• 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.

• Journal of KIEE
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• v.10 no.1
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• pp.7-15
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• 2000

Source separation is a statistical method, the goal of which is to separate the linear instantaneous mixtures of statistically independent sources without resorting to any prior knowledge. This paper addresses a source separation algorithm which is able to separate the mixtures of sub- and super-Gaussian sources. The nonlinear function in the proposed algorithm is derived from the generalized Gaussian distribution that is a set of distributions parameterized by a real positive number (Gaussian exponent). Based on the relationship between the kurtosis and the Gaussian exponent, we present a simple and efficient way of selecting proper nonlinear functions for source separation. Useful behavior of the proposed method is demonstrated by computer simulations.

• Journal of Welding and Joining
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• v.23 no.1
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• pp.48-54
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• 2005

Laser keyhole welding is investigated using a three-dimensional Gaussian heat source, and the heat source parameters such as the keyhole depth, welding efficiency and power density distribution factor are determined in a systematic way. For partial penetration, the keyhole depth is same as the penetration and is predicted using the experimental data. The welding efficiency is calculated using the ray-tracing method and the power density distribution factor is determined from the bead shape. Full penetration is classified into the transition, normal and excessive modes depending on the degree of keyhole opening. Thermal analysis of the bead-on-plate welds is conducted using the Gaussian heat source, and the calculated weld geometries show reasonably good agreements with the experimental results.

• Journal of Welding and Joining
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• v.22 no.2
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• pp.69-77
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• 2004

The Gaussian heat source has been widely used to simulate the heat flux of the welding we, and applied to calculating the temperature distribution of a workpiece. The conventional two-dimensional Gaussian heat source for the GMAW is modified in this work by decomposing the arc heat into heats of the cathode and metal transfer. The efficiency and effective arc radius of each heat source are determined analytically for the free-flight mode such as the globular and spray modes. The temperature distribution and weld geometry are calculated using the finite element method, and distribution of the drop heat is found to have significant effects on the penetration. The predicted results show good agreements with the available experimental results, especially with the penetration.

• ETRI Journal
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• v.32 no.6
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• pp.837-842
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• 2010

This paper proposes robust source localization methods for estimating the azimuth angle, elevation angle, velocity, and range using a low-complexity planar antenna array in impulsive non-Gaussian noise environments. The proposed robust source localization methods for wireless communication applications are based on nonlinear M-estimation provided from Huber and Hampel. Simulation results show the robustness performance of the proposed robust methods in impulsive non-Gaussian noise.

• ETRI Journal
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• v.33 no.5
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• pp.814-817
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• 2011

In this letter, the distributions of direct current (DC) coefficients for P-frames in H.264/AVC are analyzed, and the distortion model of the Gaussian source under the quantization of the dead-zone plus-uniform threshold quantization with uniform reconstruction quantizer is derived. Experimental results show that the DC coefficients of P-frames are best approximated by the Laplacian distribution and the Gaussian distribution at small quantization step sizes and at large quantization step sizes, respectively.

• Current Optics and Photonics
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• v.7 no.6
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• pp.701-707
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• 2023

In laser imaging, accurate extraction of the laser's center is essential. Several methods exist to extract the laser's center in an image, such as the geometric mean, the parabolic curve fitting, and the Gaussian curve fitting, etc. The Gaussian curve fitting is the most suitable because it is based on the physical properties of the laser. The width of the Gaussian laser beam depends on the distance from the laser source to the target object. It is assumed in general that the distance remains constant at a laser spot resulting in a symmetric Gaussian model for the laser image. However, on a curved surface of the object, the distance is not constant; The laser beam is narrower on the side closer to the focal point of the laser light and wider on the side closer to the laser source, which causes the distribution of the laser beam to skew. This study presents a modified Gaussian model in the laser imaging to incorporate the slant angle of a curved object. The proposed method is verified with simulation and experiments.

• Journal of Environmental Science International
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• v.15 no.4
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• pp.319-324
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• 2006

Release rate is one of the important items for the environmental impact assessment caused by radioactive materials in case of an accidental release from the nuclear facilities. In this study, the uncertainty of the estimated release rate is evaluated using Monte Carlo method. Gaussian plume model and linear programming are used for estimating the release rate of a source material. Tracer experiment is performed at the Yeoung-Kwang nuclear site to understand the dispersion characteristics. The optimized release rate was 1.56 times rather than the released source as a result of the linear programming to minimize the sum of square errors between the observed concentrations of the experiment and the calculated ones using Gaussian plume model. In the mean time, 95% confidence interval of the estimated release rate was from 1.41 to 2.53 times compared with the released rate as a result of the Monte Carlo simulation considering input variations of the Gaussian plume model. We confirm that this kind of the uncertainty evaluation for the source rate can support decision making appropriately in case of the radiological emergencies.

• Journal of Korean Society for Atmospheric Environment
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• v.18 no.5
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• pp.393-400
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• 2002

Separation distance between industrial source and residential areas to avoid odor annoyance was investigated using AUSPLUME model. A Gaussian plume model (AUSPLUME) for the dispersion was used to calculate odor emission from ground level area source. Using the dispersion model to calculate ambient odor concentrations, the separation distance between industrial source and residental areas was defined by %HA (percentage of highly annoyed person) and odor percentile concentration (C98). The result was compared with the separation distance of various nation guidelines for livestock buildings. The calculated separation distance for industrial source showed similar pattern comparing with various guidelines for livestock buildings.

• Journal of Korean Society for Atmospheric Environment
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• v.15 no.6
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• pp.757-765
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• 1999

Lagrangian particle dispersion model(LPDM) is an effective tool to calculate the dispersion from a point source since it dose not induce numerical diffusion errors in solving the pollutant dispersion equation. Fictitious particles are released to the atmosphere from the emission source and they are then transported by the mean velocity and diffused by the turbulent eddy motion in the LPDM. The concentration distribution from the dispersed particles in the calculation domain are finally estimated by applying a particle count method or a Gaussian kernel method. The two methods for calculating concentration profiles were compared each other and tested against the analytic solution and the tracer experiment to find the strength and weakness of each method and to choose computationally time saving method for the LPDM. The calculated concentrations from the particle count method was heavily dependent on the number of the particles released at the emission source. It requires lots fo particle emission to reach the converged concentration field. And resulting concentrations were also dependent on the size of numerical grid. The concentration field by the Gaussian kernel method, however, converged with a low particle emission rate at the source and was in good agreement with the analytic solution and the tracer experiment. The results showed that Gaussian kernel method was more effective method to calculate the concentrations in the LPDM.