• The Korean Journal of Applied Statistics
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• v.22 no.1
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• pp.181-188
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• 2009

This paper discusses the covariance structures of data collected from an experiment with a nested design structure, where a smaller experimental unit is nested within a larger one. Due to the nonrandomization of repeated measures factors to the nested experimental units, compound symmetry covariance structure is assumed for the analysis of data. Treatments are given as the combinations of the levels of random factors and fixed factors. So, a mixed-effects model is suggested under compound symmetry structure. An example is presented to illustrate the nesting in the experimental units and to show how to get the parameter estimates in the fitted model.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.21 no.1
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• pp.20-27
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• 2010

In this paper, we presented a signal model of STAP and actual environment of clutter. The novel estimation method of clutter covariance matrix using median value is proposed to overcome serious performance degradation after NHD in nonhomogeneous clutter. Eigen value characteristic is improved through diagonal loading. Target detection ability and SINR loss of the proposed method though MSMI statistic is also compared with conventional method using average value. The simulation results, confirm the proposed method has better performance than others.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.29 no.7
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• pp.560-565
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• 2018

The SPICE (Sparse Iterative Covariance-based Estimation) algorithm estimates the azimuth angle by applying a sparse recovery method to the covariance matrix in the time domain. In this paper, we show how the SPICE algorithm, which was originally formulated in the time domain, can be extended to the frequency domain. Furthermore, we demonstrate, through numerical results, that the performance of the proposed algorithm is superior to that of the conventional frequency domain algorithm.

• Journal of Information Processing Systems
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• v.13 no.2
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• pp.400-416
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• 2017

Music emotion is an important component in the field of music information retrieval and computational musicology. This paper proposes an approach for automatic emotion classification, based on rough set (RS) theory. In the proposed approach, four different sets of music features are extracted, representing dynamics, rhythm, spectral, and harmony. From the features, five different statistical parameters are considered as attributes, including up to the $4^{th}$ order central moments of each feature, and covariance components of mutual ones. The large number of attributes is controlled by RS-based approach, in which superfluous features are removed, to obtain indispensable ones. In addition, RS-based approach makes it possible to visualize which attributes play a significant role in the generated rules, and also determine the strength of each rule for classification. The experiments have been performed to find out which audio features and which of the different statistical parameters derived from them are important for emotion classification. Also, the resulting indispensable attributes and the usefulness of covariance components have been discussed. The overall classification accuracy with all statistical parameters has recorded comparatively better than currently existing methods on a pair of datasets.

• Structural Engineering and Mechanics
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• v.47 no.3
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• pp.361-381
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• 2013

A Bayesian probabilistic method is proposed for online estimation of the process noise and measurement noise parameters for Kalman filter. Kalman filter is a well-known recursive algorithm for state estimation of dynamical systems. In this algorithm, it is required to prescribe the covariance matrices of the process noise and measurement noise. However, inappropriate choice of these covariance matrices substantially deteriorates the performance of the Kalman filter. In this paper, a probabilistic method is proposed for online estimation of the noise parameters which govern the noise covariance matrices. The proposed Bayesian method not only estimates the optimal noise parameters but also quantifies the associated estimation uncertainty in an online manner. By utilizing the estimated noise parameters, reliable state estimation can be accomplished. Moreover, the proposed method does not assume any stationarity condition of the process noise and/or measurement noise. By removing the stationarity constraint, the proposed method enhances the applicability of the state estimation algorithm for nonstationary circumstances generally encountered in practice. To illustrate the efficacy and efficiency of the proposed method, examples using a fifty-story building with different stationarity scenarios of the process noise and measurement noise are presented.

• Journal of Labour Economics
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• v.36 no.3
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• pp.97-128
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• 2013

This paper analyzes the changing factors of income inequality by approaches to income sources focusing on the effect of husband-wife labor income relation and employment ratio change on income inequality. The level of contribution of income source to income inequality showed that the inequality of labor income between husband and wife(especially the husband's income) takes great importance. Also, the covariance of labor income of husband and wife showed growing trend and changed toward the same direction. As a result of the decomposition of the labor income covariance, it was found that the change in husband-wife employment ratio explained the covariance change better than the change in labor income inequality.

• The Korean Journal of Applied Statistics
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• v.13 no.2
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• pp.343-352
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• 2000

In this study. the author proposes a nonhierarchical clustering method. called the "Double K-Means Clustering", which performs clustering of multivariate observations with the following algorithm: Step I: Carry out the ordinary K-means clmitering and obtain k temporary clusters with sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-I: Allocate the observation x, to the cluster F if it satisfies ..... where N is the total number of observations, for -i = 1, . ,N. $\bullet$ Step II-2: Update cluster sizes $n_1$,... , $n_k$, centroids $c_$1,..., $c_k$ and pooled covariance matrix S. $\bullet$ Step II-3: Repeat Steps II-I and II-2 until the change becomes negligible. The double K-means clustering is nearly "optimal" under the mixture of k multivariate normal distributions with the common covariance matrix. Also, it is nearly affine invariant, with the data-analytic implication that variable standardizations are not that required. The method is numerically demonstrated on Fisher's iris data.

• Nuclear Engineering and Technology
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• v.46 no.3
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• pp.291-298
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• 2014

This paper concerns estimating uncertainties of the core neutronics design parameters of power reactors by direct sampling method (DSM) calculations based on the two-step McCARD/MASTER design system in which McCARD is used to generate the fuel assembly (FA) homogenized few group constants (FGCs) while MASTER is used to conduct the core neutronics design computation. It presents an extended application of the uncertainty propagation analysis method originally designed for uncertainty quantification of the FA FGCs as a way to produce the covariances between the FGCs of any pair of FAs comprising the core, or the covariance matrix of the FA FGCs required for random sampling of the FA FGCs input sets into direct sampling core calculations by MASTER. For illustrative purposes, the uncertainties of core design parameters such as the effective multiplication factor ($k_{eff}$), normalized FA power densities, power peaking factors, etc. for the beginning of life (BOL) core of Yonggwang nuclear unit 4 (YGN4) at the hot zero power and all rods out are estimated by the McCARD/MASTER-based DSM computations. The results are compared with those from the uncertainty propagation analysis method based on the McCARD-predicted sensitivity coefficients of nuclear design parameters and the cross section covariance data.

• Proceedings of the Korean Association for Survey Research Conference
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• 2007.11a
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• pp.55-65
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• 2007

In this research, it will be examined on mathematical model of AMOS software program that ues for Covariance Structure Analysis. if we have not understood to mathematical model of Covariance Structure, we fail to understand Structural equation modeling. Similarly If We were not understand to mathematical model of AMOS Software, we do not use Software adequately. Therefore we examine two sorts of Software that be designed for Structural equation modeling or Covariance Structure Analysis. In this research, We will focus on 8 assumption of Structural equation modeling and compare AMOS(Analysis of MOment Structure) program with LISREL(Linear Structure RELation) program. We found that A Program of AMOS Software have materialized with RAM(Reticular Action Model).

• Journal of Institute of Control, Robotics and Systems
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• v.21 no.3
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• pp.265-275
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• 2015

This paper describes extraction methods of five different types of geometrical features (line, arc, corner, polynomial curve, NURBS curve) from the obtained raw data by using a two-dimensional laser range finder (LRF). Natural features with their covariance matrices play a key role in the realization of feature-based simultaneous localization and mapping (SLAM), which can be used to represent the environment and correct the pose of mobile robot. The covariance matrices of these geometrical features are derived in detail based on the raw sensor data and the uncertainty of LRF. Several comparison are made and discussed to highlight the advantages and drawbacks of each type of geometrical feature. Finally, the extracted features from raw sensor data obtained by using a LRF in an indoor environment are used to validate the proposed extraction methods.