• Journal of Korean Society of Environmental Engineers
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• v.33 no.9
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• pp.630-635
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• 2011

As this study was estimation of factors of rapid OUR (Oxygen Uptake Rate) monitoring method. Experiment for estimating factors of optimal microorganism activity was carried out in this study. In addition to comparison and estimation of SCOD variation by OUR variation using real wastewaters. In consequence OUR value was highest when F/M ratio, pH and temperature were 0.03~0.05, 6.0~8.5 and $20{\sim}30^{\circ}C$ respectively. Oxygen consumption by nitrification was incomplete. OUR variation of SCOD was recognizable difference of degradable rate at before and after of inflection point OUR. This study used an experimental method for real time prediction of the influent of the sewage treatment plant for optimal operation is expected to be able to do.

• Clean Technology
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• v.8 no.1
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• pp.27-37
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• 2002

To verify the performance and effectiveness of bilinear model for the development of ozone prediction system, the simulation experiments of the model identification for ozone formation were performed by using bilinear and linear models. And the prediction results of the ozone formation by bilinear model were compared to those of linear model and the measured data of Seoul. ARMA(Autoregressive Moving Average) model was used in the model identification. A recursive parameter estimation algorithm based on an equation error method was used to estimate parameters of model. From the results of model identification experiment, the ozone formation by bilinear model showed good agreement with the ozone formation from the simulator. From the comparison of the prediction results and the measured data, it appears that the method proposed in this work is a reasonable means of developing real-time short-term prediction of ozone formation for an ozone forecast system.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.5
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• pp.60-67
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• 2014

Wind power is one of the fastest growing renewable energy sources. The blades length and tower height of wind turbine have been growing steadily in the last 10 years in order to increase the output amount of wind power energy. The amount of wind turbine energy is increased by increasing the capacity of wind turbine, but the costs of preventive, corrective and replacement maintenance are also increased accordingly. Recently, Condition Monitoring System that can repair the fault diagnose and repair of wind turbine in the real-time. However, these system have a problem that cannot predict and diagnose of the fault. In this paper, wind turbine predict methodology is proposed by using the SVM method. In the case study, correlation analysis between wind turbine fault and external environmental factors is performed by using the SVM method.

• Journal of Marine Bioscience and Biotechnology
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• v.5 no.4
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• pp.46-50
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• 2011

Phytoplankton forms the base of sea ecosystems. Various environmental factors and anthropogenic pollution, primarily, affect the concentration and photosynthetic activity algal cells, and the changes in the phytoplankton photosynthesis influence other elements of aquatic ecosystems. The increase in anthropogenic pollution markedly damages natural aquatic ecosystems, particularly, in the coastal zones, where an intense blooming of microalgae occurs, including the release of highly dangerous ecotoxic substances of various chemical natures (red tides). In this study, we tried to apply as a parameter for the algal blooming prediction in the ocean from fluorescence values in the taken samples around Busan coastal area. F0 value was almost constant but Fv/Fm value showed the irregular pattern. We presume that these results are due to the changes of the ocean environment and climate. To predict or give early warning the algal blooming, we need to investigate the specific area or fixed area through real-time monitoring. Especially, algal blooming prediction or warning can be achieved via continuously monitoring and interpretation of fluorescence changes.

• Advances in environmental research
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• v.4 no.2
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• pp.83-104
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• 2015

Air Quality Index (AQI) is a pointer to broadcast short term air quality. This paper presents one day ahead AQI forecasting on seasonal basis for three major cities in Maharashtra State, India by using Artificial Neural Networks (ANN) and Genetic Programming (GP). The meteorological observations & previous AQI from 2005-2008 are used to predict next day's AQI. It was observed that GP captures the phenomenon better than ANN and could also follow the peak values better than ANN. The overall performance of GP seems better as compared to ANN. Stochastic nature of the input parameters and the possibility of auto-correlation might have introduced time lag and subsequent errors in predictions. Spectral Analysis (SA) was used for characterization of the error introduced. Correlational dependency (serial dependency) was calculated for all 24 models prepared on seasonal basis. Particular lags (k) in all the models were removed by differencing the series, that is converting each i'th element of the series into its difference from the (i-k)"th element. New time series is generated for all seasonal models in synchronization with the original time line & evaluated using ANN and GP. The statistical analysis and comparison of GP and ANN models has been done. We have proposed a promising approach of use of GP coupled with SA for real time prediction of seasonal multicity AQI.

• Journal of Korea Water Resources Association
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• v.45 no.11
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• pp.1107-1119
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• 2012

The objectives of this study are to develop the flow nomograph for real-time flood forecasting and to assess its applicability in restored Cheonggye stream. The Cheonggye stream basin has the high impermeability and short concentration time and complicated hydrological characteristics. Therefore, the flood prediction method using runoff model is ineffective due to the limit of forecast. Flow nomograph which is able to forecast flood only with rainfall information. To set the forecast criteria of flow nomograph at selected flood forecast points and calculated criterion flood water level for each point, and in order to reflect various flood events set up simulated rainfall scenario and calculated rainfall intensity and rainfall duration time for each condition of rainfall. Besides, using a rating curve, determined scope of flood discharge following criterion flood water level and using SWMM model calculated flood discharge for each forecasting point. Using rainfall information following rainfall scenario calculated above and flood discharge following criterion flood water level developed flow nomograph and evaluated it by applying it to real flood event. As a result of performing this study, the applicability of flow nomograph to the basin of Cheonggye stream appeared to be high. In the future, it is reckoned to have high applicability as a method of prediction of flood of urban stream basin like Cheonggye stream.

• Journal of the Korean GEO-environmental Society
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• v.20 no.1
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• pp.19-25
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• 2019

In this present study, to evaluate the applicability of the monitoring system of the entire reservoir dam facility using the wireless sensor network system and a section representative of the domestic reservoir dam was selected as the test bed site and to operated a system that can evaluate the condition of the facility at the real time with monitoring. In order to set up a wireless sensor network system, the system assessment of present state was carried out for confirmation the risk factors and the limit values of the risk factors in limit state were calculated. The type and position of the sensor to be measured in the field were determined by setting the measurement items suitable for the hazardous area and the risk factor. In this paper, we evaluated the feasibility of the system by monitoring and constructing a wireless sensor network system in a field for a fill dam that can represent a domestic reservoir dam. Applicability evaluation was verified by comparing directly with the measurement of partial concentration method which is the measurement management technology of the dam.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.2
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• pp.69-79
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• 2017

In this study, we developed real-time urban stream discharge forecasting model using short-term rainfall forecasts data simulated by a regional climate model (RCM). The National Centers for Environmental Prediction (NCEP) Climate Forecasting System (CFS) data was used as a boundary condition for the RCM, namely the Global/Regional Integrated Model System(GRIMs)-Regional Model Program (RMP). In addition, we make ensemble (ESB) forecast with different lead time from 1-day to 3-day and its accuracy was validated through temporal correlation coefficient (TCC). The simulated rainfall is compared to observed data, which are automatic weather stations (AWS) data and Tropical Rainfall Measuring Mission (TRMM) Multisatellite Precipitation Analysis (TMPA 3B43; 3 hourly rainfall with $0.25^{\circ}{\times}0.25^{\circ}$ resolution) data over midland of Korea in July 26-29, 2011. Moreover, we evaluated urban rainfall-runoff relationship using Storm Water Management Model (SWMM). Several statistical measures (e.g., percent error of peak, precent error of volume, and time of peak) are used to validate the rainfall-runoff model's performance. The correlation coefficient (CC) and the Nash-Sutcliffe efficiency (NSE) are evaluated. The result shows that the high correlation was lead time (LT) 33-hour, LT 27-hour, and ESB forecasts, and the NSE shows positive values in LT 33-hour, and ESB forecasts. Through this study, it can be expected to utilizing the real-time urban flood alert using short-term weather forecast.

• Smart Structures and Systems
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• v.15 no.4
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• pp.1103-1120
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• 2015

A self-sensing magnetorheological (MR) damper with embedded piezoelectric force sensor has recently been devised to facilitate real-time close-looped control of structural vibration in a simple and reliable manner. The development and characterization of the self-sensing MR damper are presented based on experimental work, which demonstrates its reliable force sensing and controllable damping capabilities. With the use of experimental data acquired under harmonic loading, a nonparametric dynamic model is formulated to portray the nonlinear behaviors of the self-sensing MR damper based on NARX modeling and neural network techniques. The Bayesian regularization is adopted in the network training procedure to eschew overfitting problem and enhance generalization. Verification results indicate that the developed NARX network model accurately describes the forward dynamics of the self-sensing MR damper and has superior prediction performance and generalization capability over a Bouc-Wen parametric model.

• Journal of Korean Institute of Industrial Engineers
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• v.23 no.2
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• pp.249-260
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• 1997

The manufacturing environment is rife with nonlinear processes. In this context, an intelligent production controller should be able to predict the dynamic behavior of various subsystems as they react to transient environmental conditions, the varying internal condition of the manufacturing plant, and the changing demands of the production schedule. This level of adaptive capability may be achieved through a coherent methodology for a learning coordinator to predict nonlinear and stochastic processes. The system is to serve as a real time, online supervisor for routine activities as well as exceptional conditions such as damage, failure, or other anomalies. The complexity inherent in a learning coordinator can be managed by a modular architecture incorporating case based reasoning. In the interest of concreteness, the concepts are presented through a case study involving a knowledge based robotic system.