• Journal of the Korean Association of Geographic Information Studies
• /
• v.15 no.3
• /
• pp.103-118
• /
• 2012

A distributed model can easily obtain discharge at any grids spatially distributed in a watershed. But if there are subwatersheds which have various characteristics in a watershed, it is needed to apply a model calibrated at each subwatershed to obtain reliable simulation results for each subwatershed. In this study, a multi-site calibration module that can calibrate a distributed model at each subwatershed using observed flow data was developed. Methods to select multi-site calibration parameters, to apply subwatershed parameters, and to set subwatershed network information are suggested. Classes to implement multi-site calibration technique are designed and a GUI was developed, and procedures for runoff modelling using subwatershed parameters were established. Multi-site calibration module was applied to Sunsan watershed($977km^2$) of Nakdong river basin. Application results showed that the multi-site calibration technique could be applied effectively to model the calibration for each subwatershed, and the simulation results of subwatershed were improved by the application of multi-site calibration.

• The Journal of the Acoustical Society of Korea
• /
• v.39 no.6
• /
• pp.615-621
• /
• 2020

In this paper, we propose a multi-site based earthquake event classification method using graph convolution networks. In the traditional earthquake event classification methods using deep learning, they used single-site observation to estimate seismic event class. However, to achieve robust and accurate earthquake event classification on the seismic observation network, the method using the information from the multi-site observations is needed, instead of using only single-site data. Firstly, our proposed model employs convolution neural networks to extract informative embedding features from the single-site observation. Secondly, graph convolution networks are used to integrate the features from several stations. To evaluate our model, we explore the model structure and the number of stations for ablation study. Finally, our multi-site based model outperforms up to 10 % accuracy and event recall rate compared to single-site based model.

• Proceedings of the Korean Operations and Management Science Society Conference
• /
• 1993.04a
• /
• pp.36-45
• /
• 1993

This paper presents a model and a heuristic procedure to design production planning and transportation scheduling systems of critical items, components and products on the basis of material requirement planning concept and transportation planning model. These systems are stemmed from a multi-site multi-product production company in a international economic zone. An example is provided to validate the heuristic procedure developed.

• Nuclear Engineering and Technology
• /
• v.53 no.10
• /
• pp.3247-3255
• /
• 2021

As Probabilistic Safety Assessments (PSAs) are thoroughly conducted for the Site Operating States (SOSs) for a single unit, multi-unit Probabilistic Safety Assessments (MUPSAs) are ongoing worldwide to address new technical challenges or issues. In South Korea, the determination of the site operating states for a single site requires a logical approach with reasonable assumptions due to the fact that there are 4-8 operating units for each site. This paper suggests a simulation model that gives a reasonable expectation of the site operation states using the Monte-Carlo method as a stochastic approach and deterministic aspects such as operational policies. Statistical hypothesis tests were conducted so that the reliance of the simulation results can be guaranteed. In this study, 7 units of the Kori site were analysed as a case study. The result shows that the fraction of full power for all 7 units is nearly 0.45. For situations when more than two units are not in operation, the highest fraction combination was obtained for Plant Operation State (POS) 8, which is the stage of inspection and repairment. By entering various site operation scenarios, the simulation model can be used for the analysis of other site operation states.

• Journal of the Korean Operations Research and Management Science Society
• /
• v.39 no.4
• /
• pp.85-95
• /
• 2014

In the e-commerce, the conversion into the multi-media is the important issue. According to the research by Nielsen Korea, the 83% of customers who purchase the products in the e-commerce utilize multi-channel to buy the products such as mobile and online [3]. Thus, to effectively implement online advertising, marketers should understand the customers' path [15] in the multi-channel. The study of the multi-site activities plays an important role to predict customers' purchase [28]. To explain the e-commerce site visit activities of customers, we have developed research model in terms of the online advertising. This research model is based on the study of Moe and Fader [23]. There are two types of composition in the research model. First, general site visit as an exploratory search have net effect on the shopping site visit because customers could acquire or develop information on the e-commerce site via online advertising. Secondly, the e-commerce site visit as a goal-directed search cause threshold of the e-commerce site visit because customers could achieve their goal. When the threshold is increased, the probability of a shopping site visit is decreased and vice versa. Thus, we have investigated the impact of customers' previous visit activities (general site visit and shopping site visit) on the next e-commerce site visit in terms of dynamic view. Research data was provided by Cheil World Wide. This panel data include mobile and online log data of panelists from Jan. 2013 to March 2013. As the results, the customers' e-commerce site visit on the online media would decrease the probability of e-commerce site visit because these visit activities increase the threshold of e-commerce site visit. This result is similar with the previous study [23]. Otherwise, since e-commerce site visit on the mobile media decrease the threshold, the customers' probability of e-commerce site visit would increase In summary, the site visit activities on the mobile could improve the probability of e-commerce site visits.

• Nuclear Engineering and Technology
• /
• v.50 no.8
• /
• pp.1217-1233
• /
• 2018

Following a surge of interest in multi-unit risk in the last few years, many recent studies have suggested methods for multi-unit probabilistic safety assessment (MUPSA) and addressed several related aspects. Most of the existing studies though focused on two-unit nuclear power plant (NPP) sites or used rather simplified probabilistic safety assessment (PSA) models to demonstrate the proposed approaches. When considering an NPP site with three or more units, some approaches are inapplicable or yield very conservative results. Since the number of such sites is increasing, there is a strong need to develop and validate practical approaches to the related MUPSA. This article provides several detailed approaches that are applicable to multi-unit Level 1 PSA for sites with up to six or more reactor units. To validate the approaches, a multi-unit Level 1 PSA model is developed and the site core damage frequency is estimated for each of four representative multi-unit initiators, as well as for the case of a simultaneous occurrence of independent single-unit initiators in multiple units. For this purpose, an NPP site with six identical OPR-1000 units is considered, with full-scale Level 1 PSA models for a specific OPR-1000 plant used as the base single-unit models.

• Journal of Environmental Science International
• /
• v.6 no.3
• /
• pp.205-211
• /
• 1997

For the prediction of multi-site rainfall with radar data and ground meteorological data, a rainfall prediction model was proposed, which uses the neural network theory, a kind of artifical Intelligence technique. The Input layer of the prediction model was constructed with current ground meteorological data, their variation, moving vectors of rain- fall field and digital terrain of the measuring site, and the output layer was constructed with the predicted rainfall up to 3 hours. In the application of the prediction model to the Pyungchang river basin, the learning results of neural network prediction model showed more Improved results than the parameter estimation results of an existing physically based model. And the proposed model comparisonally well predicted the time distribution of ralnfall.

• Environmental Engineering Research
• /
• v.21 no.2
• /
• pp.152-163
• /
• 2016

Present study investigates the potential of cassava peel and rubber tree bark for the removal of Cr (VI) from aqueous solution. Removal efficiency of more than 99% was obtained during the kinetic adsorption experiments with dosage of 3.5 g/L for cassava peel and 8 g/L for rubber tree bark. By comparing popular isotherm models and kinetic models for evaluating the kinetics of mass transfer, it was observed that Redlich-Peterson model and Langmuir model fitted well ($R^2$ > 0.99) resulting in maximum adsorption capacity as 79.37 mg/g and 43.86 mg/g for cassava peel and rubber tree bark respectively. Validation of pseudo-second order model and Elovich model indicated the possibility of chemisorption being the rate limiting step. The multi-linearity in the diffusion model was further addressed using multi-sites models (two-site series interface (TSSI) and two-site parallel interface (TSPI) models). Considering the influence of interface properties on the kinetic nature of sorption, TSSI model resulted in low mass transfer rate (5% for cassava peel and 10% for rubber tree bark) compared to TSPI model. The study highlights the employability of two-site sorption model for simultaneous representation of different stages of kinetic sorption for finding the rate-limiting process, compared to the separate equilibrium and kinetic modeling attempts.

• Journal of the Korean Society of Industry Convergence
• /
• v.22 no.5
• /
• pp.519-527
• /
• 2019

This study was conducted to present useful information on the utilization and productivity analysis of laminated modular multi-family housing. To this end, the process of site installation was investigated and analyzed through a prior study, and the monitoring survey was conducted through the site installation case of an stacked multi-family housing. Based on the above, the results of productivity analysis using the web-cyclone technique were also presented. However, the site installation process has limitations on generalisation because the process is not the same for each construction company, and also limits that require verification through application in the actual site of the web-cyclone model presented in this study.

• Journal of Korea Water Resources Association
• /
• v.39 no.10 s.171
• /
• pp.867-880
• /
• 2006

Hydrological models with many parameters and complex model structures require a powerful and detailed model calibration/validation scheme. In this study, we proposed a multi-variable and multi-site calibration and validation framework for the Soil Water Assessment Tool (SWAT) model applied in the Gap-cheon catchment located downstream of the Geum river basin. The sensitivity analysis conducted before main calibration helped understand various hydrological processes and the characteristics of subcatchments by identifying sensitive parameters in the model. In addition, the model's parameters were estimated based on existing data prior to calibration in order to increase the validity of model. The Nash-Sutcliffe coefficients and correlation coefficient were used to estimate compare model output with the observed streamflow data: $R_{eff}\;and\;R^2$ ranged 0.41-0.84 and 0.5-0.86, respectively, at the Heuduck station. Model reproduced baseflow estimated using recursive digital filter except for 2-5% overestimation at the Sindae and Boksu stations. Model also reproduced the temporal variability and fluctuation magnitude of observed groundwater levels with $R^2$ of 0.71 except for certain periods. Therefore, it was concluded that the use of multi-variable and multi-site method provided high confidence for the structure and estimated parameter values of the model.