• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 1999.11a
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• pp.169-172
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• 1999

Matched field processing technique is performed by considering complex underwater environments. Specially, tile performance of minimum variance processor is greatly degraded by eigenvalue problem. In this paper, we .propose the minimum valiance matched field processor using shaping matrix. This shaping matrix makes that the input covariance matrix is invertible and enhances the desired acoustic source component. It was proved effectively range/depth localization of the proposed method with vertical array data collected by NATO SACLANT Center north of the island of Elba off the Italian west coast.

• The Korean Journal of Applied Statistics
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• v.4 no.1
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• pp.1-11
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• 1991

It is of great interest to consider the homogeniety of covariance matrices in MANOVA of discriminant analysis. If we lock at the problem of testing hypothesis, H : $\Sigma_1 = \Sigma_2$ from an invariance point of view where $\Sigma_i$ are the covariance matrix of two independent p-variate distribution, the testing problem is invariant under the group of nonsingular transformations and the hypothesis becomes H : $\delta_1 = \delta_2 = \cdots = \delta_p = 1$ where $\delta = (\delta_1, \delta_2, \cdots, \delta_p)$ is a vector of latent roots of $\Sigma$. Bias-corrected estimators of eigenvalues and sampling distribution of the test statistics proposed are obtained. Pooled-bootstrap method also considered for Bartlett's modified likelihood ratio statistics.

• The Journal of the Acoustical Society of Korea
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• v.39 no.2
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• pp.120-130
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• 2020

This paper solves the problem in underdetermined convolutive mixture by improving the disadvantages of the multichannel nonnegative matrix factorization technique widely used in blind source separation. In conventional researches based on Spatial Covariance Matrix (SCM), each element composed of values such as power gain of single channel and correlation tends to degrade the quality of the separated sources due to high variance. In this paper, level and frequency normalization is performed to effectively cluster the estimated sources. Therefore, we propose a novel SCM and an effective distance function for cluster pairs. In this paper, the proposed SCM is used for the initialization of the spatial model and used for hierarchical agglomerative clustering in the bottom-up approach. The proposed algorithm was experimented using the 'Signal Separation Evaluation Campaign 2008 development dataset'. As a result, the improvement in most of the performance indicators was confirmed by utilizing the 'Blind Source Separation Eval toolbox', an objective source separation quality verification tool, and especially the performance superiority of the typical SDR of 1 dB to 3.5 dB was verified.

• The Korean Journal of Applied Statistics
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• v.28 no.2
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• pp.151-158
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• 2015

This paper discusses how to use projections for the analysis of data from balanced incomplete block designs. A model is suggested as a matrix form for the interblock analysis. A second set of treatment effects can be found by projections from the suggested interblock model. The variance and covariance matrix of two estimated vectors of treatment effects is derived. The uncorrelation of two estimated vectors can be verified from their covaraince structure. The fitting constants method is employed for the calculation of block sum of squares adjusted for treatment effects.

• Proceedings of the Korean Statistical Society Conference
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• 2003.05a
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• pp.127-130
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• 2003

주변분포가 Laplace 분포인 세 가지 형태의 이변량 Laplace 분포를 연구한다. 각각의 이변량 Laplace 분포의 확률밀도함수와 누적분포함수를 유도하고, 분포의 그래프를 그려봄으로써 분포의 형태를 알아본다. 조건부 적률을 정리하여 조건부 첨도와 조건부 왜도를 구하고 분포의 성질을 파악한다. 상관계수를 구하여 다른 이변량 분포의 상관계수와 비교해 보았다. 그리고 정의된 분포함수를 응용하여 이변량 Laplace 분포를 따르는 난수벡터를 발생하는 알고리즘을 제안하였으며, 생성된 난수벡터의 표본으로부터 구한 표본평균과 중앙값의 분산-공분산 행렬식을 구하고 이변량 정규분포에 대응하는 행렬식과 비교 토론하였다.

• Communications for Statistical Applications and Methods
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• v.15 no.3
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• pp.333-342
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• 2008

Variable selection theorem in the linear regression model is extended to the analysis of covariance model. When some of regression variables are omitted from the model, it reduces the variance of the estimators but introduces bias. Thus an appropriate balance between a biased model and one with large variances is recommended.

• Journal of Korean Society of Forest Science
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• v.106 no.2
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• pp.213-218
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• 2017

The purpose of this study was to develop age-biomass model in Chungnam region containing random effect. To develop the biomass model by species and tree component, data for Pinus densiflora in central region is collected to 30 plots (150 trees). The mixed model were used to fixed effect in the age-biomass relation for Pinus densiflora, with random effect representing correlation of survey area were obtained. To verify the evaluation of the model for random effect, the akaike information criterion (abbreviated as, AIC) was used to calculate the variance-covariance matrix, and residual of repeated data. The estimated variance-covariance matrix, and residual were -1.0022, 0.6240, respectively. The model with random effect (AIC=377.2) has low AIC value, comparison with other study relating to random effects. It is for this reason that random effect associated with categorical data were used in the data fitting process, the model can be calibrated to fit the Chungnam region by obtaining measurements. Therefore, the results of this study could be useful method for developing biomass model using random effects by region.

• The Korean Journal of Applied Statistics
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• v.27 no.6
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• pp.923-932
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• 2014

The Hurdle model can to analyze zero-inflated count data. This model is a mixed model of the logit model for a binary component and a truncated Poisson model of a truncated count component. We propose a new hurdle model with a general heterogeneous random effects covariance matrix to analyze longitudinal zero-inflated count data using modified Cholesky decomposition. This decomposition factors the random effects covariance matrix into generalized autoregressive parameters and innovation variance. The parameters are modeled using (generalized) linear models and estimated with a Bayesian method. We use these methods to carefully analyze a real dataset.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.37 no.1
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• pp.36-41
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• 2009

In general, accurate attitude information is essential to perform the mission. Two algorithms are well-known to determine the attitude through two or more vector observations. One is deterministic method such as TRIAD algorithm, the other is optimal method such as QUEST algorithm. This Paper suggests the idea to improve performance of the TRIAD algorithm and to determine the attitude by combination of different sensors. First, we change the attitude matrix to Euler angle instead of using orthogonalization method and also use covariance in place of variance, then apply an unbiased minimum variance formula for more accurate solutions. We also suggest the methodology to determine the attitude when more than two measurements are given. The performance of the Averaging TRIAD algorithm upon the combination of different sensors is analyzed by numerical simulation in terms of standard deviation and probability.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.28 no.10
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• pp.810-818
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• 2017

This paper presents the approaching target above ground tracking based on Kalman filter applied to the proximity sensor for the active defense system. The proximity sensor located on the front of the countermeasure is not easy to detect when the anti-tank threat enters a fragment dispersion range due to limited antenna beamwidth. In addition, it is difficult for the proximity sensor to detect the anti-tank threat accurately at a terrestrial environment including various clutters. To solve these problems, this study presents the approaching target above ground tracking based on Kalman filter and applies the novel estimation method for a noise covariance matrix to improve a tracking performance. Then, a high tracking performance of Kalman filter applied the proposed noise covariance matrix is presented through field firing test results and the validity of the proposed study is examined.