• The Korean Journal of Financial Management
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• v.23 no.1
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• pp.243-266
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• 2006

This paper tests cross hedging performance of the KOSPI 200 stock index futures to hedge the downside risk of the KOSPI, KOSPI 200 and KOSDAQ50 spot market. For this purpose we introduce the minimum variance hedge model, bivariate GARCH(1,1) and EGARCH(1,1) model as hedge models. The main results are as follows; First, we find that the direct hedge performance of KOSPI 200 index futures is better than those of indirect hedge performance. second, in case or cross hedge performance the hedge effect of KOSPI 200 stock index futures market against KOSPI 200 stock index spot market is relatively better than those of KOSPI 200 index futures against KOSPI and KOSDAQ spot position. Third, for the out-sample, hedging effectiveness of the risk-minimization with constant hedge ratios is higher than those of the time varying bivariate GARCH(1,1) and EGARCH(1,1) model. In conclusion, investors are encouraged to use simple risk-minimization model rather than the time varying hedge models like GARCH and EGARCH model to hedge the position of the Korean stock index cash markets.

• Journal of the Korean Geotechnical Society
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• v.26 no.7
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• pp.49-58
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• 2010

This paper presents experimental results of a series of 1-g shaking table model tests performed on end-bearing single piles and pile groups to investigate the effect of particle size on the dynamic behavior of soil-pile systems. Two soil-pile models were tested twice: first using Jumoonjin sand, and second using Australian Fine sand. In the case of single-pile models, the lateral displacement was almost within 1% of pile diameter which corresponds to the elastic range of the pile. The back-calculated p-y curves show that the subgrade reaction of the Jumoonjin-sand-model ground was larger than that of the Australian Fine-sand-model ground at the same displacement. This phenomenon means that the stress-strain behavior of Jumoonjin sand was initially stiffer than that of Australian Fine sand. This difference was also confirmed by resonant column tests and compression triaxial tests. And the single pile p-y backbone curves of the Australian fine sand were constructed and compared with those of the Jumoonjin sand. As a result, the stiffness of the p-y backbone curves of Jumunjin sand was larger than those of Australian fine sand. Therefore, using the same p-y curves regardless of particle size can lead to inaccurate results when evaluating dynamic behavior of soil-pile system. In the case of the group-pile models, the lateral displacement was much larger than the elastic range of pile movement at the same test conditions in the single-pile models. The back-calculated p-y curves in the case of group pile models were very similar in both sands because the stiffness difference between the Jumoonjin-sand-model ground and the Australian Fine-sand-model ground was not significantly large at a large strain level, where both sands showed non-linear behavior. According to a series of single pile and group pile test results, the evaluation group pile effect using the p-multiplier can lead to inaccurate results on dynamic behavior of soil-pile system.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1341-1352
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• 2023

The normalized difference vegetation index (NDVI) derived from satellite images is a crucial tool to monitor forests and agriculture for broad areas because the periodic acquisition of the data is ensured. However, optical sensor-based vegetation indices(VI) are not accessible in some areas covered by clouds. This paper presented a synthetic aperture radar (SAR) based approach to retrieval of the optical sensor-based NDVI using machine learning. SAR system can observe the land surface day and night in all weather conditions. Radar vegetation indices (RVI) from the Sentinel-1 vertical-vertical (VV) and vertical-horizontal (VH) polarizations, surface elevation, and air temperature are used as the input features for an automated machine learning (AutoML) model to conduct the gap-filling of the Sentinel-2 NDVI. The mean bias error (MAE) was 7.214E-05, and the correlation coefficient (CC) was 0.878, demonstrating the feasibility of the proposed method. This approach can be applied to gap-free nationwide NDVI construction using Sentinel-1 and Sentinel-2 images for environmental monitoring and resource management.

• Korean Journal of Psychosomatic Medicine
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• v.21 no.1
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• pp.18-26
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• 2013

Objectives : Type D personality was originally introduced to study the role of personality in predicting outcomes of heart disease. However, researches showed that other medical conditions are also affected by this personality. The purpose of this study was to evaluate the relationship between type D personality and somatic symptom complaints in depressive patients. Methods : Eighty-two individuals diagnosed with depressive disorder were included. Type D personality was measured with DS14. Patient Health Questionnaire(PHQ) 9 and 15 were used to measure depression severity and somatization tendencies. For alexithymia, TAS-20 was used. Student T-test and linear regression analysis were performed. The best regression model was determined by stepwise variable selection. Results : More than half of the subjects(56%) complained at least medium degree somatic symptoms according to PHQ-15 criteria. Two-thirds of the subjects were classified as Type D personality(63.4%). The mean PHQ-15 score of the Type D individuals was significantly higher than the remaining subjects(PHQ-15 mean=12.7, $p=8.2{\times}10^{-7}$). The best regression model included age, PHQ-9 score and NA subscale score as predictor variables. Among these, only the coefficients of age($p=1.5{\times}10^{-3}$) and NA score($p=1.5{\times}10^{-7}$) were found to be statistically significant. Conclusions : The result showed that Type D personality was one of the strong predictors of somatic complaints among depressive individuals. The finding that negative affectivity rather than social inhibition was more closely associated with somatization tendencies does not fully agree with the traditional explanation that inability to express negative emotion predispose the individuals to somatic symptoms. The finding that alexithymia was not shown to be a significant predictors also substantiated this discrepancy. However, it might be possible that the high correlation between NA and SI subscore(r=0.65) and between NA and TAS-20 score(r=0.44) hid the additional effects of social inhibition and alexithymia. Further research with a larger sample would be needed to investigate the effects of the latter two components over and above the effect of negative affectivity on the somatic complaints in depressive patients.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.12 no.3
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• pp.65-79
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• 1992

This study is concerned with the optimal design of two hinged steel arches with I cross sectional type and aimed at the exact analysis of the arches and the safe and economic design of structure. The analyzing method of arches which introduces the finite difference method considering the displacements of structure in analyzing process is used to eliminate the error of analysis and to determine the sectional force of structure. The optimizing problems of arches formulate with the objective functions and the constraints which take the sectional dimensions(B, D, $t_f$, $t_w$) as the design variables. The object functions are formulated as the total weight of arch and the constraints are derived by using the criteria with respect to the working stress, the minimum dimension of flange and web based on the part of steel bridge in the Korea standard code of road bridge and including the economic depth constraint of the I sectional type, the upper limit dimension of the depth of web and the lower limit dimension of the breadth of flange. The SUMT method using the modified Newton Raphson direction method is introduced to solve the formulated nonlinear programming problems which developed in this study and tested out throught the numerical examples. The developed optimal design programming of arch is tested out and examined throught the numerical examples for the various arches. And their results are compared and analyzed to examine the possibility of optimization, the applicablity, the convergency of this algorithm and with the results of numerical examples using the reference(30). The correlative equations between the optimal sectional areas and inertia moments are introduced from the various numerical optimal design results in this study.

• Journal of agricultural medicine and community health
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• v.30 no.1
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• pp.89-100
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• 2005

Objectives: This study was to find the effected factors on customer satisfaction for medical service and the willingness to revisit among hospital users Methods: The data was collected by a questionnaire survey from February 1 to April 30, 2004, and 600 samples have been analysed among users of university hospital, private hospital and public hospital in a local city. Results and Conclusions: The satisfaction total score to use hospital was 113.54 points(out of 175 point), these scores were constituted 39.10 points(out of 55 point) on satisfaction score for kindness of hospital employee, 36.28 points(out of 60 point) for equipment utilization and service formality, 18.59 points(out of 30 point) for environmental status and 19.57 points(out of 30 points) for reliability in medical examination and treatment service. The factors effected on satisfaction total score to use hospital were type of visiting hospital, age of customer, convenience to visit the hospital, experience of using other hospitals(R2=0.171). The effected factors of willingness to revisit scores were such as satisfaction score in medical examination and treatment service, satisfaction score of kindness hospital employee, experience of health examination and age of customer($R^2=0.370$). In conclusion, to raise the response's willingness to revisit. This must be reinforced by employee's kindness education and medical service quality.

• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.337-357
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• 2021

Because the growth of paddy rice is affected by meteorological factors, the selection of appropriate meteorological variables is essential to build a rice yield prediction model. This paper examines the suitability of multiple meteorological datasets for the rice yield modeling in South Korea, 1996-2019, and a hindcast experiment for rice yield using a machine learning method by considering the nonlinear relationships between meteorological variables and the rice yield. In addition to the ASOS in-situ observations, we used CRU-JRA ver. 2.1 and ERA5 reanalysis. From the multiple meteorological datasets, we extracted the four common variables (air temperature, relative humidity, solar radiation, and precipitation) and analyzed the characteristics of each data and the associations with rice yields. CRU-JRA ver. 2.1 showed an overall agreement with the other datasets. While relative humidity had a rare relationship with rice yields, solar radiation showed a somewhat high correlation with rice yields. Using the air temperature, solar radiation, and precipitation of July, August, and September, we built a random forest model for the hindcast experiments of rice yields. The model with CRU-JRA ver. 2.1 showed the best performance with a correlation coefficient of 0.772. The solar radiation in the prediction model had the most significant importance among the variables, which is in accordance with the generic agricultural knowledge. This paper has an implication for selecting from multiple meteorological datasets for rice yield modeling.

• 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.