• Journal of Korean Society of Coastal and Ocean Engineers
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• v.23 no.1
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• pp.93-100
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• 2011

A coupled tide-surge model, which has been evaluated the utility in the previous study, is applied for simulating the inundation phenomena. The coupled model system adopts the hydrodynamic module of MIKE21 software, and the study area is identical to the previous study. The only difference is additional detailed areas for simulating inundation. An artificial scenario of a virtual typhoon striking Mokpo coastal zone at spring high tide is simulated. Then the calculated water level corresponds to the extreme high water level(556 cm) for 100 year return period. The result also shows the inundation depth is 50~100 cm not only near the Mokpo Inner Port but also near the Mokpo North Port. Finally, the coastal inundation prediction map is drawn on the basis of inundation simulation results.

• Journal of Wetlands Research
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• v.14 no.4
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• pp.645-656
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• 2012

The basin environment has been seriously damaged by reckless development during the past half century. The demand for management in the basin has increased, but the system for prediction and management is not sufficient. Therefore, the aim of this study is to design a GIS-based water quality linkage system using the most suitable simulation, HSPF (Hydrological Simulation Program-Fortran) in this basin of South Korea. To achieve this, data of HSPF model for simulation and GIS data for spatial analysis is collected. And the system applied linkages of the water quality model and GIS such as Loose coupling. Also, the major function of the system was designed as a modular unit. Ultimately, the system is developed using development language of VB.NET from Microsoft and ArcObjects component from ESRI based on design for a module unit. The water quality simulation system can be supported to prediction and management for basin environment of Yeong-San River. In the future study, scenario will be established using the result of HSPF model And will be expected to support to situation of future basin and policy making.

• International Journal of Highway Engineering
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• v.20 no.2
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• pp.57-65
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• 2018

PURPOSES : The study aims to predict the service life of national highway asphalt pavements through deep learning methods by using maintenance history data of the National Highway Pavement Management System. METHODS : For the configuration of a deep learning network, this study used Tensorflow 1.5, an open source program which has excellent usability among deep learning frameworks. For the analysis, nine variables of cumulative annual average daily traffic, cumulative equivalent single axle loads, maintenance layer, surface, base, subbase, anti-frost layer, structural number of pavement, and region were selected as input data, while service life was chosen to construct the input layer and output layers as output data. Additionally, for scenario analysis, in this study, a model was formed with four different numbers of 1, 2, 4, and 8 hidden layers and a simulation analysis was performed according to the applicability of the over fitting resolution algorithm. RESULTS : The results of the analysis have shown that regardless of the number of hidden layers, when an over fitting resolution algorithm, such as dropout, is applied, the prediction capability is improved as the coefficient of determination ($R^2$) of the test data increases. Furthermore, the result of the sensitivity analysis of the applicability of region variables demonstrates that estimating service life requires sufficient consideration of regional characteristics as $R^2$ had a maximum of between 0.73 and 0.84, when regional variables where taken into consideration. CONCLUSIONS : As a result, this study proposes that it is possible to precisely predict the service life of national highway pavement sections with the consideration of traffic, pavement thickness, and regional factors and concludes that the use of the prediction of service life is fundamental data in decision making within pavement management systems.

• Journal of Wetlands Research
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• v.24 no.1
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• pp.25-37
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• 2022

Climate change is becoming increasingly unpredictable. This has led to changes in various systems such as ecosystems, human life and hydrological cycles. In particular, the recent unpredictable climate change frequently causes extreme droughts and torrential rains, resulting in complex water resources disasters that cause water pollution due to inundation and retirement rather than primary disasters. SWAT was used as a watershed model to analyze future runoff and pollutant loads. The climate scenario analyzed the RCP4.5 climate scenario of the Meteorological Agency standard scenario (HadGEM3-RA) using the normal quantitative mapping method. Runoff and pollutant load analysis were performed by linkage simulation of climate scenario and watershed model. Finally, the results of application and verification of linkage model and analysis of future water quality change due to climate change were presented. In this study, we simulated climate change scenarios using artificial neural networks, analyzed changes in water temperature and turbidity, and compared the results of dams with artificial neural network results through W2 model, a reservoir water quality model. The results of this study suggest the possibility of applying the nonlinearity and simplicity of neural network model to Hapcheon dam water quality prediction using climate change.

• Journal of Korean Society on Water Environment
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• v.29 no.1
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• pp.88-96
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• 2013

Much attention has been needed in water resource management at the watershed due to drought and flooding issues caused by climate change in recent years. Increase in air temperature and changes in precipitation patterns due to climate change are affecting hydrologic cycles, such as evaporation and soil moisture. Thus, these phenomena result in increased runoff at the watershed. The Soil and Water Assessment Tool (SWAT) model has been used to evaluate rainfall-runoff at the watershed reflecting effects on hydrology of various weather data such as rainfall, temperature, humidity, solar radiation, wind speed. For bias-correction of RCP data, at least 30 year data are needed. However, for most gaging stations, only precipitation data have been recorded and very little stations have recorded other weather data. In addition, the RCP scenario does not provide all weather data for the SWAT model. In this study, two scenarios were made to evaluate whether it would be possible to estimate streamflow using measured precipitation and long-term average values of other weather data required for running the SWAT. With measured long-term weather data (scenario 1) and with long-term average values of weather data except precipitation (scenario 2), the estimate streamflow values were almost the same with NSE value of 0.99. Increase/decrease by ${\pm}2%$, ${\pm}4%$ in temperature and humidity data did not affect streamflow. Thus, the RCP precipitation data for Hongcheon watershed were bias-corrected with measured long-term precipitation data to evaluate effects of climate change on streamflow. The results revealed that estimated streamflow for 2055s was the greatest among data for 2025s, 2055s, and 2085s. However, estimated streamflow for 2085s decreased by 9%. In addition, streamflow for Spring would be expected to increase compared with current data and streamflow for Summer will be decreased with RCP data. The results obtained in this study indicate that the streamflow could be estimated with long-term precipitation data only and effects of climate change could be evaluated using precipitation data as shown in this study.

• International Journal of Contents
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• v.13 no.1
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• pp.53-61
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• 2017

Recently, the Social Internet of Things (IoT), the follow-up of the IoT, has been studied to expand the existing IoT services, by integrating devices into the social network of people. In the Social IoT environment, humans, devices and digital contents are connected with social relationships, to guarantee the network navigability and establish levels of trustworthiness. However, this environment handles massive data, including social data of humans (e.g., profile, interest and relationship), profiles of IoT devices, and digital contents. Hence, users and service providers in the Social IoT are exposed to arbitrary data when searching for specific information. A study about the recommender system for the Social IoT environment is therefore needed, to provide the required information only. In this paper, we propose the Social correlation group based Recommender System (SRS). The SRS generates a target group, depending on the social correlation of the service requirement. To generate the target group, we have designed an architecture, and proposed a procedure of the SRS based on features of social interest similarity and principles of the Collaborative Filtering and the Content-based Recommender System. With simulation results of the target scenario, we present the possibility of the SRS to be adapted to various Social IoT services.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2499-2522
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• 2017

Multi-hop networks are a low-setup-cost solution for enlarging an area of network coverage through multi-hop routing. Carrier sense multiple access with collision avoidance (CSMA/CA) is frequently used in multi-hop networks. Multi-hop networks face multiple problems, such as a rise in contention for the medium, and packet loss under heavy-load, saturated conditions, which consumes more bandwidth due to re-transmissions. The number of re-transmissions carried out in a multi-hop network plays a major role in the achievable quality of service (QoS). This paper presents a statistical, analytical model for the end-to-end delay of contention-based medium access control (MAC) strategies. These strategies schedule a packet before performing the back-off contention for both differentiated heterogeneous data and homogeneous data under saturation conditions. The analytical model is an application of Markov chain theory and queuing theory. The M/M/1 model is used to derive access queue waiting times, and an absorbing Markov chain is used to determine the expected number of re-transmissions in a multi-hop scenario. This is then used to calculate the expected end-to-end delay. The prediction by the proposed model is compared to the simulation results, and shows close correlation for the different test cases with different arrival rates.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2018.10a
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• pp.62-65
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• 2018

This paper comes out with the study on sensing data collection strategy in a Software-Defined Mobile Edge vehicular networking. The two cooperative data dissemination are Direct Vehicular cloud mode and edge cell trajectory prediction decision mode. In direct vehicular cloud, the vehicle observe its neighboring vehicles and sets up vehicular cloud for cooperative sensing data collection, the data collection output can be transmitted from vehicles participating in the cooperative sensing data collection computation to the vehicle on which the sensing data collection request originate through V2V communication. The vehicle on which computation originate will reassemble the computation out-put and send to the closest RSU. The SDMEVN (Software Defined Mobile Edge Vehicular Network) Controller determines how much effort the sensing data collection request requires and calculates the number of RSUs required to support coverage of one RSU to the other. We set up a simulation scenario based on realistic traffic and communication features and demonstrate the scalability of the proposed solution.

• Journal of the Earthquake Engineering Society of Korea
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• v.11 no.4
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• pp.25-31
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• 2007

For areas such as the Korean Peninsula, which have moderate seismic activity but no available records of strong ground motion, synthetic seismograms can be used to evaluate ground motion without waiting for a strong earthquake. Such seismograms represent the estimated ground motions expected from a set of possible earthquake scenarios. Local site effects are especially important in assessing the seismic hazard and possible ground motion scenarios for a specific fault. The earthquake source and rupture dynamics can be described as a two-step process of rupture initiation and front propagation controlled by a frictional sliding mechanism. The seismic wavefield propagates through heterogeneous geological media and finally undergoes near-surface modulations such as amplification or deamplification. This is a complex system in which various scales of physical phenomena are integrated. A unified approach incorporates multi-scale problems of dynamic rupture, radiated wave propagation, and site effects into an all-in-one model using a three-dimensional, fourth-order, staggered-grid, finite-difference method. The method explains strong ground motions as products of complex systems that can be modified according to a variety of fine-scale rupture scenarios and friction models. A series of such deterministic earthquake scenarios can shed light on the kind of damage that would result and where it would be located.

• Journal of Environmental Science International
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• v.25 no.6
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• pp.877-888
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• 2016

To project the effects of climate-induced change on aquatic environments, it is necessary to determine the thermal constraints affecting different fish species and to acquire time series of the current and projected water temperature (WT). Assuming that a nonlinear regression between the WT at individual stations and the ambient air temperature (AT) at nearby weather stations could represent the best relationship of air-water temperature, This study estimates future WT using a general circulation model (GCM). In addition, assuming that the grid-averaged observations of AT correspond to the AT output from GCM simulation, this study constructed a regression curve between the observations of the local WT and the concurrent GCM-simulated surface AT. Because of its low spatial resolution, downscaling is unavoidable. The projected WT under global warming scenario A2 (B2) shows an increase of about $1.6^{\circ}C$ ($0.9^{\circ}C$) for the period 2080-2100. The maximum/minimum WT shows an amount of change similar to that of the mean values. This study will provide guidelines for decision-makers and engineers in climate-induced river environment and ecosystem management.