• Asian Journal of Atmospheric Environment
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• v.8 no.2
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• pp.69-80
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• 2014

It is often assumed that atmospheric observations at remote sites represent long-range transport of airborne material, and local influences are overlooked. We evaluated the impact of local input on the rainwater composition at Gosan Station, a strategic site for monitoring the continental outflow from Asia. We analyzed a 14-year record of rainwater chemical composition archived by the Korea Meteorological Administration and detected local terrestrial contribution for nitrate, sulfate and ammonium. We also measured the chemical composition of rainwater sampled simultaneously at multiple locations within the premises of the Gosan Station, from which local influence with meter-scale spatial heterogeneity could be discerned. We estimate that the local input accounted for at least ~10% of the wet deposition of nitrogen and ~12% of the wet deposition of sulfur during the 14 years. This highlights the significance of the local influence, which should be carefully assessed when interpreting atmospheric observations at this site.

• Journal of Korean Society for Geospatial Information Science
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• v.24 no.4
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• pp.75-81
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• 2016

This study focused on exploring real transaction price of apartment and spatial and temporal heterogeneity of the variables that influence real transaction price of apartment from the spatial and temporal perspective. As independent variables that are considered to influence real transaction price of apartment, transport, local characteristics, educational conditions, population, and economic characteristics were taken into account. Accordingly, the influence of independent variables and spatial distribution pattern were analyzed from the global and local aspects. The spatial and temporal changing patterns of real transaction price of apartment which is a dependent variable were analyzed. First, to establish an analysis model, OLS analysis and GWR analysis were conducted, and thereby more efficient and proper model was selected. Secondly, to find spatial and temporal heterogeneity of independent variables with the use of the selected GWR model, Local $R^2$ was used for local analysis. Thirdly, to look into spatial distribution of independent variables, kriging analysis was carried out. Therefore, based on the results, it is considered that it is possible to carry out more microscopic housing submarket analysis and lay the foundation for establishing a policy on real property.

• Journal of the Korean Institute of Gas
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• v.3 no.2 s.7
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• pp.1-10
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• 1999

The evaluation of fracture toughness in weldment is necessary for the safety performance of industrial structures with large scale such as various power plants, LNG (liquefied natural gas) storage tanks, etc. It is generally known that weldments have material heterogeneity, which results in the serious changes in fracture characteristics of HAZ (heat-affected zone). Nevertheless, the systematic study on material heterogeneity of weldment has not been performed yet in Korea. Therefore in this paper, the effects of material heterogeneity on the fracture toughness of structural steel HAZ were introduced and reviewed.

• Proceedings of the Korean Geotechical Society Conference
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• 2006.03a
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• pp.939-948
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• 2006

The structural anisotropy and heterogeneity of rock mass, caused by discontinuities and weak zones, have a great influence on the deformation behavior of tunnel. Tunnel construction in these complex ground conditions is very difficult. No matter how excellent a geological investigation is, local uncertainties of rock mass conditions still remain. Under these uncertain circumstances, an accurate forecast of the ground conditions ahead of the advancing tunnel face is indispensable to safe and economic tunnel construction. This paper presents the effect of anisotropy and heterogeneity of the rock masses to be excavated by numerical analysis. The influences of distance from weak zone, the size or dimension, the different stiffness and the orientation of weak zones are analysedby 2-D and 3-D finite element analysis. By analysing these numerical results, the tunnel behavior due to excavation can be well understood and the prediction of rock mass condition ahead of tunnel face can be possible.

• Journal of Korean Institute of Industrial Engineers
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• v.42 no.2
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• pp.96-111
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• 2016

Effort to reduce energy consumptions or CO2 emissions is global trend. To follow this trend, spatial studies related to characteristics affecting energy consumptions or CO2 emissions have been conducted, but only with the focus on spatial dependence, not on spatial heterogeneity. The aim of this study is to investigate spatial heterogeneity patterns of CO2 emission based on socio-economic factors, land-use characteristics and traffic infrastructure of Seoul city. Geographically Weighted Regression (GWR) analysis was performed with 423 administrative district data in Seoul. The results suggest that population and employment densities, road density and railway length in most districts are found to have positive impact on the CO2 emissions. Residential and green area densities also have the highest positive impact on CO2 emissions in most districts of Gangnam-gu. The resulting model can be used to identify the spatial patterns of CO2 emissions at district level in Seoul. Eventually it can contribute to local energy policy and planning of metropolitan area.

• International Journal of Internet, Broadcasting and Communication
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• v.14 no.2
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• pp.40-46
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• 2022

Recently, as the need for data access by integrating information in a distributed cloud environment increases in enterprise-wide enterprises, interoperability for collaboration between existing legacy systems is emphasized. In order to interconnect independent legacy systems, it is necessary to overcome platform heterogeneity and semantic heterogeneity. To solve this problem, middleware was built using EMRA (Extended MetaData Registry Access) based on ISO/IEC 11179. However, the designed middleware cannot guarantee the efficiency of information utilization because it does not have an adjustment function for each node's resource status and work status. Therefore, it is necessary to manage and adjust the legacy system. In this paper, we coordinate the correct data access between the information requesting agent and the information providing agent, and integrate it by designing a cooperative agent responsible for information monitoring and task distribution of each legacy system and resource management of local nodes. to make efficient use of the available information.

• Journal of the Korean association of regional geographers
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• v.16 no.5
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• pp.610-622
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• 2010

The proportion of overweight and obese individuals in the United States has been continuously increasing up to recently. Many studies related to obesity have concentrated on jurisdictional levels of aggregation, making it very difficult to dearly illustrate at risk regions. In other words, little research has been conducted in relation to spatial patterns considering spatial dependency and heterogeneity by spatial autocorrelation models over space. In response, this research analyzes spatial patterns between overweight/obesity and risk factors, such as high blood pressure and diabetes, over space. Specifically, the Moran''s I and Geary''s C will be conducted for global and local measures. What is more, the Ordinary Least Square (OLS) linear regression and Geographically Weighted Regression methods will be applied to identify spatial dependency and spatial heterogeneity. Data provided by the Behavioral Risk Factor Surveillance System (BRFSS) have Body-Mass Index (BMI) rates, containing 4 rates of under, healthy, overweight, and obesity. In addition, high blood pressure and diabetes rates in the United States will be used as independent variables. Lastly, we are confident that this research will be beneficial for a decision maker to make a prevention plan for obesity.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.3
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• pp.958-979
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• 2023

Due to laws, regulations, privacy, etc., between 70-90 percent of providers do not share medical data, forming a "data island". It is essential to collaborate across multiple institutions without sharing patient data. Most existing methods adopt distributed learning and centralized federal architecture to solve this problem, but there are problems of resource heterogeneity and data heterogeneity in the practical application process. This paper proposes a collaborative deep learning modelling method based on the blockchain network. The training process uses encryption parameters to replace the original remote source data transmission to protect privacy. Hyperledger Fabric blockchain is adopted to realize that the parties are not restricted by the third-party authoritative verification end. To a certain extent, the distrust and single point of failure caused by the centralized system are avoided. The aggregation algorithm uses the FedProx algorithm to solve the problem of device heterogeneity and data heterogeneity. The experiments show that the maximum improvement of segmentation accuracy in the collaborative training mode proposed in this paper is 11.179% compared to local training. In the sequential training mode, the average accuracy improvement is greater than 7%. In the parallel training mode, the average accuracy improvement is greater than 8%. The experimental results show that the model proposed in this paper can solve the current problem of centralized modelling of multicenter data. In particular, it provides ideas to solve privacy protection and break "data silos", and protects all data.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2012.10a
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• pp.966-969
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• 2012

This study suggests a query conversion method based on Topic Maps MetaData Registry(TMDR) in order to solve heterogeneity problems distributed in networks and to integrate data efficiently. In order to integrate distributed data, TMDR provides global schema and it solves heterogeneity problem within local data using query conversion method. After analyzing relationship between Meta Schema Ontology(MSO) of eXtended Meta Data Registry(XMDR) and Topic Maps, this method allows integrated access through Meta Location(ML) which manages accessing information of local data. The processing method is to produce a global query for global processing by using TMDR and then to make the produced global query approach to systems distributed through networks so that allows integrated access at the end. For this, we propose a method to convert a global query into a query which is adaptive to local query.

• Journal of Korea Multimedia Society
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• v.19 no.5
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• pp.848-855
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• 2016

Face recognition has received a great deal of attention for the wide range of applications in real-world scenario. In this scenario, mismatches (so called heterogeneity) in terms of resolution and illumination between gallery and test face images are inevitable due to the different capturing conditions. In order to deal with the mismatch problem, we propose a local feature learning method using deep canonical correlation analysis (DCCA) for heterogeneous face recognition. By the DCCA, we can effectively reduce the mismatch between the gallery and the test face images. Furthermore, the proposed local feature learned by the DCCA is able to enhance the discriminative power by using facial local structure information. Through the experiments on two different scenarios (i.e., matching near-infrared to visible face images and matching low-resolution to high-resolution face images), we could validate the effectiveness of the proposed method in terms of recognition accuracy using publicly available databases.