• International Journal of Aeronautical and Space Sciences
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• v.8 no.2
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• pp.28-36
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• 2007

A multi-objective design exploration for a three-element airfoil consisted of a slat, a main wing, and a flap was carried out. The lift curve improvement is important to design high-lift system, thus design has to be performed with considered multi-angle. The objective functions considered here are to maximize the lift coefficient at landing and near stall conditions simultaneously. Kriging surrogate model which was constructed based on several sample designs is introduced. The solution space was explored based on the maximization of Expected Improvement (EI) value corresponding to objective functions on the Krigingmodels. The improvement of the model and the exploration of the optimum can be advanced at the same time by maximizing EI value. In this study, a total of 90 sample points are evaluated using the Reynolds averaged Navier-Stokes simulation(RANS) for the construction of the Kriging model. In order to obtain the information of the design space, two data mining techniques are applied to design result. One is functional Analysis of Variance(ANOVA) which can show quantitative information and the other is Self-Organizing Map(SOM) which can show qualitative information.

• Tunnel and Underground Space
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• v.19 no.5
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• pp.407-420
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• 2009

In this study geostatistical technique using indicator kriging was performed to evaluate the optimal rock mass classification by integrating the various geophysical information such as borehole data and geophysical data. To get the optimal kriging result, it is necessary to devise the suitable technique to integrate the hard (borehole) and soft (geophysical) data effectively. Also, the model parameters of the variogram must be determined as a priori procedure. Iterative non-linear inversion method was implemented to determine the model parameters of theoretical variogram. To verify the algorithm, behaviour of object function and precision of convergence were investigated, revealing that gradient of the range is extremely small. This algorithm for the field data was applied to a mountainous area planned for a large-scale tunneling construction. As for a soft data, resistivity information from AMT survey is incorporated with RMR information from borehole data, a sort of hard data. Finally, RMR profiles were constructed and attempted to be interpreted at the tunnel elevation and the upper 1D level.

• The Korean Journal of Applied Statistics
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• v.22 no.2
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• pp.425-433
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• 2009

Spatial models suitable for describing the evolving random fields in climate and environmental systems have been developed by many researchers. In general, rainfall in South Korea is highly variable in intensity and amount across space. This study characterizes the monthly and regional variation of rainfall fields using the spatial modeling. The main objective of this research is spatial prediction with the Bayesian hierarchical modeling (kriging) in order to further our understanding of water resources over space. We use the Bayesian approach in order to estimate the parameters and produce more reliable prediction. The Bayesian kriging also provides a promising solution for analyzing and predicting rainfall data.

• Journal of The Korean Society of Agricultural Engineers
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• v.53 no.5
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• pp.1-7
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• 2011

The objective of this study is to decide a spatial interpolation method on temperature data for the suitability analysis of garlic cultivation. In Korea, garlic is the second most cultivated condiment vegetable after red pepper. Nowadays warm-temperate garlic faces potential shift of its arable area according to warmer temperature in the Korean Peninsula, and the change can be drawn with the precise temperature map derived from interpolation on point-measured data. To find the preferable interpolation method in cases of germination and vegetative period of the garlic, different approaches were tested as follows: Inverse Distance Weighted (IDW), Spline, Ordinary Kriging (OK), and Universal Kriging (UK). As a result, IDW and UK show the lowest root mean square errors as for the germination and vegetative seasons, respectively. However, statistically significant difference was not revealed among the applied methods regarding the germinating period. Eventually this will contribute to mapping the suitable lands for the cultivation of warm-temperate garlic reasonably.

• International Journal of Aeronautical and Space Sciences
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• v.7 no.1
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• pp.1-12
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• 2006

In this study, a surrogate model is applied to multi-objective aerodynamic optimization design. For the balanced exploration and exploitation, each objective function is converted into the Expected Improvement (EI) and this value is used as fitness value in the multi-objective optimization instead of the objective function itself. Among the non-dominated solutions about EIs, additional sample points for the update of the Kriging model are selected. The present method was applied to a transonic airfoil design. Design results showed the validity of the present method. In order to obtain the information about design space, two data mining techniques are applied to design results: Analysis of Variance (ANOVA) and the Self-Organizing Map (SOM).

• Korean Journal of Remote Sensing
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• v.26 no.1
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• pp.29-38
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• 2010

It is necessary to develop an integration model which can account for various data acquired at different measurement scales in environmental thematic mapping with high-resolution ground survey data and relatively low-resolution remote sensing data. This paper presents and applies a multi-scale geostatistical methodology for downscaling of thematic maps generated from lowresolution remote sensing data. This methodology extends a traditional ordinary kriging system to a block kriging system which can account for both ground data and remote sensing data which can be regarded as point and block data, respectively. In addition, stochastic simulation based on block kriging is also applied to describe spatial uncertainty attached to the downscaling. Two downscaling experiments including SRTM DEM and MODIS Leaf Area Index (LAI) products were carried out to illustrate the applicability of the geostatistical methodology. Through the experiments, multi-scale geostatistics based on block kriging successfully generated relatively high-resolution thematic maps with reliable accuracy. Especially, it is expected that multiple realizations generated from simulation would be effectively used as input data for investigating the effects of uncertain input data on GIS model outputs.

• International Journal of Computer Science & Network Security
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• v.22 no.7
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• pp.91-102
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• 2022

With the evolution of wireless sensor network (WSN) technology, the routing policy has foremost importance in the Internet of Things (IoT). A systematic routing policy is one of the primary mechanics to make certain the precise and robust transmission of wireless sensor networks in an energy-efficient manner. In an IoT environment, WSN is utilized for controlling services concerning data like, data gathering, sensing and transmission. With the advantages of IoT potentialities, the traditional routing in a WSN are augmented with decision-making in an energy efficient manner to concur finer optimization. In this paper, we study how to combine IoT-based deep learning classifier with routing called, Kriging Regressive Deep Belief Neural Learning (KR-DBNL) to propose an efficient data packet routing to cope with scalability issues and therefore ensure robust data packet transmission. The KR-DBNL method includes four layers, namely input layer, two hidden layers and one output layer for performing data transmission between source and destination sensor node. Initially, the KR-DBNL method acquires the patient data from different location. Followed by which, the input layer transmits sensor nodes to first hidden layer where analysis of energy consumption, bandwidth consumption and light intensity are made using kriging regression function to perform classification. According to classified results, sensor nodes are classified into higher performance and lower performance sensor nodes. The higher performance sensor nodes are then transmitted to second hidden layer. Here high performance sensor nodes neighbouring sensor with higher signal strength and frequency are selected and sent to the output layer where the actual data packet transmission is performed. Experimental evaluation is carried out on factors such as energy consumption, packet delivery ratio, packet loss rate and end-to-end delay with respect to number of patient data packets and sensor nodes.

• Journal of Korea Water Resources Association
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• v.39 no.8 s.169
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• pp.669-676
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• 2006

Hydrological factors, especially the spatial distribution of interpretation on precipitation is often topic of interest in studying of water resource. The popular methods such as Thiessen method, inverse distance method, and isohyetal method are limited in calculating the spatial continuity and geographical characteristics. This study was intended to overcome those limitations with improved method that will yield higher accuracy. The monthly and yearly precipitation data were produced and compared with the observed daily precipitation to find correlation between them. They were then used as secondary variables in Co-kriging method, and the result was compared with the outcome of existing methods like inverse distance method and kriging method. The comparison of the data showed that the daily precipitation had high correlation with corresponding year's average monthly amounts of precipitation and the observed average monthly amounts of precipitation. Then the result from the application of these data for a Co-kriging method confirmed increased accuracy in the modeling of spatial distribution of precipitation, thus indirectly reducing inconsistency of the spatial distribution of hydrological factors other than precipitation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.4A
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• pp.677-687
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• 2006

Kriging interpolation is one of the generally used interpolation techniques in Geostatistics field. This technique includes the experimental and theoretical variograms and the formulation of kriging interpolation. In contrast to the conventional least square method for stress recovery, kriging interpolation is based on the weighted least square method to obtain the estimated exact solution from the stress data at the Gauss points. The weight factor is determined by variogram modeling for interpolation of stress data apart from the conventional interpolation methods that use an equal weight factor. In addition to this, the p-level is increased non-uniformly or selectively through a posteriori error estimation based on SPR (superconvergent patch recovery) technique, proposed by Zienkiewicz and Zhu, by auto mesh p-refinement. The cut-out plate problem under tension has been tested to validate this approach. It also provides validity of kriging interpolation through comparing to existing least square method.

• Journal of Information Processing Systems
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• v.11 no.4
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• pp.556-572
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• 2015

This paper presents the applications of spatial interpolation and assimilation methods for satellite and ground meteorological data, including temperature, relative humidity, and precipitation in regions of Vietnam. In this work, Universal Kriging is used for spatially interpolating ground data and its interpolated results are assimilated with corresponding satellite data to anticipate better gridded data. The input meteorological data was collected from 98 ground weather stations located all over Vietnam; whereas, the satellite data consists of the MODIS Atmospheric Profiles product (MOD07), the ASTER Global Digital Elevation Map (ASTER DEM), and the Tropical Rainfall Measuring Mission (TRMM) in six years. The outputs are gridded fields of temperature, relative humidity, and precipitation. The empirical results were evaluated by using the Root mean square error (RMSE) and the mean percent error (MPE), which illustrate that Universal Kriging interpolation obtains higher accuracy than other forms of Kriging; whereas, the assimilation for precipitation gradually reduces RMSE and significantly MPE. It also reveals that the accuracy of temperature and humidity when employing assimilation that is not significantly improved because of low MODIS retrieval due to cloud contamination.