• Journal of the Korean Data and Information Science Society
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• v.25 no.4
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• pp.799-805
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• 2014

This paper deals with a method for getting the Type III sums of squares on the basis of projections under the assumption of two-way fixed effects model. For unbalanced data in general total sum of squares is not equal to the sum of componentwise Type III sums of squares. There are some differencies between two quantities. The suggested method using projections can detect where the differences occur and how much they are different. The traditional ANOVA method could not explain clearly the differences. It also discusses how eigenvectors and eigenvalues of the projection matrices can be used to get the Type III sums of squares.

• The Journal of the Acoustical Society of Korea
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• v.29 no.2
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• pp.97-101
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• 2010

FIR type system identification with noisy input and output data can be solved by a total least squares (TLS) estimation. However, the performance of the TLS estimation is very sensitive to the ratio between the variances of the input and output noises. In this paper, we propose an iterative convex combination algorithm between TLS and least squares (LS). This combined algorithm shows robustness against the noise variance ratio. Consequently, the practical workability of the TLS method with noisy data has been significantly broadened.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.29 no.6 s.237
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• pp.860-875
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• 2005

A new meshfree method for the analysis of elasto-plastic deformations is presented. The method is based on the proposed first-order least-squares formulation, to which the moving least-squares approximation is applied. The least-squares formulation for the classical elasto-plasticity and its extension to an incrementally objective formulation for finite deformations are proposed. In the formulation, the equilibrium equation and flow rule are enforced in least-squares sense, while the hardening law and loading/unloading condition are enforced exactly at each integration point. The closest point projection method for the integration of rate-form constitutive equation is inherently involved in the formulation, and thus the radial-return mapping algorithm is not performed explicitly. Also the penalty schemes for the enforcement of the boundary and frictional contact conditions are devised. The main benefit of the proposed method is that any structure of cells is not used during the whole process of analysis. Through some numerical examples of metal forming processes, the validity and effectiveness of the method are presented.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.5
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• pp.2523-2538
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• 2017

In most of the existing literature, the definition of the class label has the following characteristics. First, the class label of the samples from the same object has an absolutely fixed value. Second, the difference between class labels of the samples from different objects should be maximized. However, the appearance of a face varies greatly due to the variations of the illumination, pose, and expression. Therefore, the previous definition of class label is not quite reasonable. Inspired by discriminative least squares regression algorithm (DLSR), a noisy label based discriminative least squares regression algorithm (NLDLSR) is presented in this paper. In our algorithm, the maximization difference between the class labels of the samples from different objects should be satisfied. Meanwhile, the class label of the different samples from the same object is allowed to have small difference, which is consistent with the fact that the different samples from the same object have some differences. In addition, the proposed NLDLSR is expanded to the kernel space, and we further propose a novel kernel noisy label based discriminative least squares regression algorithm (KNLDLSR). A large number of experiments show that our proposed algorithms can achieve very good performance.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.29 no.2C
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• pp.255-261
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• 2004

The problem of geolocation using multiple satellites is to determine the position of a transmitter located on the Earth by processing received signals. The specific problem addressed in this paper is that of estimating the position of a stationary transmitter located on or above the Earth's surface from measured time difference of arrivals (TDOA) by a geostationary orbiting (GSO) satellite and a low earth orbiting (LEO) satellite. The proposed geolocation method is based on the total least squares (TLS) algorithm. Under erroneous positions of the satellites together with noisy TDOA measurements, the TLS algorithm provides a better solution. By running Monte-Carlo simulations, the proposed method is compared with the ordinary least squares (LS) approach.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2216-2218
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• 2002

A system identification is to measure the output in the presence of a adequate input for the controlled system and to estimate the mathematical model in the basic of input output data. In the system identification, it is possible to estimate the true parameter values by the adjusted least squares method in the input-output case of no observed noise, and it is possible to estimate the true parameter values by the total least squares method in the input-output case with the observed noise. In recent the adjusted least squares method is suggested as a consistent estimation method in the system identification not with the observed noise input but with the observed noise output. In this paper we have developed the adjusted least squares method from the least squares method and have made certain of the efficiency in comparing the estimating results with the generating data by the computer simulations.

• The Korean Journal of Applied Statistics
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• v.21 no.2
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• pp.275-290
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• 2008

The size of microarray gene expression data is very big and its observation process is also very complex. Thus missing values are frequently occurred. In this paper we propose the sequential partial least squares(SPLS) regression fitting method to estimate missing values for time course gene expression data that has correlations among observations over time points. The SPLS method is to combine the sequential technique with the partial least squares(PLS) regression fitting method. The usefulness of method proposed is evaluated through some simulation study for three yeast time course data.

• The Journal of the Acoustical Society of Korea
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• v.40 no.3
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• pp.213-218
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• 2021

It is known that total least-squares method shows better estimation performance than least-squares method when noise is present at the input and output at the same time. When total least squares method is applied to data with time series characteristics, Recursive Total Least Squares (RTS) algorithm has been proposed to improve the real-time performance. However, RTLS has numerical instability in calculating the inverse matrix. In this paper, we propose an algorithm for reducing numerical instability as well as having similar convergence to RTLS. For this algorithm, we propose a new RTLS using Iterative Wiener Filter (IWF). Through the simulation, it is shown that the convergence of the proposed algorithm is similar to that of the RTLS, and the numerical robustness is superior to the RTLS.

• East Asian mathematical journal
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• v.15 no.1
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• pp.121-133
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• 1999

• Proceedings of the Korea Society of Design Studies Conference
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• 2001.05a
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• pp.22-23
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• 2001