• 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.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.4
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• pp.383-391
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• 2009

The detonation wave characteristics of JP-7 and oxygen mixture is investigated by one-step induction parameter model (IPM) obtained from a detailed chemistry mechanism. A general procedure of obtaining reliable one-step kinetics IPM for hydrocarbon mixture from the fully detailed chemistry is described in this study. The IPM is obtained by the reconstruction of the induction time database obtained from a detailed kinetics library. The IPM was confirmed by the comparison of the induction time calculations with that from detailed kinetics. The IPM is later implemented to a fluid dynamics code and applied for the numerical simulation of detonation wave propagation. The numerical results show the detailed characteristics of the detonation wave propagation in JP-7 and oxygen mixture at affordable computing time, which is not be possible by the direct application of the detailed chemical kinetics mechanism of hydrocarbon fuel combustion.

• Journal of the Korean Institute of Intelligent Systems
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• v.18 no.3
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• pp.387-391
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• 2008

For the detection of moving objects, background subtraction methods are widely used. In case the background has variation, we need to update the background in real-time for the reliable detection of foreground objects. Gaussian mixture model (GMM) combined with probabilistic learning is one of the most popular methods for the real-time update of the background. However, it does not work well in the complex and dynamic backgrounds with high traffic regions. In this paper, we propose a new method for modelling and updating more reliably the complex and dynamic backgrounds based on the probabilistic learning and the layered processing.

• The Journal of the Acoustical Society of Korea
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• v.18 no.8
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• pp.42-47
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• 1999

In this paper, a mixture interacting multiple model (MIMM) algorithm is proposed to enhance speech contaminated by additive nonstationary noise. In this approach, a mixture hidden filter model (HFM) is used to model the clean speech and the noise process is modeled by a single hidden filter. The MIMM algorithm, however. needs large computation time because it is a recursive method based on multiple Kalman filters with mixture HFM. Thereby, a computationally efficient implementation of the algorithm is developed by exploiting the structure of the Kalman filtering equation. The simulation results show that the proposed method offers performance gain compared to the previous results in [4,5] with slightly increased complexity.

• Proceedings of the Korean Information Science Society Conference
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• 2001.04b
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• pp.538-540
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• 2001

PCA는 다변수 데이터 해석법 중 가장 널리 알려진 방법 중 하나로 많은 응용을 가지고 있다. 그런데, PCA는 선형 모델이어서 비선형 구조를 분석하는데 효과적이지 않다. 이를 극복하기 위해서 PCA의 조합을 이용하는 PCA 혼합 모형이 제안되었다. PCA 혼합 모형의 핵심은 구조 선택, 즉 mixture 요소의 수와 PCA 기저의 수의 결정 인데 그의 체계적인 결정 방법이 필요하다. 본 논문에서는 단순화된 PCA 혼합 모형과 이를 위한 효율적인 구조 선택 방법을 제안한다. 각각의 mixture 요소 수에 대해서 모든 PCA 기저를 갖도록 한 상태에서 PCA 혼합 모형의 파라미터를 EM 알고리즘을 써서 결정한다. 최적의 mixture 요소의 수는 오류를 최소로 하는 것으로 결정한다. PCA 기저의 수는 PCA의 정렬성 특성을 이용해서 중요도가 적은 기저부터 하나씩 잘라 내며 오류가 최소로 하는 것으로 결정한다. 제안된 방법은 특히 다차원 데이터의 경우에 EM 학습의 횟수를 많이 줄인다. 인공 데이터에 대한 실험은 제안된 방법이 적절한 모델 구조를 결정한다는 것을 보여준다. 또, 눈 감지에 대한 실험은 제안된 방법이 실용적으로도 유용하다는 것을 보여준다.

• 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 Korean Institute of Gas
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• v.15 no.3
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• pp.39-46
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• 2011

This study investigates the effects of the DME and n-Butane mixture and of the stratification on combustion characteristics of HCCI engine by chemical reaction calculation. First, the existing DME reaction scheme and n-Butane is combined to make new chemical reaction model, then validating the effectiveness of new scheme. Furthermore, this study verify the HCCI combustion characteristics according to the changes of DME and n-Butane mixture ratio, which shows different auto ignition characteristics. Finally, it confirms the effects of stratification of mixture fuel on the reduction of pressure rise rate.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2015.05a
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• pp.407-409
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• 2015

In this paper, a hardware implementation of MOD(Moving Object Detection) algorithm is described, which is based GMM(Gaussian Mixture Model) and background subtraction. The EGML(Effective Gaussian Mixture Learning) is used to model and update background. Some approximations of EGML calculations are applied to reduce hardware complexity, and pipelining technique is used to improve operating speed. Gaussian parameters are adjustable according to various environment conditions to achieve better MOD performance. MOD processor is verified by using FPGA-in-the-loop verification, and it can operate with 109 MHz clock frequency on XC5VSX95T FPGA device.

• Journal of the Korea Institute of Military Science and Technology
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• v.22 no.1
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• pp.27-34
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• 2019

This paper presents a chamber pressure model development of K2 rifle by applying Gaussian mixture model. In order to materialize a real recoil force of a virtual reality shooting rifle in military combat training, the chamber pressure which is one of major components of the recoil force needs to be investigated and modeled. Over 200,000 data of the chamber pressure were collected by implementing live fire experiments with both K100 and M193 of 5.56 mm bullets. Gaussian mixture method was also applied to create a mathematical model that satisfies nonlinear, asymmetry, and deviations of the chamber pressure which is caused by irregular characteristics of propellant combustion. In addition, Polynomial and Fourier Regression were used for comparison of results, and the sum of squared errors, the coefficient of determination and root-mean-square errors were analyzed for performance measurement.

• Journal of the Korea Concrete Institute
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• v.28 no.4
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• pp.427-434
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• 2016

The purpose of this study is to propose a mixture proportioning approach based on the replacement level of natural sand for reducing $CO_2$ emissions from artificial lightweight aggregate concrete(LWAC) production. To assess the effect of natural sand on the reduction of $CO_2$ emissions and compressive strength of LWAC, a total of 379 specimens compiled from different sources were analyzed. Based on the non-linear regression analysis using the database and the previous mixture proportioning method proposed by Yang et al., simple equations were derived to determine the concrete mixture proportioning and the replacement level of natural sand for achieving the targeted performances(compressive strength, initial slump, air content, and $CO_2$ reduction ratio) of concrete. Furthermore, the proposed equations are practically applicable to straightforward determination of the $CO_2$ emissions from the provided mixture proportions of LWAC.