• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 2009.11a
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• pp.179-181
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• 2009

In this paper, we propose an adaptive noise detection and modified Gaussian removal algorithm using local statistics for impulse noise. In order to determine constraints for noise detection, the local mean, variance, and maximum values are used. In addition, a modified Gaussian filter that integrates the tuning parameter to remove the detected noises. Experimental results show that our method is significantly better than a number of existing techniques in terms of image restoration and noise detection.

• Journal of rehabilitation welfare engineering & assistive technology
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• v.5 no.1
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• pp.95-101
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• 2011

In this paper, we propose the gaussian mixture model based pattern classification algorithm of forearm electromyogram. We define the motion of 1-degree of freedom as holding and unfolding hand considering a daily life for patient with prosthetic hand. For the extraction of precise features from the EMG signals, we use the difference absolute mean value(DAMV) and the mean absolute value(MAV) to consider amplitude characteristic of EMG signals. We also propose the D_DAMV and D_MAV in order to classify the amplitude characteristic of EMG signals more precisely. In this paper, we implemented a test targeting four adult male and identified the accuracy of EMG pattern classification of two motions which are holding and unfolding hand.

• Water Engineering Research
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• v.2 no.2
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• pp.89-101
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• 2001

The theoretical treatments on jets, in which the flow is issuing into a stagnant medium, have been based on Prandtl's mixing theory. In this study, using Prandtl's mixing length hypothesis, a theoretical relationship for the velocity profile of a single round jet is derived. Furthermore, Gaussian expression is used to approximate the theoretical relationship, in which the Gaussian coefficient is assumed to be decreasing exponentially as the flow goes far from the orifice. Two data sets for a single round jet performed by tow different techniques of measurement are used to verify the suggested relationships. The theoretical and Gaussian distribution give close results in spite of the difference in approach. The observed mean velocity distributions are in good agreements with the suggested theoretical and Gaussian distributions.

• Proceedings of the KIEE Conference
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• 2007.04a
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• pp.14-16
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• 2007

The statistics for the Gaussian noise mixed with impulsive noise are modelled. The denoising algorithm called amplitude-limited sample average filter is derived, which is optimal in terms of minimizing mean square errors under the assumption that contaminating noise is heavy-tailed Gaussian distributed. Its performance is shown to be excellent when image is corrupted mainly with Gaussian noise. However, it shows visually grainy output as the amount of impulsive noise increases. In order to overcome such problems, it is combined with the myriad filter to propose an amplitude-limited myriad filter. Simulation shows it effectively removes both Gaussian and impulsive noise, not blurring edges severey.

• Journal for History of Mathematics
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• v.35 no.1
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• pp.1-18
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• 2022

This paper presents a statistical machine learning method that generates the implied volatility surface under the rareness of the market data. We apply the practitioner's Black-Scholes model and Gaussian process regression method to construct a Bayesian inference system with observed volatilities as a prior information and estimate the posterior distribution of the unobserved volatilities. The variance instead of the volatility is the target of the estimation, and the radial basis function is applied to the mean and kernel function of the Gaussian process regression. We present two types of Gaussian process regression methods and empirically analyze them.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.17 no.2
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• pp.187-195
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• 1992

This paper propose on the determination of threshold values for extracting shape information of the objects. First, surface curvatures such as mean curvature and gaussian curvature is calculated from given range data. And then local surface regions are classified into the one of 8 primitives by using the sign of mean curvature H and gaussian curvature K. Also from the statistical viewpoint. the range of the zero of H and K in the range is obtained through the analysis of the relation between mean curvature and gaussian curvature. Finally, the effectiveness of the proposed mithod in this paper is demonstrated by comparing with a case, where the zero threshold is arbitrarily obtained.

• Bulletin of the Korean Mathematical Society
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• v.61 no.2
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• pp.401-419
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• 2024

Let K, H, K_II and H_II be the Gaussian curvature, the mean curvature, the second Gaussian curvature and the second mean curvature of a timelike tubular surface T_γ(α) with the radius γ along a timelike curve α(s) in Minkowski 3-space E³₁. We prove that T_γ(α) must be a (K, H)-Weingarten surface and a (K, H)-linear Weingarten surface. We also show that T_γ(α) is (X, Y)-Weingarten type if and only if its central curve is a circle or a helix, where (X, Y) ∈ {(K, K_II), (K, H_II), (H, K_II), (H, H_II), (K_II , H_II)}. Furthermore, we prove that there exist no timelike tubular surfaces of (X, Y)-linear Weingarten type, (X, Y, Z)-linear Weingarten type and (K, H, K_II, H_II)-linear Weingarten type along a timelike curve in E³₁, where (X, Y, Z) ∈ {(K, H, K_II), (K, H, H_II), (K, K_II, H_II), (H, K_II, H_II)}.

• Smart Media Journal
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• v.4 no.3
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• pp.50-55
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• 2015

In this paper, we present background modeling method based on Gaussian mixture model to subtract background for night-time video surveillance. In night-time video, it is hard work to distinguish the object from the background because a background pixel is similar to a object pixel. To solve this problem, we change the pixel of input frame to more advantageous value to make the Gaussian mixture model using scaled histogram stretching in preprocessing step. Using scaled pixel value of input frame, we then exploit GMM to find the ideal background pixelwisely. In case that the pixel of next frame is not included in any Gaussian, the matching test in old GMM method ignores the information of stored background by eliminating the Gaussian distribution with low weight. Therefore we consider the stacked data by applying the difference between the old mean and new pixel intensity to new mean instead of removing the Gaussian with low weight. Some experiments demonstrate that the proposed background modeling method shows the superiority of our algorithm effectively.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.18 no.7
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• pp.1028-1035
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• 1993

The signed regressor (SR) algorithm employs one bit quantization on the input regressor (or tap input) in such a way that the quantized input sequences become +1 or -1. The algorithm is computationally more efficient by nature than the popular least mean square (LMS) algorithm. The behavior of the SR algorithm unfortunately is heavily dependent on the characteristics of the input signal, and there are some Inputs for which the SR algorithm becomes unstable. It is known, however, that such a stability problem does not take place with the SR algorithm when the input signal is Gaussian, such as in the case of speech processing. In this paper, we explore a statistical analysis of the SR algorithm. Under the assumption that signals involved are zero-mean and Gaussian, and further employing the commonly used independence assumption, we derive a set of nonlinear evolution equations that characterizes the mean and mean-squared behavior of the SR algorithm. Experimental results that show very good agreement with our theoretical derivations are also presented.

• Journal of KIISE:Software and Applications
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• v.35 no.7
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• pp.417-423
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• 2008

In this paper we present a probabilistic method for source separation in the case here each source has a certain temporal structure. We tackle the problem of source separation by maximum pseudo-likelihood estimation, representing the latent function which characterizes the temporal structure of each source by a random process with a Gaussian prior. The resulting pseudo-likelihood of the data is Gaussian, determined by a mixing matrix as well as by the predictive mean and covariance matrix that can easily be computed by Gaussian process (GP) regression. Gradient-based optimization is applied to estimate the demixing matrix through maximizing the log-pseudo-likelihood of the data. umerical experiments confirm the useful behavior of our method, compared to existing source separation methods.