• Proceedings of the KIEE Conference
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• 2003.07d
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• pp.2034-2036
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• 2003

본 논문에서는 21차 상태변수를 갖는 칼만필터 형태의 비행시험용 데이터 융합필터 알고리듬의 성능을 분석하였다. 실측 데이터에 대한 분석을 통하여 상태변수 선택의 적절성을 검증하였다. 공분산 해석기법을 통하여 기 개발된 데이터 융합 알고리듬의 추정값의 오차범위를 구하였다. 수치적인 성능값을 구하고자 간단한 시뮬레이터를 설계하였다. 20회 몬테칼로 시뮬레이션과 공분산 해석결과에 기반하여 필터 계수를 튜닝하였고 이를 기설계된 분산형 칼만필터에 적용하였다. 실시간 소프트웨어 모듈의 수행결과를 동일한 실측데이터를 적용한 후처리 실험결과와 비교하였다.

• Journal of Korean Tunnelling and Underground Space Association
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• v.25 no.5
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• pp.345-355
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• 2023

An analysis of covariance and Pearson correlation coefficient were performed to identify the relationship between both variables: traffic volume and crack. For this, it was carried out to analyse 216 tunnel inspection/diagnosis results with respect to the traffic. As a result, it has been proven that traffic volume and cracks in concrete linings are highly correlated. Therefore, it is recommended to consider traffic volume in planning of preemptive maintenance such as crack repair, etc.

• 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.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.35-39
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• 2011

토양과 식물의 잎에서 일어나는 증발산은 주로 증발접시, 침루계 등을 이용하여 실측하거나 에디 공분산, Bowen 비 등을 이용하여 경험적으로 측정할 수 있으나, 이러한 지점별 실측 자료는 공간적인 변동성이 큰 수문기상인자 특성상 지역적인 대표값으로 적용하는 데 어려움이 따른다. 본 연구에서는 이러한 기존 증발산 관측 방법의 단점을 보완하고자 인공위성 영상자료를 기반으로 한 원격탐사 기법을 이용하여 Penman-Monteith (PM)와 Priestley-Taylor (PT) 공간 증발산 산정 모형을 적용, 우리나라 증발산의 시공간적인 분포를 산정하였다. Terra 인공위성에 탑재된 Moderate Resolution Imaging Spectroradiometer (MODIS)로부터 제공되는 위성 영상 자료를 이용하여 기존에 연구된 증발산 모형을 이용하여 증발산을 산정하고 이를 상호 비교함으로써 우리나라에 대한 적용성을 검토하였다. 본 연구의 결과는 토양 및 식생이 소비하는 물의 양을 보다 정확하게 시공간적으로 파악하여 정부 차원의 수자원 관리 계획 수립에 유용하게 이용될 것이다.

• 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.

• Journal of the Korean Data and Information Science Society
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• v.27 no.2
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• pp.381-394
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• 2016

Graphical model represents conditional dependencies between variables as a graph with nodes and edges. It is widely used in various fields including physics, economics, and biology to describe complex association. Conditional dependencies can be estimated from a inverse covariance matrix, where zero off-diagonal elements denote conditional independence of corresponding variables. This paper proposes a efficient BCDR (block coordinate descent with random permutation) algorithm using graphics processing units and random permutation for the CONCORD (convex correlation selection method) based on the BCD (block coordinate descent) algorithm, which estimates a inverse covariance matrix based on pseudo-likelihood. We conduct numerical studies for two network structures to demonstrate the efficiency of the proposed algorithm for the CONCORD in terms of computation times.

• The Korean Journal of Applied Statistics
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• v.24 no.5
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• pp.963-977
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• 2011

PCA(Principal Component Analysis) is a well-studied statistical technique and an important tool for handling multivariate data. Although many algorithms exist for PCA, most of them are unsuitable for real time applications or high dimensional problems. Since it is desirable to avoid extensive matrix operations in such cases, alternative solutions are required to calculate the eigenvalues and eigenvectors of the sample covariance matrix. Erdogmus et al. (2004) proposed Recursive PCA(RPCA), which is a fast adaptive on-line solution for PCA, based on the first order perturbation theory. It facilitates the real-time implementation of PCA by recursively approximating updated eigenvalues and eigenvectors. However, the performance of the RPCA method becomes questionable as the size of newly-added data increases. In this paper, we modified the RPCA method by taking advantage of the mathematical relation of eigenvalues and eigenvectors of sample covariance matrix. We compared the performance of the proposed algorithm with that of RPCA, and found that the accuracy of the proposed method remarkably improved.

• Journal of the Institute of Electronics Engineers of Korea SP
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• v.37 no.6
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• pp.48-59
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• 2000

Eigendecomposition based direction-of-arrival(DOA) estimation algorithm such as MUSIC(multiple signal classification) is known to perform well and provide high resolution in white noise environment. However, its performance degrades severely when the noise process is not white. In this paper we consider the DOA estimation problem in a colored noise environment as a problem of extracting periodic signals from noise, and we take the problem to the wavelet domain. Covariance matrix of multiscale components which are obtained by taking wavelet decomposition on the noise has a special structure which can be approximated with a banded sparse matrix. Compared with noise the correlation between multiscale components of narrowband signal decays slowly, hence the covariance matrix does not have a banded structure. Based on this fact we propose a DOA estimation algorithm that transforms the covariance matrix into wavelet domain and removes noise components located in specific bands. Simulations have been carried out to analyze the proposed algorithm in colored noise processes with various correlation properties.

• 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.

• 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.