• Journal of the Institute of Electronics Engineers of Korea SC
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• v.39 no.6
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• pp.42-48
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• 2002

The design, construction and performance test of a convenient multi-purpose irradiator is described. A multi-purpose irradiator using Cesium-137 has been developed for studies of low dose radiation effects in biology and for calibration of Thermo Luminescent dosimeter(TLD). During the operation, three rods of radioactive material which are 10cm in length revolve 180 degrees and irradiate biological samples, or TLD, and return to their shielded position, after the programmed time. A programmable Logic Controller(PLC) controls the sequence of operation, interlock, motor rotation and safety system. The rotation speed of biological samples can vary up to 20 RPM. A real time monitoring system was also incorporated to check and control the operation status of the irradiator. The capacity of the irradiation chamber was 4.5 liters. The isodose distribution at arbitrary vertical planes was measured by using film dosimetry. The dose-rate was 0.13 cGy/min in air and 0.11 cGy/min in water equivalent material in the case of Cesium-137. Range of activity was 2 Ci. The homogeneity of dose distribution in the chamber was ${\pm}$7%. The actual radiation level on the surface was within permissible levels. The irradiator had a maximum 0.35 mR/min radiation leakage on its surface.

• Journal of Korea Water Resources Association
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• v.36 no.2
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• pp.285-300
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• 2003

An event-based, kinematic, infiltration-excess, and distributed rainfall-runoff model using weather radar and Geographic Information System(GIS) was developed to acknowledge and account lot the spatial variability and uncertainty of several parameters relevant to storm surface runoff and surface flow The developed model is compatible with raster GIS and spatially and temporally varied rainfall data. To calibrate the model, Monte Carlo simulation and a likelihood measure are utilized; allowing for a range of possible system responses from the calibrated model. Using rain gauge adjusted radar-rainfall estimates, the developed model was applied and evaluated to a limited number of historical events for the Ralston Creek and Goldsmith Gulch basins within the Denver Urban Drainage and Flood Control District (UDFCD) that contain mixed land use classifications. While based on a limited number of Monte Carlo simulations and considered flood events, Nash and Sutcliffe efficiency score ranges of -0.19∼0.95 / -0.75∼0.81 were obtained from the calibrated models for the Ralston Creek and Goldsmith Gulch basins, based on a comparison of observed and simulated hydrographs. For the Ralston Creek and Goldsmith Gulch basins, Nash and Sutcliffe efficiency scores of 0.88/0.10, 0.14/0.71, and 0.99/0.95 for runoff volume, peak discharge, and time to peak, respectively, were obtained from the model.

• Economic and Environmental Geology
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• v.29 no.6
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• pp.713-724
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• 1996

The time-series resident solute concentrations, monitored at two field plots using the automated 144-channel TDR system by Kim (this issue), are used to investigate the dominant transport mechanism at field scale. Two models, based on contradictory assumptions for describing the solute transport in the vadose zone, are fitted to the measured mean breakthrough curves (BTCs): the deterministic one-dimensional convection-dispersion model (CDE) and the stochastic-convective lognormal transfer function model (CLT). In addition, moment analysis has been performed using the probability density functions (pdfs) of the travel time of resident concentration. Results of moment analysis have shown that the first and second time moments of resident pdf are larger than those of flux pdf. Based on the time moments, expressed in function of model parameters, variance and dispersion of resident solute travel times are derived. The relationship between variance or dispersion of solute travel time and depth has been found to be identical for both the time-series flux and resident concentrations. Based on these relationships, the two models have been tested. However, due to the significant variations of transport properties across depth, the test has led to unreliable results. Consequently, the model performance has been evaluated based on predictability of the time-series resident BTCs at other depths after calibration at the first depth. The evaluation of model predictability has resulted in a clear conclusion that for both experimental sites the CLT model gives more accurate prediction than the CDE model. This suggests that solute transport at natural field soils is more likely governed by a stream tube model concept with correlated flow than a complete mixing model. Poor prediction of CDE model is attributed to the underestimation of solute spreading and thus resulting in an overprediction of peak concentration.

• Journal of KIISE:Software and Applications
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• v.27 no.1
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• pp.50-61
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• 2000

Recently the stereo vision based on conics has received much attention by many authors. Conics have many features such as their matrix expression, efficient correspondence checking, abundance of conical shapes in real world. Extensions to higher algebraic curves met with limited success. Although irreducible algebraic curves are rather rare in the real world, lines and conics are abundant whose products provide good examples of higher algebraic curves. We consider plane algebraic curves of an arbitrary degree $n{\geq}2$ with a fully calibrated stereo system. We present closed form solutions to both correspondence and reconstruction problems. Let $f_1,\;f_2,\;{\pi}$ be image curves and plane and $VC_P(g)$ the cone with generator (plane) curve g and vertex P. Then the relation $VC_{O1}(f_1)\;=\;VC_{O1}(VC_{O2}(f_2)\;∩\;{\pi})$ gives polynomial equations in the coefficient $d_1,\;d_2,\;d_3$ of the plane ${\pi}$. After some manipulations, we get an extremely simple polynomial equation in a single variable whose unique real positive root plays the key role. It is then followed by evaluating $O(n^2)$ polynomials of a single variable at the root. It is in contrast to the past works which usually involve a simultaneous system of multivariate polynomial equations. We checked our algorithm using synthetic as well as real world images.

• Journal of Korea Water Resources Association
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• v.35 no.3
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• pp.331-344
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• 2002

This study is aimed at the development of a deterministic runoff model which can be used for flood runoff. The model is formulated by the watershed runoff model. Based on the assumptions that runoff system is nonlinear, the proposed watershed runoff model is the conceptual model. In the model structure, the conceptual model divides the runoff system into a surface structure and a subsurface structure corresponding to the surface flow, and inter flow and ground water flow respectively. The lag time effect of surface can be represented by the sub-tank of surface structure in the conceptual model. The parameter calibration of inter flow and ground water flow in the subsurface structure of the conceptual model is performed by separating the components with numeric filter The runoff coefficient($\alpha$$_2$) is expressed as the function of antecedent precipitation index(API). The parameters with the surface flow can be calibrated with the runoff coefficient($\alpha$$_1$ and $\alpha$/$_{11}$) in the conceptual model. In the conceptual model, an algorithm is developed to calibrate the parameters automatically based on efficiency criteria. The comparative study shows that simulated value from the conceptual model well agreed to observed value.

• Journal of Korea Water Resources Association
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• v.40 no.6 s.179
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• pp.431-446
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• 2007

Groundwater flow in a basin is greatly affected by many hydrogeological and hydrological characteristics of the basin. A groundwater flow model for the Kap-cheon basin ($area=648.3km^2$) in the Geum river basin was established using MODFLOW by fully considering major features obtained from observed data of 438 wells and 24 streams. Furthermore, spatial groundwater recharge distribution was estimated employing accurately calibrated watershed model developed using SWAT, a physically semi-distributed hydrological model. Model calibration using observed groundwater head data at 86 observation wells yielded the deterministic coefficient of 0.99 and the water budget discrepancy of 0.57%, indicating that the model well represented the regional groundwater flow in the Kap-cheon basin. Model simulation results showed that groundwater flow in the basin was strongly influenced by such factors as topological features, aquifer characteristics and streams. The streams in mountainous areas were found to alternate gaining and losing steams, while the streams in the vicinity of the mid-stream and down-stream, especially near the junction of Kap-cheon and Yudeong-cheon, areas were mostly appeared as gaining streams. Analysis of water budget showed that streams in mountainous areas except for the mid-stream and up-stream of Yudeong-cheon were mostly fed by groundwater recharge while the streams in the mid and down-stream areas were supplied from groundwater inflows from adjacent sub-basins. Hence, it was concluded that the interactions between surface water-groundwater in the Kap-cheon basin would be strongly inter-connected with not only streams but also groundwater flow system itself.

• Korean Journal of Remote Sensing
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• v.35 no.5_1
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• pp.625-636
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• 2019

Soil moisture is a representative factor that plays a key role in hydrological cycle. It is involved in the interaction between atmosphere and land surface, and is used in fields such as agriculture and water resources. Advanced Microwave Scanning Radiometer 2 (AMSR2), Advanced SCATterometer (ASCAT), and European Space Agency Climate Change Initiative (ESACCI) data were used to analyze the applicability and uncertainty of satellite soil moisture product in the Korean peninsula. Cumulative distribution function (CDF) matching and triple collocation (TC) analysis were carried out to investigate uncertainty and correction of satellite soil moisture data. Comparisons of pre-calibration satellite soil moisture data with the Automated Agriculture Observing System (AAOS) indicated that ESACCI and ASCAT data reflect the trend of AAOS well. On the other hand, AMSR2 satellite data showed overestimated values during the freezing period. Correction of satellite soil moisture data using CDF matching improved the error and correlation compared to those before correction. Finally, uncertainty analysis of soil moisture was carried out using TC method. Clearly, the uncertainty of the satellite soil moisture, corrected by CDF matching, was diminished in both freezing and thawing periods. Overall, it is expected that using ASCAT and ESACCI rather than AMSR2 soil moisture data will give more accurate soil moisture information when correction is performed on the Korean peninsula.

• Journal of the Korea Society for Simulation
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• v.31 no.1
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• pp.29-41
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• 2022

As one of the alternatives to solve the problem of unstable food supply and demand imbalance caused by abnormal climate change, the need for plant factories is increasing. Airflow in plant factory is recognized as one of important factor of plant which influence transpiration and heat transfer. On the other hand, Digital Twin (DT) is getting attention as a means of providing various services that are impossible only with the real system by replicating the real system in the virtual world. This study aimed to develop a digital twin model for airflow prediction that can predict airflow in various situations by applying the concept of digital twin to a plant factory in operation. To this end, first, the mathematical formalism of the digital twin model for airflow analysis in plant factories is presented, and based on this, the information necessary for airflow prediction modeling of a plant factory in operation is specified. Then, the shape of the plant factory is implemented in CAD and the DT model is developed by combining the computational fluid dynamics (CFD) components for airflow behavior analysis. Finally, the DT model for high-accuracy airflow prediction is completed through the validation of the model and the machine learning-based calibration process by comparing the simulation analysis result of the DT model with the actual airflow value collected from the plant factory.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.30 no.6B
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• pp.579-587
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• 2010

The mid-range streamflow forecast was performed using NWP(Numerical Weather Prediction) provided by KMA. The NWP consists of RDAPS for 48-hour forecast and GDAPS for 240-hour forecast. To enhance the accuracy of the NWP, QPM to downscale the original NWP and Quantile Mapping to adjust the systematic biases were applied to the original NWP output. The applicability of the suggested streamflow prediction system which was verified in Geum River basin. In the system, the streamflow simulation was computed through the long-term continuous SSARR model with the rainfall prediction input transform to the format required by SSARR. The RQPM of the 2-day rainfall prediction results for the period of Jan. 1~Jun. 20, 2006, showed reasonable predictability that the total RQPM precipitation amounts to 89.7% of the observed precipitation. The streamflow forecast associated with 2-day RQPM followed the observed hydrograph pattern with high accuracy even though there occurred missing forecast and false alarm in some rainfall events. However, predictability decrease in downstream station, e.g. Gyuam was found because of the difficulties in parameter calibration of rainfall-runoff model for controlled streamflow and reliability deduction of rating curve at gauge station with large cross section area. The 10-day precipitation prediction using GQPM shows significantly underestimation for the peak and total amounts, which affects streamflow prediction clearly. The improvement of GDAPS forecast using post-processing seems to have limitation and there needs efforts of stabilization or reform for the original NWP.

• Journal of Korean Tunnelling and Underground Space Association
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• v.19 no.6
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• pp.915-936
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• 2017

In this study, current status of Korean hazard mitigation guideline for tunnel operation is summarized. It shows that requirement for CCTV installation has been gradually stricted and needs for tunnel incident detection system in conjunction with the CCTV in tunnels have been highly increased. Despite of this, it is noticed that mathematical algorithm based incident detection system, which are commonly applied in current tunnel operation, show very low detectable rates by less than 50%. The putative major reasons seem to be (1) very weak intensity of illumination (2) dust in tunnel (3) low installation height of CCTV to about 3.5 m, etc. Therefore, an attempt in this study is made to develop an deep-learning based tunnel incident detection system, which is relatively insensitive to very poor visibility conditions. Its theoretical background is given and validating investigation are undertaken focused on the moving vehicles and person out of vehicle in tunnel, which are the official major objects to be detected. Two scenarios are set up: (1) training and prediction in the same tunnel (2) training in a tunnel and prediction in the other tunnel. From the both cases, targeted object detection in prediction mode are achieved to detectable rate to higher than 80% in case of similar time period between training and prediction but it shows a bit low detectable rate to 40% when the prediction times are far from the training time without further training taking place. However, it is believed that the AI based system would be enhanced in its predictability automatically as further training are followed with accumulated CCTV BigData without any revision or calibration of the incident detection system.