• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.4
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• pp.122-134
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• 2003

In recent, different kinds of nationwide thematic map have been developed based on NGIS, and each related research field has tried to develop GIS by utilizing this map. Also, Many researches on the geographic information data model has been conducted to improve the compatibility of developed system. The developed system in water resource field should reflect the dynamic characteristics of river flow. Because it should be considered from the design of data model, this study suggests the datamodel for designing geographical information database on water resources which is possible to linear reference capacity based on stream network. In order to examine the applicability of the suggested model, network component based system has developed. Finally, the river network based system shows the superiority in terms of its applicability comparing with other system.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05a
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• pp.65-73
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• 2002

Weather Radar have played an important role in both precipitation observation and hydrological operations over several countries and evaluated its efficient and necessities for the developed flood management and control. This paper describe the factors influencing the design the hydrological radar network in Korea and develop Hydrological Radar Network Simulation Model (HRNSM) based on GIS and UI system. Moreover, the methodologies for geographical and hydrological feasibility analysis for radar network were provided in detail manner.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.617-625
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• 2017

Both domestic and overseas urban drainage systems have been actively researched to solve the problems of urban flash floods and the flood damage that is caused by local downpours. Recent urban planning has been designed to better manage the floods of decentralized rainfall-management systems, and the installation of green infrastructure and low-impact development (LID) facilities at national ministries has been recommended. In this study, we use the EPA SWMM model to construct a decentralized rainfall-management network for each small watershed, and we analyze the effect of the drainage-capacity improvement from the installation of the LID technologies in vulnerable areas that replaces the network-expansion process. In the design of the existing urban piping systems, it is common to increase the pipe size due to the increment of the impervious area, the steep terrain, and the sensitive entrance-ramp junction; however, the installation of green infrastructure and LID facilities will be sufficient for the construction of a safe urban drainage system. The applications of LID facilities and green infrastructure in urban areas can positively affect the recovery of the corresponding water cycles to a healthy standard, and it is expected that further research will occur in the future.

• Smart Structures and Systems
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• v.26 no.2
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• pp.175-184
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• 2020

Conventional Monte Carlo simulation-based methods for seismic risk assessment of water networks often require excessive computational time costs due to the hydraulic analysis. In this study, an Artificial Neural Network-based surrogate model was proposed to efficiently evaluate the flow-based system reliability of water distribution networks. The surrogate model was constructed with appropriate training parameters through trial-and-error procedures. Furthermore, a deep neural network with hidden layers and neurons was composed for the high-dimensional network. For network training, the input of the neural network was defined as the damage states of the k-dimensional network facilities, and the output was defined as the network system performance. To generate training data, random sampling was performed between earthquake magnitudes of 5.0 and 7.5, and hydraulic analyses were conducted to evaluate network performance. For a hydraulic simulation, EPANET-based MATLAB code was developed, and a pressure-driven analysis approach was adopted to represent an unsteady-state network. To demonstrate the constructed surrogate model, the actual water distribution network of A-city, South Korea, was adopted, and the network map was reconstructed from the geographic information system data. The surrogate model was able to predict network performance within a 3% relative error at trained epicenters in drastically reduced time. In addition, the accuracy of the surrogate model was estimated to within 3% relative error (5% for network performance lower than 0.2) at different epicenters to verify the robustness of the epicenter location. Therefore, it is concluded that ANN-based surrogate model can be utilized as an alternative model for efficient seismic risk assessment to within 5% of relative error.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.27 no.1
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• pp.56-62
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• 2015

The potential impact of water temperature on sea level and air temperature rise in response to recent global warming has been noticed. To predict the effect of temperature change on river water quality and aquatic environment, it is necessary to understand and predict the change of water temperature. Air-water temperature relationship was analyzed using air temperature data at Buan and water temperature data of Shinsi, Garyeok, Mangyeong and Dongjin. Maximum and minimum water temperature was predicted by neural network and the results show a very high correlation between measured and predicted water temperature.

• Nuclear Engineering and Technology
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• v.32 no.5
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• pp.485-494
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• 2000

In this work, a fuzzy neural network is used to estimate the relevant sensor signal using other sensor signals. Noise components in input signals into the fuzzy neural network are removed through the wavelet denoising technique . Principal component analysis (PCA) is used to reduce the dimension of an input space without losing a significant amount of information. A lower dimensional input space will also usually reduce the time necessary to train a fuzzy-neural network. Also, the principal component analysis makes easy the selection of the input signals into the fuzzy neural network. The fuzzy neural network parameters are optimized by two learning methods. A genetic algorithm is used to optimize the antecedent parameters of the fuzzy neural network and a least-squares algorithm is used to solve the consequent parameters. The proposed algorithm was verified through the application to the pressurizer water level and the hot-leg flowrate measurements in pressurized water reactors.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.35 no.3
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• pp.187-191
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• 2012

The methods for constructing network of observational locations for measuring water quality in water reservoirs have been widely proposed, but they had some limitations to be applied to river areas, which lie in awkward clustering and finding representative observational locations among locations within each clustering. In this paper, a statistical approach to detect anomaly locations which were significantly different in important measurements for the water quality from the previous locations and construct observational network with them was proposed. Anomaly was detected with the sampling distribution of each primary principal component score, sum of primary PCs, or sum of residual PCs. The empirical study with the data of Nakdong Dam for guiding how to use our proposed approach and showing limitations of previous studied was described.

• The Journal of the Acoustical Society of Korea
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• v.17 no.1E
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• pp.54-65
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• 1998

This study attempts to test the classifying performance of a neural network and thereby examine its applicability to the signals distorted in a shallow water environment. Linear frequency modulated(LFM) signals are simulated by using an acoustic model and also measured through sea experiment. The network is constructed to have three layers and trained on both data sets. To get normalized power spectra as feature vectors, the study considers the three transforms : shot-time Fourier transform (STFT), wavelet transform (WT) and pseudo Wigner-Ville distribution (PWVD). After trained on the simulated signals over water depth, the network gives over 95% performance with the signal to noise ratio (SNR) being up to-10 dB. Among the transforms, the PWVD presents the best performance particularly in a highly noisy condition. The network performs worse with the summer sound speed profile than with the winter profile. It is also expected to present much different performance by the variation of bottom property. When the network is trained on the measured signals, it gives a little better results than that trained on the simulated data. In conclusion, the simulated signals are successfully applied to training a network, and the trained network performs well in classifying the signals distorted by a surrounding environment and corrupted by noise.

• Journal of Korean Society of Water and Wastewater
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• v.32 no.2
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• pp.193-200
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• 2018

Owing to the development in information and communications technologies have improved the technology for high-speed transmission of massive data, which has changed closed-circuit television (CCTV) video transmission technology. In particular, digitization of the CCTV video format and streaming technology has made it possible to minimize transmission loss and integrate video transmission and camera control(pan/tilt). It has also become possible to provide additional services like remote emergency warning broadcasting with just Internet Protocol (IP). However, because of the structural problems of IP, these changes have also brought about the threat of hacking of CCTV monitoring systems. In this study, we propose a methode to optimize network management by examining cases of enhancement of operational efficiency and security by improving the structure of CCTV monitoring network.

• Proceedings of the Korean Vacuum Society Conference
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• 2010.02a
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• pp.363-363
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• 2010

The high capillarity of a plastic fiber network having superhydrophilic Si-DLC coating is studied. Although the superhydrophilic surface maximize wetting ability on the flat surface, there remains a requirement for the more wettable surface for various applications such as air-filters or liquid-filters. In this research, the PET non-woven fabric surface was realized by superhydrophilic coating. PTE non-woven fabric network was chosen due to its micro-pore structure, cheap price, and productivity. Superhydrophobic fiber network was prepared with a coating of oxgyen plasma treated Si-DLC films using plasma-enhanced chemical vapor deposition (PECVD). We first fabricated superhydrophilic fabric structure by using a polyethylene terephthalate (PET) non-woven fabric (NWF) coated with a nanostructured films of the Si-incorporated diamond-like carbon (Si-DLC) followed by the plasma dry etching with oxygen. The Si-DLC with oxygen plasma etching becomes a superhydrophilic and the Si-DLC coating have several advantages of easy coating procedure at room temperature, strong mechanical performance, and long-lasting property in superhydrophilicity. It was found that the superhydrophobic fiber network shows better wicking ability through micro-pores and enables water to have much faster spreading speed than merely superhydrophilic surface. Here, capillarity on superhydrophilic fabric structure is investigated from the spreading pattern of water flowing on the vertical surface in a gravitational field. As water flows on vertical flat solid surface always fall down in gravitational direction (i.e. gravity dominant flow), while water flows on vertical superhydrophilic fabric surface showed the capillary dominant spreading.