• International Journal of Fluid Machinery and Systems
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• v.3 no.4
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• pp.369-378
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• 2010

The creation of the hydraulic turbine flow factor map will undoubtedly benefit its design by decreasing both the design cycle time and product cost. In this paper, the geometry and flow variables, which effectively affect the flow factor, are proposed, analyzed and determined. These flow variables are further used to create the operating condition maps by using different model approaches categorized into Response Surface Method (RSM) and Artificial Neural Network (ANN). The accuracies of models created by different approaches are compared and the performances of model approaches are analyzed. The influences of chosen variables and the combination of Principle Component Analysis (PCA) and model approaches are also studied. The comparison results between predicted and actual flow factors suggest that two-hidden-layer Feed-forward Neural Network (FFNN), and one.hidden-layer FFNN with PCA has the best performance on forming this mapping, and are accurate sufficiently for hydraulic turbine design.

• Proceedings of the Korea Water Resources Association Conference
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• 2001.05a
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• pp.45-51
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• 2001

The daily inflow is apparently one of nonlinear and complicated phenomena. The nonlinear and complexity make it difficult to model the prediction of daily flow, but attractive to try the neural networks approach which contains inherently nonlinear schemes. The study focuses on developing the forecasting models of daily inflows to a large dam site using neural networks. In order to reduce the error caused by high or low outliers, the back propagation algorithm which is one of neural network structures is modified by combining a regression algorithm. The study indicates that continuous forecasting of a reservoir inflow in real time is possible through the use of modified neural network models. The positive effect of the modification using tole regression scheme in BP algorithm is showed in the low and high ends of inflows.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.157-157
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• 2018

A reliable and accurate downscaling model which can provide climate change information, obtained from global climate models (GCMs), at finer resolution has been always of great interest to researchers. In order to achieve this model, linear methods widely have been studied in the past decades. However, nonlinear methods also can be potentially beneficial to solve downscaling problem. Therefore, this study explored the applicability of some nonlinear machine learning techniques such as neural network (NN), extreme learning machine (ELM), and ELM autoencoder (ELM-AE) as well as a linear method, least absolute shrinkage and selection operator (LASSO), to build a reliable temperature downscaling model. ELM is an efficient learning algorithm for generalized single layer feed-forward neural networks (SLFNs). Its excellent training speed and good generalization capability make ELM an efficient solution for SLFNs compared to traditional time-consuming learning methods like back propagation (BP). However, due to its shallow architecture, ELM may not capture all of nonlinear relationships between input features. To address this issue, ELM-AE was tested in the current study for temperature downscaling.

• Proceedings of the Korea Information Processing Society Conference
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• 2013.11a
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• pp.486-488
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• 2013

In the earth more than half of the space filled with water. In that water most of the part is in the form of oceans. The ocean atmosphere determines climate on the land. Combining the Underwater Acoustic Sensor Network (UWASN) system with Internet Of Things (IoT) is called Internet of Underwater Things (IoUT). Using IoUT we can find the changes in the ocean environment. Underwater sensor nodes are used in UWASN. Underwater sensor nodes are constructive in offshore investigation, disaster anticipation, data gathering, assisted navigation, pollution checking and strategic inspection. By using IoT components such as Database, Server and Internet, ocean data can be broadcasted. This paper introduces IoUT architecture and and explains fish forming application scenario with this IoUT architecture.

• Computers and Concrete
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• v.31 no.4
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• pp.315-325
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• 2023

Calcium leaching is one of the main deterioration factors in concrete structures contact with water, such as dams, water treatment structures, and radioactive waste structures. It causes a porous microstructure and may be coupled with various harmful factors resulting in mechanical degradation of concrete. Several numerical modeling studies focused on the calcium leaching depth prediction. However, these required a lot of cost and time for many experiments and analyses. This study presents an artificial neural network (ANN) approach to predict the leaching depth quickly and accurately. Totally 132 experimental data are collected for model training and validation. An optimal ANN model was proposed by ANN topology. Results indicate that the model can be applied to estimate the calcium leaching depth, showing the determination coefficient of 0.91. It might be used as a simulation tool for engineering problems focused on durability.

• Magazine of the Korean Society of Agricultural Engineers
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• v.30 no.3
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• pp.114-120
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• 1988

A water surface profile computation model using a standard step procedure was developed for gradually varied flow at an irrigation channel network. Flow characteristics ab Banweol district near Suweon were field monitored during irrigation periol of 1987. The model was applied to the main system at the district and the simulation results were compared to the field data. The results are sumrnarized as follows ; 1. The simulated water surface profiles from the model were in good agreement with the measured water surface profiles at different flow rates. 2. The model applicability for defining a stage-discharge relationship at a channel reach was demonstrated with reasonable accuracy when water stage and friction factor were given. 3. The roughness coefficient was found to be a major factor sigrificantly affecting computed water surface profile among a few physical input parameters for the model.

• Water Engineering Research
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• v.2 no.3
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• pp.171-178
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• 2001

There are three methods for analyzing flow and pressure distribution in looped water distribution networks (the loop method, the node method, the element method) taking into consideration hydraulic parameters chosen as unknown. For all these methods the non-linear system of equations can be solved by iterative procedures. The paper presents a different approach to this problem by using the method of variational formulations for hydraulic analysis of water distribution networks. This method has the advantage that it uses a specialized optimization algorithm which minimizes directly an objective multivariable function without constraints, implemented in a computer program. The paper compares developed method to the classic Hardy-Cross method. This shows the good performance of the new method.

• Journal of Digital Convergence
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• v.7 no.3
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• pp.103-111
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• 2009

In this paper, we introduce automatic meter reading system for tap water. The system is composed of automatic meter, router using RF and CDMA network, and data server. This system will easily extend to fire detect, gas, and electric charge meter system. In addition, this system will be used to monitoring a water leak and human which live in solitude. Proposed system is installed at Gimhae-City and Gijang-gun. As a result of the automatic meter reading system for tap water, The leak of water and complain of user is decreased. But The building cost is still an obstacle to expand into entire city.

• Journal of Korean Society of Water and Wastewater
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• v.27 no.5
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• pp.611-619
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• 2013

In this study, Back Tracing Calculation Method was developed to determine the leakage location and leakage amount. Previously developed determination method of monitoring location and newly developed Back Tracing Calculation Method were applied to the sample pipe network and real size pilot plant. After leakage was assumed in the pilot plant, leakage location and leakage amount could be traced by Back Tracing Calculation Method. From the results, it was found that Back Tracing Calculation Method can be applied for the leakage detection in water distribution system. Furthermore, this method can be applied for the pressure management or leakage detection as a pressure control method in water distribution system.

• Journal of Korean Society on Water Environment
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• v.27 no.1
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• pp.30-35
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• 2011

2D pollutant transport model was applied to the simulation of contaminant transport in the channel. At first, two kinds of virtual channels having different slopes were designed. The distribution of contaminant, which flows from one of the three drainages to the main channel, was simulated by each 2D model. Concentrations of 745 nodes were converted to input data of neural network model (Multi-perceptron) for training and verification using matrix. The first three cases (Case A-1, A-2, A-3) were used for training Multi-perceptron, the other three cases (Case B-1, B-2, B-3) were used for verification. As a result, Multi-perceptron reasonably divided the cases into the three characteristics which have different contaminant distributions due to the different input point of water pollution source. It can be a useful methodology for the water quality monitoring and backtracking.