• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.8 no.2
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• pp.91-99
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• 2009

Mobile WiMAX is an emerging technology that can provide ubiquitous Internet access. To provide seamless service in mobile WiMAX environment, delay or disruption in dealing with mobility must be minimized. However offering seamless services on IEEE 802.16e networks is very hard due to long handover latency both in layer 2 and 3. In this paper, we propose a fast cross-layer handover scheme based on prediction algorithm. With the help of the prediction, layer-3 handover activities are able to occur prior to layer-2 handover, and therefore, total handover latency can be reduced. The experiments conducted with system parameters and propagation model defined by WiMAX Forum demonstrate that the proposed method predicts the future signal level accurately and reduces the total handover latency.

• Journal of Korean Society of Environmental Engineers
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• v.34 no.4
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• pp.232-238
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• 2012

In this study, the noise mapping simulation is executed to design an effective barrier reducing noise levels of a school site. The geographical features of the ambient site and the school buildings are modelled in detail in order to consider sound propagation, deflection, and absorption phenomena etc. The main sound source, sound power level of expressway, is estimated on the basis of measured noise levels at several points of the site. The noise mapping simulation is performed by using ENPro, environmental noise prediction program based on ISO 9613 to analysis the effectiveness of noise barrier. Consequently, the noise barrier is designed to meet an environmental noise standard and satisfy low cost and safety conditions.

• 전자공학회논문지 IE
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• v.44 no.1
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• pp.27-32
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• 2007

This study was to suggest the predictive model of propagation, considering the effect by the multipath waves produced by the sending and receiving vehicles' left/right reflectors and the adjacent vehicles when the communication between the vehicles on the one-way two-lanes road in the urban city with a lot of traffic jams. Then, the radius of curved road was 600[m], the length of curved roads $52.4\sim471.2[m]$, and the bridge's pier of road was $5o\sim45o$. Also, it was simulated by changing the receiving vehicle located on the curved road's gap from minimum 3.3[m] to maximum 29.5[m], corresponding to the change of distance of the bridge's pier of road and curved road. As a result of this research above, in case of $5o\sim15o$ bridge's pier of road, it was within l[dB] regardless of the receiving vehicle's position on the curved road in case of propagation path loss. In case of $15o\sim45o$, it was approximately $1\sim8[dB]$ as the bridge's pier of road is changed. And, in case of propagation path, it found out that it was changed to $0.4\sim120[m]$ according to the change of bridge's pier of road. Then, the delay time of propagation was 400[nsec] as it produced 120[m] in the difference of propagation path.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.367-367
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• 2020

최근 전세계적으로 기후변화로 인해 가뭄의 발생 가능성이 높아지고 있으며 그에 대한 인적피해와 경제적 손실로 인한 피해액은 증가하고 있다. 특히, 국내의 충남지역은 최근 강수량이 평년 대비 75% 수준으로 감소하고 있으며, 지속적인 가뭄이 발생하여 용수 확보에 어려움을 겪고 있다. 또한, 2015년에는 강수량 감소로 인해 보령댐을 상수원으로 사용하고 있는 충남 서북부지역 8개 시군의 용수공급에 큰 차질이 있었다. 지속적인 가뭄 상황이 반복되면서 기후변화의 영향에 따라 미래의 물공급량 변화 및 물부족에 대한 연구와 이를 분석하여 정확성을 높이는 물수지 분석모형이 지속적으로 개발되고 있다. 이에 본 연구에서는 가뭄이 발생한 충남지역을 대상으로 기후변화에 따른 미래 물부족 변화에 대한 경향성을 파악하고자 한다. 미래의 물부족 변화를 파악하기 위해 기상청 RCP 8.5 시나리오의 40년(2008~2047년) 중권역별 강수량과 잠재증발산량 일단위 자료를 수집하였다. 수집된 중권역별 기상자료를 하천유역별 일단위 기상자료를 활용하여 TANK 모델을 이용한 하천 일유출모의와 비유량법을 이용하여 저수지의 일유량을 산정하였다. 미래의 수혜면적변화와 생활/공업/농업 수요량 추정을 통해서 하천 및 저수지 물수지 분석을 진행하여 미래기간 30년에 대한 일단위 물수급을 산정하였다. 미래기간에 대한 분석은 기준기간 R0(2008~2017년) 대비, 미래기간 S1(2018~2027년), S2(2028~2037년), S3(2038~2047년)에 대한 연평균 물부족량과 경로별 경향성을 파악하였다. 대상지역의 물부족량에 대한 분석결과, 기준기간 대비 S1, S2, S3 기간에 각각 27.8%, 10.8%, 23.1% 감소하는 경향이 나타났다. 이는 미래의 강우량이 점차 증가하는 경향과 수혜면적 변화시 논과 밭 면적이 지속적인 감소로 인해 수요량에 영향을 받았을 것으로 판단된다. 본 연구 결과를 바탕으로 장래 하천유역의 가뭄에 대한 피해 예측 및 물수급 파악에 활용될 수 있을 것으로 판단된다.

• 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.

• 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.