• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.34-39
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• 1993

Presented in this paper is a complete error covariance analysis for strapdown inertial navigation system(SDINS). We have found that in SDINS the cross-coupling terms in gyrocompass alignment errors can significantly influence the SDINS error propagation. Initial heading error has a close correlation with the east component of gyro bias erro, while initial level tilt errors are closely related to accelerometer bias errors. In addition, pseudo-state variables are introduced in covariance analysis for SDINS utilizing the characteristics of gyrocompass alignment errors. This approach simplifies the covariance analysis because it makes the initial error covariance matrix to a diagonal form. Thus a real implementation becomes easier. The approach is conformed by comparing the results for a simplified case with the covariance analysis obtained from the conventional SDINS error model.

• Phonetics and Speech Sciences
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• v.1 no.1
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• pp.69-73
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• 2009

This paper proposes an efficient global covariance-based principal component analysis (GCPCA) for speaker identification. Principal component analysis (PCA) is a feature extraction method which reduces the dimension of the feature vectors and the correlation among the feature vectors by projecting the original feature space into a small subspace through a transformation. However, it requires a larger amount of training data when performing PCA to find the eigenvalue and eigenvector matrix using the full covariance matrix by each speaker. The proposed method first calculates the global covariance matrix using training data of all speakers. It then finds the eigenvalue matrix and the corresponding eigenvector matrix from the global covariance matrix. Compared to conventional PCA and Gaussian mixture model (GMM) methods, the proposed method shows better performance while requiring less storage space and complexity in speaker identification.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.264-267
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• 1991

In this paper, a new application method of the Kalman filter to desigin a gyro is proposed. The role of a gyro is the estimation of an input rate with minimal error covariance. The size of the error covariance depends on gyro's parameters, which makes it possible to use the parameters of gyro to minimze the estimation error covariance. Numerical analysis shows that the error covariance becomes smaller as the spin axis momentum becomes larger and the damping coefficient smaller, but production cost must be considered. Through numerical analysis the parameter set for an acceptable - performance gyro with small cost can be selected.

• Proceedings of the KIEE Conference
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• 1990.07a
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• pp.72-76
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• 1990

A new statistical linearization technique for nonlinear system called covariance matching method is proposed in this paper. The covariance matching method makes the mean and variance of an approximated output be identical real functional output, and the distribution of the approximated output have identical shape with a given random input. Also, the covariance matching method can be easily implemented for statistical analysis of nonlinear systems with a combination of linear system covariance analysis.

• Korean Journal of Clinical Laboratory Science
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• v.45 no.2
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• pp.60-65
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• 2013

Data from repeated measurements are accomplished through repeatedly processing the same subject under different conditions and different points of view. The power of testing enhances the choice of pertinent analysis methods that agrees with the characteristics of data concerned and the situation involved. Along with the clinical example, this paper compares the analysis of the variance on ex-post tests, gain score analysis, analysis by mixed design and analysis of covariance employable for repeating measure. Comparing the analysis of variance on ex post test, and gain score analysis on correlations, leads to the fact that the latter enhances the power of the test and diminishes the variance of error terms. The concluded probability, identified that the gain score analysis and the mixed design on interaction between "between subjects factor" and "within subjects factor", are identical. The analysis of covariance, demonstrated better power of the test and smaller error terms than the gain score analysis. Research on four analysis method found that the analysis of covariance is the most appropriate in clinical data than two repeated test with high correlation and ex ante affects ex post.

• Journal of Preventive Medicine and Public Health
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• v.6 no.1
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• pp.77-79
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• 1973

The Mechanism for sorting out the covariance effect is known as the covariance analysis. The sorting out of regression and correlation effect is an obvious application of the covariance analysis. The result of Do population by age groups (15 ages interval) from 1966 Census and from 1970 Census has been applied to analyzing covariability by the analysis of covariance. The results are as follows. (1) The signicance of the regression of 1970 population on 1966 population is assured as F=116.5 (2) There is a significant difference between mean of each age group. (F=88.1) (3) There is very little evidence of significant heterogeneity of regression between age groups. (F=0.72)

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2002.05a
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• pp.257-260
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• 2002

The impressions for odors are subjective and have individual differences. In this study, the Impressions of odors were investigated by covariance structure analysis. 46 subjects (men in their twenty) recorded their reactions to ten odorants by grading them on a seven-point scale in terms of twelve adjective pairs. Their reactions were quantified by using factor analysis and covariance structure analysis. The factors were extracted as "preference", "arousal" and "persistency". The subjects were classified into three groups according to the most suitable causal models (structural equation models). Each group had different causal relationship and different impression structure for odors. It was suggested that there is a possibility to evaluate the subjective impression of odor using covariance structure analysis.

• Journal of the Korean Statistical Society
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• v.23 no.1
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• pp.53-62
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• 1994

When a comparison is made with respect to the unknown best treatment, Hsu (1984, 1985) proposed the so called multiple comparisons procedures with the best in the analysis of variance model. Applying Hsu's results to the analysis of covariance model, simultaneous confidence intervals for multiple comparisons with the best in a balanced one-way layout with a random covariate are developed and are applied to a real data example.

• Journal of Astronomy and Space Sciences
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• v.29 no.3
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• pp.287-293
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• 2012

Satellite operating agencies are constantly monitoring conjunctions between satellites and space objects. Two line element (TLE) data, published by the Joint Space Operations Center of the United States Strategic Command, are available as raw data for a preliminary analysis of initial conjunction with a space object without any orbital information. However, there exist several sorts of uncertainties in the TLE data. In this paper, we suggest and analyze a method for estimating the uncertainties in the TLE data through mean, standard deviation of state vector residuals and covariance matrix. Also the estimation results are compared with actual results of orbit determination to validate the estimation method. Characteristics of the state vector residuals depending on the orbital elements are examined by applying the analysis to several satellites in various orbits. Main source of difference between the covariance matrices are also analyzed by comparing the matrices. Particularly, for the Korea Multi-Purpose Satellite-2, we examine the characteristics of the residual variation of state vector and covariance matrix depending on the orbital elements. It is confirmed that a realistic consideration on the space situation of space objects is possible using information from the analysis of mean, standard deviation of the state vector residuals of TLE and covariance matrix.

• Journal of Broadcast Engineering
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• v.21 no.1
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• pp.60-65
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• 2016

This paper introduces the non-local means (NLM) algorithm for image denoising, and also introduces an improved algorithm which is based on the principal component analysis (PCA). To do the PCA, a covariance matrix of a given image should be evaluated first. If we let the size of neighborhood patches of the NLM S × S², and let the number of pixels Q, a matrix multiplication of the size S² × Q is required to compute a covariance matrix. According to the characteristic of images, such computation is inefficient. Therefore, this paper proposes an efficient method to compute the covariance matrix by sampling the pixels. After sampling, the covariance matrix can be computed with matrices of the size S² × floor (Width/l) × (Height/l).