deteriorating ward patients to transfer to the ICU. Therefore the purpose of this study was to identify risk factors in predicting deteriorating ward patients transferred to intensive care unit (ICU). Methods: We reviewed retrospectively clinical data of 2,945 deteriorating ward patients who referred medical emergency team. Data were analyzed with multivariate logistic regression. Results: The solid cancer that diagnosed at hospitalization (odds ratio[OR] 0.39; 95% confidence interval [CI] 0.32-0.47), when the cause of deterioration was respiratory problem (1.51; 95% CI 1.17-1.95), high MEWS (1.22; 1.17-1.28) and SpO2/FiO2 score (2.41; 2.23-2.60) were predictive of ICU transfer. Conclusion: These findings suggest that early prediction and treatment of patients with high risk of ICU transfer may improve the prognosis of patients.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.18 no.12
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• pp.581-588
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• 2017

This study was conducted to determine which factors would predict metabolic syndrome (MetS) perception and exercise by applying a machine learning classifier, or Extreme Gradient Boosting algorithm (XGBoost) from July 2014 to December 2015. Data were obtained from the Korean Community Health Survey (KCHS), representing different community-dwelling Korean adults 19 years and older, from 2009 to 2013. The dataset includes 370,430 adults. Outcomes were categorized as follows based on the perception of MetS and physical activity (PA): Stage 1 (no perception, no PA), Stage 2 (perception, no PA), and Stage 3 (perception, PA). Features common to all questionnaires for the last 5 years were selected for modeling. Overall, there were 161 features, categorical except for age and the visual analogue scale (EQ-VAS). We used the Extreme Boosting algorithm in R programming for a model to predict factors and achieved prediction accuracy in 0.735 submissions. The top 10 predictive factors in Stage 3 were: age, education level, attempt to control weight, EQ mobility, nutrition label checks, private health insurance, EQ-5D usual activities, anti-smoking advertising, EQ-VAS, education in health centers for diabetes, and dental care. In conclusion, the results showed that XGBoost can be used to identify factors influencing disease prevention and management using healthcare bigdata.

• 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.