• Journal of The Korean Astronomical Society
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• v.52 no.5
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• pp.173-180
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• 2019

We present IR flux density measurements, models of the broadband SED, and results of SED modeling for the Pulsar Wind Nebula (PWN) 3C 58. We find that the Herschel flux density seems to be slightly lower than suggested by interpolation of previous measurements in nearby wavebands, implying that there may be multiple electron populations in 3C 58. We model the SED using a simple stationary one-zone and a more realistic time-evolving multi-zone scenario. The latter includes variations of flow properties in the PWN (injected energy, magnetic field, and bulk speed), radiative energy losses, adiabatic expansion, and diffusion, similar to previous PWN models. From the modeling, we find that a PWN age of 2900-5400 yrs is preferred and that there may be excess emission at ${\sim}10^{11}Hz$. The latter may imply multiple populations of electrons in the PWN.

• Interaction and multiscale mechanics
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• v.2 no.4
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• pp.353-374
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• 2009

A new seamless multiscale simulation was developed for coupling the continuum model with its molecular dynamics. Kriging-based Finite Element Method (K-FEM) is employed to model the continuum base of the entire domain, while the molecular dynamics (MD) is confined in a localized domain of interest. In the coupling zone, where the MD domain overlaps the continuum model, the overall Hamiltonian is postulated by contributions from the continuum and the molecular overlays, based on a quartic spline scaling parameter. The displacement compatibility in this coupling zone is then enforced by the Lagrange multiplier technique. A multiple-time-step velocity Verlet algorithm is adopted for its time integration. The validation of the present method is reported through numerical tests of one dimensional atomic lattice. The results reveal that at the continuum/MD interface, the commonly reported spurious waves in the literature are effectively eliminated in this study. In addition, the smoothness of the transition from MD to the continuum can be significantly improved by either increasing the size of the coupling zone or expanding the nodal domain of influence associated with K-FEM.

• Journal of information and communication convergence engineering
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• v.13 no.2
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• pp.105-112
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• 2015

The estimation of missing sensor values is an important problem in sensor network applications, but the existing approaches have some limitations, such as the limitations of application scope and estimation accuracy. Therefore, in this paper, we propose a new estimation model based on a spatial-temporal correlation analysis (STCAM). STCAM can make full use of spatial and temporal correlations and can recognize whether the sensor parameters have a spatial correlation or a temporal correlation, and whether the missing sensor data are continuous. According to the recognition results, STCAM can choose one of the most suitable algorithms from among linear interpolation algorithm of temporal correlation analysis (TCA-LI), multiple regression algorithm of temporal correlation analysis (TCA-MR), spatial correlation analysis (SCA), spatial-temporal correlation analysis (STCA) to estimate the missing sensor data. STCAM was evaluated over Intel lab dataset and a traffic dataset, and the simulation experiment results show that STCAM has good estimation accuracy.

• Journal of Environmental Science International
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• v.22 no.2
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• pp.151-161
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• 2013

The assessment of personal exposure is a critical component in population-based epidemiologic studies of air pollution. This study was conducted to apply and compare the four exposure estimation methods of individual-level to air pollution concentration in a cohort including 2,283 subjects in Gwangyang, Korea. Individual-level exposure of air pollution were estimated using multiple approaches, including average across all monitors, nearest monitor, and spatial interpolation by inverse distance weighting and kriging. The mean concentrations of $PM_{10}$, $NO_2$, $SO_2$, CO, $O_3$ by four exposure estimation methods were slightly different but not significantly different from each other. Cross-validation showed that kriging was more accurate than other exposure estimation methods because kriging has probably predicted individual exposure levels equivalent to residential locations after estimating the parameters of a model according to the spatial surface of air pollution concentration. These data support that spatial interpolation methods may provide better estimates than selecting the value from the nearest monitor and averaging across values from all monitors by reflecting spatial attributes of air pollution on personal level.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.3
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• pp.381-386
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• 2003

Gradually engineering designers are determined based on computer simulations. Modeling of the computer simulation however is too expensive and time consuming in a complicate system. Thus, designers often use approximation models called metamodels, which represent approximately the relations between design and response variables. There arc general metamodels such as response surface model and kriging metamodel. Response surface model is easy to obtain and provides explicit function. but it is not suitable for highly nonlinear and large scaled problems. For complicate case, we may use kriging model that employs an interpolation scheme developed in the fields of spatial statistics and geostatistics. This class of into interpolating model has flexibility to model response data with multiple local extreme. In this study. metamodeling techniques are adopted to carry out the shape optimization of a funnel of Cathode Ray Tube. which finds the shape minimizing the local maximum principal stress Optimum designs using two metamodels are compared and proper metamodel is recommended based on this research.

• Proceedings of the KSME Conference
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• 2001.06c
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• pp.537-542
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• 2001

In recent engineering, the designer has become more and more dependent on computer simulation. But defining exact model using computer simulation is too expensive and time consuming in the complicate systems. Thus, designers often use approximation models, which express the relation between design variables and response variables. These models are called metamodel. In this paper, we introduce one of the metamodel, named Kriging. This model employs an interpolation scheme and is developed in the fields of spatial statistics and geostatistics. This class of interpolating model has flexibility to model response data with multiple local extreme. By reason of this multi modality, we can't use any gradient-based optimization algorithm to find global extreme value of this model. Thus we have to introduce global optimization algorithm. To do this, we introduce DE(Differential Evolution). DE algorithm is developed by Ken Price and Rainer Storn, and it has recently proven to be an efficient method for optimizing real-valued multi-modal objective functions. This algorithm is similar to GA(Genetic Algorithm) in populating points, crossing over, and mutating. But it introduces vector concept in populating process. So it is very simple and easy to use. Finally, we show how we determine Kriging metamodel and find global extreme value through two mathematical examples.

• Geophysics and Geophysical Exploration
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• v.16 no.3
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• pp.139-144
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• 2013

A computer program has been developed to estimate time-domain electromagnetic (EM) responses for a onedimensional model with multiple source and receiver dipoles that are finite in length. The time-domain solution can be obtained by applying an inverse fast Fourier transform (FFT) to frequency-domain fields for efficiency. Frequency-domain responses are first obtained for 10 logarithmically equidistant frequencies per decade, and then cubic spline interpolated to get the FFT input. In the case of phases, the phase curve must be made to be continuous prior to the spline interpolation. The spline interpolated data are convolved with a source current waveform prior to FFT. In this paper, only a step-off waveform is considered. This time-domain code is verified with an analytic solution and EM responses for a marine hydrocarbon reservoir model. Through these comparisons, we can confirm that the accuracy of the developed program is fairly high.

• Journal of Korea Water Resources Association
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• v.43 no.12
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• pp.1083-1091
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• 2010

Distributed hydrologic models typically require spatial estimates of precipitation interpolated from sparsely located observational points to the specific grid points. However, widely used estimation schemes fail to describe the realistic variability of daily precipitation field. We compare and contrast the performance of statistical methods for the spatial estimation of precipitation in two hydrologically different basins, and propose a two-step process for effective daily precipitation estimation. The methods assessed are: (1) Inverse Distance Weighted Average (IDW); (2) Multiple Linear Regression (MLR); (3) Climatological MLR; and (4) Locally Weighted Polynomial Regression (LWP). In the suggested simple two-step estimation process, precipitation occurrence is first generated via a logistic regression model before applying IDW scheme (one of the local scheme) to estimate the amount of precipitation separately on wet days. As the results, the suggested method shows the better performance of daily rainfall interpolation which has spatial differences compared with conventional methods. And this technique can be used for streamflow forecasting and downscaling of atmospheric circulation model effectively.

• Journal of Urban Science
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• v.9 no.2
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• pp.31-44
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• 2020

This study aims to analyze the relations among greenbelt, air pollution empirically in order to assess the environmental effects of the greenbelt in the Seoul metropolitan area, objectively. For this purpose, this study conducts an empirical analysis of impacts of greenbelt on urban air pollution using a multiple-regression model. The major findings are summarized as follows. As a result of an empirical analysis of the impacts of greenbelt on air pollution, it is found that the characteristics of the city have impacts on air pollution concentration. It is found that the population and employment are the causes of increases in CO and NO2 concentrations, and the number of employees in the manufacturers has impacts on increases of O3 and SO2, while power plants have impacts on PM10, CO and NO2. Intersections have impacts on O3 and SO2, while the areas of the roads have impacts on CO and NO2. In addition, as for the spatial distribution of air pollutants, it is found that CO and NO2 concentrations are relatively higher in the center of the Seoul metropolitan area, while PM10, O3 and SO2 concentrations are relatively higher in the suburbs. It is found that air pollution concentration is low in greenbelt zone. In the greenbelt zone, PM10, CO and SO2 concentrations are low.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.197-200
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• 2005

Super resolution image reconstruction method refers to image processing algorithms that produce a high resolution(HR) image from observed several low resolution(LR) images of the same scene. This method is proved to be useful in many practical cases where multiple frames of the same scene can be obtained, such as satellite imaging, video surveillance, video enhancement and restoration, digital mosaicking, and medical imaging. In this paper we applied super resolution reconstruction method in spatial domain to video sequences. Test images are adjacently sampled images from continuous video sequences and overlapped for high rate. We constructed the observation model between the HR images and LR images applied by the Maximum A Posteriori(MAP) reconstruction method that is one of the major methods in the super resolution grid construction. Based on this method, we reconstructed high resolution images from low resolution images and compared the results with those from other known interpolation methods.