• International Area Studies Review
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• v.14 no.2
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• pp.25-40
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• 2010

Accurate forecasting of volatility is of considerable interest in financial volatility research, particularly in regard to portfolio allocation, option pricing and risk management because volatility is equal to market risk. So, we attempted to delineate a model with good ability to forecast and identified stylized features of volatility, with a focus on volatility persistence or long memory in the Australian futures market. In this context, we assessed the long-memory property in the volatility of index futures contracts using three conditional volatility models, namely the GARCH, IGARCH and FIGARCH models. We found that the FIGARCH model better captures the long-memory property than do the GARCH and IGARCH models. Additionally, we found that the FIGARCH model provides superior performance in one-day-ahead volatility forecasts. As discussed in this paper, the FIGARCH model should prove a useful technique in forecasting the long-memory volatility in the Australian index futures market.

• The Bulletin of The Korean Astronomical Society
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• v.46 no.1
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• pp.41.2-41.2
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• 2021

In this study, we generate He I 1083 nm images from Solar Dynamic Observatory (SDO)/Atmospheric Imaging Assembly (AIA) images using a novel deep learning method (pix2pixHD) based on conditional Generative Adversarial Networks (cGAN). He I 1083 nm images from National Solar Observatory (NSO)/Synoptic Optical Long-term Investigations of the Sun (SOLIS) are used as target data. We make three models: single input SDO/AIA 19.3 nm image for Model I, single input 30.4 nm image for Model II, and double input (19.3 and 30.4 nm) images for Model III. We use data from 2010 October to 2015 July except for June and December for training and the remaining one for test. Major results of our study are as follows. First, the models successfully generate He I 1083 nm images with high correlations. Second, the model with two input images shows better results than those with one input image in terms of metrics such as correlation coefficient (CC) and root mean squared error (RMSE). CC and RMSE between real and AI-generated ones for the model III with 4 by 4 binnings are 0.84 and 11.80, respectively. Third, AI-generated images show well observational features such as active regions, filaments, and coronal holes. This work is meaningful in that our model can produce He I 1083 nm images with higher cadence without data gaps, which would be useful for studying the time evolution of chromosphere and coronal holes.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.29 no.4D
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• pp.555-561
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• 2009

In lieu of section profile data, a fitting of the bored tunnel shape is more significant confirmation for maintenance of a tunnel. Before the permit on the completion of a tunnel, deformation of the completed tunnel with respect to the design model are considered. And deformation can be produced at continuously along the entire of the tunnel section. This study firstly includes an analysis of algebraic approach and test it with an observed field data. And then a number of methods, line search method, genetic algorithm, and pattern search methods, are compared with the 3D tunnel shape fitting. Algebraic methods can solve a simple circular cylinder type as like a railway tunnel. However, a more complex model (compound circular curve and non circular) as like a highway tunnel has to be solved with soft computing tools in the cause of conditional constraints. The genetic algorithm and pattern search methods are computationally more intensive, but they are more flexible at a complex condition. The line search method is fastest, but it needs a narrow bounds of the initial values.

• Journal of Educational Research in Mathematics
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• v.23 no.4
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• pp.467-490
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• 2013

Correlation is a basic statistical concept which is necessary for understanding the relationship between two variables when they change values. In the middle school curriculum of Korea, only informal definition of correlation is taught with two-way data representations such as scatter plots and contingency tables. In this study, we investigated Korean high school students' understanding of correlation using a test consisting of 35 items about interpretation of scatter plot, contingency table, and text in realistic situation. 216 students from a high school in Seoul took the test for 20 minutes. From the results, we could observe the following: First, students did not have right criteria for determining the strength of correlation presented in scatter plots. Most of students could determine if there is correlation/no correlation and if the correlation is positive/negative by seeing the data presented in scatter plots. However, they did not judge by the closeness to the regression line but rather judged by the closeness between data points. Second, when statements about comparing the strength of correlation in the context of real life situation were given in text, the students had difficulty in understanding the distribution-related characteristic of the bi-variate data. Students had difficulty in figuring out the local distribution characteristic of data, which cannot be guessed merely based on the expression 'The correlation is strong' without statistical knowledge of correlation. Third, a large number of students could not judge the association between two variabels using conditional proportions when qualitative data are given in 2-by-2 tables. They made judgement by the absolute cell count and when the marginal sum of two categories are different for explanatory variable they thought the association could not be determined. From these results, we concluded that educational measures are required in order to remove such misconceptions and to improve understanding of correlation. Considering that the current mathematics curriculum does not cover the concept of correlation, we need to improve the curriculum as well.

• Korean Journal of Radiology
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• v.22 no.11
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• pp.1918-1928
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• 2021

Objective: With the recent development of various MRI-conditional cardiac implantable electronic devices (CIEDs), the accurate identification and characterization of CIEDs have become critical when performing MRI in patients with CIEDs. We aimed to develop and evaluate a deep learning-based algorithm (DLA) that performs the detection and characterization of parameters, including MRI safety, of CIEDs on chest radiograph (CR) in a single step and compare its performance with other related algorithms that were recently developed. Materials and Methods: We developed a DLA (X-ray CIED identification [XCID]) using 9912 CRs of 958 patients with 968 CIEDs comprising 26 model groups from 4 manufacturers obtained between 2014 and 2019 from one hospital. The performance of XCID was tested with an external dataset consisting of 2122 CRs obtained from a different hospital and compared with the performance of two other related algorithms recently reported, including PacemakerID (PID) and Pacemaker identification with neural networks (PPMnn). Results: The overall accuracies of XCID for the manufacturer classification, model group identification, and MRI safety characterization using the internal test dataset were 99.7% (992/995), 97.2% (967/995), and 98.9% (984/995), respectively. These were 95.8% (2033/2122), 85.4% (1813/2122), and 92.2% (1956/2122), respectively, with the external test dataset. In the comparative study, the accuracy for the manufacturer classification was 95.0% (152/160) for XCID and 91.3% for PPMnn (146/160), which was significantly higher than that for PID (80.0%,128/160; p < 0.001 for both). XCID demonstrated a higher accuracy (88.1%; 141/160) than PPMnn (80.0%; 128/160) in identifying model groups (p < 0.001). Conclusion: The remarkable and consistent performance of XCID suggests its applicability for detection, manufacturer and model identification, as well as MRI safety characterization of CIED on CRs. Further studies are warranted to guarantee the safe use of XCID in clinical practice.

• Journal of Intelligence and Information Systems
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• v.22 no.3
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• pp.113-127
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• 2016

Among the techniques for recommendation, collaborative filtering (CF) is commonly recognized to be the most effective for implementing recommender systems. Until now, CF has been popularly studied and adopted in both academic and real-world applications. The basic idea of CF is to create recommendation results by finding correlations between users of a recommendation system. CF system compares users based on how similar they are, and recommend products to users by using other like-minded people's results of evaluation for each product. Thus, it is very important to compute evaluation similarities among users in CF because the recommendation quality depends on it. Typical CF uses user's explicit numeric ratings of items (i.e. quantitative information) when computing the similarities among users in CF. In other words, user's numeric ratings have been a sole source of user preference information in traditional CF. However, user ratings are unable to fully reflect user's actual preferences from time to time. According to several studies, users may more actively accommodate recommendation of reliable others when purchasing goods. Thus, trust relationship can be regarded as the informative source for identifying user's preference with accuracy. Under this background, we propose a new hybrid recommender system that fuses CF and social network analysis (SNA). The proposed system adopts the recommendation algorithm that additionally reflect the result analyzed by SNA. In detail, our proposed system is based on conventional memory-based CF, but it is designed to use both user's numeric ratings and trust relationship information between users when calculating user similarities. For this, our system creates and uses not only user-item rating matrix, but also user-to-user trust network. As the methods for calculating user similarity between users, we proposed two alternatives - one is algorithm calculating the degree of similarity between users by utilizing in-degree and out-degree centrality, which are the indices representing the central location in the social network. We named these approaches as 'Trust CF - All' and 'Trust CF - Conditional'. The other alternative is the algorithm reflecting a neighbor's score higher when a target user trusts the neighbor directly or indirectly. The direct or indirect trust relationship can be identified by searching trust network of users. In this study, we call this approach 'Trust CF - Search'. To validate the applicability of the proposed system, we used experimental data provided by LibRec that crawled from the entire FilmTrust website. It consists of ratings of movies and trust relationship network indicating who to trust between users. The experimental system was implemented using Microsoft Visual Basic for Applications (VBA) and UCINET 6. To examine the effectiveness of the proposed system, we compared the performance of our proposed method with one of conventional CF system. The performances of recommender system were evaluated by using average MAE (mean absolute error). The analysis results confirmed that in case of applying without conditions the in-degree centrality index of trusted network of users(i.e. Trust CF - All), the accuracy (MAE = 0.565134) was lower than conventional CF (MAE = 0.564966). And, in case of applying the in-degree centrality index only to the users with the out-degree centrality above a certain threshold value(i.e. Trust CF - Conditional), the proposed system improved the accuracy a little (MAE = 0.564909) compared to traditional CF. However, the algorithm searching based on the trusted network of users (i.e. Trust CF - Search) was found to show the best performance (MAE = 0.564846). And the result from paired samples t-test presented that Trust CF - Search outperformed conventional CF with 10% statistical significance level. Our study sheds a light on the application of user's trust relationship network information for facilitating electronic commerce by recommending proper items to users.

• The Journal of Fisheries Business Administration
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• v.40 no.2
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• pp.49-70
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• 2009

This study proviedes GARCH model(Bollerslev, 1986) to analyze the structural characteristics of price volatility in domestic aquacultural fish market of Korea. As a case study, flatfish and rock-fish are analyzed as major species with relatively high portion in an aspect of production volume among fish captured in Korea. For analyzing, this study uses daily market data (dating from Jan 1 2000 to June 30, 2008) published by the Noryangjin Fisheries Wholesale Market which is located in Seoul of Korea. This study performs normality test on trading volume and price volatility of flatfish and rock-fish as an advanced empirical approach. The normality test adopted is Jarque-Bera test statistic. As a result, first, a null hypothesis that "an empirical distribution follows normal distribution" was rejected in both fishes. The distribution of daily market data of them were not only biased toward positive(+) direction in terms of kurtosis and skewness, but also characterized by leptokurtic distribution with long right tail. Secondly, serial correlations were found in data on market trading volume and price volatility of two species during very long period. Thirdly, the results of unit root test and ARCH-LM test showed that all data of time series were very stationary and demonstrated effects of ARCH. These statistical characteristics can be explained as a reasonable ground for supporting the fitness of GARCH model in order to estimate conditional variances that reveal price volatility in empirical analysis. From empirical data analysis above, this study drew the following conclusions. First of all, from an empirical analysis on potential effects of seasonality and the day of week on price volatility of aquacultural fish, Monday effects were found in both species and Thursday and Friday effects were also found in flatfish. This indicates that Monday is effective in expanding price volatility of aquacultural fish market and also Monday has higher effects upon the price volatility of fish than other days of week have since it has more new information for weekend. Secondly, the empirical analysis led to a common conclusion that there was very high price volatility of flatfish and rock-fish. This points out that the persistency parameter($\lambda$), an index of possibility for current volatility to sustain similarly in the future, was higher than 0.8-equivalently nearly to 1-in both flatfish and rock-fish, which presents volatility clustering. Also, this study estimated and compared and model that hypothesized normal distributions in order to determine fitness of respective models. As a result, the fitness of GARCH(1, 1)-t model was better than model where the distribution of error term was hypothesized through-distribution due to characteristics of fat-tailed distribution, was also better than model, as described in the results of basic statistic analysis. In conclusion, this study has an important mean in that it was introduced firstly in Korea to investigate in price volatility of Korean aquacultural fishery products, although there was partially a limited of official statistic data. Therefore, it is expected that the results of this study will be useful as a reference material for making and assessing governmental policies. Also, it is looked forward that the results will be helpful to build a fishery business plan as and aspect of producer, and also to take timely measures to potential price fluctuations of fishery products in market. Hence, it is advisable that further studies related to such price volatility in fishery market will extend and evolve into a wider variety of articles and issues in near future.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.19 no.7
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• pp.174-182
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• 2018

Given the ongoing debate in many aspects of finance, more attention may need to focus on corporate R&D expenditures. This study empirically tests financial determinants of R&D expenditures for NYSE-listed and KOSPI-listed firms. Three major hypotheses were postulated to test for corporate R&D outlay. First, proposed variables such as one-year lagged R&D expenditures, market value based leverage, profitability and cash holdings showed significant influence on corporate R&D costs for the sample firms. Moreover, financial factors inclusive of squared one-year lagged R&D expenditures, the interaction effect between one-lagged R&D expenditures and high-growth firm, non-debt tax shield, Tobin's q and a dummy variable to explain differences in accounting treatment between the U.S. and Korea, revealed significant differences between the two samples. Finally, in the conditional quantile regression (CQR) analysis for the R&D-related variables in relation to corporate growth rate, it was found that the NYSE-listed firms had a statistically significant linkage between growth potential and one-year lagged R&D expenditures at lower quantile levels. This study may shed new light on identifying financial factors affecting differences between the U.S. market (as an advanced market) and the Korean market (as an emerging market) regarding the optimal level of R&D investments for shareholders.

• Journal of Distribution Science
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• v.10 no.12
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• pp.35-43
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• 2012

Purpose - This study analyzes the effects of types of leadership on the employees of Italian restaurants, its efficacy, and organizational citizenship behavior, utilizing a causal assessment model. In this study, independent variables such as the type of leadership perceived in the manager or chef by an Italian restaurant's employees, and its efficacy were parameters, and the organizational citizenship behavior and organizational effectiveness were the variables representing the results in the hypothesis. The study aimed to draw implications by verifying the leadership via efficacy and the impact on organizational citizenship behavior of Italian restaurants. Research design, data, methodology - For the purpose of this analysis, specific questionnaire items were configured according to the theory and efficacy of the study. From a questionnaire used in organizational citizenship behavior comprising 22 questions, six were modified to suit the research purpose of this study. The configured questionnaire comprised 5 parts and 40 items. A Likert (Likert) 5-point scale was utilized to measure responses to the questionnaire items from the employees of an Italian restaurant in Seoul who participated in the survey. For data collection, 400 questionnaires were distributed, and 344 collected. Factor analysis and reliability verification were conducted using SPSS18.0 and AMOS18.0. A covariance structure analysis was conducted to test the research hypotheses. Results - Based on the results of the analyses, the summary and suggested implications of the research are as follows: The covariance structure analysis used to analyze the kind of effect transformational and transactional leadership styles in Italian restaurant employees had on self-efficacy, group-efficacy, and organizational citizenship behavior, indicated that among the characteristics of transformational leadership (such as, idealized influence, inspirational motivation, individual consideration, and intellectual stimulation), idealized influence and individual consideration had a positive influence on self-efficacy. Idealized influence, individual consideration, conditional reward, and management by exception also positively influenced self-efficacy and altruistic and conscientious behavior (organizational citizenship behavior). Conclusions - Results suggest that with regard to self-efficacy and group efficacy, managers in different departments and chefs should provide team members with a vision for the future, increase their confidence in their abilities, and build their trust in the organization. By evaluating employee performance and experiences, management can demonstrate leadership and encourage organizational citizenship behavior through enjoyable, voluntary participation. Transformational and transactional leadership is effective in group processes that include social-exchange relationships, self-efficacy and group efficacy, and organizational citizenship behavior. However, as this research study utilizes only self-reported data, it has several limitations, such as a vulnerability of errors caused by the various experiment types. A significant limitation of this study is the lack of potential for the duplication of results. The covariance structure analysis, however, provides complementation to limit the impact of errors from self-reporting studies. A future study can extend this research by utilizing different data collection methods.

• Asian Pacific Journal of Cancer Prevention
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• v.17 no.8
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• pp.4175-4179
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• 2016

Background: Oral cavity cancer (OCC) is one of the most common cancers worldwide. No studies have reported on the prevalence and epidemiologic risk factors of oral premalignant lesions (OPLs) in Thailand. The purpose of this study was to investigate the prevalence of OPLs and associated factors in Roi Et Province, Thailand. Materials and Methods: To investigate the prevalence of OPLs, a cross-sectional descriptive study was conducted in which 2,300 subjects over 40 years of age were recruited and screened for the prevalence of OPLs. To identify factors associated with OPLs, a matched case-control study was used in which the subjects were 102 cases with OPL and 102 matched controls without OPLs. The studies were conducted in Roi Et Province during the period 1 February, 2014, to 30 April, 2014, and the data were collected by the use of a structured interview questionnaire and by extraction of information from medical records. Data analyses involved the use of descriptive statistics, McNemar's test, and conditional logistic regression. Results: The overall prevalence of OPLs was 3.8%, and no-one was diagnosed with more than one type of OPL. The factors found to be associated with a statistically significant higher risk of an OPL were betel nut chewing, smoking, and alcohol consumption. The associations with these factors were strong, especially for betel nut chewing and smoking. Conclusions: The habits of betel nut chewing, smoking, and alcohol use are confirmed as factors associated with OPLs in a population of Roi Et Province, Thailand. Campaigns to reduce such risk healthy behaviour are needed, but whether any actual decrease will prevent the eventual transformation of an OPL into an OCC remains an open question.