• International Journal of Highway Engineering
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• v.9 no.4
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• pp.193-204
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• 2007

Deformation strength($S_D$) is a property which shows relatively good correlation with rut resistance of asphalt mixtures at high temperature. The Asphalt Pavement Analyzer (APA) is widely used as an equipment for estimating rut resistance of asphalt mixtures. The APA was used as corresponding property of the $S_D$ to estimate rutting resistance of asphalt mixtures. Many data were collected to establish the correlation of $S_D$ with APA. For $S_D$ test, the specimen is submerged into the $60^{\circ}C$ water for 30 minutes before applying a vertical load at the speed of 50mm/min to obtain peak load (P) and deformation (y) for $S_D$ calculation. For the same materials, APA test was performed. Relation of the $S_D$ with APA rut depth was evaluated using regression analysis. The $R^2$ value was 0.76, indicating this simple test procedure being a possible method for predicting deformation resistance of asphalt concretes at high temperature. It was also shown that, using the regression model, minimum value(s) of $S_D$ of surface course asphalt mixture or binder course for a particular road level can be determined. The limit values may be possible to use as cut-off value(s) of asphalt mixtures for the layer after further elaborated studies.

• Journal of the Korean GEO-environmental Society
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• v.12 no.9
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• pp.55-61
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• 2011

This study investigate the use of ultraviolet(UV) light with hydrogen peroxide($H_2O_2$) for Methyl Tert Butyl Ether(MTBE) degradation in photolysis reactor. The process in general demands the generation of OH radicals in solution at the presence of UV light. These radicals can then attack the MTBE molecule and it is finally destroyed or converted into a simple harmless compound. The MTBE removal by photolysis were mathematically described as the independent variables such as irradiation intensity, initial concentration of MTBE and $H_2O_2$/MTBE ratio, and these were modeled by the use of response surface methodology(RSM). These experiments were carried out as a Box-Behnken Design(BBD) consisting of 15 experiments. Regression analysis term of Analysis of Variance(ANOVA) shows significantly p-value(p<0.05) and high coefficients for determination values($R^2$=94.60%) that allow satisfactory prediction of second-order regression model. And Canonical analysis yields the stationery point for response, with the estimate ridge of maximum responses and optimal conditions for Y(MTBE removal efficiency, %) are $x_1$=25.75 W of irradiation intensity, $x_2$=7.69 mg/L of MTBE concentration and $x_3$=11.04 of $H_2O_2$/MTBE molecular ratio, respectively. This study clearly shows that RSM is available tool for optimizing the operating conditions to maximize MTBE removal.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.21 no.3
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• pp.96-107
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• 2020

This study aimed to identify the associated factors of protective behaviors for radiation exposure among some radiology technologists using the Health Belief Model. The subjects of the study were 541 radiology technologists working at hospitals or clinics in Honam Province. Using the SPSS version 18.0 program, data were analyzed using a t-test, ANOVA, Pearson's correlation analysis, and hierarchical multiple logistic regression analysis. To modify the factors, the performance of subjects who had a higher level of education and nuclear medicine rooms were higher than those who worked in simple radiography rooms. The radiation protective behaviors performance of the subjects who had more exercise, medium-level stress, and worked in higher-quality protection facilities was higher. Regarding the personal perceptions, the cues to action (β=.292, p=.0001), and perceived seriousness (β =.075, p=.010) were factors that had effects on the performance of radiation protection behaviors. Regarding the likelihood of action, the benefits (β=.168, p<.0001), self-efficacy (β=.148, p=.007), and the performance of protective behaviors were higher. In conclusion, protection education as a cue to action should be provided to stimulate protective behaviors, and the benefits of protective behaviors should be emphasized. To increase the performance of protection behaviors, self-efficacy should be enhanced, and the subjects are offered appropriate information that helps perceive seriousness.

• Journal of Korea Water Resources Association
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• v.45 no.12
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• pp.1321-1330
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• 2012

In this study, the daily inflow at the basin of Chungju dam is predicted using wavelet-artificial neural network for nonlinear model. Time series generally consists of a linear combination of trend, periodicity and stochastic component. However, when framing time series model through these data, trend and periodicity component have to be removed. Wavelet transform which is denoising technique is applied to remove nonlinear dynamic noise such as trend and periodicity included in hydrometeorological data and simple noise that arises in the measurement process. The wavelet-artificial neural network (WANN) using data applied wavelet transform as input variable and the artificial neural network (ANN) using only raw data are compared. As a results, coefficient of determination and the slope through linear regression show that WANN is higher than ANN by 0.031 and 0.0115 respectively. And RMSE and RRMSE of WANN are smaller than those of ANN by 37.388 and 0.099 respectively. Therefore, WANN model applied in this study shows more accurate results than ANN and application of denoising technique through wavelet transforms is expected that more accurate predictions than the use of raw data with noise.

• International Area Studies Review
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• v.13 no.3
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• pp.287-308
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• 2009

This article examines the distributional characteristics of the return of Chinese stock market indices. The majority of previous empirical researches have tended to focus upon the simple stock market index. However, this study focuses on the four indices which represent the characteristics of each stock market index. The empirical findings indicate that the returns of the four chinese indices are not normally distributed at conventional levels. The Ljimg-Box -statistics indicate the returns of the index of A shares are not serially autocorrelated. However, the returns of the index of B shares are serially autocorrelated. The empirical findings also indicate returns of the four chinese indices are not serially autocorrelated. The statistics of Regression Specification Error Test and ARCH indicate the returns of all four indices are not serially linear. The findings also indicate that E- GARCH model is the most fittest model for the returns of the four chinese indices and the forecast error can be reduced by using student t distribution rather normal distribution.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.384-395
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• 2020

Breaking waves generated by wave shoaling in coastal areas have a close relationship with various physical phenomena in coastal regions, such as sediment transport, longshore currents, and shock wave pressure. Therefore, it is crucial to accurately predict breaker index such as breaking wave height and breaking depth, when designing coastal structures. Numerous scientific efforts have been made in the past by many researchers to identify and predict the breaking phenomenon. Representative studies on wave breaking provide many empirical formulas for the prediction of breaking index, mainly through hydraulic model experiments. However, the existing empirical formulas for breaking index determine the coefficients of the assumed equation through statistical analysis of data under the assumption of a specific equation. In this paper, we applied a representative linear-based supervised machine learning algorithms that show high predictive performance in various research fields related to regression or classification problems. Based on the used machine learning methods, a model for prediction of the breaking index is developed from previously published experimental data on the breaking wave, and a new linear equation for prediction of breaker index is presented from the trained model. The newly proposed breaker index formula showed similar predictive performance compared to the existing empirical formula, although it was a simple linear equation.

• Journal of Intelligence and Information Systems
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• v.23 no.3
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• pp.155-175
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• 2017

As social data become into the spotlight, mainstream web search engines provide data indicate how many people searched specific keyword: Web Search Traffic data. Web search traffic information is collection of each crowd that search for specific keyword. In a various area, web search traffic can be used as one of useful variables that represent the attention of common users on specific interests. A lot of studies uses web search traffic data to nowcast or forecast social phenomenon such as epidemic prediction, consumer pattern analysis, product life cycle, financial invest modeling and so on. Also web search traffic data have begun to be applied to predict tourist inbound. Proper demand prediction is needed because tourism is high value-added industry as increasing employment and foreign exchange. Among those tourists, especially Chinese tourists: Youke is continuously growing nowadays, Youke has been largest tourist inbound of Korea tourism for many years and tourism profits per one Youke as well. It is important that research into proper demand prediction approaches of Youke in both public and private sector. Accurate tourism demands prediction is important to efficient decision making in a limited resource. This study suggests improved model that reflects latest issue of society by presented the attention from group of individual. Trip abroad is generally high-involvement activity so that potential tourists likely deep into searching for information about their own trip. Web search traffic data presents tourists' attention in the process of preparation their journey instantaneous and dynamic way. So that this study attempted select key words that potential Chinese tourists likely searched out internet. Baidu-Chinese biggest web search engine that share over 80%- provides users with accessing to web search traffic data. Qualitative interview with potential tourists helps us to understand the information search behavior before a trip and identify the keywords for this study. Selected key words of web search traffic are categorized by how much directly related to "Korean Tourism" in a three levels. Classifying categories helps to find out which keyword can explain Youke inbound demands from close one to far one as distance of category. Web search traffic data of each key words gathered by web crawler developed to crawling web search data onto Baidu Index. Using automatically gathered variable data, linear model is designed by multiple regression analysis for suitable for operational application of decision and policy making because of easiness to explanation about variables' effective relationship. After regression linear models have composed, comparing with model composed traditional variables and model additional input web search traffic data variables to traditional model has conducted by significance and R squared. after comparing performance of models, final model is composed. Final regression model has improved explanation and advantage of real-time immediacy and convenience than traditional model. Furthermore, this study demonstrates system intuitively visualized to general use -Youke Mining solution has several functions of tourist decision making including embed final regression model. Youke Mining solution has algorithm based on data science and well-designed simple interface. In the end this research suggests three significant meanings on theoretical, practical and political aspects. Theoretically, Youke Mining system and the model in this research are the first step on the Youke inbound prediction using interactive and instant variable: web search traffic information represents tourists' attention while prepare their trip. Baidu web search traffic data has more than 80% of web search engine market. Practically, Baidu data could represent attention of the potential tourists who prepare their own tour as real-time. Finally, in political way, designed Chinese tourist demands prediction model based on web search traffic can be used to tourism decision making for efficient managing of resource and optimizing opportunity for successful policy.

• Journal of agricultural medicine and community health
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• v.41 no.1
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• pp.13-26
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• 2016

Purpose: This study was performed to identify the association between family support, activities of daily living (ADL) and depression among hospitalized older patients with chronic diseases. Methods: This study subjects were 100 elderly patients with chronic diseases including chronic respiratory diseases, diabetes mellitus and et al. in a general hospital. The collected data were patient characteristics, family support, ADL, and depression by structured questionnaire and medical chart review. The used statistical analyses were t-test, analysis of variance, Pearson's correlational analysis and multiple regression analysis. Results: The mean scores of family support, ADL and depression were $49.95{\pm}8.68$, $8.65{\pm}2.65$, $6.66{\pm}3.78$, respectively. The prevalence rate of depression was 64.0%. In simple analysis, the statistically significant associated factors with depression were age, spouse, economic status, social activity, subjective health status, and number of pain. Depression had statistically a significant positive correlation with ADL and a negative correlation with family support. The final result of hierarchial multiple regression analysis (Model 3), the factors related to depression were family support (b=-.135, p<.001), subjective health status (b=2.510, p=.001). Conclusions: It is necessary to develop and apply the program for controlling the depression of elderly patients with health education, reinforcement of supportive systems in hospital. And, further multidisciplinary studies should be done.

• Management & Information Systems Review
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• v.39 no.2
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• pp.17-37
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• 2020

The purpose of this study is to investigate the global spillover effects through the existence of linear and nonlinear causal relationships between the US, European and BRIC financial markets after the period from the introduction of the Euro, the financial crisis and the subsequent EU debt crisis in 2007~2010. Although the global spillover effects of the financial crisis are well described, the nature of the volatility effects and the spread mechanisms between the US, Europe and BRIC stock markets have not been systematically examined. A stepwise filtering methodology was introduced to investigate the dynamic linear and nonlinear causality, which included a vector autoregressive regression model and a multivariate GARCH model. The sample in this paper includes the post-Euro period, and also includes the financial crisis and the Eurozone financial and sovereign crisis. The empirical results can have many implications for the efficiency of the BRIC stock market. These results not only affect the predictability of this market, but can also be useful in future research to quantify the process of financial integration in the market. The interdependence between the United States, Europe and the BRIC can reveal significant implications for financial market regulation, hedging and trading strategies. And the findings show that the BRIC has been integrated internationally since the sub-prime and financial crisis erupted in the United States, and the spillover effects have become more specific and remarkable. Furthermore, there is no consistent evidence supporting the decoupling phenomenon. Some nonlinear causality persists even after filtering during the investigation period. Although the tail distribution dependence and higher moments may be significant factors for the remaining interdependencies, this can be largely explained by the simple volatility spillover effects in nonlinear causality.

• Korean Journal of Agricultural and Forest Meteorology
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• v.20 no.3
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• pp.233-242
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• 2018

This study explored the richness patterns of vascular plant species and evaluated the effects of the climatic and habitat variables on the observed patterns along elevational gradients on the Mt. Baekhwa, South Korea. Plant data were recorded from 70 plots and a total of 187 plant species with 78 woody and 109 herbaceous species were recorded along two study transects, the Banyasa and Bohyunsa transects, on the Mt. Baekhwa. A total of 154 plant species with 66 woody and 88 herbaceous species and 131 plant species with 58 woody and 73 herbaceous species were recorded along the Banyasa and Bohyunsa transects, respectively. We used simple ordinary least squares regression model, multi-model inference and variation partitioning to analyze the relative contribution of climatic and habitat variables on the elevational richness patterns. Species richness pattern for vascular plants along the Banyasa transect monotonically decreased with elevation, whereas plant species richness showed reversed hump-shaped pattern along the Bohyunsa transect. Although the elevational patterns of species richness for vascular plants were different between the both transects, habitat variables are more important predictors than climatic variables for the elevational patterns of plant species richness along our study transects on the Mt. Baekhwa. These results indicate that elevational diversity patterns of vascular plants may be different even between nearby elevational transects in a mountain ecosystem but the diversity patterns may be controlled by same drivers.