• Nuclear Engineering and Technology
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• v.39 no.3
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• pp.215-220
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• 2007

The existing flooding Probabilistic Safety Analysis(PSA) was updated to reflect the Korean plant specific operating experience data into the flooding frequency to improve the PSA quality. Both the Nuclear Power Experience(NPE) database and the Korea Nuclear Pipe Failure Database(NuPIPE) databases were used in this study, and from these databases, only the Pressurized Water Reactor(PWR) data were used for the flooding frequencies of the flooding areas in the primary auxiliary building. With these databases and a Bayesian method, the flooding frequencies for the flooding areas were estimated. Subsequently, the Core Damage Frequency(CDF) for the flooding PSA of the Ulchin(UCN) unit 3 and 4 plants based on the Korean Standard Nuclear Power Plant(KSNP) internal full-power PSA model was recalculated. The evaluation results showed that sixteen flooding events are potentially significant according to the screening criterion, while there were two flooding events exceeding the screening criterion of the existing UCN 3 and 4 flooding PSA. The result was compared with two kinds of cases: (1) the flooding frequency and CDF from the method of the existing flooding PSA with the PWR and Boiled Water Reactor(BWR) data of the NPE database and the Maximum Likelihood Estimate(MLE) method and (2) the flooding frequency and CDF with the NPE database(PWR and BWR data), NuPIPE database, and a Bayesian method. From the comparison, a difference in CDF results was revealed more clearly between the CDF from this study and case (2) than between case (1) and case (2). That is, the number of flooding events exceeding the screen criterion further increased when only the PWR data were used for the primary auxiliary building than when the Korean specific data were used.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.95-95
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• 2020

Urban flooding occurs in the form of internal-water inundation on roads and lowlands due to heavy rainfall. Unlike in the case of rivers, inundation in urban areas there is lacking in research on predicting and warning through measurement data. In order to analyze urban flood patterns and prevent damage, it is necessary to analyze flooding measurement data for various rainfalls. In this study, the pattern of urban flooding caused by rainfall was analyzed by utilizing the urban flooding measuring sensor, which is being test-run in the flood prone zone for urban flooding management. For analysis, 2019 rainfall data, surface water depth data, and water level data of a street inlet (storm water pipeline) were used. The analysis showed that the amount of rainfall that causes flooding in the target area was identified, and the timing of inundation varies depending on the rainfall pattern. The results of the analysis can be used as verification data for the urban inundation limit rainfall under development. In addition, by using rainfall intensity and rainfall patterns that affect the flooding, it can be used as data for establishing rainfall criteria of urban flooding and predicting that may occur in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.18 no.1
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• pp.91-98
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• 2023

With the increasing flood damage by frequently localized heavy rains, flood prediction research are being conducted to prevent flooding damage in advance. In this paper, we present a machine-learning scheme for developing a flooding depth and location prediction model using real-time rainfall data. This scheme proposes a dataset configuration method using the data as input, which can robustly configure various rainfall distribution patterns and train the model with less memory. These data are composed of two: valid total data and valid local. The one data that has a significant effect on flooding predicted the flooding location well but tended to have different values for predicting specific rainfall patterns. The other data that means the flood area partially affects flooding refers to valid local data. The valid local data was well learned for the fixed point method, but the flooding location was not accurately indicated for the arbitrary point method. Through this study, it is expected that a lot of damage can be prevented by predicting the depth and location of flooding in a real-time manner.

• Journal of KIISE:Computing Practices and Letters
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• v.16 no.7
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• pp.804-808
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• 2010

In this paper, we propose Restricted Angle-control Flooding considered density network of sensor nodes and node‘s limit energy. Restricted Angle-control Flooding, increase energy efficiency by reducing unnecessary candidate nodes involved in forwarding closer to the destination. And The Hole when faced with our proposal to raise rates data through Hole Detection which is sender‘s forwarding area is extended or broadcast to the entire network. Compared to the traditional flooding, we show the superiority at the node’s energy consumption, data rate and network lifetime through the performance.

• Journal of KIISE:Information Networking
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• v.33 no.2
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• pp.165-174
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• 2006

Data dissemination using either flooding or legacy ad-hoc routing protocol is not realistic approach in the wireless sensor networks, which are composed of sensor nodes with very weak computing power, small memory and limited battery. In this paper, we propose the ELF(Energy-efficient Localized Flooding) protocol. The ELF is energy-efficient data dissemination protocol for wireless sensor networks. In the ELF protocol, there are two data delivery phases between fixed source and mobile sink node. The first phase, before the tracking zone, sensing data are forwarded by unicasting. After that, within the tracking zone, sensing data are delivered by localized flooding. Namely, the ELF Properly combines advantages from both unicasting and flooding. According to evaluation results by simulation, the proposed ELF protocol maintains very high data delivery ratio with using a little energy. Also, the property of average delay is better than others. From our research results, the ELF is very effective data dissemination protocol for wireless sensor networks.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.97-97
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• 2020

Flooding in urban areas is caused by heavy rains for a short period of time and drains within 1 to 2 hours. It is also characterized by a small flooding area. In addition, flooding is often caused by various and complex causes such as land use, basin slope, pipe, street inlet, drainage pumping station, making it difficult to predict flooding. Therefore, this study analyzes the effect of each basin characteristic on the occurrence of flooding in urban areas by correlating various basin characteristics, whether or not flooding occurred, and rainfall(Limit Rainfall), and intends to use the data for urban flood prediction. As a result of analyzing the relationship between the imperviousness and the urban slope, pipe, threshold rainfall and limit rainfall, the pipe showed a correlation coefficient of 0.32, and the remaining factors showed low correlation. However, the multiple correlation analysis showed the correlation coefficient about 0.81 - 0.96 depending on the combination, indicating that the correlation was relatively high. In the future, I will further analyze various urban characteristics data, such as area by land use, average watershed elevation, river and coastal proximity, and further analyze the relationship between flooding occurrence and urban characteristics. The relationship between the urban characteristics, the occurrence of flooding and the limiting rainfall amount suggested in this study is expected to be used as basic data for the study to predict urban flooding in the future.

• Journal of People, Plants, and Environment
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• v.24 no.1
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• pp.39-51
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• 2021

Background and objective: Urban topology can be characterized as impervious, which changes the hydrologic features of an area, increasing surface water flow during local heavy rain events. The pluvial flooding is also influenced by the vertical structures of the urban area. This study suggested a modified digital elevation model (DEM) to identify changes in urban hydrological conditions and segmentalized urban micro catchment areas using a geographical information system (GIS). Methods: This study suggests using a modified DEM creation process based on Rolling Ball Method concepts along with a GIS program. This method proposes adding realized urban vertical data to normal DEM data and simulating hydrological analyses based on RBM concepts. The most important aspect is the combination of the DEM with polygon data, which includes urban vertical data in three datasets: the contour polyline, the locations of buildings and roads, and the elevation point data from the DEM. DEM without vertical data (DCA) were compared with the DEM including vertical data (VCA) to analyze catchment areas in Shin-wol district, Seoul, Korea. Results: The DCA had 136 catchments, and the area of each catchment ranged from 3,406 m² to 423,449 m². The VCA had 2,963 catchments, with the area of each ranging from 50 m² to 16,209 m². The most important finding is that in the overlapped VCA; the boundary of areas directly affected by flooding and the direction of surface water flow could be identified. Flooding data from September 21, 2010 and July 27, 2011 in the Shin-wol district were applied as ground reference data. The finding is that in the overlapped VCA; the boundary of areas directly affected by flooding and the direction of surface water flow could be identified. Conclusion: The analysis of the area vulnerable to surface water flooding (SWF) was more accurately determined using the VCA than using the DCA.

• Convergence Security Journal
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• v.15 no.2
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• pp.63-68
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• 2015

Current ICT(Information & Communication Technology) infrastructures (Internet and server/client communication) are struggling for a wide variety of devices, services, and business and technology evolution. Cloud computing originated simply to request and execute the desired operation from the network of clouds. It means that an IT resource that provides a service using the Internet technology. It is getting the most attention in today's IT trends. In the distributed cloud environments, management costs for the network and computing resources are solved fundamentally through the integrated management system. It can increase the cost savings to solve the traffic explosion problem of core network via a distributed Micro DC. However, traditional flooding methods may cause a lot of traffic due to transfer to all the neighbor DCs. Restricted Path Flooding algorithms have been proposed for this purpose. In large networks, there is still the disadvantage that may occur traffic. In this paper, we developed Lightweight Path Flooding algorithm to improve existing flooding algorithm using hop count restriction.

• IEMEK Journal of Embedded Systems and Applications
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• v.18 no.3
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• pp.133-139
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• 2023

This paper proposes a two-stage Water Shield Automation System (WSAS) to predict the possibility of river overflow and vehicle flooding due to sudden rainfall. The WSAS uses a two-stage Deep Neural Network (DNN) model. First, a river overflow prediction module is designed with LSTM to decide whether the river is flooded by predicting the river's water level rise. Second, a vehicle flooding prediction module predicts flooding of underground parking lots by detecting flooded tires with YOLOv5 from CCTV images. Finally, the WSAS automatically installs the water barrier whenever the river overflow and vehicle flooding events happen in the underground parking lots. The only constraint to implementing is that collecting training data for flooded vehicle tires is challenging. This paper exploits the Image C&S data augmentation technique to synthesize flooded tire images. Experimental results validate the superiority of WSAS by showing that the river overflow prediction module can reduce RMSE by three times compared with the previous method, and the vehicle flooding detection module can increase mAP by 20% compared with the naive detection method, respectively.

• Convergence Security Journal
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• v.23 no.5
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• pp.73-79
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• 2023

Cyber threats are evolving and becoming more sophisticated with the development of new technologies, and consequently the number of service failures caused by DDoS attacks are continually increasing. Recently, DDoS attacks have numerous types of service failures by applying a large amount of traffic to the domain address of a specific service or server. In this paper, after generating the data of the Syn Flooding attack, which is the representative attack type of bandwidth exhaustion attack, the data were compared and analyzed using Random Forest, Decision Tree, Multi-Layer Perceptron, and KNN algorithms for the effective detection of attacks, and the optimal algorithm was derived. Based on this result, it will be useful to use as a technique for the detection policy of Syn Flooding attacks.