• Journal of Korea Water Resources Association
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• v.46 no.12
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• pp.1221-1234
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• 2013

The purpose of this study is to suggest the methodology for the computation of uplift pressure and discharge of the seepage flow under gravity dam. A 3-dimensional FDM model is developed for this purpose and this model can simulate the saturated Darcian flow in heterogeneous media. For the verification of the numeric model, test simulation has been executed and the mass balance has been checked. The error does not exceed 3%. Using the developed model, The uplift pressure and seepage flow discharge under gravity dam has been calculated. The uplift pressure shows the similar pattern, comparing with the result of flow-net method. As the length of grout curtain increases, the uplift pressure decreases linearly, but the seepage flow discharge shows the non-linear decreasing pattern. The coefficients of the formulas in the dam-design criteria have been analysed, and ${\alpha}=1/3$ corresponds to the value when the length of curtain grout is 70% of the aquifer height. The uplift pressure near the pressure relief drain has the big curvature vertically and horizontally. The developed model in this study can be used for the evaluation of the effects of seepage flow under gravity dam.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.505-519
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• 2006

We consider zero-inflated count data, which is discrete count data but has too many zeroes compared to the Poisson distribution. Zero-inflated data can be found in various areas. Despite its increasing importance in practice, appropriate statistical inference on zero-inflated data is limited. Classical inference based on a large number theory does not fit unless the sample size is very large. And regular Poisson model shows lack of St due to many zeroes. To handle the difficulties, a mixture of distributions are considered for the zero-inflated data. Specifically, a mixture of a point mass at zero and a Poisson distribution is employed for the data. In addition, when there exist meaningful covariates selected to the response variable, loglinear link is used between the mean of the response and the covariates in the Poisson distribution part. We propose a Bayesian inference for the zero-inflated Poisson regression model by using a Markov Chain Monte Carlo method. We applied the proposed method to a Korean oral hygienic data and compared the inference results with other models. We found that the proposed method is superior in that it gives small parameter estimation error and more accurate predictions.

• Journal of Korean Society on Water Environment
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• v.33 no.6
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• pp.661-669
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• 2017

A distributed watershed model CAMEL (Chemicals, Agricultural Management and Erosion Losses) was applied to a small rural watershed where intensive livestock farming sites are located to estimate nitrate leaching rates from soil to groundwater. The model was calibrated against the stream flows, and T-N and $NO_3-N$ concentrations were observed at the watershed outlet for three rainfall events in 2014. The simulation results showed good agreement with the observed stream flows ($R^2=0.67{\sim}0.93$), T-N concentrations ($R^2=0.40{\sim}0.58$) and $NO_3-N$ concentrations ($R^2=0.43{\sim}0.65$). The estimated annual nitrate leaching rate of the watershed was 33.0 kg N/ha/yr. The contributing proportions of individual activities to the total nitrate leaching rate of the watershed were estimated for livestock farming, applications of chemical fertilizer, and manure. The simulation results showed that the highest contributor to the nitrate leaching rate of the watershed was chemical fertilizer applications. The simulation period was for one year only, however, and results may vary depending on different conditions. Gathering input data over a longer period of time and monitoring data for calibration is needed. When this has been accomplished, it is expected that this model can be applied to small rural watersheds for evaluating temporal and spatial variations of nitrogen transformations and transport processes.

• Journal of Astronomy and Space Sciences
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• v.34 no.4
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• pp.315-330
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• 2017

Halo coronal mass ejections (CMEs) originating from solar activities give rise to geomagnetic storms when they reach the Earth. Variations in the geomagnetic field during a geomagnetic storm can damage satellites, communication systems, electrical power grids, and power systems, and induce currents. Therefore, automated techniques for detecting and analyzing halo CMEs have been eliciting increasing attention for the monitoring and prediction of the space weather environment. In this study, we developed an algorithm to sense and detect halo CMEs using large angle and spectrometric coronagraph (LASCO) C3 coronagraph images from the solar and heliospheric observatory (SOHO) satellite. In addition, we developed an image processing technique to derive the morphological and dynamical characteristics of halo CMEs, namely, the source location, width, actual CME speed, and arrival time at a 21.5 solar radius. The proposed halo CME automatic analysis model was validated using a model of the past three halo CME events. As a result, a solar event that occurred at 03:38 UT on Mar. 23, 2014 was predicted to arrive at Earth at 23:00 UT on Mar. 25, whereas the actual arrival time was at 04:30 UT on Mar. 26, which is a difference of 5 hr and 30 min. In addition, a solar event that occurred at 12:55 UT on Apr. 18, 2014 was estimated to arrive at Earth at 16:00 UT on Apr. 20, which is 4 hr ahead of the actual arrival time of 20:00 UT on the same day. However, the estimation error was reduced significantly compared to the ENLIL model. As a further study, the model will be applied to many more events for validation and testing, and after such tests are completed, on-line service will be provided at the Korean Space Weather Center to detect halo CMEs and derive the model parameters.

• Journal of Environmental Science International
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• v.10 no.1
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• pp.21-25
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• 2001

The Keum river has been utilized for drinking water supply of several city including Kunsan city and is deepening pollution state due to numerous municipal and industrial discharges. The concentration BOD in river is affected by the organic loading from a tributary and the algae biomass that largely happen to under eutrophication state. In the eutrophic water mass such as the Keum river, the autochthonous BOD was very important part for making a decision of water quality management, because it was accounted for majority of the total BOD. The predict of water quality has important meaning for management of water quality pollution of the Keum river. The purpose of this study will manage and predict water quality of the Keum river using QUAL-2E model considering the autochthonous BOD. The estimation of autochthonous BOD represented that the relationship between BOD and chlorophyll a. The regression equation was shown to be autochthonous BOD=$\beta$(sub)5$\times$chlorophyll a. The results of this study may be summarized as followed; The QUAL-2E model was calibrated with the data surveyed in the field of the study area in June, 1998. The calculated value by QUAL-2E model are in good agree to measured value within relative error of 7.80~20.33%. Especially, in the case of the considering autochthonous BOD, the calculated value of BOD were fairly good coincided with the observed values within relative error of 15%. But the case of not considering autochthonous BOD, relative error of BOD was shown to be 43.2%. In order to attain II grade of water quality standard in Puyo station which has a intake facility of water supply, we reduced to the pollutants loading of tributaries. In the case of removed 100% BOD of tributaries, the BOD of Puyo station was 4.07mg/$\ell$, belong to III grade of water quality standard. But in the case of removed 88% nutrient of tributaries, it was satisfied to II grade of water quality standard as below 3mg/$\ell$ of BOD. For estimation of autochthonous BOD in Keum river, we are performed simulating in accordance with reduction of nutrient load(50~100%) under conditions removal 90% organic load. Occupancy of autochthonous BOD according to nutrient loading reductions were varied from 25.97~79.51%. Occupancy of autochthonous BOD was shown to be a tendency to increasing in accordance with reduction of nutrient loading. Showing the above results, the nutrient that one of the growing factor of algae was important role in decision of BOD in the Keum river. For the water quality management of the Keum river, therefore, it is necessary to considering autochthonous BOD and to construction of advanced sewage treatment plant for nutrient removal.

• Computers and Concrete
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• v.20 no.3
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• pp.275-282
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• 2017

Grouting is an operation often carried out to consolidate and seal the rock mass in dam sites and tunnels. One of the important parameters in this operation is grouting pressure. In this paper, analytical models used to estimate pressure are investigated. To validate these models, grouting data obtained from Seymareh and Aghbolagh dams were used. Calculations showed that P-3 model from Groundy and P-25 model obtained from the results of grouting in Iran yield the most accurate predictions of the pressure and measurement errors compared to the real values in P-25 model in this dams are 12 and 14.33 Percent and in p-3 model are 12.25 and 16.66 respectively. Also, SPSS software was applied to define the optimum relation for pressure estimation. The results showed a high correlation between the pressure with the depth of the section, the amount of water take, rock quality degree and grout volume, so that the square of the multiple correlation coefficient among the parameters in this dams were 0.932 and 0.864, respectively. This indicates that regression results can be used to predict the amount of pressure. Eventually, the relationship between the parameters was obtained with the correlation coefficient equal to 0.916 based on the data from both dams generally and shows that there is a desirable correlation between the parameters. The outputs of the program led to the multiple linear regression equation of P=0.403 Depth+0.013 RQD+0.011 LU-0.109 V+0.31 that can be used in estimating the pressure.

• Journal of Korean Society on Water Environment
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• v.23 no.4
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• pp.490-497
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• 2007

A Combined sewer overflows (CSOs) are themselves a significant source of water pollution. Therefore, the control of urban drainage for CSOs reduction and receiving water quality protection is needed. Examples in combined sewer systems include downstream storage facilities that detain runoff during periods of high flow and allow the detained water to be conveyed by an interceptor sewer to a centralized treatment plant during periods of low flow. The design of such facilities as stormwater detention storage is highly dependant on the temporal variability of storage capacity available (which is influenced by the duration of interevent dry periods) as well as the infiltration capacity of soil and recovery of depression storage. As a result, a continuous approach is required to adequately size such facilities. This study for the continuous long-term analysis of urban drainage system used analytical probabilistic model based on derived probability distribution theory. As an alternative to the modeling of urban drainage system for planning or screening level analysis of runoff control alternatives, this model have evolved that offer much ease and flexibility in terms of computation while considering long-term meteorology. This study presented rainfall and runoff characteristics of the subject area using analytical probabilistic model. This study presented the average annual COSs and number of COSs when the interceptor capacity is in the range $3{\times}DWF$ (dry weather flow). Also, calculated the average annual mass of pollutant lost in CSOs using Event Mean Concentration. Finally, this study presented a decision of storage volume for CSOs reduction and water quality protection.

• Smart Media Journal
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• v.12 no.11
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• pp.57-66
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• 2023

In this paper, we present a study on an AI-based system utilizing the CCTV system within city buses to predict the intentions of boarding and alighting passengers, with the aim of preventing accidents. The proposed system employs the YOLOv7 Pose model to detect passengers, while utilizing an LSTM model to predict intentions of tracked passengers. The system can be installed on the bus's CCTV terminals, allowing for real-time visual confirmation of passengers' intentions throughout driving. It also provides alerts to the driver, mitigating potential accidents during passenger transitions. Test results show accuracy rates of 0.81 for analyzing boarding intentions and 0.79 for predicting alighting intentions onboard. To ensure real-time performance, we verified that a minimum of 5 frames per second analysis is achievable in a GPU environment. his algorithm enhance the safety of passenger transitions during bus operations. In the future, with improved hardware specifications and abundant data collection, the system's expansion into various safety-related metrics is promising. This algorithm is anticipated to play a pivotal role in ensuring safety when autonomous driving becomes commercialized. Additionally, its applicability could extend to other modes of public transportation, such as subways and all forms of mass transit, contributing to the overall safety of public transportation systems.

• Journal of Environmental Science International
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• v.23 no.3
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• pp.511-520
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• 2014

The MFFn(Mass first flush), EMCs(Event mean concentrations) and runoff loads were analyzed for various rainy events(monitoring data from 2011 to 2012) in transportation area(rail road in station). The pollutant EMCs by volume of stormwater runoff showed the BOD5 9.6 mg/L, COD 29.9 mg/L, SS 16.7 mg/L, T-N 3.271 mg/L, T-P 0.269 mg/L in the transportation areas(Railroad in station). The average pollutant loading by unit area of stormwater runoff showed the BOD5 $27.26kg/km^2$, COD $92.55kg/km^2$, SS $50.35kg/km^2$, T-N $10.13kg/km^2$ and T-P $10.13kg/km^2$ in the transportation areas. Estimated NCL-curve(Normalized cumulated-curve) was evaluated by comparison with observed MFFn. MFFn was estimated by varying n-value from 10% to 90% on the rainy events. The n-value increases, MFFn is closed to '1'. As time passed, the rainfall runoff was getting similar to ratio of pollutants accumulation. The result of a measure of the strength of the linear relationship between observed data and expected data under model was good.

• Journal of Korean Tunnelling and Underground Space Association
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• v.7 no.1
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• pp.37-49
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• 2005

In a tunnel, failure of its supports can cause failure of the tunnel. Therefore it is important to estimate safety factor of the tunnel which the failure of its supports is taken into account. In previous studies, supports of tunnels were usually modelled as beam elements. The failure of the supports was decided by comparing the allowable stress and the calculated bending stresses inside the beam elements in estimating safety factor of the tunnel considering the failure of its supports. In this study, it is suggested how to model the supports properly. To this end, supports of a tunnel were modelled by both beam (elastic) elements and continuum (elasto-plastic) elements in two dimensional numerical analyses. Meanwhile, it was analyzed how rock mass class, coefficient of lateral pressure, shotcrete thickness, the existence of rock bolt, and excavation method had an effect on the safety factor of a tunnel.