• 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.32 no.4C
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• pp.411-417
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• 2007

The rate control is an essential component in video coding to provide better quality under given coding constraints, such as channel capacity, frame rates, etc. In general, source data cannot be described as a single distribution in a video coding, hence it can cause an exhaustive approximation problem. It drops a coding efficiency under weak channel environments, such as mobile communications. In this paper, we design a new quantization parameter decision model that is based on a rate-distortion function of generalized Gaussian distribution. In order to adaptively express various source data distribution, we decide a shape parameter by observing a ratio of samples, which have a small value. For experiment, the proposed algorithm is implemented into H.264/AVC video codec, and its performance is compared with that of MPEG-2 TM5, H.263 TMN8 rate control algorithm. As shown in simulation results, the proposed algorithm provides an improved quality rather than previous algorithms and generates the number of bits closed to the target bits.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.469-473
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• 2007

For the quantitative analysis of inclusion using OES data, separating of noise and inclusion is needed. In previous methods assuming that noises come from a normal distribution, intensity levels beyond a specific threshold are determined as inclusions. However, it is not possible to classify inclusions in low intensity region using this method, even though every inclusion is an element of some chemical compound. In this paper, we assume that distribution of OES data is a Gaussian mixture and estimate the parameters of the mixture model using EM algorithm. Then, we calculate mixing ratio of noise and inclusion using these parameters to separate noise and inclusion.

• Corrosion Science and Technology
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• v.3 no.6
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• pp.257-261
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• 2004

Leaking of fuel gas in a building creates flammable atmosphere and gives rise to explosion. Observations from accidents suggest that some explosions are caused by quantity of gas significantly less than the lower explosion limit amount required to fill the whole confined space, which might be attributed to inhomogeneous mixing of the leaked gas. The minimum amount of leaked gas for explosion is highly dependent on the degree of mixing in the building. This paper proposes a method for estimating minimum amount of flammable gas for explosion assuming Gaussian distribution of flammable gas.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.16 no.6
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• pp.1260-1265
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• 2012

This paper have presented the change for subthreshold characteristics for double gate(DG) MOSFET based on scaling theory and the shape of Gaussian function. To obtain the analytical solution of Poisson's equation, Gaussian function been used as carrier distribution and consequently potential distributions have been analyzed closely for experimental results, and the subthreshold characteristics have been analyzed for the shape parameters of Gaussian function such as projected range and standard projected deviation. Since this potential model has been verified in the previous papers, we have used this model to analyze the subthreshold chatacteristics. The scaling theory is to sustain constant outputs for the change of device parameters. As a result to apply the scaling theory for DGMOSFET, we know the subthreshold characteristics have been greatly changed, and the change of threshold voltage is bigger relatively.

• Journal of Korean Society for Atmospheric Environment
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• v.12 no.5
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• pp.529-540
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• 1996

Dispersion experiment using SF$_{6}$ tracer was performed in the flat field of Chunchon Basin during four nights from August 29 to September 2, 1991. The purpose of this study is to analyze toe horizontal distribution of tracer concentration under the strong stable conditions and to evaluate the results calculated by INPUFF model. Incase of high wind speed, plume spread of SF$_{6}$ concentration appeared in narrow area of the downwind and the standard deviation of the horizontal wind angle (.sigma.$_{a}$) was amall. However, the SF$_{6}$ was spread widely in cases of low wind speed because of the large .sigma.$_{a}$. The result of the INPUFF model was similar to the observed distribution of the SF$_{6}$ concentration. It is proved that the Gaussian puff model is useful when wind direction varies significantly.tly.tly.tly.

• The Korean Journal of Applied Statistics
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• v.22 no.4
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• pp.759-770
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• 2009

A problem of separating signals from noises is considered, when they are randomly mixed in the observation. It is assumed that the noise follows a Gaussian distribution and the signal follows a Gamma distribution, thus the underlying distribution of an observation will be a mixture of Gaussian and Gamma distributions. The parameters of the mixture model will be estimated from the EM algorithm. Then the signals and noises will be classified by a fixed threshold approach based on multiple testing using positive false discovery rate and Bayes error. The proposed method is applied to a real optical emission spectroscopy data for the quantitative analysis of inclusions. A simulation is carried out to compare the performance with the existing method using 3 sigma rule.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.9 no.5
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• pp.1249-1254
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• 2008

The crucial part of graphical model is to compute the posterior distribution of parameters plus with the hidden variables given the observed data. In this paper, implementation of variational Bayes method for Gaussian mixture model and derivation of factorial variational approximation have been proposed. This result can be used for data analysis tasks like information retrieval or data visualization.

• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.225-228
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• 2007

This paper presents a geostatistical methodology to model local uncertainty in spatial estimation of sediment grain size with high-resolution remote sensing imagery. Within a multi-Gaussian framework, the IKONOS imagery is used as local means both to estimate the grain size values and to model local uncertainty at unsample locations. A conditional cumulative distribution function (ccdf) at any locations is defined by mean and variance values which can be estimated by multi-Gaussian kriging with local means. Two ccdf statistics including condition variance and interquartile range are used here as measures of local uncertainty and are compared through a cross validation analysis. In addition to local uncertainty measures, the probabilities of not exceeding or exceeding any grain size value at any locations are retrieved and mapped from the local ccdf models. A case study of Baramarae beach, Korea is carried out to illustrate the potential of geostatistical uncertainty modeling.

• Journal of the Korea Society of Computer and Information
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• v.15 no.1
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• pp.177-183
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• 2010

In continuous vocabulary recognition system by statistical method vocabulary recognition to be performed using probability distribution it also modeling using phoneme clustering for based sample probability parameter presume. When vocabulary search that low recognition rate problem happened in express vocabulary result from presumed probability parameter by not defined phoneme and insert phoneme and it has it's bad points of gaussian model the accuracy unsecure for one clustering modeling. To improve suggested probability distribution mixed gaussian model to optimized for based resemble Euclidean and Bhattacharyya distance measurement method mixed clustering modeling that system modeling for be searching phoneme probability model in clustered model. System performance as a result of represent vocabulary dependence recognition rate of 98.63%, vocabulary independence recognition rate of 97.91%.