• International Area Studies Review
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• v.21 no.4
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• pp.65-83
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• 2017

As Global value chain(GVC) is deepening, the importance of intermediated good trade is growing in international trade issues. Such facts lead to much discussions about the relation between Global Outsourcing and pollution. This study analyzed the effect of Global outsourcing on Environment using the data including 21 industries for 2004-14. $CO_2$ intensity is used as a proxy for the environment variable and to measure Global outsourcing and I employed the method suggested by Feenstra and Hanson(1999), Amiti and Wei(2006). To examine the effect Global outsourcing on the Environment more precisely, this paper controlled the factors that can affect the environment level on the basis of the theory suggested by Copeland and Taylor(1994). In the methodology, System GMM is employed to solve endogenous problem. The results show that for overall industries, Global outsourcing effect cannot be identified and for polluting industries, the result is identical. However, Global outsourcing has a negative effect on the pollution level for China and developing countries. In other words, as Global outsourcing is increasing, the national pollution level is decreasing.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2000.04b
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• pp.449-457
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• 2000

The paper describes the study of global approximate optimization utilizing soft computing techniques such as genetic algorithms (GA's), neural networks (NN's), and fuzzy inference systems(FIS). GA's provide the increasing probability of locating a global optimum over the entire design space associated with multimodality and nonlinearity. NN's can be used as a tool for function approximations, a rapid reanalysis model for subsequent use in design optimization. FIS facilitates to handle the quantitative design information under the case where the training data samples are not sufficiently provided or uncertain information is included in design modeling. Properties of soft computing techniques affect the quality of global approximate model. Evolutionary fuzzy modeling (EFM) and adaptive neuro-fuzzy inference system (ANFIS) are briefly introduced for structural optimization problem in this context. The paper presents the success of EFM depends on how optimally the fuzzy membership parameters are selected and how fuzzy rules are generated.

• Communications for Statistical Applications and Methods
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• v.25 no.2
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• pp.185-197
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• 2018

Measuring market fear is an important way of understanding fundamental economic phenomena related to financial crises. There have been several approaches to measure market fear or panic level in a financial market. Recently, herd behavior has gained its popularity as important economic phenomena explaining the fear in the financial market. In this paper, we investigate herd behavior in global stock markets with a focus on intercontinental comparison. While various risk measures are available for the detection of herd behavior in the market, we use the standardized herd behavior index in Dhaene et al. (Insurance: Mathematics and Economics, 50, 357-370, 2012b) and Lee and Ahn (Dependence Modeling, 5, 316-329, 2017) for the comparison of herd behaviors in global stock markets. A global stock market data from Morgan Stanley Capital International is used to study herd behavior especially during periods of financial crises.

• Journal of the Korea Society of Computer and Information
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• v.22 no.8
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• pp.15-23
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• 2017

In this paper, the block reweighting and global offset initialization methods are proposed to complement the improved IRLMS algorithm which is the effective algorithm in registration based SBNUC algorithm. Proposed block weighting method reweights the error map whose abnormal data are excluded. The global offset initialization method compensates the global nonuniformity initially. The ordinary registration based SBNUC algorithm is hard to compensate global nonuniformity because of low scene motion. We employ the proposed methods to improved IRLMS algorithm, and apply it to real-world infrared raw image stream. The result shows that new implementation provides 3.5~4.0dB higher PSNR and convergence speed 1.5 faster then the improved IRLMS algorithm.

• The Journal of Korea Robotics Society
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• v.3 no.1
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• pp.33-41
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• 2008

Based on object recognition technology, we present a new global localization method for robot navigation. For doing this, we model any indoor environment using the following visual cues with a stereo camera; view-based image features for object recognition and those 3D positions for object pose estimation. Also, we use the depth information at the horizontal centerline in image where optical axis passes through, which is similar to the data of the 2D laser range finder. Therefore, we can build a hybrid local node for a topological map that is composed of an indoor environment metric map and an object location map. Based on such modeling, we suggest a coarse-to-fine strategy for estimating the global localization of a mobile robot. The coarse pose is obtained by means of object recognition and SVD based least-squares fitting, and then its refined pose is estimated with a particle filtering algorithm. With real experiments, we show that the proposed method can be an effective vision- based global localization algorithm.

• International Union of Geodesy and Geophysics Korean Journal of Geophysical Research
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• v.24 no.1
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• pp.19-27
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• 1996

The ozone layer monitoring program of the Global Environment Laboratory at Yonsei University in Seoul, established as one of the Global Ozone Observing System($GO_3OS$) of the World Meteorological Organization(WMO), has been carried out daily by measuring total ozone and its vertical distribution using a Dobson Ozone Spectrophotometer(Beck #124) since 1984. In this paper, we review the organization and the historical background of ozone measurements in Korea, describe data acquisition and analysis systems, and briefly summarize the results from our ozone observations.

• Journal of Information Technology Services
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• v.11 no.1
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• pp.305-323
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• 2012

This study examines online social media diffusion across different countries that will help to provide a picture of current global online social network services (SNSs). Analyzing country-level data drawn from 78 nations, we find that non-technological factors such as culture and language as well as technological factors including Internet penetration rate and smartphone adoption rate have significant effects on online social media diffusion. These findings, derived from a broad range of different countries, not only provide theoretical insights into understanding critical factors that enable successful global expansion of online social media services but also help practitioners plan their marketing strategies more effectively in a global context.

• Journal of KIISE:Databases
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• v.34 no.6
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• pp.564-571
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• 2007

One of the methods for integrating XML data in distributed environments is using XML view. User can query toward distributed local XML views by using global XQuery queries in XQuery which is a standard query language for searching XML data. The global XQuery queries naturally contain join operations because of integrating and searching distributed heterogeneous data. Since join operations are generally expensive for processing a query, its processing technique is very important for efficient processing of global XQuery queries. Therefore there are some studies on the efficient processing of join operations and one of these studies is that selects minimum join cost by estimating a join selectivity. In case of SQL, there are already some researches for estimating a join selectivity and join cost of global SQL queries. However we can not apply their methods for estimating the selectivity of join operations in SQL queries into XQuery queries because of the structural difference between relational data and XML data. Therefore this paper proposes a method for estimating a selectivity of join operations in XQuery queries using the information of XML views. Our contribution is three threefold. First, we define the difference point for estimating join selectivity between SQL and XQuery. Second, we estimate join selectivity in XQuery queries by referring XML views. Third, we evaluate our estimating method.

• Korean Journal of Agricultural and Forest Meteorology
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• v.12 no.4
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• pp.298-306
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• 2010

In this study, we evaluated the performance of Korea Land Data Assimilation System (KLDAS) for the estimation of evapotranspiration (ET) by comparing the modeled against the observed ET at Gwangneung deciduous forest of KoFlux site (GDK) from 2006 to 2008. Although the magnitudes of ET by KLDAS overestimated the observed ET, the seasonal patterns of KLDAS ET were comparable with the correlation coefficient of 0.78. The difference between the KLDAS ET and the observed ET was larger in spring and summer due to rapid plant growth and frequent rainfalls with high cloud cover, respectively. Compared to the ET estimated by NASA Global Land Data Assimilation System (GLDAS) with $0.25^{\circ}$ and $1^{\circ}$ resolution, the ET by KLDAS with 10 km resolution showed better agreement with the observation at the GDK site. Albeit further improvement is necessary, our results suggest that KLADS can be used as a practical tool to map ET and to examine its spatiotemporal variability over the Korean Peninsula.

• Journal of Korea Water Resources Association
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• v.32 no.6
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• pp.617-625
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• 1999

Global warming has begun since the industrial revolution and it is getting worse recently. Even though the increase of greenhouse gases such as $CO_2$ is thought to be the main cause for global warming, its impact on global climate has not been revealed clearly in rather quantitative manners. However, researches using General Circulation Models(GCMs) has shown the accumulation of greenhouse gases increases the global mean temperature, which in turn impacts on the global water circulation pattern. This changes in global water circulation pattern result in abnormal and more frequent meteorological events such as severe floods and droughts, generally more severe than the normal ones, which are now common around the world and is referred as a indirect proof of global warming. Korean peninsula also cannot be an exception and have had several extremes recently. The main objective of this research is to analyze the impact of global warming on the change of flood and drought frequency. Based on the assumption that now is a point in a continuously changing climate due to global warming, we analyzed the observed daily rainfall data to find out how the increase of annual rainfall amount affects the distribution of daily rainfall. Obviously, the more the annual rainfall depth, the more frequency of much daily rainfall, and vice versa. However, the analysis of the 17 points data of Keum river basin in Korea shows that especially the number of days of under 10mm or over 50mm daily rainfall depth is highly correlated with the amount of annual rainfall depth, not the number of dry days with their correlation coefficients quite high around 0.8 to 0.9.