• Geomechanics and Engineering
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• v.18 no.6
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• pp.585-594
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• 2019

In-situ leaching could be one of the promising mining methods to extract the minerals from deep fractured rock mass. Constrained by the low permeability at depth, however, the performance does not meet the expectation. In fact, the rock mass permeability mainly depends on the pre-existing natural fractures and therefore play a crucial role in in-situ leaching performance. More importantly, fractures have various characteristics, such as aperture, persistence, and density, which have diverse contributions to the promising method. Hence, it is necessary to study the variation of fluid rate versus fracture parameters to enhance in-situ leaching performance. Firstly, the subsurface fractures from the depth of 1500m to 2500m were mapped using the discrete fracture network (DFN) in this paper, and then the numerical model was calibrated at a particular case. On this basis, the fluid flow through fractured rock mass with various fracture characteristics was analyzed. The simulation results showed that with the increase of Fisher' K value, which determine the fracture orientation, the flow rate firstly decreased and then increased. Subsequently, as another critical factor affecting the fluid flow in natural fractures, the fracture transmissivity has a direct relationship with the flow rate. Sensitive study shows that natural fracture characteristics play a critical role in in-situ leaching performance.

• Journal of Korea Water Resources Association
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• v.52 no.4
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• pp.279-289
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• 2019

Unlike other natural disasters, drought is a reoccurring and region-wide phenomenon after being triggered by a prolonged precipitation deficiency. Considering that remote sensing products provide consistent temporal and spatial measurements of precipitation, this study developed a remote sensing data-based drought outlook model. The meteorological drought was defined by the Standardized Precipitation Index (SPI) achieved from PERSIANN_CDR, TRMM 3B42 and GPM IMERG images. Bayesian networks were employed in this study to combine the historical drought information and dynamical prediction products in advance of drought outlook. Drought outlook was determined through a decision-making model considering the current drought condition and forecasted condition from the Bayesian networks. Drought outlook condition was classified by four states such as no drought, drought occurrence, drought persistence, and drought removal. The receiver operating characteristics (ROC) curve analysis were employed to measure the relative outlook performance with the dynamical prediction production, Multi-Model Ensemble (MME). The ROC analysis indicated that the proposed outlook model showed better performance than the MME, especially for drought occurrence and persistence of 2- and 3-month outlook.

• Journal of Digital Convergence
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• v.20 no.4
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• pp.83-92
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• 2022

With the digital transformation of the entire industry, software competency has become the core competency for the future talent. However, it is difficult to find researches related to the corporate education for improving employee's software capability. Therefore, this study tried to verify the relationship between factors affecting the learning performance of employees in online software education. For this purpose, a survey of 223 employees with online software education experience was analyzed using the SPSS PROCESS macro. As a result of analysis, perceived usefulness and self-regulated learning have been found to have a significant multiple mediating effect on learning performance by digital literacy and satisfaction. This suggests that not only learner factors but also the characteristics of education should be considered. The results of this study are expected to be helpful in designing effective online education programs.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.231-231
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• 2020

Unlike conventional treatment technologies, the performance of nature-based facilities were susceptible to seasonal changes and climatological variabilities. This study evaluated the effects of seasonal variables on the treatment performance of constructed wetlands (CWs). Two CWs treating runoff and discharge from agricultural and livestock areas were monitored to determine the efficiency of the systems in reducing particulates, organics, and nutrients in the influent. For all four seasons, the mean effluent suspended solids concentration in the agricultural CW (ACW) increased by -2% to -39%. The occurrence of algal blooms in the system during summer and fall seasons resulted to the greatest increase in the amount of suspended materials in the overlying water. unlike ACW, the livestock CW (LCW) performed efficiently throughout the year, with mean suspended solids removal amounting to 61% to 68%. Algal blooms were still present in LCW seasonally; however, the constant inflow in the system limited the proliferation of phytoplankton through continuous flushing. The total nitrogen (TN) and total phosphorus (TP) removal efficiencies in ACW were higher during the summer (21% to 25%) and fall (8% to 21%) seasons since phytoplankton utilize nitrogen and phosphorus during the early stages of phytoplankton blooms. In the case of LCW, the most efficient reduction in TN (24%) and TP (54%) concentrations were also noted in summer, which can be attributed to the favorable environmental conditions for microbial activities. The mean removal of organics in ACW was lowest during summer season (-52% to 35%), wherein the onset of algal decay triggered a relative increase in organic matter and stimulate bacterial growth. The removal of organics in LCW was highest (54 % to 55%) during the fall and winter seasons since low water temperatures may limit the persistence of various algal species. Variations in environmental conditions due to seasonal changes can greatly affect the performance of CW systems. This study effectively established the contributory factors affecting the feasibility of utilizing CW systems for treating agricultural and livestock discharges and runoff.

• The Plant Pathology Journal
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• v.22 no.3
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• pp.303-308
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• 2006

Ethaboxam is a fungicide controlling plant diseases caused by Oomycetes. Efforts were made to improve its fungicidal activity applying formulation technology. Fungicidal activity of ethaboxam against cucumber downy mildew caused by Pseudoperonospora cubensis was improved by incorporating polyoxyethylene cetyl ether (PCE) in a wettable powder formulation. It was found that the optimum combination ratio of PCE and ethaboxam was 3:1, and a tank-mix of $150{\mu}g/ml$ of ethaboxam and $450{\mu}g/ml$ of PCE would be as good as the standard 25 % WP formulation diluted to $250{\mu}g/ml$ ethaboxam without PCE in controlling cucumber downy mildew. Based on this results, a wettable powder (WP) co-formulation containing 15% of ethaboxam and 45% of PCE was developed in this study, and tested for its performance in the fields. This co-formulation showed significant improvement in persistence of fungicidal activity and curative efficacy of ethaboxam against cucumber downy mildew. The improved control efficacy was also confirmed for control of grape downy mildew caused by Plasmopara viticola and potato late blight caused by Phytophthora infestans in the field tests.

• Journal of the Korea Society of Computer and Information
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• v.22 no.2
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• pp.1-7
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• 2017

Recently, NAND flash memory, which is used as a storage medium, is replacing HDD (Hard Disk Drive) at a high speed due to various advantages such as fast access speed, low power, and easy portability. In order to apply NAND flash memory to a computer system, a Flash Translation Layer (FTL) is indispensably required. FTL provides a number of features such as address mapping, garbage collection, wear leveling, and hot data identification. In particular, hot data identification is an algorithm that identifies specific pages where data updates frequently occur. Hot data identification helps to improve overall performance by identifying and managing hot data separately. MHF (Multi hash framework) technique, known as hot data identification technique, records the number of write operations in memory. The recorded value is evaluated and judged as hot data. However, the method of counting the number of times in a write request is not enough to judge a page as a hot data page. In this paper, we propose hot data identification which considers not only the number of write requests but also the persistence of write requests.

• Environmental Engineering Research
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• v.21 no.4
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• pp.427-435
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• 2016

In this study, change in water-dissolved oxygen (DO) was analyzed under various synthetic water qualities and nanobubbles (NBs) application conditions, such as gas type, initial DO as well as water dissolved, suspended and organic matters contents. When oxygen, rather than air, was introduced into nitrogen-desorbed ultra-pure water, the stagnation time was significantly increased. It took ten days for DO concentration to drop back to saturation. The higher the initial DO concentration, the longer particles were observed above saturation due to particle stability improvement. The oxygen mass transfer rate of 0.0482 mg/L/min was found to reach a maximum at an electrolytic concentration of 0.75 g/L, beyond which the transfer rate decreased due to adsorption of negative ions of the electrolyte at the interface. High levels of turbidity caused by suspended solids have become a barrier to dissolution of NBs oxygen into the water solution, and thus affected the transfer performance. On the other hand, by applying NBs for just an hour, up to 7.2% degradation of glucose as representative organic matter was achieved. Thus, NBs technology would maintain a high DO extent for an extended duration, and thus can improve water quality provided that water chemistry is closely monitored during its application.

• The Korean Journal of Applied Statistics
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• v.27 no.3
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• pp.431-444
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• 2014

We introduce the empirical model decomposition (EMD) to decompose a time series into a set of components in the time-frequency domain. By using EMD, we also extract cycle and trend components from major Korean macroeconomic indices and forecast the indices with the components combined. In order to evaluate their efficiencies, we investigate volatility, autocorrelation, persistence, Granger causality, nonstationarity, and forecasting performance. They are then compared with those by Hodrick-Prescott filter which is the most commonly used method.

• Korean Management Science Review
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• v.26 no.1
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• pp.77-91
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• 2009

It is the most important sucess factor for the electricity generation industry to minimize operations cost of surplus electricity generation through accurate demand forecasts. Temperature forecast is a significant input variable, because power demand is mainly linked to the air temperature. This study estimates the information value of the temperature forecast by analyzing the relationship between electricity load and daily air temperature in Korea. Firstly, several characteristics was analyzed by using a population-weighted temperature index, which was transformed from the daily data of the maximum, minimum and mean temperature for the year of 2005 to 2007. A neural network-based load forecaster was derived on the basis of the temperature index. The neural network then was used to evaluate the performance of load forecasts for various types of temperature forecasts (i.e., persistence forecast and perfect forecast) as well as the actual forecast provided by KMA(Korea Meteorological Administration). Finally, the result of the sensitivity analysis indicates that a $0.1^{\circ}C$ improvement in forecast accuracy is worth about $11 million per year.

• Tunnel and Underground Space
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• v.5 no.1
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• pp.1-10
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• 1995

The behavior of a jointed rock mass depends mainly on the geometrical and mechanical properties of joints. The failure mode of a rock mass and kinematics of rock blocks are governed by the orientation, spacing, and persistence of joints. The mechanical properties such as dilation angle, shear strength, maximum closure, strength of asperities and friction coeffiient play important roles on the stability and deformation of the rock mass. The normal and shear behaviour of a joint are coupled due to dilation, and the joint deformation depends also on the boundary conditions such as stiffness conditons. In this paper, the joint constitutive law including the dilatant behaviour of a joint is numerically modelled using the edge-to-edge contact logic in distinct element method. Also, presented is the method to quantify the input parameters used in the joint law. The results from uniaxial compression and direct shear tests using the numeical model of the single joint were compared to the analytic results from them. The boundary effect on the behaviour of a joint is verified by comparing the results of direct shear test under constant stress boundary condition with those under constant stiffness boundary condition. The numerical model developed is applied to a complex jointed rock mass to examine its performance and to evaluate the effect of joint dilation on tunnel stability.