• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.42 no.2
• /
• pp.108-118
• /
• 2014

On the optimization design problem using surrogate model, it requires considerable number of sampling points to construct a surrogate model which retains the accuracy. As an alternative to reduce construction cost of the surrogate model, Variable-Fidelity Modeling(VFM) technique, where correct high fidelity model based on the low fidelity surrogate model is introduced. In this study, hierarchical kriging model for variable-fidelity surrogate modeling is used and an optimization framework with multi-objective genetic algorithm(MOGA) is presented. To prove the feasibility of this framework, airfoil design optimization process is performed for the transonic region. The parameters of PARSEC are used to design variables and the optimization process is performed in case of varying number of grid and varying fidelity. The results showed that pareto front of all variable-fidelity models are similar with its single-level of fidelity model and calculation time is considerably reduced. Based on computational results, it is shown that VFM is a more efficient way and has an accuracy as high as that single-level of fidelity model optimization.

• The Korean Journal of Applied Statistics
• /
• v.26 no.1
• /
• pp.93-109
• /
• 2013

Geostatistical data or point-referenced data have the information on the monitoring stations of interest where the observations are measured. Practical geostatistical data are obtained from a wide variety of observational monitoring networks that are mainly operated by the Korean government. When we analyze geostatistical data and predict the expectations at unobservable locations, we can improve the reliability of the prediction by utilizing some relevant spatial data obtained from different observational monitoring networks and blend them with the measurements of our main interest. In this paper, we consider the hierarchical spatial linear model that enables us to link spatial variables from different resources but with similar patterns and guarantee the precision of the prediction. We compare the proposed model to a classical linear regression model and simple kriging in terms of some information criteria and one-leave-out cross-validation. Real application deals with Sulfur Dioxide($SO_2$) measurements from the urban air pollution monitoring network and wind speed data from the surface observation network.

• Proceedings of the Korea Water Resources Association Conference
• /
• 2007.05a
• /
• pp.1081-1083
• /
• 2007

The study in river basin has been performed for the identify water quality impaired stream segments, to create a priority ranking of those segments, and to calculate the heavy metal ion distribution for each impaired segment based on chemical and physical water quality standards. Two methods for modeling the potential area-specific heavy metal distribution are pursued in this study. First, a novel approach focuses on distance. Heavy metal distribution can be associated with a particular small geographic area. Based on the derived estimates an distribution map can be generated. Second, the approach is used the near watershed by means of kriging interpolation algorithm. These approaches provide an alternative distribution mapping of the area. The exposure estimates from both of these modeling methods are then compared with other environmental monitoring data. A GIS-based model will be used to mimic the hierarchical stream structure and processes found in natural watershed. Specifically, the relationship between landscape variables and reach scale habitat conditions most influential found in the Abandoned mine will be explored.