• Journal of the Korean Geographical Society
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• v.41 no.1 s.112
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• pp.106-120
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• 2006

Natural or man-made disaster has been expected to be one of the potential themes that can integrate human geography and physical geography. Typhoons like Rusa and Maemi caused great loss to insurance companies as well as public sectors. We have implemented a natural disaster management system for a private insurance company to produce better estimation of hazards from high wind as well as calculate vulnerability of damage. Climatic gauge sites and addresses of contract's objects were geo-coded and the pressure values along all the typhoon tracks were vectorized into line objects. National GIS topog raphic maps with scale of 1: 5,000 were updated into base maps and digital elevation model with 30 meter space and land cover maps were used for reflecting roughness of land to wind velocity. All the data are converted to grid coverage with $1km{\times}1km$. Vulnerability curve of Munich Re was ad opted, and preprocessor and postprocessor of wind velocity model was implemented. Overlapping the location of contracts on the grid value coverage can show the relative risk, with given scenario. The wind velocities calculated by the model were compared with observed value (average $R^2=0.68$). The calibration of wind speed models was done by dropping two climatic gauge data, which enhanced $R^2$ values. The comparison of calculated loss with actual historical loss of the insurance company showed both underestimation and overestimation. This system enables the company to have quantitative data for optimizing the re-insurance ratio, to have a plan to allocate enterprise resources and to upgrade the international creditability of the company. A flood model, storm surge model and flash flood model are being added, at last, combined disaster vulnerability will be calculated for a total disaster management system.

• Knowledge Management Research
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• v.22 no.2
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• pp.119-144
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• 2021

Recently, as competition in the market evolves from the competition among companies to the competition among their supply chains, companies are struggling to enhance their supply chain management (hereinafter SCM). In particular, as blockchain technology with various technical advantages is combined with SCM, a lot of domestic manufacturing and distribution companies are considering the adoption of blockchain-oriented SCM (BOSCM) services today. Thus, it is an important academic topic to examine the factors affecting the use of blockchain-oriented SCM. However, most prior studies on blockchain and SCMs have designed their research models based on Technology Acceptance Model (TAM) or the Unified Theory of Acceptance and Use of Technology (UTAUT), which are suitable for explaining individual's acceptance of information technology rather than companies'. Under this background, this study presents a novel model of blockchain-oriented SCM acceptance model based on the Technology-Organization-Environment (TOE) framework to consider companies as the unit of analysis. In addition, Value-based Adoption Model (VAM) is applied to the research model in order to consider the benefits and the sacrifices caused by a new information system comprehensively. To validate the proposed research model, a survey of 126 companies were collected. Among them, by applying PLS-SEM (Partial Least Squares Structural Equation Modeling) with data of 122 companies, the research model was verified. As a result, 'business innovation', 'tracking and tracing', 'security enhancement' and 'cost' from technology viewpoint are found to significantly affect 'perceived value', which in turn affects 'intention to use blockchain-oriented SCM'. Also, 'organization readiness' is found to affect 'intention to use' with statistical significance. However, it is found that 'complexity' and 'regulation environment' have little impact on 'perceived value' and 'intention to use', respectively. It is expected that the findings of this study contribute to preparing practical and policy alternatives for facilitating blockchain-oriented SCM adoption in Korean firms.

• Journal of KIISE:Databases
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• v.30 no.6
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• pp.651-665
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• 2003

To reduce cache misses emerges as the most important issue in today's situation of main memory databases, in which CPU speeds have been increasing at 60% per year, and memory speeds at 10% per year. Recent researches have demonstrated that cache-conscious index structure such as the CR-tree outperforms the R-tree variants. Its search performance can be poor than the original R-tree, however, since it uses a lossy compression scheme. In this paper, we propose alternatively a cache-conscious version of the R-tree, which we call MR-tree. The MR-tree propagates node splits upward only if one of the internal nodes on the insertion path has empty room. Thus, the internal nodes of the MR-tree are almost 100% full. In case there is no empty room on the insertion path, a newly-created leaf simply becomes a child of the split leaf. The height of the MR-tree increases according to the sequence of inserting objects. Thus, the HeightBalance algorithm is executed when unbalanced heights of child nodes are detected. Additionally, we also propose the CCMR-tree in order to build a more cache-conscious MR-tree. Our experimental and analytical study shows that the two-dimensional MR-tree performs search up to 2.4times faster than the ordinary R-tree while maintaining slightly better update performance and using similar memory space.

• KIPS Transactions on Computer and Communication Systems
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• v.5 no.12
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• pp.455-462
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• 2016

Wireless sensor networks have been researched to gather data about events on sensor fields from sources at sinks. Multipath routing is one of attractive approaches to reliably send data against the problem of frequent breakages on paths from sources to sinks due to node and link failures. As mobile events such as humans, animals, and vehicles are considered, sources may be continuously generated according to the movement of the mobile event. Thus, mobile events provide new challenging issue in multipath routing. However, the research on multipath routing mainly focus on both efficient multipath construction from sources to static sinks and fast multipath reconstruction against path breakages. Accordingly, the previous multipath routing protocols request each source continuously generated by a mobile event to construct individual multipath from the source to sinks. This induces the increase of multipath construction cost in the previous protocols in proportion to the number of source. Therefore, we propose efficient multipath routing protocol for supporting continuous sources generated by mobile events. In the proposed protocol, new source efficiently reconstructs its multipath by exploiting the existing multipath of previous sources. To do this, the proposed protocol selects one among three reconstruction methods: a local reconstruction, a global partial one, and a global full one. For a selection decision, we provide an analytical energy consumption cost model that calculates the summation of both the multipath reconstruction cost and the data forwarding cost. Simulation results show that the proposed protocol has better performance than the previous protocol to provide multipath routing for mobile events.

• Journal of the Korean Society of Food Science and Nutrition
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• v.43 no.10
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• pp.1500-1509
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• 2014

The object of this study was to investigate the inhibitory effects of chios mastic gum (MG) on gastric acid secretion in an ethanol-induced SD rat model and primary parietal cells. Rats were randomly divided into four groups: Vehicle (normal group), Control (treated with ethanol), MG50 (treated with ethanol and mastic gum at 50 mg/kg b.w), MG100 (treated with ethanol and mastic gum at 100 mg/kg b.w). Groups treated with both MG50 and MG100 showed attenuation of gastric mucosal injury, sub-epithelial loss, hemorrhaging, and gastric juice secretion. We also examined the acidity of gastric juice during gastric injury. Oral administration of both MG50 and MG100 significantly decreased acidity of gastric juice by % and %, respectively. To examine the stimulatory factors related to gastric acid secretion, mRNA expression levels of H2r, M3r, CCK2r, and $H^+/K^+$ ATPase were measured by real-time PCR. Compared with a vehicle group, mRNA expression levels of H2r, CCK2r, and $H^+/K^+$ ATPase clearly increased in the control group. However, levels of H2r, CCK2r, and $H^+/K^+$ ATPase slightly but significantly decreased in MG-treated groups compared with control. Blood level of histamine significantly decreased in MG-treated groups, which indicates the involvement of MG on in histamine-related acid secretion. To identify the mode of action of MG in regulating histamine-related pathways, intracellular level of cAMP and mRNA levels of H2r, M3r, CCK2r, and $H^+/K^+$ ATPase were measured in primary parietal cells. While mRNA levels of M3r and CCK2r remained unchanged, levels of H2r and $H^+/K^+$ ATPase significantly decreased upon MG treatment. Subsequently, intracellular levels of cAMP decreased. These results suggest that mastic gum has the ability to inhibit gastric acid secretion by regulating a histamine-related pathway.

• 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.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.4
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• pp.421-426
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• 2010

Due to the ease of deployment and the extended coverage, wireless mesh networks (WMNs) are gaining popularity and research focus. For example, the routing protocols that enhance the throughput on the WMNs and the link quality measurement schemes are among the popular research topics. However, most of these works assume that the locations of the mesh routers are predetermined. Since the operators in an Indoor mesh network can determine the locations of the mesh routers by themselves, it is essential to the WMN performance for the mesh routers to be initially placed by considering the performance issues. In this paper, we propose a mesh router placement scheme based on genetic algorithms by considering the characteristics of WMNs such as interference and topology. There have been many related works that solve similar problems such as base station placement in cellular networks and gateway node selection in WMNs. However, none of them actually considers the interference to the mesh clients from non-associated mesh routers in determining the locations of the mesh routers. By simulations, we show that the proposed scheme improves the performance by 30-40% compared to the random selection scheme.

• Journal of the Institute of Electronics and Information Engineers
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• v.53 no.9
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• pp.18-25
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• 2016

In D2D communications underlaying a multicell network, it is of primary importance to ensure coexistence of cellular links and D2D links with minimal interference. Therefore, resource allocation scheme for D2D links should be designed to limit the interference between cellular links and D2D links. In this paper, we propose an effective resource allocation scheme for multiple D2D links which share the uplink spectrum resource with cellular users in a multicell network. Under the assumption that the locations of users are known to the base station, the proposed scheme allocates cellular resources to D2D links, such that the interference between a cellular link and multiple D2D links is minimized. In particular, we compute two constants from the path loss model and then use the constants to protect both cellular and D2D links. Simulation results are provided to verify the performance of the proposed scheme.