• Journal of Information Processing Systems
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• v.17 no.6
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• pp.1170-1178
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• 2021

This paper proposed a versatile algorithm based on a dual-tree complex wavelet transform for intensifying the visual aspect of medical images. First, the decomposition of the input image into a high sub-band and low-sub-band image is done. Further, to improve the resolution of the resulting image, the high sub-band image is interpolated using Lanczos interpolation. Also, contrast enhancement is performed by singular value decomposition (SVD). Finally, the image reconstruction is achieved by using an inverse wavelet transform. Then, the Gaussian filter will improve the visual quality of the image. We have collected images from the hospital and the internet for quantitative and qualitative analysis. These images act as a reference image for comparing the effectiveness of the proposed algorithm with the existing state-of-the-art. We have divided the proposed algorithm into several stages: preprocessing, contrast enhancement, resolution enhancement, and visual quality enhancement. Both analyses show the proposed algorithm's effectiveness compared to existing methods.

• The KIPS Transactions:PartB
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• v.10B no.6
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• pp.657-664
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• 2003

Reliable tracking of moving humans is essential to motion estimation, video surveillance and human-computer interface. This paper presents a new approach to human motion tracking that combines appearance-based and model-based techniques. Monocular color video is processed at both pixel level and object level. At the pixel level, a Gaussian mixture model is used to train and classily individual pixel colors. At the object level, a 3D human body model projected on a 2D image plane is used to fit the image data. Our method does not use inverse kinematics due to the singularity problem. While many others use stochastic sampling for model-based motion tracking, our method is purely dependent on nonlinear programming. We convert the human motion tracking problem into a nonlinear programming problem. A cost function for parameter optimization is used to estimate the degree of the overlapping between the foreground input image silhouette and a projected 3D model body silhouette. The overlapping is computed using computational geometry by converting a set of pixels from the image domain to a polygon in the real projection plane domain. Our method is used to recognize various human motions. Motion tracking results from video sequences are very encouraging.

• The Korean Journal of Applied Statistics
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• v.26 no.3
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• pp.483-494
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• 2013

The importance of financial risk management has been highlighted after several recent incidences of global financial crisis. One of the issues in financial risk management is how to measure the risk; currently, the most widely used risk measure is the Value at Risk(VaR). We can consider to estimate VaR using extreme value theory if the financial data have heavy tails as the recent market trend. In this paper, we study estimations of VaR using Peaks over Threshold(POT), which is a common method of modeling fat-tailed data using extreme value theory. To use POT, we first estimate parameters of the Generalized Pareto Distribution(GPD). Here, we compare three different methods of estimating parameters of GPD by comparing the performance of the estimated VaR based on KOSPI 5 minute-data. In addition, we simulate data from normal inverse Gaussian distributions and examine two parameter estimation methods of GPD. We find that the recent methods of parameter estimation of GPD work better than the maximum likelihood estimation when the kurtosis of the return distribution of KOSPI is very high and the simulation experiment shows similar results.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.5
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• pp.35-44
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• 2021

In recent times, several studies have been conducted focusing on crashes occurring on the main segment of the highway. However, there is a dearth of research dealing with traffic safety relating to other highway facilities, especially ramp areas. According to the Korea Expressway Corporation's Expressway Information Service, 6,717 crashes have occurred on ramps in the five years from 2015~2019, which accounts for about 15% of all highway accidents. In this study, the simple and full safety performance functions (SPFs) were evaluated and explored using different statistical distributions (i.e., Poisson Gamma (PG) and Poisson Inverse Gaussian (PIG)) and techniques (i.e., fixed effects (FE) and random effects (RE)) to provide more accurate crash prediction models for highway ramp sections. Data on the geometric characteristics of traffic and roadways were collected from various systems and with extensive efforts using a street-view application. The results showed that the PIG models present more accurate crash predictions in general. The results also indicated that the RE models performed better than FE models for simple and full SPFs. The findings from this study offer transportation practitioners using the Korea Expressway Corporation's Expressway a dependable reference to enhance and understand traffic safety in ramp areas based on accurate crash prediction models and empirical evidence.

• Journal of Ocean Engineering and Technology
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• v.27 no.3
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• pp.61-66
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• 2013

This paper presents the results of finite element simulations of elastic wave propagation in an underwater steel plate and the verification of a proposed method utilizing elastic wave-based damage detection. For the simulation and verification, we carried out the following procedures. First, three-dimensional finite element models were constructed using a general purpose finite element program. Second, two types of damages (mechanical defects and deteriorations) were applied to the underwater steel plate and three parameters (defect location, defect width, and depth) were considered to adjust the severity of the applied damages. Third, elastic waves were generated using the oblique incident method with a Gaussian tone burst, and the response signals were obtained at the receiving point for each defect or deterioration case. In addition, the received time domain signals were analyzed, particularly by measuring the magnitudes of the maximum amplitudes. Finally, the presence and severity of each type of damage were identified by the decreasing ratios of the maximum amplitudes. The results showed that the received signals for the models had the same global pattern with minor changes in the amplitudes and phases, and the decreasing ratio generally increased as the damage area increased. In addition, we found that the defect depth was more critical than the width in the decrease of the amplitude. This mainly occurred because the layout of the depth interfered with the elastic wave propagation in a more severe manner than the layout of the width. An inverse analysis showed that the proposed method is applicable for detecting mechanical defects and quantifying their severity.

• Water for future
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• v.29 no.5
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• pp.215-222
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• 1996

An expression for the cross covariance of the logconductivity and the head in nonstationary porous formation is obtained. This cross covariance plays a key role in the inverse problem, i.e., in inferring the statistical characteristics of the conductivity field from head data. The nonstationary logconductivity is modeled as superposition of definite linear trend and stationary fluctuation and the hydraulic head in saturated aquifers is found through stochastic analysis of a steady, two-dimensional flow. The cross covariance with a Gaussian correlation function is investigated for two particular cases where the trend is either parallel or normal to the head gradient. The results show that cross covariances are stationary except along separation distances parallel to the mean flow direction for the case where the trend is parallel to head gradient. Also, unlike the stationary model, the cross covariance along distances normal to flow direction is non-zero. From these observations we conclude that when a trend in the conductivity field is suspected, this information must be incorporated in the analysis of groundwater flow and solute transjport.

• Korean Journal of Remote Sensing
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• v.24 no.5
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• pp.483-496
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• 2008

Qualified ground control points (GCPs) are required to construct a digital elevation model (DEM) from a pushbroom stereo pair. An inverse geolocation algorithm for extracting GCPs from ERS SAR data and the SRTM DEM was recently developed. However, not all GCPs established by this method are accurate enough for direct application to the geometric correction of pushbroom images such as SPOT, IRS, etc, and thus a method for selecting and removing inaccurate points from the sets of GCPs is needed. In this study, we propose a method for evaluating GCP accuracy and winnowing sets of GCPs through orientation modeling of pushbroom image and validate performance of this method using SPOT stereo pair of Daejon City. It has been found that the statistical distribution of GCP positional errors is approximately Gaussian without bias, and that the residual errors estimated by orientation modeling have a linear relationship with the positional errors. Inaccurate GCPs have large positional errors and can be iteratively eliminated by thresholding the residual errors. Forty-one GCPs were initially extracted for the test, with mean the positional error values of 25.6m, 2.5m and -6.1m in the X-, Y- and Z-directions, respectively, and standard deviations of 62.4m, 37.6m and 15.0m. Twenty-one GCPs were eliminated by the proposed method, resulting in the standard deviations of the positional errors of the 20 final GCPs being reduced to 13.9m, 8.5m and 7.5m in the X-, Y- and Z-directions, respectively. Orientation modeling of the SPOT stereo pair was performed using the 20 GCPs, and the model was checked against 15 map-based points. The root mean square errors (RMSEs) of the model were 10.4m, 7.1m and 12.1m in X-, Y- and Z-directions, respectively. A SPOT DEM with a 20m ground resolution was successfully constructed using a automatic matching procedure.

• Journal of Korea Multimedia Society
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• v.15 no.4
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• pp.449-471
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• 2012

In intelligent surveillance systems, it is required to robustly track multiple people. Most of the previous studies adopted a Gaussian mixture model (GMM) for discriminating the object from the background. However, it has a weakness that its performance is affected by illumination variations and shadow regions can be merged with the object. And when two foreground objects overlap, the GMM method cannot correctly discriminate the occluded regions. To overcome these problems, we propose a new method of tracking and identifying multiple people. The proposed research is novel in the following three ways compared to previous research: First, the illuminative variations and shadow regions are reduced by an illumination normalization based on the median and inverse filtering of the L*a*b* image. Second, the multiple occluded and overlapped people are tracked by combining the GMM in the still image and the Lucas-Kanade-Tomasi (LKT) method in successive images. Third, with the proposed human tracking and the existing face detection & recognition methods, the tracked multiple people are successfully identified. The experimental results show that the proposed method could track and recognize multiple people with accuracy.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.32 no.4C
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• pp.418-424
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• 2007

This paper proposes a scheme to track channel response in OFDM systems with null sub-carriers. The proposed channel tracking scheme estimates the channel response first in the frequency domain by using the decision directed data. The time domain channel estimation is then performed to remove additive white Gaussian noise (AWGN) components further. Due to the channel estimation in the frequency domain, no inverse matrix calculation is required in the time domain channel estimation. Computational reduction in the proposed method is about 93%, compared with the conventional time domain channel estimation method. Mean square error (MSE) and bit error rate (BER) performances are evaluated by using computer simulation. The proposed method shows the same performance as that of the conventional time domain channel estimation even though the significant computational reduction.

• The Transactions of the Korean Institute of Electrical Engineers C
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• v.51 no.11
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• pp.551-558
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• 2002

A time-domain combined field integral equation (CFIE) is presented to obtain the transient scattering response from arbitrarily shaped three-dimensional conducting bodies. This formulation is based on a linear combination of the time-domain electric field integral equation (EFIE) with the magnetic field integral equation (MFIE). The time derivative of the magnetic vector potential in EFIE is approximated using a central finite difference approximation and the scalar potential is averaged over time. The time-domain CFIE approach produces results that are accurate and stable when solving for transient scattering responses from conducting objects. The incident spectrum of the field may contain frequency components, which correspond to the internal resonance of the structure. For the numerical solution, we consider both the explicit and implicit scheme and use two different kinds of Gaussian pulses, which may contain frequencies corresponding to the internal resonance. Numerical results for the EFIE, MFIE, and CFIE are presented and compared with those obtained from the inverse discrete Fourier transform (IDFT) of the frequency-domain CFIE solution.