• Journal of Korean Society of Water and Wastewater
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• v.20 no.6
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• pp.799-811
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• 2006

As an alternative measure to replace reactive stance with proactive one, a risk based management scheme has been commonly applied to enhance public satisfaction on water service by providing a higher creditable solution to handle a rehabilitation problem of pipe having high potential risk of leaks. This study intended to examine the feasibility of a simulation model to predict a recurrence probability of pipe leaks. As a branch of the data mining technique, probabilistic neural network (PNN) algorithm was applied to infer the extent of leaking recurrence probability of water network. PNN model could classify the leaking level of each unit segment of the pipe network. Pipe material, diameter, C value, road width, pressure, installation age as input variable and 5 classes by pipe leaking probability as output variable were built in PNN model. The study results indicated that it is important to pay higher attention to the pipe segment with the leak record. By increase the hydraulic pipe pressure to meet the required water demand from each node, simulation results indicated that about 6.9% of total number of pipe would additionally be classified into higher class of recurrence risk than present as the reference year. Consequently, it was convinced that the application of PNN model incorporated with a data base management system of pipe network to manage municipal water distribution network could make a promise to enhance the management efficiency by providing the essential knowledge for decision making rehabilitation of network.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.865-868
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• 2008

Remote Sur-veillance network system for water contamination is developed using sensor network. The wireless sensor network is one of effective solutions for monitoring water contamination on wide area such s river. Existing sur-veillance system for water contamination has the disadvantage in installation cost, complexity of adding a new node replacing a defective node. The proposed system has cost effective solutions compared with the existing system.

• Proceedings of the Korea Water Resources Association Conference
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• 2002.05b
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• pp.829-836
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• 2002

The paper presents a multi-objective genetic algorithm approach to the design of a water distribution network. The objectives considered are minimization of network cost and maximization of a reliability measure. In this study, a new reliability measure, called network resilience, is introduced. This measure mimics a designer's desire of providing excess power at nodes and designing reliable loops with practicable pipe diameters. The proposed method produces a set of Pareto-optimal solutions in the search space of cost and network resilience. Genetic algorithms are observed to be poor in handling constraints. To handle constraints in a better way, a constraint handling technique that does not require a penalty coefficient and applicable to water distribution systems is presented. The present model is applied to two example problems, which were widely reported. Pipe failure analysis carried out on some of the solutions obtained revealed that the network resilience based approach gave better results in terms of network reliability.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.5
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• pp.423-432
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• 2018

Surface water mapping has been widely used in various remote sensing applications. Water indices have been commonly used to distinguish water bodies from land; however, determining the optimal threshold and discriminating water bodies from similar objects such as shadows and snow is difficult. Deep learning algorithms have greatly advanced image segmentation and classification. In particular, FCN (Fully Convolutional Network) is state-of-the-art in per-pixel image segmentation and are used in most benchmarks such as PASCAL VOC2012 and Microsoft COCO (Common Objects in Context). However, these data sets are designed for daily scenarios and a few studies have conducted on applications of FCN using large scale remotely sensed data set. This paper aims to fine-tune the pre-trained FCN network using the CRMS (Coastwide Reference Monitoring System) data set for surface water mapping. The CRMS provides color infrared aerial photos and ground truth maps for the monitoring and restoration of wetlands in Louisiana, USA. To effectively learn the characteristics of surface water, we used pre-trained the DeepWaterMap network, which classifies water, land, snow, ice, clouds, and shadows using Landsat satellite images. Furthermore, the DeepWaterMap network was fine-tuned for the CRMS data set using two classes: water and land. The fine-tuned network finally classifies surface water without any additional learning process. The experimental results show that the proposed method enables high-quality surface mapping from CRMS data set and show the suitability of pre-trained FCN networks using remote sensing data for surface water mapping.

• Proceedings of the Korea Water Resources Association Conference
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• 2022.05a
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• pp.458-458
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• 2022

It is essential to reduce global carbon emissions, mainly from energy use. The water supply and distribution sector is a vital part of human society and is one of the primary energy consumers. The procurement and distribution of water require electricity to operate the pump to deliver water to users with sufficient pressure. As the water users are spatially distributed over a wide area, the energy required to deliver water to each user differs depending on the corresponding supplying element (reservoir, tank, pipe, pump, and valve). This difference in energy required for each user also comes with a difference in pressure availability which affects the level of service for individual users and the whole network. Typically, there is a disproportion where users close to the source experience excessively high pressure with low energy consumption. In contrast, remote users need more energy to get the minimum pressure. This study proposes the Energy Return Index (ERI) to quantify the pressure return from particular energy consumption to supply water to each node. The disproportionality can be quantified and identified in the network using the proposed ERI. The index can be applied to optimize the network elements such as pump operation and tank location/size to reach a balanced energy consumption with the appropriate level of service.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.11
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• pp.3083-3098
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• 2023

As demands for efficient and echo-friendly production of marine products increase, smart aquaculture based on information and communication technology (ICT) has become a promising trend. The smart aquaculture is expected to control fundamental farm environment variables including water temperature and dissolved oxygen (DO) levels with less human intervention. A recirculating aquaculture system (RAS) is required for the smart aquaculture which utilizes a purification tank to reuse water drained from the water tank while blocking the external environment. Elaborate water treatment should be considered to properly operate RAS. However, analyzing the water treatment performance is a challenging issue because fish farm circumstance continuously changes and recursively affects water fluidity. To handle this issue, we introduce computational fluid dynamics (CFD) aided water treatment analysis including water fluidity and the solid particles removal efficiency. We adopt RAS parameters widely used in the real aquaculture field to better reflect the real situation. The simulation results provide several indicators for users to check performance metrics when planning to select appropriate RAS without actually using it which costs a lot to operate.

• Journal of Korea Water Resources Association
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• v.36 no.1
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• pp.105-115
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• 2003

The computation model which evaluates combined hydraulic and mechanical reliability, is developed to analyze the integrated reliability in water distribution system. The hydraulic reliability is calculated by considering uncertain variables like water demand, hydraulic pressure, pipe roughness as random variables according to proper distribution type. The mechanical reliability is evaluated by analyzing the effect of pipe network with sequential failure of network components. The result of this study model applied to the real pipe network shows that this model can be used to simulate the uncertain factors effectively in real pipe network. Therefore, The pipe-line engineers can design and manage the network system with more quantitative reliability, through applying this model to reliable pipe network design and diagnosis of existing systems.

• Journal of the Korean Chemical Society
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• v.57 no.3
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• pp.370-376
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• 2013

A back propagation artificial neural network model with one hidden layer is established to correlate the liquid-liquid equilibrium data of hydrocarbon-water systems. The model has four inputs and two outputs. The network is systematically trained with 48 data points in the range of 283.15 to 405.37K. Statistical analyses show that the optimised neural network model can yield excellent agreement with experimental data(the average absolute deviations equal to 0.037% and 0.0012% for the correlated mole fractions of hydrocarbon in two coexisting liquid phases respectively). The comparison in terms of average absolute deviation between the correlated mole fractions for each binary system and literature results indicates that the artificial neural network model gives far better results. This study also shows that artificial neural network model could be developed for the phase equilibria for a family of hydrocarbon-water binaries.

• Geomechanics and Engineering
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• v.21 no.6
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• pp.537-549
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• 2020

The presence of cracks changes the water content pattern during seepage through a cracked soil as compared to that of intact soil. In addition, several different crack networks may form in one soil type. These two factors result in a variation of water contents in the soil matrix part of a cracked soil during seepage. This paper presents an investigation of the effect of crack network representation on the water content of the soil matrix part of cracked soil using numerical models. A new method for the numerical generation of crack networks incorporating connections among crack endpoints was developed as part of the investigation. Numerical analysis results indicated that the difference in the point water content was large, whereas the difference in the average water content was relatively small, indicating the uniqueness of the crack network representation on the average water content of the soil matrix part of cracked soil.

• Proceedings of the Korea Water Resources Association Conference
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• 1989.07a
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• pp.63-70
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• 1989