• Water Engineering Research
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• v.6 no.1
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• pp.31-38
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• 2005

Groundwater recharge is defined in an addition of water to groundwater reservoir. Recently, many people have been moving to the Edwards aquifer and urban and agricultural industry have been expending. Hydrologists and water planning managers concern about insufficient groundwater amounts and irrigation water price variability. In this paper, I focus on estimates of local recharge volumes and quantify preferential flow through GIS technique. Chloride Mass Balance (CMB) and hydrochemical components have been widely applied to recharge rate and evaluate flow paths. The CMB method is based on relationship between wet-dry chloride deposition data and Rainfall data. These data are manipulated using ArcGIS. Especially, hydrochemical concentration distribution is good index for groundwater residence times or flow paths such as $[Mg^{2+}]/[Ca^{2+}],[Cl]$ and log$([Ca^{2+}]+[Mg^{2+}])/[Na^+]$. Well information such as hydrological-hydrochemical data are imported into ArcGIS and manipulated by interpolation techniques. For each potentiometric surface and water quality, point data are converted to spatial data through each Kriging and Inverse Distance Weighted (IDW) techniques.

• The Journal of Information Systems
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• v.29 no.3
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• pp.119-144
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• 2020

Purpose Real estate usually takes charge of the highest proportion of physical properties which individual, organizations, and government hold and instability of real estate market affects the economic condition seriously for each economic subject. Consequently, practices for predicting the real estate market have attention for various reasons, such as financial investment, administrative convenience, and wealth management. Additionally, development of machine learning algorithms and computing hardware enhances the expectation for more precise and useful prediction models in real estate market. Design/methodology/approach In response to the demand, this paper aims to provide a framework for forecasting the real estate market with machine learning algorithms. The framework consists of demonstrating the prediction efficiency of each machine learning algorithm, interpreting the interior feature effects of prediction model with a state-of-art algorithm, LIME(Local Interpretable Model-agnostic Explanation), and comparing the results in different cities. Findings This research could not only enhance the academic base for information system and real estate fields, but also resolve information asymmetry on real estate market among economic subjects. This research revealed that macroeconomic indicators, real estate-related indicators, and Google Trends search indexes can predict real-estate prices quite well.

• Journal of the East Asian Society of Dietary Life
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• v.27 no.2
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• pp.104-113
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• 2017

The purpose of this study was to analyze and consumption patterns diet/low-calorie food. Ubjects were 353 adult women aged 20s~50s. Ubjects were divided into three groups according to body mass index (BMI): Normal group (BMI>23.0), overweight group (23.0${\leq}$BMI<25.0), and obesity group (BMI${\geq}$25.0). This study collected all information by self-administrated questionnaires. The SPSS version 21.0 was used for analysis of data. The obesity group lower education level (p<0.001), higher age (p<0.001) and higher income (p<0.001) than normal group. However, score of health status was highest in normal group (p<0.001). Proportion of obesity group pill type diet/low-calorie (p<0.034), drug (diuretic, appetite suppressant and riental medicine) (p<0.001), and cosmetic surgery (p<0.001). The main reason for consumption of diet/low-calorie was control without starving (28.0%). Obese group emphasized manufacturer, ingredient and reputation, whereas the normal group emphasized price and expected effectiveness (p<0.001).

• Journal of Information Technology Applications and Management
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• v.21 no.1
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• pp.177-184
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• 2014

This research analyzes the effects of factors on the demands for outbound to the countries such as Japan, China, the United States of America, Thailand, Philippines, Hong Kong, Singapore and Australia, the countries preferred by many Koreans. The factors for this research are (1) economic variables such as Korea Composite Stock Price Index (KOSPI), which could have influences on outbound tourism and exchange rate and (2) unpredictable events such as diseases, financial crisis and terrors. Regression analysis was used to identify relationship based on the monthly data from January 2001 to December 2010. The results of the analysis show that both exchange rate and KOSPI have impacts on the demands for outbound travel. In the case of travels to the United States of America and Philippines, Korean tourists usually have particular purposes such as studying, visiting relatives, playing golf or honeymoon, thus they are less influenced by the exchange rate. Moreover, Korean tourists tend not to visit particular locations for some time when shock reaction happens. As the demands for outbound travels are different from country to country accompanied by economic variables and shock variables, differentiated measure to should be considered to come close to the target numbers of tourists by switching as well as creating the demands. For further study we plan to build outbound tourism forecasting models using Artificial Neural Networks.

• International Journal of Internet, Broadcasting and Communication
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• v.8 no.3
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• pp.12-24
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• 2016

Recently, as the price per bit is decreasing at a fast rate, flash memory is considered to be used as primary storage of large-scale database systems. Although flash memory shows off its high speeds of page reads, however, it has a problem of noticeable performance degradation in the presence of increasing update workloads. When updates are requested for pages with random page IDs, in particular, the shortcoming of flash tends to impair significantly the overall performance of a flash-based database system. Therefore, it is important to have a way to efficiently update the B+-tree, when it is stored in flash storage. This is because most of updates in the B+-tree arise at leaf nodes, whose page IDs are in random. In this light, we propose a new flash B+-tree that stores up-to-date versions of leaf nodes in sibling-leaf blocks (SLBs), while updating them. The use of SLBs improves the update performance of B-trees and provides the mechanism for fast key range searches. To verify the performance advantages of the proposed flash B+-tree, we developed a mathematical performance evaluation model that is suited for assessing B-tree operations. The performance comparisons from it show that the proposed flash B+-tree provides faster range searches and reduces more than 50% of update costs.

• Proceedings of the KSR Conference
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• 2005.05a
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• pp.957-962
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• 2005

Since the nation's currency crisis in 1997. Korea reioined the USD 10.000 per capita income group after collapse of per capita income to USD 6.000 due to the minus GDP growth and sharp hike of exchange rate. It has also been expected for Korea to achieve per capita income of USD 20.000. provided that it maintains $10\%$ export increase rate. $5\%$ nominal GDP growth rate. $3\%$ consumer price index. $2\%$ increase in KRW/USD exchange rate. and $1\%$ net population increase rate. Yet. it should be noted that the nation needs to fulfill the necessity of various SOC infrastructure investment in order to achieve this goal. This paper will address the prospects for the future direction of the national SOC policies through the historical examination of the industrialized nations. such as U.S.A.. U.K.. France. and Japan. with regard to the relationships between economic growth and SOC provision. Some efforts will be made to forecast the optimal budget allocation of the national SOC, in particular, between highway and railway sectors.

• The Journal of Asian Finance, Economics and Business
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• v.7 no.9
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• pp.169-178
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• 2020

In this paper, we provide an overview of financial development in Vietnam. Particularly, a new approach of this study is to measure financial development through improvements in depth, efficiency and access of the banking system and stock market. Further, the study examines the factors significantly affecting financial development in Vietnam. The data are collected in Vietnam, an emerging country with a limited financial development. We employ the Autoregressive Distributed Lag (ARDL) approach, which generates a high reliability and suits data characteristics of emerging countries like Vietnam. We observe that Vietnam's banking system plays a key role in supplying credits to the economy while the nascent stock market at a limited size shows its potential for a considerable growth in the future. We also find the influential determinants of financial development in Vietnam including real estate market (RE), economic growth (EG), consumer price index (CPI), and global financial crisis (GFC). These findings are essential for Vietnamese authorities in providing practical solutions in order to build a sustainable and synchronous financial development. They are also first empirical evidence relating to an overview of financial development in an emerging country, so they are not only valuable to Vietnam but also crucial to other emerging economies.

• Management Science and Financial Engineering
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• v.11 no.3
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• pp.79-93
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• 2005

Despite the fact that the sourcing process in B2B e-Marketplaces is one of the most important tasks, the evaluation and selection process of suppliers have been ad-hoc based and mainly dependent on the experience of sourcing managers' subjective knowledge. To remedy the problem, we developed a decision support System (called Wise - I) that helps sourcing managers evaluate suppliers in a more systematic way. The system reflects company's strategy and know-how by adopting company enforced weighted scores for different factors and employing a more scientific method of considering factors other than price and on-time delivery rate, utilizing the AHP method. This paper reports the effectiveness of the system as well as the detailed description of the system. To investigate the effectiveness of the system, we collected information through interview and questionnaire survey. The information was also augmented through the firm key index system, which monitors average delivery lead time and on-time delivery rate. The result indicates that the system leads to the efficiency of purchasing section and the transparency of buying process, therefore reduces delivery time and cost.

• Korean Management Science Review
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• v.32 no.2
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• pp.37-50
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• 2015

The number of contracts for bundling services in a domestic telecommunication market records 1.3 miilion as of the first half year in 2007, and steadily increased to 8.97 million in 2010, to 12.76 million in 2013. In 2014, 70.4% of telecommunication service consumers are found to subscribe to bundling service. Bundling services provide consumers with benefits such as price discount, convenience, increase ARPU. However, a market dominant player in a specific market may transfer its market power to another market by selling bundling services. SSNIP has been adopted to provide a market definition. However, SSNIP is not suitable to measure the effect of market power transition through bundling services because SSNIP cannot measure the effect of changeover sales of bundling services. Thus, in this study, we have investigated the effect of market power transition through bundling services reflecting market power effect and quality upgrade using Gross Upward Market Power Pressure Index metho and reviewed UPP and derivative UPP models.

• The Korean Journal of Applied Statistics
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• v.25 no.5
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• pp.757-773
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• 2012

Traditional value at risk(S-VaR) has a difficulity in predicting the future risk of financial asset prices since S-VaR is a backward looking measure based on the historical data of the underlying asset prices. In order to resolve the deficiency of S-VaR, an economic value at risk(E-VaR) using the risk neutral probability distributions is suggested since E-VaR is a forward looking measure based on the option price data. In this study E-VaR is estimated by assuming the generalized gamma distribution(GGD) as risk neutral density function which is implied in the option. The estimated E-VaR with GGD was compared with E-VaR estimates under the Black-Scholes model, two-lognormal mixture distribution, generalized extreme value distribution and S-VaR estimates under the normal distribution and GARCH(1, 1) model, respectively. The option market data of the KOSPI 200 index are used in order to compare the performances of the above VaR estimates. The results of the empirical analysis show that GGD seems to have a tendency to estimate VaR conservatively; however, GGD is superior to other models in the overall sense.