• Proceedings of the KSR Conference
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• 2011.05a
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• pp.1161-1166
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• 2011

A camera is mounted vertically downwards viewing the people heads from the top. This configuration is successful in people counting technique especially when only a few isolated people pass through a counting region in a non-crowded situation. Thus, this paper describes object counting which detects and count moving people using mixture of gaussian and motion vector. This method is intended to estimates the number of people in outdoor environment. This method use single gaussian background modeling which is more robust an noise and has adaptiveness. The experimental results that is based on mixture of gaussian and motion vector is also helpful to design intelligent surveillance.

• Honam Mathematical Journal
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• v.38 no.3
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• pp.593-611
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• 2016

This paper is concerned with the classifications of quadric surfaces of first and second kinds in Euclidean 3-space satisfying the Jacobi condition with respect to their curvatures, the Gaussian curvature K, the mean curvature H, second mean curvature $H_{II}$ and second Gaussian curvature $K_{II}$. Also, we study the zero and non-zero constant curvatures of these surfaces. Furthermore, we investigated the (A, B)-Weingarten, (A, B)-linear Weingarten as well as some special ($C^2$, K) and $(C^2,\;K{\sqrt{K}})$-nonlinear Weingarten quadric surfaces in $E^3$, where $A{\neq}B$, A, $B{\in}{K,H,H_{II},K_{II}}$ and $C{\in}{H,H_{II},K_{II}}$. Finally, some important new lemmas are presented.

• Journal of Korea Multimedia Society
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• v.22 no.12
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• pp.1396-1403
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• 2019

The radar wind profiler data contaminates with various non-atmospheric components that produce errors in moments and wind velocity estimations. This study implemented an adaptive Gaussian model to detect and remove the clutter from the radar return. This model includes DC filtering, ground clutter recognition, Gaussian fitting, and cost function to mitigate the clutter component. The adaptive model tested for the various types of clutter components and found that it is effective in clutter removal process. It is also applied for the both time series and spectrum datasets. The moments estimated using this method are compared with those derived using conventional DC-filtering clutter removal method. The comparisons show that the proposed method effectively removes the clutter and produce reliable moments.

• Kyungpook Mathematical Journal
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• v.47 no.4
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• pp.579-593
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• 2007

In this paper, we study some properties of ruled surfaces in a three-dimensional Lorentz-Minkowski space related to their Gaussian curvature, the second Gaussian curvature and the mean curvature. Furthermore, we examine the ruled surfaces in a three-dimensional Lorentz-Minkowski space satisfying the Jacobi condition formed with those curvatures, which are called the II-W and the II-G ruled surfaces and give a classification of such ruled surfaces in a three-dimensional Lorentz-Minkowski space.

• Journal of Korea Multimedia Society
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• v.6 no.3
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• pp.508-517
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• 2003

This paper presents perceptual model with a stochastic multiresolution characteristic that can be applied with watermark embedding in the biorthogonal wavelet domain. The perceptual model with adaptive watermarking algorithm embeds at the texture and edge region for more strongly embedded watermark by the SSQ. The watermark embedding is based on the computation of a NVF that has local image properties. This method uses non- stationary Gaussian and stationary Generalized Gaussian models because watermark has noise properties. The particularities of embedding in the stationary GG model use shape parameter and variance of each subband regions in multiresolution. To estimate the shape parameter, we use a moment matching method. Non-stationary Gaussian model uses the local mean and variance of each subband. The experiment results of simulation were found to be excellent invisibility and robustness. Experiments of such distortion are executed by Stirmark 3.1 benchmark test.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.17 no.6
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• pp.1273-1280
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• 2013

The UKF algorithm for tracking moving objects has fast convergence speed and good tracking performance without the derivative computation. However, this algorithm has serious drawbacks which limit its use in conditions such as Gaussian noise distribution. Meanwhile, the particle filter(PF) is a state estimation method applied to nonlinear and non-Gaussian systems without these limitations. But this method also has some disadvantages such as computation increase as the number of particles rises. In this paper, we propose the Unscented Particle Filter (UPF) algorithm which combines Unscented Kalman Filter (UKF) and Particle Filter (PF) in order to overcome these drawbacks.The performance of the UPF algorithm was tested to compare with Particle Filter using a 2-DOF (Degree of Freedom) Pendulum System. The results show that the proposed algorithm is more suitable to the nonlinear and non-Gaussian state estimation compared with PF.

• Journal of Information Processing Systems
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• v.15 no.5
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• pp.1108-1118
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• 2019

Non-local means (NLM) algorithm is an effective and successful denoising method, but it is computationally heavy. To deal with this obstacle, we propose a novel NLM algorithm with fuzzy metric (FM-NLM) for image denoising in this paper. A new feature metric of visual features with fuzzy metric is utilized to measure the similarity between image pixels in the presence of Gaussian noise. Similarity measures of luminance and structure information are calculated using a fuzzy metric. A smooth kernel is constructed with the proposed fuzzy metric instead of the Gaussian weighted L2 norm kernel. The fuzzy metric and smooth kernel computationally simplify the NLM algorithm and avoid the filter parameters. Meanwhile, the proposed FM-NLM using visual structure preferably preserves the original undistorted image structures. The performance of the improved method is visually and quantitatively comparable with or better than that of the current state-of-the-art NLM-based denoising algorithms.

• Journal of the Earthquake Engineering Society of Korea
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• v.7 no.5
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• pp.11-17
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• 2003

It is well known that the double integration of measured acceleration records is one of the most difficult signal processing, particularly in the measurements on civil engineering structures, The measured accelerations of civil engineering structures are usually non-stationary and contain non-gaussian low-frequency noises, which can be significant causes of numerical instabilities in double Integration, For the de-noising of this kind of signals, wavelet transform can be very effective because of its inherent processing features for non-stationary signals, In this paper, the de-noising algorithm for the double integration is proposed using the modified wavelet transform, which is extended version of ordinary wavelet transform to process non-gaussian and low-frequency noises, using the median filter concept, The example studies show that the integration can be improved by the proposed method.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.16 no.3
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• pp.301-309
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• 2003

The non linear control algorithm with actuator saturation for a randomly excited oscillator has been widely explored and has shown promising results, but the probabilistic analysis of the algorithm has been rarely made due to its non-linear nature and the fact that the analytical solution of probability density function (PDF) for controlled responses does not exist. In this paper, a method for the probabilistic analysis on the non linear control algorithm with actuator saturation is proposed based on the equivalent non linear system method. Numerical examples are given to verify the approximation solution of PDF comparing to a statistically obtained PDF using a Gaussian white noise and a Kanai - Tagimi filtered Gaussian white noise.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.8
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• pp.3865-3883
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• 2016

Infrastructure as a Service (IaaS) encapsulates computer hardware into a large amount of virtual and manageable instances mainly in the form of virtual machine (VM), and provides rental service for users. Currently, VM anomaly incidents occasionally occur, which leads to performance issues and even downtime. This paper aims at detecting anomalous VMs based on performance metrics data of VMs. Due to the dynamic nature and increasing scale of IaaS, detecting anomalous VMs from voluminous correlated and non-Gaussian monitored performance data is a challenging task. This paper designs an anomaly detection framework to solve this challenge. First, it collects 53 performance metrics to reflect the running state of each VM. The collected performance metrics are testified not to follow the Gaussian distribution. Then, it employs independent components analysis (ICA) instead of principal component analysis (PCA) to extract independent components from collected non-Gaussian performance metric data. For anomaly detection, it employs multi-class Bayesian classification to determine the current state of each VM. To evaluate the performance of the designed detection framework, four types of anomalies are separately or jointly injected into randomly selected VMs in a campus-wide testbed. The experimental results show that ICA-based detection mechanism outperforms PCA-based and LDA-based detection mechanisms in terms of sensitivity and specificity.