• Magazine of the Korean Society of Agricultural Engineers
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• v.39 no.6
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• pp.54-66
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• 1997

The objectives of this study is to introduce and apply neural network theory to real hydrologic systems for stochastic nonlinear predicting of daily runoff discharge in the river catchment. Back propagation algorithm of neural network model is applied for the estimation of daily stochastic runoff discharge using historical daily rainfall and observed runoff discharge. For the fitness and efficiency analysis of models, the statistical analysis is carried out between observed discharge and predicted discharge in the chosen runoff periods. As the result of statistical analysis, method 3 which has much processing elements of input layer is more prominent model than other models(method 1, method 2) in this study.Therefore, on the basis of this study, further research activities are needed for the development of neural network algorithm for the flood prediction including real-time forecasting and for the optimal operation system of dams and so forth.

• Journal of Korea Water Resources Association
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• v.37 no.2
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• pp.145-154
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• 2004

The purpose of this study is to develop a real-time forecasting model in order to predict the flood runoff which has the nature of non-linearity and to verify applicability of neural network model for flood warning system. Developed model based on neural network, NRDFM(Neural River Discharge-Stage Forecasting Model) is applied to predict the flood discharge on Waekwann and Jindong stations in Nakdong river basin. As a result of flood forecasting on these two stations, it can be concluded that NRDFM-II is the best predictive model for real-time operation. In addition, the results of forecasting used on NRDFM-I and NRDFM-II model are not bad and these models showed sufficient probability for real-time flood forecasting. Consequently, it is expected that NRDFM in this study can be utilized as suitable model for real-time flood warning system and this model can perform flood control and management efficiently.

• Journal of Ocean Engineering and Technology
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• v.18 no.5
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• pp.22-28
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• 2004

This study constructed a 3D real-time numerical model that predicts the water quality and movement characteristics of the inner bay, considering the characteristics of the wind-driven current and density current in estuaries, generated by the river discharge from the Hyeong-san river and oceanic water of the Eastern sea. The numerical model successfully calculated the seawater circulation current of Yeong-il Bay, using the input conditions oj the real-time tidal current, river discharge, and weather conditions during March 2001. This study also observed the wind-driven current and density current in estuaries that are effected by the seawater circulation pattern of the inner bay. We investigated and analyzed each impact factor, and its relationship to the water quality of Yeong-il bay.

• Proceedings of the Korean Institute of Electrical and Electronic Material Engineers Conference
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• 1997.11a
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• pp.451-455
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• 1997

The partial discharge has been blown as the chief breakdown of power equipments. The analysis and the recognition is much difficult because the partial discharge signal is very small and has complex aging pattern. Recently, insulation aging diagnosis based on pattern of phase(Ф), partial discharge magnitude(q), number(n) has been very important. Owing to depreciate the reappearance of aging progress at the electrical tree pattern and to be difficult to analyze visually, the study on partial discharge pattern is suggested to normalizing analysis method of partial discharge signals. This parer is purposed on prediction of life-time measurement of cv-cable, on decision of risk degree with normalization and real-time measurement of partial discharge signals for aging diagnosis of cv-cable. As normalizing the aging signals of electrical tree in cv-cable, it is able to confirm risk degree of insulation material with the distribution of Ф-q-n and recognize the process of aging pattern using neural network.

• Journal of Korea Water Resources Association
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• v.38 no.7 s.156
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• pp.565-574
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• 2005

It is used water quality data that was measured at Pyeongchanggang real time monitoring stations in Namhan river. These characteristics were analyzed with the water qualify of rainy and nonrainy periods. TOC (Total Organic Carbon) data of rainy periods has correlation with discharge and shows high values of mean, maximum, and standard deviation. DO (Dissolved Oxygen) value of rainy periods is lower than those of nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water qualify forecasting models were applied. LMNN, MDNN, and ANFIS models have achieved the highest overall accuracy of TOC data. LMNN (Levenberg-Marquardt Neural Network) and MDNN (MoDular Neural Network) model which are applied for DO forecasting shows better results than ANFIS (Adaptive Neuro-Fuzzy Inference System). MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. The observation of discharge and water quality are effective at same point as well as same time for real time management. But there are some of real time water quality monitoring stations far from the T/M water stage. Pyeongchanggang station is one of them. So discharge on Pyeongchanggang station was calculated by developed runoff neural network model, and the water quality forecasting model is linked to the runoff forecasting model. That linked model shows the improvement of waterquality forecasting.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 1999.10c
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• pp.486-492
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• 1999

One of the purpose of the reservoir operation is minimizing theinnudation area in the downstream reaches during flood period.l To execute the gate operation properly , it requires lots of real-time data such as rainfall, reservoir level, and water level in the downstrea. Gate operation model was developed with the flood discharge obtained from real-time flood forecasting model and the criterion prepared from the past history of gate operation. Water level in the downstream would be increased by the releasing discharge from the spillway and the area of paddy land flooded in a certain detph and time would be estimated usnig GIS map. Gate operation model was applied to the Yedang reservoir, and the flooded area, depth and time in the paddy land was estimaged.

• Journal of Institute of Control, Robotics and Systems
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• v.6 no.4
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• pp.320-326
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• 2000

The real-time simulator of a pneumatic actuation system that is composed of differential PWM signal generator, charge solenoid valve, discharge solenoid valve, actuator, load, and rotational potentiometer is developed using a DSP board and a PC. The simulator receives the control signals from the external controller through the A/D converter, updates the state and output variables of the Pneumatic actuation system responding to the input signals every sampling time, and sends out the output signals through the D/A converter in real time. The user can observe the displacements, velocities, pressures, and mass flows representing the operation of pneumatic actuation system through the PC monitor in real time. Also the user can see the moving images between the pistons and rotating arm realistically in real time. The accuracy of the real-time simulator is verified by the good agreement of the real-time simulation results and the experimental results of the pneumatic actuation system.

• Journal of Korea Water Resources Association
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• v.44 no.5
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• pp.377-388
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• 2011

Surface Image Velocimetry (SIV) is a recently-developed discharge measurement instrument. It uses image processing techniques to measure the water surface velocity and estimate water discharge with given cross section. The present study aims to implement a FSIV (Fixed-type Surface Image Velocimetry) at Soojeon Bridge in the Dalcheon. The hardware system consists of two digital cameras, a computer, and a pressure-type water stage gauge. The images taken with the hardware system are sent to a server computer via a wireless internet, and analyzed with a image processing software (SIV software). The estimated discharges were compared with the observed discharges through Goesan dam spillway and index velocity method using ADVM. The computed results showed a good agreement with the observed one, except for the night time. The results compared with discharges through Goesan dam spillway reached around 5~10% in the case of discharge over 30 m3/s, and the results compared with discharges through index velocity method using ADVM reached around 5% in the case of discharge over 200 $m^3/s$. Considering the low cost of the system and the visual inspection of the site situation with the images, the SIV would be fairly good way to measure water discharge in real time.

• Journal of Korean Society of Water and Wastewater
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• v.19 no.6
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• pp.738-748
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• 2005

Considering pollution source is transferred by discharge, it is very important to analyze the correlation between discharge and water quality. And temperature also influent to the water quality. In this paper, it is used water quality data that was measured DO (Dissolved Oxygen), TOC (Total Organic Carbon), TN (Total Nitrogen), TP (Total Phosphorus) at Dalchun real time monitoring stations in Namhan river. These characteristics were analyzed with the water quality of rainy and nonrainy periods. Input data of the water quality forecasting models that they were constructed by neural network and neuro-fuzzy was chosen as the reasonable data, and water quality forecasting models were applied. LMNN (Levenberg-Marquardt Neural Network), MDNN (MoDular Neural Network), and ANFIS (Adaptive Neuro-Fuzzy Inference System) models have achieved the highest overall accuracy of TOC data. LMNN and MDNN model which are applied for DO, TN, TP forecasting shows better results than ANFIS. MDNN model shows the lowest estimation error when using daily time, which is qualitative data trained with quantitative data. If some data has periodical properties, it seems effective using qualitative data to forecast.

• Journal of the Korean institute of surface engineering
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• v.38 no.2
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• pp.73-78
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• 2005

A real-time monitoring of ICP(inductively coupled plasma) assisted magnetron sputtering of MgO was carried out using a QMS(quadrupole mass spectrometer), an OES(optical emission spectrometer), and a digital oscilloscope with a high voltage probe and a current monitor. At the time of ICP ignition, the most distinct impurity was OH emission (308.9 nm) which was dissociated from water molecules. For reactive deposition oxygen was added to Ar and the OH emission intensity was reduced abruptly When the discharge voltage was regulated by a PID controller from 240V(metallic mode) to 120V(oxide mode), the emission intensity from Mg (285.2 nm) changed proportionally to the discharge voltage, but the intensity of Ar I(811.6 nm) was constant. At 100V of discharge voltage, Mg sputtering was almost stopped. Emissions from Ar I(420.1 nm) and Mg I were dropped down to 1/10, but Ar I(811.6 nm) didn't change. And the emission from atomic oxygen (O I, 777.3 nm) was increased to 10 times. These results are compatible with those from QMS study.