• The Bulletin of The Korean Astronomical Society
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• v.46 no.2
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• pp.70.1-70.1
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• 2021

We examine the HI gas kinematics and distributions of galaxy pairs in group or cluster environments from high-resolution Australian Square Kilometer Array Pathfinder (ASKAP) WALLABY pilot observations. We use 32 well-resolved close pair galaxies from the Hydra, Norma, and NGC 4636, two clusters and a group of which are identified by their spectroscopy information and additional visual inspection. We perform profile decomposition of HI velocity profiles of the galaxies using a new tool, BAYGAUD which allows us to separate a line-of-sight velocity profile into an optimal number of Gaussian components based on Bayesian MCMC techniques. Then, we construct super profiles via stacking of individual HI velocity profiles after aligning their central velocities. We fit a model which consists of double Gaussian components to the super profiles, and classify them as kinematically cold and warm HI gas components with respect to their velocity dispersions, narrower or wider 𝜎, respectively. The kinematically cold HI gas reservoir (M_cold/M_HI) of the paired galaxies is found to be relatively higher than that of unpaired control samples in the clusters and the group, showing a positive correlation with the HI mass in general. Additionally, we quantify the gravitational instability of the HI gas disk of the sample galaxies using their Toomre Q parameters and HI morphological disturbances. While no significant difference is found for the Q parameter values between the paired and unpaired galaxies, the paired galaxies tend to have larger HI asymmetry values which are derived using their moment0 map compared to those of the non-paired control sample galaxies in the distribution.

• Korean Journal of Optics and Photonics
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• v.4 no.1
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• pp.28-35
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• 1993

In the case of an imaging system affected by aberrations which are not precisely known, the effect of aberrations can be minimized and near-diffraction-limited images can be restored by introducing artificial random phase fluctuations in the exit pupil of the imaging system and using bispectral speckle imaging. In order to determine the optimum value of the correlation length for Gaussian random phase model, computer simulation is performed for 50 image frames of a point object in the presence of defocus, spherical aberration, coma, astigmatism of 1 wave, respectively. In terms of the criterion of performance, the FWHM of the point spread function, normalized peak intensity, MTF and visual inspection of the restored object are employed. The optimum value for the rms difference $\sigma$ of aberration on the exit pupil in the interval of Fried parameter ${\Upsilon}_0$ is given by 0.27-0.53 wave for spherical aberration, and 0.24-0.36 wave for defocus and astigmatism, respectively. It is found that the bispectral speckle imaging technique does not give good results in the case of coma.

• Journal of the Korean Geotechnical Society
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• v.24 no.8
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• pp.111-123
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• 2008

Geotechnical engineering problems are characterized by many sources of uncertainty. Some of these sources are connected to the uncertainties of soil properties involved in the analysis. In this paper, a numerical procedure for a probabilistic analysis that considers the spatial variability of soil properties is presented to study the response of spatially random soil. The approach integrates a commercial finite difference method and random field theory into the framework of a probabilistic analysis. Two-dimensional non-Gaussian random fields are generated based on a Karhunen-$Lo{\grave{e}}ve$ expansion in a fashion consistent with a specified marginal distribution function and an autocorrelation function. A Monte Carlo simulation is then used to determine the statistical response based on the random fields. A series of analyses were performed to study the effects of uncertainty due to the spatial heterogeneity on the settlement and bearing capacity of a rough strip footing. The simulations provide insight into the application of uncertainty treatment to the geotechnical problem and show the importance of the spatial variability of soil properties with regard to the outcome of a probabilistic assessment.

• Korean Journal of Remote Sensing
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• v.28 no.3
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• pp.307-318
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• 2012

To understand forest structures, the Geoscience Laser Altimeter System (GLAS) instrument have been employed to measure and monitor forest canopy with feasibility of acquiring three dimensional canopy structure information. This study tried to examine the potential of GLAS dataset in measuring forest canopy structures, particularly maximum canopy height estimation. To estimate maximum canopy height using feasible GLAS dataset, we simply used difference between signal start and ground peak derived from Gaussian decomposition method. After estimation procedure, maximum canopy height was derived from airborne Light Detection and Ranging (LiDAR) data and it was applied to evaluate the accuracy of that of GLAS estimation. In addition, several influences, such as topographical and biophysical factors, were analyzed and discussed to explain error sources of direct maximum canopy height estimation using GLAS data. In the result of estimation using direct method, a root mean square error (RMSE) was estimated at 8.15 m. The estimation tended to be overestimated when comparing to derivations of airborne LiDAR. According to the result of error occurrences analysis, we need to consider these error sources, particularly terrain slope within GLAS footprint, and to apply statistical regression approach based on various parameters from a Gaussian decomposition for accurate and reliable maximum canopy height estimation.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.41 no.2
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• pp.56-64
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• 2018

In recent years, imbalanced data is one of the most important and frequent issue for quality control in industrial field. As an example, defect rate has been drastically reduced thanks to highly developed technology and quality management, so that only few defective data can be obtained from production process. Therefore, quality classification should be performed under the condition that one class (defective dataset) is even smaller than the other class (good dataset). However, traditional multi-class classification methods are not appropriate to deal with such an imbalanced dataset, since they classify data from the difference between one class and the others that can hardly be found in imbalanced datasets. Thus, one-class classification that thoroughly learns patterns of target class is more suitable for imbalanced dataset since it only focuses on data in a target class. So far, several one-class classification methods such as one-class support vector machine, neural network and decision tree there have been suggested. One-class support vector machine and neural network can guarantee good classification rate, and decision tree can provide a set of rules that can be clearly interpreted. However, the classifiers obtained from the former two methods consist of complex mathematical functions and cannot be easily understood by users. In case of decision tree, the criterion for rule generation is ambiguous. Therefore, as an alternative, a new one-class classifier using hyper-rectangles was proposed, which performs precise classification compared to other methods and generates rules clearly understood by users as well. In this paper, we suggest an approach for improving the limitations of those previous one-class classification algorithms. Specifically, the suggested approach produces more improved one-class classifier using hyper-rectangles generated by using Gaussian function. The performance of the suggested algorithm is verified by a numerical experiment, which uses several datasets in UCI machine learning repository.

• Journal of the Institute of Convergence Signal Processing
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• v.20 no.3
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• pp.125-131
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• 2019

Through the 4th Industrial Revolution, various digital equipments are being distributed, and accordingly, the importance of data processing is increasing. As data processing has a great effect on the reliability of equipment, its importance is increasing, and various studies are being conducted. In this paper, we propose an algorithm to remove AWGN in consideration of the noise in the image. The proposed algorithm is used in the filtering process by inferring the standard deviation of the image noise. The noise is removed by dividing the filter for the high frequency component and the filter for the low frequency component compared with the standard deviation of the filtering mask. The proposed algorithm is simulated with the existing methods for evaluation and compared and analyzed by difference image, PSNR and profile. The proposed algorithm minimizes the effect of noise and preserves the important characteristics of the image and shows the performance of efficient noise removal.

• Korean Journal of Remote Sensing
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• v.19 no.6
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• pp.465-478
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• 2003

This paper presents two methods for automatic estimation of threshold values in unsupervised change detection of multi-temporal remote sensing images. The proposed methods consist of two analytical steps. The first step is to compute the parameters of a 3-component Gaussian mixture model from difference or ratio images. The second step is to determine a threshold value using Bayesian rule for minimum error. The first method which is an extended version of Bruzzone and Prieto' method (2000) is to apply an Expectation-Maximization algorithm for estimation of the parameters of the Gaussian mixture model. The second method is based on an iterative thresholding algorithm that successively employs thresholding and estimation of the model parameters. The effectiveness and applicability of the methods proposed here were illustrated by two experiments and one case study including the synthetic data sets and KOMPSAT-1 EOC images. The experiments demonstrate that the proposed methods can effectively estimate the model parameters and the threshold value determined shows the minimum overall error.

• Journal of the Institute of Electronics Engineers of Korea SD
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• v.37 no.8
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• pp.76-85
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• 2000

Effects of the random fluctuation in grating half-period have been studied by an effective index transfer matrix method in DFB lasers. The laser facets are assumed to be perfectly antireflection coated, and the period fluctuation is modeled as a Gaussian random variable. The random fluctuation breaks spectral symmetry in both uniform-grating and quarter-wavelength -shifted(QWS) DFB lasers, and decreases the effective coupling coefficient. This leads to increased average mirror loss of ${\pm}$1 modes and reduced stopband width in uniform grating DFB lasers, and degradation in the wavelength accuracy and the single mode stability in QWS-DFB lasers. Threshold gain difference decreases with increasing period fluctuation irrespective of grating coupling coefficient in QWS-DFB lasers, while spatial hole-burning effect is exacerbated or alleviated when the normalized coupling coefficient is lower and higher than 1.5, respectively.

• Korean Journal of Optics and Photonics
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• v.13 no.5
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• pp.400-407
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• 2002

Two dimensional solitary waves are stably propagated in a saturable medium which has a saturable nonlinear index as input intensity. However, in the case of low intensity. a negative fifth-order nonlinear medium has properties of a saturable medium. So a Gaussian beam travels stably. The propagation process into the fifth order nonlinear medium of the Gaussian beam with a weak intensity is investigated by using the computer simulation of the two-dimensional nonlinear Schrodinger equation. As a result, it is confirmed that the two-dimensional spatial solitary waves are stably propagated in this medium when the incident powers are self-trapping powers. In the condition of the phase difference and collisional angle between two input beams of 180 degree and 0.05 degree, respectively, we can confirm that all optical switching is as simple as controlling the incident power of one input beam.

• Journal of the Institute of Electronics Engineers of Korea TC
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• v.45 no.8
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• pp.75-81
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• 2008

The authors present a more accurate analysis of the bit error rate (BER) performance for multicode code division multiple access (CDMA) systems equipped with a multipath interference canceler (MPIC) over multipath fading channels. At first, we consider the problem of previous researches. It is that the previous analyses do not match with simulations even in the case without MPIC. This mismatch also causes the difference between the analysis and simulations when an MPIC is applied. Therefore, what is needed is to verify the reason of this mismatch at the initial stage. Numerical results obtained from the proposed analytical method indicate that the performance mismatch between the previous analysis and simulations is caused by not fie Gaussian approximation which is generally used at the performance analysis of CDMA systems but the additional Gaussian approximation (AGA) for multipath interferences. Based on this effect, we propose a more accurate performance analysis not using AGA and its accuracy is verified by simulations. Furthermore, it is confirmed that the proposed analysis presents an analytical lower bound which can be achieved by MPIC.