• The Korean Journal of Applied Statistics
• /
• v.24 no.6
• /
• pp.1033-1043
• /
• 2011

In this paper, we use a nested family of models of Generalized Autoregressive Conditional Heteroscedasticity(GARCH) to verify asymmetric conditional heteroscedasticity in the KOSPI and Won-Dollar exchange rate. This study starts from an investigation of whether time series data have asymmetric features not explained by standard GARCH models. First, we use kernel density plot to show the non-normality and asymmetry in data as well as to capture asymmetric conditional heteroscedasticity. Later, we use three representative asymmetric heteroscedastic models, EGARCH(Exponential Garch), GJR-GARCH(Glosten, Jagannathan and Runkle), APARCH(Asymmetric Power Arch) that are improved from standard GARCH models to give a better explanation of asymmetry. Thereby we highlight the fact that volatility tends to respond asymmetrically according to positive and/or negative values of past changes referred to as the leverage effect. Furthermore, it is verified that how the direction of asymmetry is different depending on characteristics of time series data. For the KOSPI and Korean won-US dollar exchange rate, asymmetric heteroscedastic model analysis successfully reveal the leverage effect. We obtained predictive values of conditional volatility and its prediction standard errors by using moving block bootstrap.

• Journal of the Korean Society of Groundwater Environment
• /
• v.1 no.2
• /
• pp.113-120
• /
• 1994

One of the delicate problems in aquifer management is the identification of the spatial distribution of tile hydrological parameters. The observed data are insufficient to identify the distribution of transmissivities in LAN aquifer. To determine the distribution of the transmissivity in LAN aquifer, it would be required to transform the observed heads at the pilot points into transmissivities. Therefore, three procedures wire tackled for the identification of the spatial distribution of the hydrological parameters; geostatistical estimate of the parameter field on the basis of known well point, heads reconstructed by a numerical model, and modification of the values at pilot points by a minimization algorithm. The variogram of Kriging has been applied to a total of 258 transmissivity value in attempt to quantify their distribution of LAN aquifer. Variogram of the observed and optimized transmissivities at pilot points are adapted to the exponential form. So, it is fitted by theoretical one with coefficients of w=0.623, a=2.743. Values of head obtained through numerical analysis are adjusted to the observed values so that heads have been transformed completely into the transmissivities at the observation wells. The procedure represented contour map of the estimated transmissivities and the calculated head.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.17 no.3
• /
• pp.288-296
• /
• 1992

This paper describes an improved 2D QPS(quadrature polar separable) filter design and its applications to texture processing. The filter kernel pair consists of the product of a radial weighting function based on the finite PSS (prolate spheroidal sequences) and an exponential at tenuation function for the orientational angle. It is quadrature and polar separable in the frequency domain. It is near optimal in the energy loss because we let the orientational angle function approximate the radial weighting function. The filter frequency characteristics is easy to control as it depends only upon the design specifications such as the bandwidth, the directional angle, the attenuation constant, and the shift constant of the central frequency. Some applications of the filter in texture processing, such as the generation of the texture image, the estimation of orientation angles, and the segmentations for the synthetic texture image, are considered. The result shows that the filter with the wide bandwidth can be used for the generation of discrimination of the strong orientational textures and the segmentation results are good.

• Korean Journal of Microbiology
• /
• v.28 no.2
• /
• pp.98-103
• /
• 1990

Brevibacterium lactofermentum SWA (arg trp) and B. lactofermentum SWB (met ser) were obtained from UV and NTG treatment. The rates of protoplast formation by B. lactofermentum SWA and SWB were 99.93% and 99.98%, respectively when each strain was treated with penicillin G in mid exponential growth phase, followed by incubation with 400 $\mu\textrm{g}$/ml of lysozyme in lysis fluid supplemented with 0.4M sucrose. Frequencies of protoplast regeneration in B. lactofermentum SWA and B. lactofermentum SWB were 9.27% and 10.32% respectively, on regeneration medium containing 0.5M sodium succinate, 50 mM $Mg^{2+}$, and 3% PVP. In intraspecific protoplast fusion between B. lactofermentum SWA and B. lactofermentum SWB, fusion frequency of $2.30\times 10^{-5}$ was observed by using the 100mM $CaCl_{2}$ and 30% PEG 6,000 in fusion fluid. Relative recombinant frequencies in each marker by means of selective media could be used for genetic analysis.

• Journal of the Korean Institute of Gas
• /
• v.18 no.1
• /
• pp.8-16
• /
• 2014

In this study, the number of gas accidents prediction model was suggested by analyzing the gas accidents occurred in Korea. In order to predict the number of gas accidents, simple moving average method (3, 4, 5 period), weighted average method and exponential smoothing method were applied. Study results of the sum of mean-square error acquired by the models of moving average method for 4 periods and weighted moving average method showed the highest value of 44.4 and 43 respectively. By developing the number of gas accidents prediction model, it could be actively utilized for gas accident prevention activities.

• Korean Journal of Remote Sensing
• /
• v.33 no.4
• /
• pp.423-436
• /
• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• The Korean Journal of Applied Statistics
• /
• v.25 no.2
• /
• pp.311-319
• /
• 2012

The simplest and the most important distribution in survival analysis is exponential distribution. Koziol and Green (1976) derived Cram$\acute{e}$r-von Mises statistic's randomly censored version based on the Kaplan-Meier product limit estimate of the distribution function; however, it could not be practical for a real data set since the statistic is for testing a simple goodness of fit hypothesis. We generalized it to the composite hypothesis for exponentiality with an unknown scale parameter. We also considered the classical Kolmogorov-Smirnov statistic and generalized it by the exact same way. The two statistics are compared through a simulation study. As a result, we can see that the generalized Koziol-Green statistic has better power in most of the alternative distributions considered.

• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.40 no.2
• /
• pp.185-198
• /
• 2016

In this work, experimental errors from an impact hammer test were investigated to develop a statistic-based technique for updating a finite element model. Digital signal processing was analyzed by using theoretical models and experiments when errors occurred during the experimental procedure. First, the duration time and peak level of the excitation signal, the stiffness and position of elastic springs connecting the specimen as well as the support, position and mass of the accelerometer were considered as error factors during the experiment. Then the picket fence effect, leakage, and exponential window function were considered as candidate error factors during the digital signal processing. Finally, methods to reduce errors are suggested.

• Journal of Mushroom
• /
• v.14 no.4
• /
• pp.244-248
• /
• 2016

In order to isolate thermophilic bacteria with high activity of CMCase and xylanase, spent mushroom substrates was collected from an oyster mushroom cultivation farm in Jinju, Gyeongnam, Korea. Among the isolates, one strain designated as YJ09 was selected by agar diffusion method. The isolate YJ09 was identified as a member of Bacillus licheniformis based on biochemical characteristics using Bacillus ID kit and MicroLog system. Comparative 16S rDNA sequence analysis showed that isolate YJ09 formed a distinct phylogenetic tree within the genus Bacillus and was most closely related to Bacillus licheniformis with sequence similarity of 98.9%. Based on its physiological properties, biochemical characteristics and phylogenetic distinctiveness, the isolate YJ09 was classified as Bacillus licheniformis. The CMCase and xylanase activity of B. licheniformis YJ09 was slightly increased corresponding to the bacterial population from exponential phase to stationary phase in the growth curve of B. licheniformis YJ09.

• Journal of Microbiology and Biotechnology
• /
• v.15 no.6
• /
• pp.1267-1272
• /
• 2005

The effects of coexpression of GroEL/ES and DnaK/DnaJ/GrpE chaperones on the productivity of the soluble form of human granulocyte colony stimulating factor (hG-CSF) in E. coli were examined. Recombinant hG-CSF protein was coexpressed with DnaK/DnaJ/GrpE or GroEL/ES chaperones under the control of the araB or Pzt-1 promoter, respectively. The optimal concentration of L-arabinose for the expression of DnaK/DnaJ/GrpE was found to be 1 mg/ml. When L-arabinose was added at $OD_{600}$=0.2 (early-exponential phase), soluble hG-CSF production was greatly increased. In addition, it was observed that the DnaK/DnaJ/GrpE and GroEL/ES chaperones had no synergistic effects on preventing aggregation of hG-CSF protein. Consequently, by coexpression of the DnaK/DnaJ/GrpE chaperone, the signal intensity of the hG-CSF protein band in the soluble fraction of cell lysate was increased from $3.5\%\;to\;13.9\%$, and Western blot analysis also revealed about a 4-5-fold increase of production of soluble hG-CSF over the non-induction case of DnaK/DnaJ/GrpE.