• Journal of Global Scholars of Marketing Science
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• v.20 no.3
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• pp.249-261
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• 2010

As consumers' concern for the environment has continued to increase, many firms have actively engaged in environmental marketing to achieve their objectives. However, consumers' high concerns about the environment are not always reflected in their purchasing behavior. This indicates the need for an in-depth understanding of the development of green consumption within the individual's belief system. In consideration of psychological approaches, a large body of research has examined the factors underlying ecologically conscious "green" consumer behavior and the interrelationships of these factors. However, most previous studies have concentrated on Western countries. Using a sample of Korean consumers, this study attempts to understand the basis of Korean green consumerism and find universal values that are cross-culturally important in guiding consumers' environmental attitudes and behaviors. To this end, this study relates Schwartz's 10 universal values (Schwartz 1992) to environmental behaviors in a hierarchical model of value-attitude-behavior. With reference to the value-attitude-behavior framework, the conceptual model developed for the study explains what motivations can be manifested in Korean consumers' environmental attitudes, and subsequently how the attitudes affect their green choices. Using the pattern of relationships among values that can be related to environmentalism, the first hypothesis holds that there would be particular relationships between motivational value types and environmental attitudes. Hypothesis 2 assumes that environmental attitudes predict environmental behaviors. On the basis of the claim that favorable attitudes toward the environment may be expressed in many different behaviors, the assumption is that consumers' favorable attitudes toward the environment would be linked to a variety of environmental behaviors because people with high environmental attitudes can be more interested in and knowledgeable about environmental actions. Consistent with H2, H3 hypothesizes that there would be a positive relationship between different types of environmental behavior. A total of 564 university students participated in the study. The sample included 308 men, 254 women, and two participants who did not indicate their gender. The average age of the participants was 22.5 years, with a range of 19 to 39. Regarding majors, special efforts were made to draw the participants from different departments of the university. Data were collected by a survey administered via self-completion questionnaires., which assessed the participants' value priorities, environmental attitudes, and behaviors. Path analysis conducted to test the proposed model found the overall fit to be ${\chi}^2$=72.01 (p=0.00), GFI=0.983, CFI=0.982, NFI=0.970, RMR=0.070, and REMSEA=0.050. Thus, most of the fit measures indicated a good fit of the model with the data, and a hierarchical relationship from values to environmental attitudes to environmental non-purchasing behavior to environmental purchasing behavior was confirmed. An assessment of all the predicted paths by path coefficients led to several major hypothesized effects being confirmed. Out of the ten value types, universalism and power were significantly but conversely related to environmental attitudes. In line with the other studies, these findings confirm that environmental attitudes are an important factor in leading to a variety of green behaviors. Finally, significant relationships were found between environmental purchasing and non-purchasing behaviors. The path analysis supported the idea that universalism values provide a motivation for Korean consumers' greenness and indirectly promote environmental acts through favorable attitudes toward the environment. Participants with high environmental attitudes were found to actively engage in diverse forms of green consumer behavior. This research provides an opportunity to examine cross-cultural differences with respect to values leading to environmentalism, and, further, to verify previous findings. The study also examined the attitude-behavior relationship with respect to three distinct types of environmental behaviors. The different strengths of paths between green attitudes and behaviors suggest that researchers should consider the specificity of behavior explained as an effort to improve the low attitude-behavior correlation. Finally, the findings here illustrate that with increased environmental concerns among people, they come to include more such behaviors in their green portfolios.

• Journal of dental hygiene science
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• v.14 no.3
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• pp.332-341
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• 2014

This study was aimed to define the predicting factor account for the Korean Dental Hygienist Licensing Examination (KDHLE) by analyzing the academic grade score and the KDHLE score. The subjects included 185 graduates (2007, 2009, 2011~2014 graduates). The ratio of successful applicants of the subjects was 99.2%. The academic grade scores were calculated to grade point average, the KDHLE was scoring marks out of 300 (200 of written examination score and 100 of performance evaluation score) for correlation and regression analysis. The graduation grades and comprehensive examination scores correlated significantly with the KDHLE written examination scores (correlation coefficient=0.612), and KDHLE total score (correlation coefficient=0.258). First~third grade score and comprehensive examination scores correlated significantly with KDHLE total scores (p<0.05). Especially, there are the highest correlated between second comprehensive examination scores and KDHLE total scores (correlation coefficient=0.455), the last score in time sequence is the important factors account for the KDHLE total score. But there is no correlation between academic grade score and KDHLE performance evaluation scores, therefore it is necessary to study for determine the reason. The results of multiple linear regression analysis, second grade score and the average score of comprehensive examination were the main predicting factors account for the KDHLE total score, the explanatory power was 31.6%. Our results show that KDHLE total and written examination scores are predicted by the academic grade score reliably, but not the KDHLE performance evaluation scores. Further studies are needed to determine relationship between dental hygiene education and KDHLE.

• Korean Chemical Engineering Research
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• v.56 no.2
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• pp.169-175
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• 2018

This study deals with a process for the recovery of hydrogen isotopes from methane ($CQ_4$) and water ($Q_2O$) containing tritium in the nuclear fusion exhaust gas (Q is Hydrogen, Deuterium, Tritium). Steam Methane Reforming and Water Gas Shift reactions are used to convert $CQ_4$ and $Q_2O$ to $Q_2$ and the produced $Q_2$ is recovered by the subsequent Pd membrane. In this study, one circulation loop consisting of catalytic reactor, Pd membrane, and circulation pump was applied to recover H components from $CH_4$ and $H_2O$, one of $CQ_4$ and $Q_2O$. The conversion of $CH_4$ and $H_2O$ was measured by varying the catalytic reaction temperature and the circulating flow rate. $CH_4$ conversion was 99% or more at the catalytic reaction temperature of $650^{\circ}C$ and the circulating flow rate of 2.0 L/min. $H_2O$ conversion was 96% or more at the catalytic reaction temperature of $375^{\circ}C$ and the circulating flow rate of 1.8 L/min. In addition, the amount of $CQ_4$ generated by Korean Demonstration Fusion Power Plant (K-DEMO) in the future was predicted. Then, the treatment process for the $CQ_4$ was proposed and HAZOP (hazard and operability) analysis was conducted to identify the risk factors and operation problems of the process.

• The Journal of Korean Society for Radiation Therapy
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• v.31 no.1
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• pp.17-24
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• 2019

Purpose: The present study aims to assess the level of coherency and the accuracy of Point dose of the Isocenter of VERO, a linear accelerator developed for the purpose of the Stereotactic Body Radiation Therapy(SBRT). Materials and Method: The study was conducted randomly with 10 treatment plans among SBRT patients in Kyungpook National University Chilgok Hospital, using VERO, a linear accelerator between June and December, 2018. In order to assess the equipment's power stability level, we measured the output constancy by using PTW-LinaCheck, an output detector. We also attempted to measure the level of accuracy of the equipment's Laser, kV(Kilo Voltage) imaging System, and MV(Mega Voltage) Beam by using Tofu Phantom(BrainLab, Germany) to assess the accuracy level of geometrical Isocenter. We conducted a comparative analysis to assess the accuracy level of the dose by using an acrylic Phantom($30{\times}30{\times}20cm$), a calibrated ion chamber CC-01(IBA Dosimetry), and an Electrometer(IBA, Dosimetry). Results: The output uniformity of VERO was calculated to be 0.66 %. As for geometrical Isocenter accuracy, we analyzed the error values of ball Isocenter of inner Phantom, and the results showed a maximum of 0.4 mm, a minimum of 0.0 mm, and an average of 0.28 mm on X-axis, and a maximum of -0.4 mm, a minimum of 0.0 mm, and an average of -0.24 mm on Y-axis. A comparison and evaluation of the treatment plan dose with the actual measured dose resulted in a maximum of 0.97 % and a minimum of 0.08 %. Conclusion: The equipment's average output dose was calculated to be 0.66 %, meeting the ${\pm}3%$ tolerance, which was considered as a much uniform fashion. As for the accuracy assessment of the geometric Isocenter, the results met the recommended criteria of ${\pm}1mm$ tolerance, affirming a high level of reproducibility of the patient's posture. The difference between the treatment plan dose and the actual measurement dose was calculated to be 0.52 % on average, significantly less than the 3 % tolerance, confirming that it obtained predicted does. The current study suggested that VERO equipment is suitable for SBRT, and would result in notable therapeutic effect.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• 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.

• Korean Journal of Remote Sensing
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• v.38 no.6_2
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• pp.1723-1735
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• 2022

Landslides are one of the most prevalent natural disasters, threating both humans and property. Also landslides can cause damage at the national level, so effective prediction and prevention are essential. Research to produce a landslide susceptibility map with high accuracy is steadily being conducted, and various models have been applied to landslide susceptibility analysis. Pixel-based machine learning models such as frequency ratio models, logistic regression models, ensembles models, and Artificial Neural Networks have been mainly applied. Recent studies have shown that the kernel-based convolutional neural network (CNN) technique is effective and that the spatial characteristics of input data have a significant effect on the accuracy of landslide susceptibility mapping. For this reason, the purpose of this study is to analyze landslide vulnerability using a pixel-based deep neural network model and a patch-based convolutional neural network model. The research area was set up in Gangwon-do, including Inje, Gangneung, and Pyeongchang, where landslides occurred frequently and damaged. Landslide-related factors include slope, curvature, stream power index (SPI), topographic wetness index (TWI), topographic position index (TPI), timber diameter, timber age, lithology, land use, soil depth, soil parent material, lineament density, fault density, normalized difference vegetation index (NDVI) and normalized difference water index (NDWI) were used. Landslide-related factors were built into a spatial database through data preprocessing, and landslide susceptibility map was predicted using deep neural network (DNN) and CNN models. The model and landslide susceptibility map were verified through average precision (AP) and root mean square errors (RMSE), and as a result of the verification, the patch-based CNN model showed 3.4% improved performance compared to the pixel-based DNN model. The results of this study can be used to predict landslides and are expected to serve as a scientific basis for establishing land use policies and landslide management policies.

• 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.

• Korean Journal of Culture and Social Issue
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• v.28 no.2
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• pp.187-217
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• 2022

The aim of this study is to examine the psychometric properties of the Korean version of the Post-traumatic Stress Disorder Checklist for DSM-5(PCL-5). For this purpose, online surveys were conducted for two times with a one year interval using the data from 1,077 Korean adults at time 1, and 563 Korean adults at time 2. First, from the result of the confirmatory factor analysis, comparing the model fit of the 1, 4, 6, and 7-factor model, the 4, 6, and 7-factor model showed a acceptable fit, and the best fit was seen in the order of the 7, 6, 4-factor model. Second, the internal consistency, omega coefficient, construct validity, average variance extracted, and test-retest reliability results were all satisfactory.. Third, a correlation analysis with the K-PC-PTSD-5 and the sub-factors of BSI-18 was conducted to check the validity of the Korean Version of PCL-5. As a result, a positive correlation was seen with both K-PC-PTSD-5 and BSI-18. Fourth, a hierarchical multiple regression was performed to examine whether the Korean Version of PCL-5 predicts future PTSD, depression, anxiety, and somatization. As a result, the Korean Version of PCL-5 measured at time 1 significantly predicted PTSD, depression, anxiety, and somatization symptoms at time 2. Fifth, by analyzing the ROC curve, the discriminant power of PCL-5 for screening PTSD symptom groups was confirmed, and the best cut-off score was suggested. As a result of the longitudinal validation of Korean version of PCL-5, it was found that this scale is a reliable and valid measure for Korean adults. By looking into the predictive validity of the scale, it was found that the Korean version of PCL-5 can predict not only PTSD symptoms but also PTSD-related symptoms such as depression, anxiety, and somatization. Also, this study differs from previous validation studies measuring PTSD symptoms in that it suggested a cut-off score to help differentiate PTSD symptom groups.

• Clean Technology
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• v.29 no.3
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• pp.193-199
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• 2023

Biomass has been used to secure renewable energy certificates (REC) in domestic and overseas coal-fired power plants. In recent years, biofuel has been diversified from traditional wood pellets to non-woody biomass. Non-woody biomass has a higher content of alkaline metals such as K and Na than wood-based biomass, resulting in a lower melting point and an increase in slagging on boiler tubes, which reduces boiler efficiency. This study analyzed the effect of kaolin, an additive commonly used to increase melting points, on biomass co-firing to coal through thermochemical equilibrium calculations. In a previous experiment on biomass co-firing to coal conducted at 80 kW_th, it was interpreted that the use of kaolin actually increased the amount of fouling. In this study, analysis showed that when kaolin was added, aluminosilicate compounds were generated due to Al₂O₃, which is abundant in coal, and mullite was formed. Thus, it was confirmed that the amount of slag increased when more kaolin was used. Further analysis was conducted by increasing the biomass co-firing rate from 0% to 100% at 10% intervals, and the results showed non-linear liquid slag generation. As a result, it was found that the least amount of liquid slag was generated when the biomass co-firing rate was between 50 and 60%. The phase diagram analysis showed that high melting point compounds such as leucite and feldspar were most abundantly generated under these conditions.