• Proceedings of the Korea Water Resources Association Conference
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• 2008.05a
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• pp.389-394
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• 2008

The purpose of this research was to develop a methodology to determine whether conjunctive surface water and groundwater management could significantly reduce deficits in a river basin with a relatively limited alluvial aquifer. The Geum River basin is one of major river basins in South Korea. The upper region of the Geum River basin is typical of many river basins in Korea where the shape of river basin is narrow with small alluvial aquifer depths from 10m to 20m and where most of the groundwater pumped comes quickly from the steamflow. The basin has two surface reservoirs, Daecheong and Yongdam. The most recent reservoir, Yongdam, provides water to a trans-basin diversion, and therefore reduces the water resources available in the Geum River basin. After the completion of Yongdam reservoir, the reduced water supply in the Geum basin resulted in increasing conflicts between downstream water needs and required instream flows, particularly during the low flow season. Historically, the operation of groundwater pumping has had limited control and is administered separately from surface water diversions. Given the limited size of the alluvial aquifer, it is apparent that groundwater pumping is essentially taking its water from the stream. Therefore, the operation of the surface water withdrawals and groundwater pumping must be considered together. The major component of the conjunction water management in this study is a goal-programmin g based optimization model that simultaneously considers surface water withdrawals, groundwater pumping and instream flow requirements. A 10-day time step is used in the model. The interactions between groundwater pumping and the stream are handled through the use of response and lag coefficients. The impacts of pumping on streamflow are considered for multiple time periods. The model is formulated as a linear goal-programming problem that is solved with the commercial LINGO optimization software package.

• Ecology and Resilient Infrastructure
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• v.7 no.4
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• pp.345-352
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• 2020

Securing the lead time for evacuation is crucial to minimize flood damage. In this study, downstream water levels for heavy rainfall were predicted using measured water level observation data. Multiple regression analysis and artificial neural networks were applied to the Seom River experimental watershed to predict the water level. Water level observation data for the Seom River experimental watershed from 2002 to 2010 were used to perform the multiple regression analysis and to train the artificial neural networks. The water level was predicted using the trained model. The simulation results for the coefficients of determination of the artificial neural network level prediction ranged from 0.991 to 0.999, while those of the multiple regression analysis ranged from 0.945 to 0.990. The water level prediction model developed using an artificial neural network was better than the multiple-regression analysis model. This technique for forecasting downstream water levels is expected to contribute toward flooding warning systems that secure the lead time for streams.

• Journal of Korean Society on Water Environment
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• v.25 no.4
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• pp.580-588
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• 2009

In this study daily flow rates and delivered pollutant loads of Nakdong river basin are simulated with modified TANK model and minimum variance unbiased estimator. Based on the simulation results, flow duration curves, load duration curves, and delivery ratio duration curves have been established. Then GIS analysis is performed to obtain several hydrological geomorphic characteristics such as watershed area, stream length, watershed slope and runoff curve number. Finally, multiple regression analysis is carried out to estimate empirical equations for pollutants delivery ratio. The results show that there is positive relation between the flow rates and delivery ratios, and the proposed empirical formulas for delivery ratio can predict well river pollutant loads.

• Journal of Environmental Science International
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• v.4 no.1
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• pp.71-80
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• 1995

A discrete state space model for a multiple-reach river system is formulated using the dynamics of biochemical oxygen demand(BOD) and dissolved oxygen(DO). A hierarchical optimization technique, which is applicable to large-scale systems with time-delays in states, is also described to control stream quality in a river as an optimal manner based on the interaction prediction method. The steady state tracking error of the proposed method is determined analytically and a necessary and sufficient condition on which a constant target tracking problem has zero steady-state error is derived. Computer simulations for the river pollution model illustrate the algorithm.

• Journal of Information Processing Systems
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• v.1 no.1 s.1
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• pp.14-21
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• 2005

Even though mainly statistical methods have been used in anomaly network intrusion detection, to detect various attack types, machine learning based anomaly detection was introduced. Machine learning based anomaly detection started from research applying traditional learning algorithms of artificial intelligence to intrusion detection. However, detection rates of these methods are not satisfactory. Especially, high false positive and repeated alarms about the same attack are problems. The main reason for this is that one packet is used as a basic learning unit. Most attacks consist of more than one packet. In addition, an attack does not lead to a consecutive packet stream. Therefore, with grouping of related packets, a new approach of group-based learning and detection is needed. This type of approach is similar to that of multiple-instance problems in the artificial intelligence community, which cannot clearly classify one instance, but classification of a group is possible. We suggest group generation algorithm grouping related packets, and a learning algorithm based on a unit of such group. To verify the usefulness of the suggested algorithm, 1998 DARPA data was used and the results show that our approach is quite useful.

• 한국전산유체공학회:학술대회논문집
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• 1999.11a
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• pp.71-76
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• 1999

The base flow regions of a three-body sounding rocket containing multiple exhaust plumes were numerically investigated in three dimensions for a free stream Mach number of 2.7 at flight altitude 18.5 km. The flowfields were calculated using the full compressible Navier-Stokes equations with an one-equation turbulence model of Baldwin-Earth. The present calculations were executed based upon a chemically frozen, single perfect gas model assumption. Due to the symmetry of the three-body rocket of each single nozzle, only one fourth of the computational domain was considered for the analysis. The results indicate that a babe heating effect is not considerable due to the small expansion of the plumes. In the base, however, a low speed recirculating flow dominates the region.

• Journal of Environmental Impact Assessment
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• v.22 no.6
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• pp.591-607
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• 2013

The objectives of the study were to evaluate the aquatic environment of an urban stream using various ecological parameters of biological biomarkers, physical habitat quality and chemical water quality and to develop a "Multimetric Eco-Model" ($M_m$-E Model) for the ecosystem evaluations. For the applications of the $M_m$-E model, three zones including the control zone ($C_Z$) of headwaters, transition zone ($T_Z$) of mid-stream and the impacted zone ($I_Z$) of downstream were designated and analyzed the seasonal variations of the model values. The biomarkers of DNA, based on the comet assay approach of single-cell gel electrophoresis (SCGE), were analyzed using the blood samples of Zacco platypus as a target species, and the parameters were used tail moment, tail DNA(%) and tail length (${\mu}m$) in the bioassay. The damages of DNA were evident in the impacted zone, but not in the control zone. The condition factor ($C_F$) as key indicators of the population evaluation indicator was analyzed along with the weight-length relation and individual abnormality. The four metrics of Qualitative Habitat Evaluation Index (QHEI) were added for the evaluations of physical habitat. In addition, the parameters of chemical water quality were used as eutrophic indicators of nitrogen (N) and phosphorus (P), chemical oxygen demand (COD) and conductivity. Overall, our results suggested that attributes of biomarkers and bioindicators in the impacted zone ($I_Z$) had sensitive response largely to the chemical stress (eutrophic indicators) and also partially to physical habitat quality, compared to the those in the control zone.

• Journal of Korean Society on Water Environment
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• v.25 no.6
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• pp.863-871
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• 2009

For the assessment of climate change impacts on river flow condition, CGCM 3.1 T63 is selected as future climate information. The projections come from CGCM used to simulate the GHG emission scenario known as A2. Air temperature and precipitation information from the GCM simulations are converted to regional scale data using the statistical downscaling method known as MSPG. Downscaled climate data from GCM are then used as the input data for the modified TANK model to generate regional runoff estimates for 44 river locations in Nakdong river basin. Climate change is expected to reduce the reliability of water supplies in the period of 2021~2030. In the period of 2051~2060, stream flow is expected to be reduced in spring season and increased in summer season. However, it should be noted that there are a lot of uncertainties in such multiple-step analysis used to convert climate information from GCM-based future climate projections into hydrologic information.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.24 no.6B
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• pp.1098-1107
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• 1999

In multimedia applications, a source usually generates multiple streams. By heterogeneous multimedia multicast, we mean a recipient can receive some of them, not necessarily all of them. A recipient bids for what it wants to receive and the source gains the same amount when a connection is established. The problem of distributing streams for heterogeneous multicast to maximize the source's gain, can be solved using a 0-1 integer programming, hewn as NP-complete. In this paper, we propose efficient stream distribution algorithms in two different types of multicast models. The first restricted model assumes that the capacity for a link in the multicast tree is grater than or equal to the capacities of its descendant links. In the second unrestricted model, we drop out the restriction in the restricted model. Proposed algorithms have better time and space complexities compared with any existing one. In addition, distributed implementations are straightforward, which is very useful for large networks.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.