• Journal of Korea Multimedia Society
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• v.16 no.3
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• pp.330-335
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• 2013

It is expected that the vehicle safety systems using vehicle-to-vehicle communication can reduce the possibility of vehicle collision and prevent the chain crash by promptly delivering the status of neighboring vehicles. Many IEEE 802.11 DCF based Flooding schemes have been proposed, but they may generally expose the problems that the chances of a chain-collision reaction are sharply increased as the vehicle density has increased. Therefore, this paper proposes the chain-collision prevention scheme using a broadcasting-based adaptive report. The proposed method can adaptively allocate the preoccupancy right based on a quantitative priority order and then promptly deliver the warning messages in neighboring areas. Moreover, it is shown from simulation that the proposed scheme provides the performance gains over the existing Flooding based scheme.

• Journal of Korea Multimedia Society
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• v.15 no.10
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• pp.1221-1227
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• 2012

It is expected that the vehicle safety systems using vehicle-to-vehicle communication can reduce the possibility of vehicle collision and prevent the chain crash by promptly delivering the status of neighboring vehicles. Many IEEE 802.11 DCF based Flooding schemes have been proposed, but they may generally expose the problems that the transmission efficiency is sharply declined as the vehicle density has increased and then is related to the low possibility of the channel access. Therefore, this paper proposes a collision prevention scheme using adaptively controlling the frequency of the message exchanges based on the current status of neighboring vehicles. Moreover, it is shown from simulation that the proposed scheme provides the performance gains over the existing Flooding based scheme.

• Journal of Navigation and Port Research
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• v.37 no.4
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• pp.409-418
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• 2013

On the assumption of the rise of sea level, the inundation vulnerabilities on coastal areas of Korea are evaluated in different ways. The propose of this study is to find out the influences of sea level rise caused by global warming at Young-do coastal area, and to suggest the prevention schemes against the flooding damage caused by the sea level rise. The potential rates of sea level rise are assumed and with these rates the inundation vulnerabilities are simulated using CAD program. With the virtual maps, as the results of the previous CAD simulation, this study attempts to suggest the flood prevention schemes for each sector of damage-expected coastal area.

• Journal of the Korean Institute of Intelligent Systems
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• v.22 no.6
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• pp.722-728
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• 2012

Urban sewer systems has a limitation of capacity of rainwater storage and problem of occurrence of untreated sewage, so adopting a storage facility for sewer flooding prevention and urban non-point pollution reduction has a big attention. The Korea Ministry of Environment has recently introduced a new concept of "multi-functional storage facility", which is crucial not only in preventive stormwater management but also in dealing with combined sewer overflow and sanitary sewer discharge, and also has been promoting its adoption. However, reserving a space for a single large-scale storage facility might be difficult especially in urban areas. Thus, decentralized construction of small- and midium-sized storage facilities and its operation have been introduced as an alternative way. In this paper, we propose a model predictive control scheme for an optimized operation of distributed storage facilities and sewer networks. To this aim, we first describe the mathematical model of each component of networks system which enables us to analyze its detailed dynamic behavior. Second, overflow locations and volumes will be predicted based on the developed network model with data on the external inflow occurred at specific locations of the network. MPC scheme based on the introduced particle swarm optimization technique then produces the optimized the gate setting for sewer network flow control, which minimizes sewer flooding and maximizes the potential storage capacity. Finally, the operational efficacy of the proposed control scheme is demonstrated by simulation study with virtual rainstorm event.

• Journal of Environmental Science International
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• v.28 no.1
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• pp.125-135
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• 2019

For the purposes of enhancing usability of Numerical Weather Prediction (NWP), the quantitative precipitation prediction scheme by machine learning has been proposed. In this study, heavy rainfall was corrected for by utilizing rainfall predictors from LENS and Radar from 2017 to 2018, as well as machine learning tools LightGBM and XGBoost. The results were analyzed using Mean Absolute Error (MAE), Normalized Peak Error (NPE), and Peak Timing Error (PTE) for rainfall corrected through machine learning. Machine learning results (i.e. using LightGBM and XGBoost) showed improvements in the overall correction of rainfall and maximum rainfall compared to LENS. For example, the MAE of case 5 was found to be 24.252 using LENS, 11.564 using LightGBM, and 11.693 using XGBoost, showing excellent error improvement in machine learning results. This rainfall correction technique can provide hydrologically meaningful rainfall information such as predictions of flooding. Future research on the interpretation of various hydrologic processes using machine learning is necessary.