• 대한전자공학회:학술대회논문집
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• 대한전자공학회 2002년도 하계종합학술대회 논문집(4)
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• pp.195-198
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• 2002

We propose an efficient mixture Gaussian synthesis method for decision tree based state tying that produces better context-dependent models in a short period of training time. This method makes it possible to handle mixture Gaussian HMMs in decision tree based state tying algorithm, and provides higher recognition performance compared to the conventional HMM training procedure using decision tree based state tying on single Gaussian GMMs. This method also reduces the steps of HMM training procedure. We applied this method to training of PBS, and we expect to achieve a little point improvement in phoneme accuarcy and reduction in training time.

• Journal of KIEE
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• 제10권1호
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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.

• ETRI Journal
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• 제33권6호
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• pp.949-952
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• 2011

In this letter, a simplified suboptimum receiver based on soft-limiting for the detection of binary antipodal signals in non-Gaussian noise modeled as a generalized normal-Laplace (GNL) distribution combined with Gaussian noise is presented. The suboptimum receiver has low computational complexity. Furthermore, when the number of diversity branches is small, its performance is very close to that of the Neyman-Pearson optimum receiver based on the probability density function obtained by the Fourier inversion of the characteristic function of the GNL-plus-Gaussian distribution.

• 응용통계연구
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• 제28권2호
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• pp.353-360
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• 2015

공간적으로 관측되는 연속형 자료를 분석하는 모형으로 공간적 상관관계를 고려한 다양한 정규모형이 지난 수십 년간 제안되었다. 그 중에서 공간효과를 랜덤효과로 모형화하는 공간선형모형(Spatial Linear Mixed Model; SLMM)이 가장 널리 활용되는 모형 중 하나일 것이다. 연결함수(link function)을 사용하면 SLMM을 비정규 데이터도 적용할 수 있는 일반화된 공간선형모형(Spatial Generalized Linear Mixed Model; SGLMM)으로 자연스럽게 확장할 수 있다. 이 논문에서는 가장 널리 활용되는 SGLMM을 알아보고 실제 데이터 적용사례를 R 패키지를 활용하여 제시하고자 한다.

• Communications for Statistical Applications and Methods
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• 제25권6호
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• pp.633-645
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• 2018

Gaussian error distributions are a common choice in traditional regression models for the maximum likelihood (ML) method. However, this distributional assumption is often suspicious especially when the error distribution is skewed or has heavy tails. In both cases, the ML method under normality could break down or lose efficiency. In this paper, we consider the log-concave and Gaussian scale mixture distributions for error distributions. For the log-concave errors, we propose to use a smoothed maximum likelihood estimator for stable and faster computation. Based on this, we perform comparative simulation studies to see the performance of coefficient estimates under normal, Gaussian scale mixture, and log-concave errors. In addition, we also consider real data analysis using Stack loss plant data and Korean labor and income panel data.

• Journal of information and communication convergence engineering
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• 제19권1호
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• pp.54-60
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• 2021

Various digital devices were supplied throughout the Fourth Industrial Revolution. Accordingly, the importance of data processing has increased. Data processing significantly affects equipment reliability. Thus, the importance of data processing has increased, and various studies have been conducted on this topic. This study proposes a modified Gaussian filter algorithm based on a fuzzy membership function. The proposed algorithm calculates the Gaussian filter weight considering the standard deviation of the filtering mask and computes an estimate according to the fuzzy membership function. The final output is calculated by adding or subtracting the Gaussian filter output and estimate. To evaluate the proposed algorithm, simulations were conducted using existing additive white Gaussian noise removal algorithms. The proposed algorithm was then analyzed by comparing the peak signal-to-noise ratio and differential image. The simulation results show that the proposed algorithm has superior noise reduction performance and improved performance compared to the existing method.

• 한국정보처리학회:학술대회논문집
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• 한국정보처리학회 2011년도 추계학술발표대회
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• pp.326-329
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• 2011

안개나 스모그 등으로 인한 영상의 왜곡에 대해 Dark Channel Prior 를 이용해 안개제거를 하면 깨끗한 결과 영상을 얻을 수 있다. 하지만 이 기법에서 전달량을 정련할 때 많은 시간이 걸리는데 계산 속도 면을 개선하기 위해 Gaussian Filter 를 사용해 정련한다. 이 때 단순한 Gaussian Filter 를 사용하게 되면 결과영상에서 후광효과가 생기게 된다. 후광효과를 줄이기 위해 본 논문에서 제안한 적응 Gaussian Filter 를 사용해 영상을 복원시킨다.

• 한국수학사학회지
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• 제35권1호
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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.

• Wind and Structures
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• 제36권1호
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• pp.15-29
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• 2023

To better estimate the non-Gaussian extreme wind pressures for high-rise buildings, a data-driven revised Hermitetype peak factor estimation model is proposed in this papar. Subsequently, a comparative study on three types of methods, such as Hermite-type models, short-time estimate Gumbel method (STE), and new translated-peak-process method (TPP) is carried out. The investigations show that the proposed Hermite-type peak factor has better accuracy and applicability than the other Hermite-type models, and its absolute accuracy is slightly inferior to the STE and new TPP methods for non-Gaussian wind pressures by comparing with the observed values. Moreover, these methods generally overestimate the Gaussian wind pressures especially the STE.

• 한국전자통신학회논문지
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• 제8권8호
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• pp.1137-1142
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• 2013

FBG센서는 구조물의 변형과 온도 측정에 주로 사용되고 있다. 본 논문에서는 FBG센서를 이용하여 균열을 측정하기 위하여 FPGA와 DSP를 이용하여 인테로게이터를 개발 구현하였다. 개발한 인테로게이터는 광원, 광서큘레이터, 광그레이팅 그리고 CCD센서 및 제어기로 구성된다. FBG센서로부터 반사된 광원의 스펙트럼을 분석하여 피크 파장를 검출하고 피크 파장의 변이를 이용하여 구조물의 변형 정도를 측정한다. 본 논문에서는 FBG센서 인테로게이터의 피크 검출 알고리즘에 주로 적용하는 중심점 알고리즘과 Gaussian 근사법을 비교한다. Gaussian 근사법에 개발한 인테로게이터에 적합함을 실험을 통하여 확인한다.