• Journal of the Economic Geographical Society of Korea
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• v.12 no.4
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• pp.627-644
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• 2009

In the knowledge-based economy highlighting the importance of human capital, there has been a growing interest in human capital externalities as a fundamental engine of growth and development of a region. The purpose of this study is to analyze human capital externalities using 3-level hierarchical linear model(3-HLM), decomposing determinants of wages into three levels involving workers(level-1) nested within firms(level-2) nested within regions(level-3). This study separately estimates the effect of the average education level on the wages by three different schooling groups on the assumption that the intensity of knowledge spillovers varies with each group's schooling level. The main results are as follows; First, the coefficient of the average education level of a region shows 0.044, indicating that one-year increase in the average level of schooling could increase average individual earnings by 4.4%. Secondly, the external effects of human capital on three different schooling groups are considerably different, raising less than high school graduates' wages by 3.0%, college graduates' wages by 4.7%, and graduate schools' wages by 11.8%, respectively. Thirdly, well educated workers are much more sensitive to the variation of the regional education level than less educated ones when we apply the shares of each schooling group as alternative measures for the average level of education. Such findings of this study draw an implication that local governments could speed up regional economic growth in the knowledge-based economy by not only raising total human capital stock in a region but building the close networks that promote productivity-enhancing human capital external effects.

• Journal of KIISE:Databases
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• v.33 no.2
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• pp.201-213
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• 2006

A time-series database is a set of time-series data sequences, each of which is a list of changing values of the object in a given period of time. Subsequence matching is an operation that searches for such data subsequences whose changing patterns are similar to a query sequence from a time-series database. This paper addresses a performance issue of time-series subsequence matching. First, we quantitatively examine the performance degradation caused by the window size effect, and then show that the performance of subsequence matching with a single index is not satisfactory in real applications. We argue that index interpolation is fairly useful to resolve this problem. The index interpolation performs subsequence matching by selecting the most appropriate one from multiple indexes built on windows of their inherent sizes. For index interpolation, we first decide the sites of windows for multiple indexes to be built. In this paper, we solve the problem of selecting optimal window sizes in the perspective of physical database design. For this, given a set of query sequences to be peformed in a target time-series database and a set of window sizes for building multiple indexes, we devise a formula that estimates the cost of all the subsequence matchings. Based on this formula, we propose an algorithm that determines the optimal window sizes for maximizing the performance of entire subsequence matchings. We formally Prove the optimality as well as the effectiveness of the algorithm. Finally, we perform a series of extensive experiments with a real-life stock data set and a large volume of a synthetic data set. The results reveal that the proposed approach improves the previous one by 1.5 to 7.8 times.

• International Area Studies Review
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• v.20 no.2
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• pp.85-111
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• 2016

Global semiconductor companies is investing enormous capital worldwide. And direct investment in China is increasing greatly these days, Especially, global semiconductor companies are setting up a factory in China due to expanding market rather than utilizing low labor cost. Therefore, this study is trying to analyze the background and process of direct investment from global Korean and Taiwanese semiconductor companies in China. Firstly, In 1996, Samsung semiconductor established a back end process factory in Suzhou. And in 2014, Samsung semiconductor set up a front and back end factory in Xian. Secondly, In 2006, SK Hynix built a front and back end factory in Wuxi. and SK Hynix set up a back end factory named Hitech semiconductor with Chinese company in 2009. Later in 2015, SK Hynix established a back end factory in Chongqing. Thirdly, In 2004, TSMC started to operate a factory in Shanghai, and in 2018, TSMC is going to establish a factory in Nanjing. Lastly, UMC bought a stock to produce product in Chinese local company named HJT, and at the end of 2016, UMC is going to finish building a factory in Xiamen. As a result, it was proved that most companies hoped to expand the chinese market by setting up a factory in china. In addition, Samsung expected to avoid a risk by setting up a factory in china, and SK Hynix wanted to avoid a countervailing duty by setting up a factory in china. Based on the result of this study, this study indicates some implications for other semiconductor companies which are very helpful for their future foreign direct investment.

• Journal of Animal Environmental Science
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• v.11 no.3
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• pp.197-206
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• 2005

This study was carried out to investigate the concentration and characteristics of dust originating from windowless broiler building in each season. 12.0m width and 46m tenth with side wall height of 3.0m was investigated and capacity was 12,800 birds at a stock density of 23.2 birds per square meter. Dust concentrations in terms of total suspended particles (TSP), and particulate matter of sizes $10{\mu}m(PM10),\;2.5{\mu}m (PM2.5),\;and\;1{\mu}m(PM1)$ were measured at 30-minute intervals. On the basis of broiler age, the average dust concentration in summer in TSP as follows: 1,229 904.5 558.8 and $1,053{\mu}g/m^3$ on the broilers' first to fourth week of age, respectively. But during winter, the average dust concentration showed an increasing pattern, as follows: 465.4, 1,401, 4,497, 5,097 and $6,873{\mu}g/m^3$ on the broilers' first to fifth week of age, respectively. The maximum dust concentration of $11,132{\mu}g/m^3$ was observed on the fifth week. On a daily basis, the maximum dust concentration during summer was detected in early morning, and the minimum in the afternoon. The aerial dust particle size of $0.05\~0.35{\mu}m$ was the highest in number. But on volume basis, particle size of 16~99 un had the largest percentage in the broiler house. Crude protein of the dust $(42.8\~65.2\%)$, on dry matter basis, was higher than that $(20.5\~24.5\%)$ fed to the broilers. Heavy metal concentration of the dust also had high levels compared with that of the feed.

• Journal of Korean Society of Forest Science
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• v.105 no.4
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• pp.401-413
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• 2016

Forest resources utilization and variable disturbance history have been affected the rarity and conservation value of forest relict trees, which served as habitat for forest biodiversity, important carbon stock and cultural role include human and natural history in South Korea. This study was conducted to establish the baseline data for forest resources conservation by clarifying species diversity, spatial distribution and ecological characteristics (individual and habitat) of forest relict trees (DBH > 300 cm) based on the data getting from mountain trail, high resolution aerial photos and field professionals and field survey. As results, 54 taxa (18 family 32 genus 48 species 1 subspecies 3 variety and 2 form) as about 22% of tree species in Korea was identified in the field. 837 individuals of forest relict trees were observed and the majority of the trees was in Pinaceae, deciduous Fagaceae and Rosaceae, which families are abundant in population diversity. High elevation area was important to relict trees as mean altitudinal distribution was 1,200 m a.s.l as likely affected by human activity gradients and mid-steep slope and North aspect was important environment for the trees remain. Many individuals exhibited 'damage larger branch' (55.6%) and consequent relatively lower mean canopy coverages (below 80%). Synthetically, present diversity and abundance of relict forest trees in South Korea were the result of complex process among climate variation, local weather and biological factors and the trees of big and old were estimated to important forest biodiversity elements. In the future, clarifying the role and function of relict trees in forest ecosystem, in- and ex- situ programmes for important trees and habitat, and activities for building the background of conservation policy such as "Guideline for identifying and measurement of forest relict trees".

• Journal of KIISE:Databases
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• v.33 no.1
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• pp.42-55
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• 2006

We propose a single Index approach for subsequence matching that supports moving average transform of arbitrary order in time-series databases. Using the single index approach, we can reduce both storage space overhead and index maintenance overhead. Moving average transform is known to reduce the effect of noise and has been used in many areas such as econometrics since it is useful in finding overall trends. However, the previous research results have a problem of occurring index overhead both in storage space and in update maintenance since tile methods build several indexes to support arbitrary orders. In this paper, we first propose the concept of poly-order moving average transform, which uses a set of order values rather than one order value, by extending the original definition of moving average transform. That is, the poly-order transform makes a set of transformed windows from each original window since it transforms each window not for just one order value but for a set of order values. We then present theorems to formally prove the correctness of the poly-order transform based subsequence matching methods. Moreover, we propose two different subsequence matching methods supporting moving average transform of arbitrary order by applying the poly-order transform to the previous subsequence matching methods. Experimental results show that, for all the cases, the proposed methods improve performance significantly over the sequential scan. For real stock data, the proposed methods improve average performance by 22.4${\~}$33.8 times over the sequential scan. And, when comparing with the cases of building each index for all moving average orders, the proposed methods reduce the storage space required for indexes significantly by sacrificing only a little performance degradation(when we use 7 orders, the methods reduce the space by up to 1/7.0 while the performance degradation is only $9\%{\~}42\%$ on the average). In addition to the superiority in performance, index space, and index maintenance, the proposed methods have an advantage of being generalized to many sorts of other transforms including moving average transform. Therefore, we believe that our work can be widely and practically used in many sort of transform based subsequence matching methods.

• Journal of Intelligence and Information Systems
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• v.24 no.4
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• pp.137-154
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• 2018

Animal infectious diseases, such as avian influenza and foot and mouth disease, occur almost every year and cause huge economic and social damage to the country. In order to prevent this, the anti-quarantine authorities have tried various human and material endeavors, but the infectious diseases have continued to occur. Avian influenza is known to be developed in 1878 and it rose as a national issue due to its high lethality. Food and mouth disease is considered as most critical animal infectious disease internationally. In a nation where this disease has not been spread, food and mouth disease is recognized as economic disease or political disease because it restricts international trade by making it complex to import processed and non-processed live stock, and also quarantine is costly. In a society where whole nation is connected by zone of life, there is no way to prevent the spread of infectious disease fully. Hence, there is a need to be aware of occurrence of the disease and to take action before it is distributed. Epidemiological investigation on definite diagnosis target is implemented and measures are taken to prevent the spread of disease according to the investigation results, simultaneously with the confirmation of both human infectious disease and animal infectious disease. The foundation of epidemiological investigation is figuring out to where one has been, and whom he or she has met. In a data perspective, this can be defined as an action taken to predict the cause of disease outbreak, outbreak location, and future infection, by collecting and analyzing geographic data and relation data. Recently, an attempt has been made to develop a prediction model of infectious disease by using Big Data and deep learning technology, but there is no active research on model building studies and case reports. KT and the Ministry of Science and ICT have been carrying out big data projects since 2014 as part of national R &D projects to analyze and predict the route of livestock related vehicles. To prevent animal infectious diseases, the researchers first developed a prediction model based on a regression analysis using vehicle movement data. After that, more accurate prediction model was constructed using machine learning algorithms such as Logistic Regression, Lasso, Support Vector Machine and Random Forest. In particular, the prediction model for 2017 added the risk of diffusion to the facilities, and the performance of the model was improved by considering the hyper-parameters of the modeling in various ways. Confusion Matrix and ROC Curve show that the model constructed in 2017 is superior to the machine learning model. The difference between the2016 model and the 2017 model is that visiting information on facilities such as feed factory and slaughter house, and information on bird livestock, which was limited to chicken and duck but now expanded to goose and quail, has been used for analysis in the later model. In addition, an explanation of the results was added to help the authorities in making decisions and to establish a basis for persuading stakeholders in 2017. This study reports an animal infectious disease prevention system which is constructed on the basis of hazardous vehicle movement, farm and environment Big Data. The significance of this study is that it describes the evolution process of the prediction model using Big Data which is used in the field and the model is expected to be more complete if the form of viruses is put into consideration. This will contribute to data utilization and analysis model development in related field. In addition, we expect that the system constructed in this study will provide more preventive and effective prevention.