• Korean Journal of Social Welfare
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• v.61 no.2
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• pp.325-348
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• 2009

This study intends to investigate the main and mediating effects which caregiving appraisal and positive reappraisal exert on quality of life (QOL) of primary family caregivers of cancer patient considering the relationship with social support. The processes of this study areas follows. First, the variables which research model were chosen on the basis of stress-appraisal-coping theory through reviews of the previous studies. Second, a survey was conducted upon 295 primary caregiver of patient with cancer at National Cancer Center. Collected data were analyzed by SPSS 12.0 and SEM (Structural Equation Modeling) method using AMOS 5.0. The summary of the result is as follows. First, the entire model including measurement and structural model shows sufficient fit index of CFI(.951), TLI(.940) and RMSEA(.062). Second, the results of analysis of direct effects among variables are as follows. The 'Social support' has statistically significant direct effect on the 'feeling of burden' and 'growth'. The 'feeling of burden' has statistically significant direct effect on the 'growth' and 'QOL-mental and physical'. The 'growth' has statistically significant direct effect on the 'QOL-mental'. Third, the results of analysis of mediating effects of the 'social support and QOL' and 'feeling of burden and QOL' are as follows. The effects of 'social support' on 'QOL-mental' are significantly mediated by the 'feeling of burden' and 'growth'. The effects of 'social support' on 'QOL-physical' are significantly mediated by the 'feeling of burden'. The effects of 'feeling of burden' on 'QOL-mental' are significantly mediated by 'growth'. Through this research, these implications in social work study and practice are found: (1) this study extended the scope of study in the caregiver's health area from negative sides into positive ones by using growth variables as positive reappraisalof caregiving in research model, which has not been tried on the Korean family caregivers of the cancer patient. (2) The effects of positive reappraisal on QOL-mental can provide a foundational necessity for social workers to help family caregivers find positive meaning in their caregiving experience. This approach of social work practice will improve QOL of family caregivers. (3) This study present a framework including social support, negative appraisal, positive reappraisal, and QOL variables available to social work practice and explaining affective relationships among these variables in various aspects.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.488-495
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• 2005

This study was conducted to understand the influences of forest structure on throughfall, stemflow and interception loss. The study plots included the natural old-growth deciduous, Pinus koraiensis and Abies holophylla forests in Gwangneung and the rehabilitated young mixed forest in Yangju, Gyeonggido. The Pinus koraiensis and Abies hotophylla had been planted in 1976. The rehabilitated young mixed forest had been established to control erosion in 1974. Total and net rainfall were monitored from March, 2003 to October, 2004. Tipping bucket rain gauge recorded total rainfall. Throughfall and stemflow were measured by custom-made tipping bucket and CR10X data logger at each $10m{\times}10m$ plots at intervals of 30 minutes. Interception loss in the Pinus koraiensis plot were most as 37.2% of total rainfall and least as 22.6% in the rehabilitated young mixed forest. Stemflow in the rehabilitated young mixed forest was 10.7% of total rainfall and stemflow in the Pinus koraiensis plot was 2.4%. The average throughfall ratio ranged from 66% to 77% depending on the canopy coverage. The relationship of stemflow and total rainfall represented in a linear regression equation though the variation of data was large. The ratio of stemflow-conversion was 2% of total rainfall in the Pinus koraiensis plot and 12% in the rehabilitated young mixed forest, respectively. The stem storage of the natural old-growth deciduous was the largest of 0.21 mm whereas that of the Pinus koraiensis plot was the least of 0.003 mm. A deciduous forest produced stemflow more than a coniferous forest due to a smooth bark and steeply angled branches. Interception loss of all study plots increased linearly as total rainfall increased. The distribution of interception loss data related in total rainfall became wider in a deciduous forest than a coniferous. It resulted from seasonality of leaf area index in a deciduous forest. As considered above results, it was confirmed that there were great differences of throughfall, stemflow and interception loss depending on forest stand structures. The simulation model for predicting interception loss must have parameters such as forest stand characteristics and LAI in order to describe the influence of forest structure on interception loss.

• Journal of Intelligence and Information Systems
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• v.22 no.4
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• pp.177-192
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• 2016

Machine learning is a field of artificial intelligence. It refers to an area of computer science related to providing machines the ability to perform their own data analysis, decision making and forecasting. For example, one of the representative machine learning models is artificial neural network, which is a statistical learning algorithm inspired by the neural network structure of biology. In addition, there are other machine learning models such as decision tree model, naive bayes model and SVM(support vector machine) model. Among the machine learning models, we use SVM model in this study because it is mainly used for classification and regression analysis that fits well to our study. The core principle of SVM is to find a reasonable hyperplane that distinguishes different group in the data space. Given information about the data in any two groups, the SVM model judges to which group the new data belongs based on the hyperplane obtained from the given data set. Thus, the more the amount of meaningful data, the better the machine learning ability. In recent years, many financial experts have focused on machine learning, seeing the possibility of combining with machine learning and the financial field where vast amounts of financial data exist. Machine learning techniques have been proved to be powerful in describing the non-stationary and chaotic stock price dynamics. A lot of researches have been successfully conducted on forecasting of stock prices using machine learning algorithms. Recently, financial companies have begun to provide Robo-Advisor service, a compound word of Robot and Advisor, which can perform various financial tasks through advanced algorithms using rapidly changing huge amount of data. Robo-Adviser's main task is to advise the investors about the investor's personal investment propensity and to provide the service to manage the portfolio automatically. In this study, we propose a method of forecasting the Korean volatility index, VKOSPI, using the SVM model, which is one of the machine learning methods, and applying it to real option trading to increase the trading performance. VKOSPI is a measure of the future volatility of the KOSPI 200 index based on KOSPI 200 index option prices. VKOSPI is similar to the VIX index, which is based on S&P 500 option price in the United States. The Korea Exchange(KRX) calculates and announce the real-time VKOSPI index. VKOSPI is the same as the usual volatility and affects the option prices. The direction of VKOSPI and option prices show positive relation regardless of the option type (call and put options with various striking prices). If the volatility increases, all of the call and put option premium increases because the probability of the option's exercise possibility increases. The investor can know the rising value of the option price with respect to the volatility rising value in real time through Vega, a Black-Scholes's measurement index of an option's sensitivity to changes in the volatility. Therefore, accurate forecasting of VKOSPI movements is one of the important factors that can generate profit in option trading. In this study, we verified through real option data that the accurate forecast of VKOSPI is able to make a big profit in real option trading. To the best of our knowledge, there have been no studies on the idea of predicting the direction of VKOSPI based on machine learning and introducing the idea of applying it to actual option trading. In this study predicted daily VKOSPI changes through SVM model and then made intraday option strangle position, which gives profit as option prices reduce, only when VKOSPI is expected to decline during daytime. We analyzed the results and tested whether it is applicable to real option trading based on SVM's prediction. The results showed the prediction accuracy of VKOSPI was 57.83% on average, and the number of position entry times was 43.2 times, which is less than half of the benchmark (100 times). A small number of trading is an indicator of trading efficiency. In addition, the experiment proved that the trading performance was significantly higher than the benchmark.

• Investigative Magnetic Resonance Imaging
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• v.13 no.1
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• pp.40-46
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• 2009

Purpose : The purpose of this study was to investigate the quantitative evaluation of the corticospinal tract (CST) at the multiple levels by using functional MRI (fMRI) co-registered to diffusion tensor tractography (DTT). Materials and Methods : Ten normal subjects without any history of neurological disorder participated in this study. fMRI was performed at 1.5 T MR scanner using hand grasp-release movement paradigm. DTT was performed by using DtiStudio on the basis of fiber assignment continuous tracking algorithm (FACT). The seed region of interest (ROI) was drawn in the area of maximum fMRI activation during the motor task of hand grasp-release movement on a 2-D fractional anisotropy (FA) color map, and the target ROI was drawn in the cortiocospinal portion of anterior lower pons. We have drawn five ROIs for the measurement of FA and apparent diffusion coefficient (ADC) along the corona radiata (CR) down to the medulla. Results : The contralateral primary sensorimotor cortex (SM1) was mainly found to be activated in all subjects. DTT showed that tracts originated from SM1 and ran to the medulla along the known pathway of the CST. In all subjects, FA values of the CST were higher at the level of the midbrain and posterior limb of internal capsule (PLIC) than the level of others. Conclusion : Our study showed that co-registered fMRI and DTT has elucidated the state of CST on 3-D and analyzed the quantitative values of FA and ADC at the multiple levels. We conclude that co-registered fMRI and DTT may be applied as a useful tool for clarifying and investigating the state of CST in the patients with brain injury.

• Journal of Korea Port Economic Association
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• v.33 no.2
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• pp.53-82
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• 2017

The purpose of this study is to show the clustering trend and compare empirical results, as well as to choose the clustering ports for 3 Korean ports (Busan, Incheon, and Gwangyang) by using meta-frontier DEA (Data Envelopment Analysis) and integer models on 38 Asian container ports over the period 2005-2014. The models consider 4 input variables (birth length, depth, total area, and number of cranes) and 1 output variable (container TEU). The main empirical results of the study are as follows. First, the meta-frontier DEA for Chinese seaports identifies as most efficient ports (in decreasing order) Shanghai, Hongkong, Ningbo, Qingdao, and Guangzhou, while efficient Korean seaports are Busan, Incheon, and Gwangyang. Second, the clustering results of the integer model show that the Busan port should cluster with Dubai, Hongkong, Shanghai, Guangzhou, Ningbo, Qingdao, Singapore, and Kaosiung, while Incheon and Gwangyang should cluster with Shahid Rajaee, Haifa, Khor Fakkan, Tanjung Perak, Osaka, Keelong, and Bangkok ports. Third, clustering through the integer model sharply increases the group efficiency of Incheon (401.84%) and Gwangyang (354.25%), but not that of the Busan port. Fourth, the efficiency ranking comparison between the two models before and after the clustering using the Wilcoxon signed-rank test is matched with the average level of group efficiency (57.88 %) and the technology gap ratio (80.93%). The policy implication of this study is that Korean port policy planners should employ meta-frontier DEA, as well as integer models when clustering is needed among Asian container ports for enhancing the efficiency. In addition Korean seaport managers and port authorities should introduce port development and management plans accounting for the reference and clustered seaports after careful analysis.

• The Journal of Information Systems
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• v.25 no.1
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• pp.1-23
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• 2016

Purpose Self-congruity deals with the effect of symbolic value-expressive attributes on consumer decision and behavior, which is the theoretical foundation of the "non-utilitarian destination positioning". Functional congruity refers to utilitarian evaluation of a product or service by consumers. In addition, recent years, social network services, especially mobile social network services have created many opportunities for e-WOM communication that enables consumers to share personal consumption related information anywhere at any time. Moreover, self-construal is a hot and popular topic that has been discussed in the field of modem psychology as well as in marketing area. This study aims to examine the moderating effect of self-construal on the relationship between self-congruity, functional congruity and tourists' positive electronic word of mouth (e-WOM). Design/methodology/approach In order to verify the hypotheses, we developed a questionnaire with 32 survey items. We measured all the items on a five-point Likert-type scale. We used Sojump.com to collect questionnaire and gathered 218 responses from whom have visited Korea before. After a pilot test, we analyzed the main survey data by using SPSS 20.0 and AMOS 18.0, and employed structural equation modeling to test the hypotheses. We first estimated the measurement model for its overall fit, reliability and validity through a confirmatory factor analysis and used common method bias test to make sure that whether measures are affected by common-method variance. Then we tested the hypotheses through the structural model and used regression analysis to measure moderating effect of self-construal. Findings The results reveal that the effect of self-congruity on tourists' positive e-WOM is stronger for tourists with an independent self-construal compared with those with interdependent self-construal. Moreover, it shows that the effect of functional congruity on tourists' positive e-WOM becomes salient when tourists' self-construal is primed to be interdependent rather than independent. We expect that the results of this study can provide important implications for academic and practical perspective.

• Journal of the Korea Concrete Institute
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• v.17 no.4 s.88
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• pp.569-579
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• 2005

An investigation for long-term strength characteristics of crushed sand concrete using crushed sands produced in Yang-san, Kim-hae and Jin-hae that can be assumed to respectively represent eastern, middle and western suburbs of Busan has been carried out. Concrete is composed of 70~80% of aggregates in whole volume so the effect of aggregates quality to the characteristics of concrete is very important. Since 1980s, aggregates used in concrete have already been substituted crushed stone because of the exhaustion of natural gravel and sand. Crushed sand tends to increase in using quantity because of the prohibition of sea sand picking and deterioration of river sand. Crushed sand is blended with river sand in order to investigate the quality changes and characteristics of concrete as variation of blend ratio of crushed sand (n, 50, 70, 80, 90, 100%). Slump and air content were measured to investigate the properties of fresh concrete. Unit weight, compressive strength and modulus of elasticity in age of 7, 28, 60, n, 180 days were measured to investigate properties of hardened concrete. Compressive strength, unit weight and modulus of elasticity were increased with a passage of time and they are expected to keep on increasing in long-term age as well. The experimental results of the qualifies of crushed aggregates in each producing area, were all satisfied with Korea Standard. The results of the measurement of slump exposed that slump preferably decreased as mixing rate increased till 70~80% but it increased to mixing rate 70~80%. The air content was exposed that it decreased by micro filler phenomenon according to that crushed sand b)ended ratio increased. According to the result of measuring unit weight in age of 7, 28, 60, 90, 180days, it increased in accordance with that blended ratio of crushed aggregates increases. As a result of measuring compressive strength and modulus of elasticity in age of 7, 28, 50, 90, 180days, compressive strength was highest when it is 70% of blended ratio.

• Journal of the Korean association of regional geographers
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• v.20 no.2
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• pp.163-175
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• 2014

This study is to explain social capital characteristics of Filipino immigrant women at the level of neighborhood. This research targeted Filipino immigrant women in the metropolis, small town and rural area in Korea to find out the relevance of individual property and characteristics of the community and social capital of neighboring communities- school community, cathedral community, etc- through measurement of the participants' recognition. This study reveals that differences exist in the relationship between length of residence and social capital in the school community and the catholic church community. There is a significant positive relationship between length of residence and political factors in the catholic church community, thereby having a better relationship with longer period of stay, while length of residence and confidence show a negative trend in the school community, leading to less confidence. The catholic church community holds a dominant position in homogeneity, cohesion, and the amount of social capital. According to the findings, social capital 'relation' is more closely related to homogeneity of the community, 'norms' to cohesion. 'Relation and norms' and 'confidence and politics' factors are recognized similarly in both communities, thus resulting in the recognition that decision making within the community, the share of value, and observance of social norms approximate a friendly relationship among members, and satisfaction level, emotional support, and confidence among members approach politics that members can talk about their personal matters. It is noted in the research process that the symbolism of the cathedral community as a transnational circuit behavior occurs where collective culture and personal desires of Filipino immigrant women were combined with production of social capital. Filipino immigrant women's awareness of community and social capital appearing in the cathedral community show that not only residence, along with the cultural identity of Filipino immigrant women, but also collective social and cultural characteristics, such as 'family reunion' can not be overlooked. In particular, at this time when discussion and debate on the interculturalism over multiculturalism is heating up, communal spirit and social capital based on the ethnic identity are important in that they can be a crucial path to the cross-cultural interaction with our society, therefore, a study on the social capital of the ethnic community needs to be encouraged and extended to more diverse communities, to the space of the multilayered scale.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.2
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• pp.135-145
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• 2020

Wave observation is widely used to direct observation method for observing the water surface elevation using wave buoy or pressure gauge and remote-sensing wave observation method. The wave buoy and pressure gauge can produce high-quality wave data but have disadvantages of the high risk of damage and loss of the instrument, and high maintenance cost in the offshore area. On the other hand, remote observation method such as radar is easy to maintain by installing the equipment on the land, but the accuracy is somewhat lower than the direct observation method. This study investigates the data quality of MIROS Wave and Current Radar (MWR) installed at Dokdo and improve the data quality of remote wave observation data using the wave buoy (CWB) observation data operated by the Korea Meteorological Administration. We applied and developed the three types of wave data quality control; 1) the combined use (Optimal Filter) of the filter designed by MIROS (Reduce Noise Frequency, Phillips Check, Energy Level Check), 2) Spike Test Algorithm (Spike Test) developed by OOI (Ocean Observatories Initiative) and 3) a new filter (H-Ts QC) using the significant wave height-period relationship. As a result, the wave observation data of MWR using three quality control have some reliability about the significant wave height. On the other hand, there are still some errors in the significant wave period, so improvements are required. Also, since the wave observation data of MWR is different somewhat from the CWB data in high waves of over 3 m, further research such as collection and analysis of long-term remote wave observation data and filter development is necessary.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1117-1132
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• 2019

Forest covers 30% of the Earth's land area and plays an important role in global carbon flux through its ability to store much greater amounts of carbon than other terrestrial ecosystems. The Gross Primary Production (GPP) represents the productivity of forest ecosystems according to climate change and its effect on the phenology, health, and carbon cycle. In this study, we estimated the daily GPP for a forest ecosystem using remote-sensed data from Moderate Resolution Imaging Spectroradiometer (MODIS) and machine learning algorithms Support Vector Machine (SVM). MODIS products were employed to train the SVM model from 75% to 80% data of the total study period and validated using eddy covariance measurement (EC) data at the six flux tower sites. We also compare the GPP derived from EC and MODIS (MYD17). The MODIS products made use of two data sets: one for Processed MODIS that included calculated by combined products (e.g., Vapor Pressure Deficit), another one for Unprocessed MODIS that used MODIS products without any combined calculation. Statistical analyses, including Pearson correlation coefficient (R), mean squared error (MSE), and root mean square error (RMSE) were used to evaluate the outcomes of the model. In general, the SVM model trained by the Unprocessed MODIS (R = 0.77 - 0.94, p < 0.001) derived from the multi-sites outperformed those trained at a single-site (R = 0.75 - 0.95, p < 0.001). These results show better performance trained by the data including various events and suggest the possibility of using remote-sensed data without complex processes to estimate GPP such as non-stationary ecological processes.