• Water Engineering Research
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• v.1 no.2
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• pp.129-136
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• 2000

Predicting water levels is a difficult task because a lot of uncertainties are included. Therefore the neural network which is appropriate to such a problem, is introduced. One day ahead forecasting of river stage in the Bocheong River is carried out by using the neural network model. Historical water levels at Snagye gauging point which is located at the downstream of the Bocheong River and average rainfall of the Bocheong River basin are selected as training data sets. With these data sets, the training process has been done by using back propagation algorithm. Then waters levels in 1997 and 1998 are predicted with the trained algorithm. To improve the accuracy, a filtering method is introduced as predicting scheme. It is shown that predicted results are in a good agreement with observed water levels and that a filtering method can overcome the lack of training patterns.

• Journal of Korea Water Resources Association
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• v.43 no.8
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• pp.721-731
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• 2010

The rainfall-runoff relationship is very difficult to predict because it is complicate factor affected by many temporal and spatial parameters of the basin. In recent, models which is based on artificial intelligent such as neural network, genetic algorithm fuzzy etc., are frequently used to predict discharge while stochastic or deterministic or empirical models are used in the past. However, the discharge data which are generally used for prediction as training and validation set are often estimated from rating curve which has potential error in its estimation that makes a problem in reliability. Therefore, in this study, water stage is predicted from antecedent rainfall and water stage data for short term using three models of neural network which trained by error back propagation algorithm and optimized by genetic algorithm and training error back propagation after it is optimized by genetic algorithm respectively. As the result, the model optimized by Genetic Algorithm gives the best forecasting ability which is not much decreased as the forecasting time increase. Moreover, the models using stage data only as the input data give better results than the models using precipitation data with stage data.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.6
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• pp.792-798
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• 2021

In this paper, an algorithm for determining the coagulant input rate in the drug-injection tank during the process of the water purification plant was derived through big data analysis and prediction based on artificial intelligence. In addition, analysis of big data technology and AI algorithm application methods and existing academic and technical data were reviewed to analyze and review application cases in similar fields. Through this, the goal was to develop an algorithm for determining the coagulant input rate and to present the optimal input rate through autonomous driving simulator and pilot operation of the coagulant input process. Through this study, the coagulant injection rate, which is an output variable, is determined based on various input variables, and it is developed to simulate the relationship pattern between the input variable and the output variable and apply the learned pattern to the decision-making pattern of water plant operating workers.

• Spatial Information Research
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• v.21 no.6
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• pp.91-98
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• 2013

Government introduces a Total Maximum Daily Loads(TMDL) which can be implemented for total pollutant amounts in 2004. Normally, the local governments have been calculated the amounts of pollutant discharge of each watershed using a water quality model. However, among the input data to use the water quality model, creating a schematic diagram of the stream or the modeling usually requires considerable amount of time and efforts due to the manual work. Therefore, this study tried to develop an algorithm which automates the creation of a schematic diagram for water quality modeling using the Korean Reach File capable of river network analysis. Further, this study creates a schematic diagram with the shape of a stream utilizing GIS capabilities. The diagram can be easily analyzed with overlapping various spatial information such as pollution sources and discharge points. This study mainly has automated element segmentation algorithm to divide streamflows into equal distance using line graphic data of Koran Reach File. Also, automated attribute input algorithm has also been developed to enable to insert element order and type into elements using point graphic data of Korean Reach File. For the verification of the developed algorithm, the algorithm was applied to kyungan stream basin to see the acceptable results. To conclude, it was possible to automate generating of schematic diagram of water quality model and it is expected to be able to save time and cost required for the water modeling. In future study, it is necessary to develop an automatic creation system of various types of input data for water quality modeling and this will lead to relatively easier and simple water quality modeling.

• Journal of Korean Society of Water and Wastewater
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• v.18 no.2
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• pp.145-154
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• 2004

A long-term sewer rehabilitation project consuming an enormous budget needs to be conducted systematically using an optimization skill. The optimal budgeting and ordering of priority for sewer rehabilitation projects are very important with respect to the effectiveness of investment. In this study, the sewer rehabilitation optimization model using fast-messy genetic algorithm is developed to suggest a schedule for optimal sewer rehabilitation in a subcatchment area by modifying the existing GOOSER$^{(R)}$ model having been developed using simple genetic algorithm. The sewer rehabilitation optimization model using fast-messy genetic algorithm can improve the speed converging to the optimal solution relative to GOOSER$^{(R)}$, suggesting that it is more advantageous to the sewer rehabilitation in a larger-scale subcatchment area than GOOSER.

• Journal of Biosystems Engineering
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• v.33 no.2
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• pp.115-123
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• 2008

This study was conducted to develop an automatic soil water content control system for greenhouse, which consisted of drip irrigation nozzles, soil water content sensors, an on/off valve, a servo-motor assembly and a control program. The control logic adopted in the system was Ziegler-Nichols algorithm and rising time, time constant and over/undershoot ratio as control variables in the system was selected and determined by various control experiments to maintain small delay time and low overshoot. Based on the experimental results, it was concluded that the control system developed in the study could replace the unreliable conventional greenhouse soil water management.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.999-1001
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• 1995

In this study, the algorithm of optimal parameter inference is proposed. At this inferring method, we tried to acquire the follow-up to reference pattern through comparing the plant output pattern with random reference pattern. As an inference method, the fuzzy theory was applied and the proposed algorithm was proved by computer simulation.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.153-159
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• 2009

In order to estimate release water from reservoirs located on ungaged watersheds, an algorithm was suggested based on hydrologic reservoir routing and real time calibrating watershed parameters. A prototype - simple computer program was developed to implement the algorithm with Genetic Algorithm technic. The program was applied to a mid-size reservoir and its ungauged watershed area using observed rainfall data, spillway gates operation data and reservoir water stage time series data under a existing storm event. The result shows that the algorithm and the prototype would be useful to simulate released water from reservoirs.

• International Journal of Control, Automation, and Systems
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• v.1 no.1
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• pp.111-118
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• 2003

This paper proposes a two dimensional behavior analysis method for fish in a water tank based on the ARX method and the Kalman filter algorithm using image processing technology. In modeling the behavior of fish, the input is denoted as the environmental change and uses M-sequence. The output is expressed by the partnership between fish. The behavior model of individual fish is identified by the ARX method. It is then estimated by the Kalman filter algorithm. Finally, the fish behavior is analyzed by FFT. To prove the effectiveness of the pro-posed algorithm, it is applied to two tilapias in a water tank with dimensions of 100cm$\times$100cm$\times$50cm. The effectiveness of the proposed method is demonstrated through ARX identification, estimation of Kalman filter, and FFT analysis.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 2004.10a
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• pp.993-997
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• 2004

A data error inspection algorithm for wireless digital data communication was developed. Original data converted By wireless digital data error inspection algorithm. Wireless digital data is high possibility to get distortion and lose by noise and barrier on wireless. If the data check damaged and lost at receiver, can't make it clear and can't judge whether this data is right or not. Therefore, by wireless transmission data need the data error inspection algorithm in order to decrease the data distortion and lose and to monitoring the transmission data as real time. This study consists of RF station for wireless transmission, Water Level Meter station for water level measurement and Error inspection algorithm for error check of transmission data. This study is also that investigation and search for error inspection algorithm in order to wireless digital data transmission in condition of the least data's damage and lose. Designed transmitter and receiver with one - chip micro process to protect to swell the volume of circuit. Had designed RF transmitter - receiver station simply by means of ATMEL one - chip micro processing the systems. Used 10mW of the best RF power and 448MHz-449MHz on frequency band which is open to public touse free within the limited power.