• Smart Media Journal
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• v.11 no.11
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• pp.84-91
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• 2022

Baseboard Management Controller(BMC) supports server monitoring, maintenance, and control functions using various communication interfaces. However, if an unexpected problem occurs during the device driver initialization process, the BMC may not operate normally. Therefore, a boot process profiling tool that accurately analyzes the device driver initialization process and provides a function to check the analysis result is essential. Existing boot process profiling tools do not specifically provide the device driver initialization process and results required for BMC boot process analysis, forcing developers to use a combination of tools to analyze the boot process in detail. In this paper, we propose an integrated profiling tool for BMC's booting process. The proposed tool provides device driver initialization process analysis, CPU and memory usage analysis, and kernel version management functions. Users can easily analyze the booting process using the proposed tool, and the analysis result can be used to shorten the booting time. Also, the proposed tool is implemented in Linux-based BMC, and it is shown that the proposed tool is more efficient than the existing profiling tool.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.46 no.3
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• pp.96-104
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• 2009

In this paper, we propose a no-reference objective Quality assessment metrics of image. The blockiness and blurring of edge areas which are sensitive to the human visual system are modeled as step functions. Blocking and blur metrics are obtained by estimating local visibility of blockiness and edge width, For the blocking metric, horizontal and vertical blocking lines are first determined by accumulating weighted differences of adjacent pixels and then the local visibility of blockiness at the intersection of blocking lines is obtained from the total difference of amplitudes of the 2-D step function which is modelled as a blocking region. The blurred input image is first re-blurred by a Gaussian blur kernel and an edge mask image is generated. In edge blocks, the local edge width is calculated from four directional projections (horizontal, vertical and two diagonal directions) using local extrema positions. In addition, the kurtosis and SSIM are used to compute the blur metric. The final no-reference objective metric is computed after those values are combined using an appropriate function. Experimental results show that the proposed objective metrics are highly correlated to the subjective data.

• Journal of Korea Water Resources Association
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• v.34 no.4
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• pp.303-316
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• 2001

In this study, Radial Basis Function(RBF) Neural Networks Model, a kind of Hybrid Neural Networks was applied to hydrological forecasting in a small watershed. RBF Neural Networks Model has four kinds of parameters in it and consists of unsupervised and supervised training patterns. And Gaussian Kernel Function(GKF) was used among many kinds of Radial Basis Functions(RBFs). K-Means clustering algorithm was applied to optimize centers and widths which ate the parameters of GKF. The parameters of RBF Neural Networks Model such as centers, widths weights and biases were determined by the training procedures of RBF Neural Networks Model. And, with these parameters the validation procedures of RBF Neural Networks Model were carried out. RBF Neural Networks Model was applied to Wi-Stream basin which is one of the IHP Representative basins in South Korea. 10 rainfall events were selected for training and validation of RBF Neural Networks Model. The results of RBF Neural Networks Model were compared with those of Elman Neural Networks(ENN) Model. ENN Model is composed of One Step Secant BackPropagation(OSSBP) and Resilient BackPropagation(RBP) algorithms. RBF Neural Networks shows better results than ENN Model. RBF Neural Networks Model spent less time for the training of model and can be easily used by the hydrologists with little background knowledge of RBF Neural Networks Model.

• Korean Journal of Breeding Science
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• v.42 no.1
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• pp.50-55
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• 2010

The R-r:standard (R-r:std) allele of maize R gene complex consists of S subcomplex and P component; the S subcomplex regulates anthocyanin pigmentation of seed aleurone layer, and the P component confers pigmentation of the other plant parts. The S subcomplex contains two functional genes, S1 and S2 components. In the presence of Pl gene some alleles of R gene induce anthocyanin pigmentation of pericarp. In the present study, the effects of different R alleles on the anthocyanin pigmentation of pericarp in the presence of Pl gene were analyzed in order to identify the R gene component responsible for pericarp pigmentation. The results show that R-ch and r-ch alleles condition similar degrees of pericarp pigmentation, and that R-r:Ecuador (R-r:Ec) conditions stronger pigmentation. The r-ch allele, which is inferred that its S subcomplex has lost function but the P component is normal, induces pericarp pigmentation in the presence of Pl gene. On the contrary, the R-g:g1111 allele, derived from R-r:Ec and inferred that its S subcomplex functions normal but the P component has lost its function, did not induce pericarp pigmentation in the presence of Pl gene. Moreover, PCR analysis of genomic DNA's of R-ch and r-ch indicate that R-ch maintains both P and S1 components, whereas r-ch lacks for the S1 component. Taken together, The results suggest that the P components of R alleles inducing pericarp pigmentation in the presence of Pl gene are responsible for pericarp pigmentation.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.28 no.1B
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• pp.95-109
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• 2008

We numerically analyzed the nonlinear shoaling, a plunging breaker and its accompanying energetic suspension of sediment at a bed, and a redistribution of suspended sediments by a down rush of preceding waves and the following plunger using SPH with a Gaussian kernel function, Lagrangian Dynamic Smagorinsky model (LDS), Van Rijn's pick up function. In that process, we came to the conclusion that the conventional model for the tractive force at a bottom like a quadratic law can not accurately describe the rapidly accelerating flow over a swash zone, and propose new methodology to accurately estimate the bottom tractive force. Using newly proposed wave model in this study, we can successfully duplicate severely deformed water surface profile, free falling water particles, a queuing splash after the landing of water particles on the free surface and a wave finger due to the structured vortex on a rear side of wave crest (Narayanaswamy and Dalrymple, 2002), a circulation of suspended sediments over a swash zone, net transfer of sediments clouds suspended over a swash zone toward the offshore, which so far have been regarded very difficult features to mimic in the computational fluid mechanics.

• Food Science and Preservation
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• v.22 no.5
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• pp.652-659
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• 2015

The physical properties of wheat kernels were determined as a function of moisture content to optimize the design of equipment for post-harvest management. Several properties, including bulk density, dynamic repose angle, one thousand-kernel weight, specific gravity, length, width, thickness, and area of two wheat cultivars (Baekjjung: B and Jogyeong: J ), were studied in the moisture range from approximately 9% to 30% wet basis. As moisture increased, length increased linearly from 6.42 to 7.20 mm (B) and 8.71 to 9.15 mm (J), width increased from 2.90 to 3.49 mm (B) and 4.12 to 4.43 mm (J), thickness from 2.94 to 3.20 mm (B) and 3.29 to 3.63 mm (J), and area from 14.13 to $19.44mm^2$ (B) and 27.75 to $31.25mm^2$ (J). Additionally, the dynamic repose angle and one thousand-kernel weight increased linearly from $46.3^{\circ}$ to $54.0^{\circ}$ (B) and $46.3^{\circ}$ to $54.5^{\circ}$ (J) and from 32.26 to 41.51 g (B) and 45.30 to 63.07 g (J), respectively, as the moisture content increased. Based on the experimental measurements, only the bulk density and specific gravity decreased from 754.0 to $664.1kgm^{-3}$ (B) and 776.1 to $660.0kgm^{-3}$ (J) and from 1.2950 to 1.2265 (B) and 1.3379 to 1.2671 (J), respectively, as moisture content increased.

• The Korean Journal of Financial Management
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• v.20 no.2
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• pp.73-94
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• 2003

In general, the interest rate is forecasted by the parametric method which assumes the interest rate follows a certain distribution. However the method has a shortcoming that forecasting ability would decline when the interest rate does not follow the assumed distribution for the stochastic behavior of interest rate. Therefore, the nonparametric method which assumes no particular distribution is regarded as a superior one. This paper compares the interest rate forecasting ability between the two method for the Monetary Stabilization Bond (MSB) market in Korea. The daily and weekly data of the MSB are used during the period of August 9th 1999 to February 7th 2003. In the parametric method, the drift term of the interest rate process shows the linearity while the diffusion term presents non-linear decline. Meanwhile in the nonparametric method, both drift and diffusion terms show the radical change with nonlinearity. The parametric and nonparametric methods present a significant difference in the market price of interest rate risk. This means in forecasting the interest rate and the market price of interest rate risk, the nonparametric method is more appropriate than the parametric method.

• Journal of the Institute of Electronics Engineers of Korea SC
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• v.48 no.2
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• pp.47-55
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• 2011

Hemorrhagic shock is a cause of one third of death resulting from injury in the world. Early diagnosis of hemorrhagic shock makes it possible for physician to treat successfully. The objective of this paper was to select an optimal classifier model using physiological signals from rats measured during hemorrhagic experiment. This data set was used to train and predict survival rate using artificial neural network (ANN) and support vector machine (SVM). To avoid over-fitting, we chose the best classifier according to performance measured by a 10-fold cross validation method. As a result, we selected ANN having three hidden nodes with one hidden layer and SVM with Gaussian kernel function as trained prediction model, and the ANN showed 88.9 % of sensitivity, 96.7 % of specificity, 92.0 % of accuracy and the SVM provided 97.8 % of sensitivity, 95.0 % of specificity, 96.7 % of accuracy. Therefore, SVM was better than ANN for survival prediction.

• Journal of the Korean Data and Information Science Society
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• v.25 no.2
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• pp.271-280
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• 2014

Foot-and-mouth Disease (FMD) is a highly infectious and fatal viral livestock disease that affects cloven-hoofed animals domestic and wild and the FMD outbreak in Korea in 2010/2011 was a disastrous incident for the country and the economy. Thus, efforts at the national level are put to prevent foot-and-mouth disease and to reduce the damage in the case of outbreak. As one of these efforts, it is useful to study the spread of the disease by using probabilistic model. In fact, after the FMD epidemic in the UK occurred in 2001, many studies have been carried on the spread of the disease using a variety of stochastic models as an effort to prepare future outbreak of FMD. However, for the FMD outbreak in Korea occurred in 2010/2011, there are few study by utilizing probabilistic model. This paper assumes a stochastic spatial-temporal susceptible-infectious-removed (SIR) epidemic model for the 2010/2011 FMD outbreak to understand spread of the disease. Since data on infections of FMD disease during 2010/2011 outbreak of Aniaml and Plant Quarantine Agency and on the livestock farms from the nationwide census in 2011 of Statistics Korea do not have detail informations on address or missing values, we generate detail information on address by randomly allocating farms within corresponding Si/Gun area. The kernel function is estimated using the infection data and by using simulations, the susceptibility and transmission of the spatial-temporal stochastic SIR models are determined.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.3
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• pp.14-28
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• 2010

The aim of this study is to investigate crime hotspot areas using the spatio-temporal cluster analysis which is possible to search simultaneously time range as well as space range as an alternative method of existing hotspot analysis only identifying crime occurrence distribution patterns in urban area. As for research method, first, crime data were collected from criminal registers provided by official police authority in M city, Gyeongnam and crime occurrence patterns were drafted on a map by using Geographic Information Systems(GIS). Second, by utilizing Ripley K-function and Space-Time Scan Statistics analysis, the spatio-temporal distribution of crime was examined. The results showed that the risk of crime was significantly clustered at relatively few places and the spatio-temporal clustered areas of crime were different from those predicted by existing spatial hotspot analysis such as kernel density analysis and k-means clustering analysis. Finally, it is expected that the results of this study can be not only utilized as a valuable reference data for establishing urban planning and crime prevention through environmental design(CPTED), but also made available for the allocation of police resources and the improvement of public security services.