• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.757-765
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• 1999

Lagrangian particle dispersion model(LPDM) is an effective tool to calculate the dispersion from a point source since it dose not induce numerical diffusion errors in solving the pollutant dispersion equation. Fictitious particles are released to the atmosphere from the emission source and they are then transported by the mean velocity and diffused by the turbulent eddy motion in the LPDM. The concentration distribution from the dispersed particles in the calculation domain are finally estimated by applying a particle count method or a Gaussian kernel method. The two methods for calculating concentration profiles were compared each other and tested against the analytic solution and the tracer experiment to find the strength and weakness of each method and to choose computationally time saving method for the LPDM. The calculated concentrations from the particle count method was heavily dependent on the number of the particles released at the emission source. It requires lots fo particle emission to reach the converged concentration field. And resulting concentrations were also dependent on the size of numerical grid. The concentration field by the Gaussian kernel method, however, converged with a low particle emission rate at the source and was in good agreement with the analytic solution and the tracer experiment. The results showed that Gaussian kernel method was more effective method to calculate the concentrations in the LPDM.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.645-652
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• 2023

The aleurone layer in maize is crucial as it contains essential nutrients such as minerals, vitamins, and high-quality proteins. While most of the maize varieties are known to possess a single aleurone layer, several multi-aleurone layer mutants and landraces have been suggested for hierarchical genetic control of aleurone development. Conventional microscopy analysis often involves using immature seeds or sampling only a portion of the kernel sample, and whole kernel section analysis using a microtome is technically difficult and time-consuming. Additionally, the larger size of maize kernels posed challenges for comprehensive cross-sectional analysis compared to other cereal crops. Consequently, this study aimed to develop an efficient method to comprehensively understand the aleurone layer characteristics of the entire cross-section in maize. Through observations of diverse maize genetic resources, we confirmed irregular aleurone layer patterns in those with multiple aleurone layers, and we discovered a landrace having multiple aleurone layers. By selectively identifying genetic resources with multiple aleurone layers, this method may contribute to efficient breeding processes in maize.

• Korean Journal of Remote Sensing
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• v.5 no.1
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• pp.41-56
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• 1989

Improvement of the multichannel sea surface temperature(MCSST) software, which had been developed for the purpose of operating under mainframe computer system, was seeked in order to operate effectively in a mini computer system. CPU time and processing time, which is not a major factor under mainframe computer system, become a critical factor in real time image processing under mini computer system. Due to fixed kernel size(3$\times$4) of the old MCSST software, high spatial resolution characteristics of the original image received from satellites were apparently degraded when images are transformed into a cartesian coordinate system after geometrical distortions of the image due to earth curvature are removed. CPU and processing time were reduced to 0.13 and 0.15~0.22 comparing with the old MCSST's, respectively, by applying disk block I/O and M/T queue I/O method under VAX-11/750 computer. The high resolution quality (1.1km in AVHRR) of the processed image was guaranted using 2$\times$2 kernel size and applying moving window techniques without sacrificing CPU and processing time much.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.25 no.4
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• pp.863-872
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• 2015

Kernel rootkit is recognized as one of the most severe and widespread threats to corrupt the integrity of an operating system. Without an external monitor as a root of trust, it is not easy to detect kernel rootkits which can intercept and modify communications at the interfaces between operating system components. To provide such a monitor isolated from an operating system that can be compromised, most existing solutions are based on external hardware. Unlike those solutions, we develop a kernel introspection system based on the ARM TrustZone technology without incurring extra hardware cost, which can provide a secure memory space in isolation from the rest of the system. We particularly use a secure timer to implement an autonomous switch between secure and non-secure modes. To ensure integrity of reference, this system measured reference from vmlinux which is a kernel original image. In addition, the flexibility of monitoring block size can be configured for efficient kernel introspection system. The experimental results show that a secure kernel introspection system is provided without incurring any significant performance penalty (maximum 6% decrease in execution time compared with the normal operating system).

• Communications for Statistical Applications and Methods
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• v.15 no.5
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• pp.709-717
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• 2008

The difference of two one-sided kernel estimators is usually used to detect the location of the discontinuity points of regression function. The large absolute value of the statistic imply discontinuity of regression function, so we may use the difference of two one-sided kernel estimators as the test statistic for testing null hypothesis of a smooth regression function. The problem is, however, we only know the asymptotic distribution of the test statistic under $H_0$ and we hardly expect the good performance of test if we rely solely on the asymptotic distribution for determining the critical points. In this paper, we show that if we adjust the bias of test statistic properly, the asymptotic rules hold for even small sample size situation.

• Communications for Statistical Applications and Methods
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• v.26 no.6
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• pp.611-622
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• 2019

This paper considers the estimation of the long memory parameter in nonparametric regression with strongly correlated errors. The key idea is to minimize a unified mean squared error of long memory parameter to select both kernel bandwidth and the number of frequencies used in exact local Whittle estimation. A unified mean squared error framework is more natural because it provides both goodness of fit and measure of strong dependence. The block bootstrap is applied to evaluate the mean squared error. Finite sample performance using Monte Carlo simulations shows the closest performance to the oracle. The proposed method outperforms existing methods especially when dependency and sample size increase. The proposed method is also illustreated to the volatility of exchange rate between Korean Won for US dollar.

• Journal of Korean Society for Atmospheric Environment
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• v.14 no.4
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• pp.303-312
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• 1998

To obtain the solution to the time-dependent particle size distribution of an aerosol undergoing gravitational coagulation, the moment method was used which converts the non linear integro-differential equation to a set of ordinary differential equations. A semi-numerical solution was obtained using this method. Subsequently, an analytic solution was given by approximating the collision kernel into a form suitable for the analysis. The results show that during gravitational coagulation, the geometric standard deviation increases and the geometric mean radius decreases as time increases.

• Proceedings of the Korean Society of Crop Science Conference
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• 2006.04a
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• pp.384-385
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• 2006

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.8 no.7
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• pp.2496-2511
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• 2014

Face recognition is considered a complex biometrics in the field of image processing mainly due to the constraints imposed by variation in the appearance of facial images. These variations in appearance are affected by differences in expressions and/or occlusions (sunglasses, scarf etc.). This paper discusses incremental Kernel Fisher Discriminate Analysis on sub-classes for dealing with partial occlusions and variant expressions. This framework focuses on the division of classes into fixed size sub-classes for effective feature extraction. For this purpose, it modifies the traditional Linear Discriminant Analysis into incremental approach in the kernel space. Experiments are performed on AR, ORL, Yale B and MIT-CBCL face databases. The results show a significant improvement in face recognition.

• Journal of Korea Multimedia Society
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• v.16 no.4
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• pp.444-452
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• 2013

Operation time of a function or procedure is a thing that always needs to be optimized. Parallelizing the operation is the general method to reduce the operation time of the function. One of the most powerful parallelizing methods is using GPU. In image processing field, one of the most commonly used operations is morphology operation. Three types of morphology operations kernel, na$\ddot{i}$ve, global and shared, are presented in this paper. All kernels are made using CUDA and work parallel on GPU. Four morphology operations (erosion, dilation, opening, and closing) using square structuring element are tested on MRI images with different size to measure the speedup of the GPU implementation over CPU implementation. The results show that the speedup of dilation is similar for all kernels. However, on erosion, opening, and closing, shared kernel works faster than other kernels.