• Journal of the Korean Data and Information Science Society
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• 제28권1호
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• pp.227-235
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• 2017

When the spatial information of each location is given specifically as coordinates it is popular to use the geographically weighted regression to incorporate the spatial information by assuming that the regression parameters vary spatially across locations. In this paper, we relax the linearity assumption of geographically weighted regression and propose a geographically weighted least squares-support vector machine for estimating geographically weighted mean by using the basic concept of kernel machines. Generalized cross validation function is induced for the model selection. Numerical studies with real datasets have been conducted to compare the performance of proposed method with other methods for predicting geographically weighted mean.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1992년도 하계학술대회 논문집 A
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• pp.319-322
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• 1992

This paper proposes indirect adaptive pole placement adaptive controller using a modified least squares method. If an adaptive controller has good performance, it is necessary that an estimator have fast convergence. This paper presents a modified least squares method which guarantees the stability of estimator and has fast convergence. In this algorithm, information on signal level is obtained from the determinent of covariance matrix and according to it, weighting factor is tuned.

• Journal of Mechanical Science and Technology
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• 제17권7호
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• pp.1026-1035
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• 2003

A computer simulation method for investigating the form generation mechanism in the centerless infeed (plunge) grinding process is described. For a 3-D simulation model of form generation, contact points are assumed to be on least squares contact lines at the grinding wheel, regulating wheel, and work-rest blade. Using force and deflection analyses, the validity of this assumption is shown. Based on the 2-D simulation model developed in the previous work and the least squares contact line assumption, a 3-D model is presented. To validate this model, simulation results were compared with the experimental works. The experiments and computer simulations were carried out using three types of cylindrical workpiece shapes with varying flat length. The experimental results agree well with the simulation. It can be seen that the effect of flat end propagated to the opposite end through workpiece reorientation.

• 자원환경지질
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• 제28권4호
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• pp.433-438
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• 1995

The most popular minimization method is based on the least-squares criterion, which uses the $L_2$ norm to quantify the misfit between observed and synthetic data. The solution of the least-squares problem is the maximum likelihood point of a probability density containing data with Gaussian uncertainties. The distribution of errors in the geophysical data is, however, seldom Gaussian. Using the $L_2$ norm, large and sparsely distributed errors adversely affect the solution, and the estimated model parameters may even be completely unphysical. On the other hand, the least-absolute-deviation optimization, which is based on the $L_1$ norm, has much more robust statistical properties in the presence of noise. The solution of the $L_1$ problem is the maximum likelihood point of a probability density containing data with longer-tailed errors than the Gaussian distribution. Thus, the $L_1$ norm gives more reliable estimates when a small number of large errors contaminate the data. The effect of outliers is further reduced by M-fitting method with Cauchy error criterion, which can be performed by iteratively reweighted least-squares method.

• Asian Pacific Journal of Cancer Prevention
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• 제15권7호
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• pp.3145-3149
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• 2014

Gene expression profiling facilitates the understanding of biological characteristics of gliomas. Previous studies mainly used regression/variance analysis without considering various background biological and environmental factors. The aim of this study was to investigate gene expression differences between grade III and IV gliomas through partial least squares (PLS) based analysis. The expression data set was from the Gene Expression Omnibus database. PLS based analysis was performed with the R statistical software. A total of 1,378 differentially expressed genes were identified. Survival analysis identified four pathways, including Prion diseases, colorectal cancer, CAMs, and PI3K-Akt signaling, which may be related with the prognosis of the patients. Network analysis identified two hub genes, ELAVL1 and FN1, which have been reported to be related with glioma previously. Our results provide new understanding of glioma pathogenesis and prognosis with the hope to offer theoretical support for future therapeutic studies.

• Journal of applied mathematics & informatics
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• 제22권1_2호
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• pp.95-112
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• 2006

In this paper, the authors investigate the condition number with their condition numbers for weighted Moore-Penrose inverse and weighted least squares solution of min /Ax - b/M, where A is a rank-deficient complex matrix in $C^{m{\times}n} $ and b a vector of length m in $C^m$, x a vector of length n in $C^n$. For the normwise condition number, the sensitivity of the relative condition number itself is studied, the componentwise perturbation is also investigated.

• Journal of applied mathematics & informatics
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• 제27권5_6호
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• pp.1061-1071
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• 2009

For real generalized reflexive matrices R, S, i.e., $R^T$ = R, $R^2$ = I, $S^T$ = S, $S^2$ = I, we say that real matrix X is (R,S)-symmetric, if RXS = X. In this paper, an iterative algorithm is proposed to solve the least-squares problem of matrix equation AXB = C with (R,S)-symmetric X. Furthermore, the optimal approximation solution to given matrix $X_0$ is also derived by this iterative algorithm. Finally, given numerical example and its convergent curve show that this method is feasible and efficient.

• Journal of Mechanical Science and Technology
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• 제17권5호
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• pp.698-707
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• 2003

A new quadratic response surface modeling method is presented. In this method, the incomplete small composite design (ISCD) is newly proposed to .educe the number of experimental runs than that of the SCD. Unlike the SCD, the proposed ISCD always gives a unique design assessed on the number of factors, although it may induce the rank-deficiency in the normal equation. Thus, the singular value decomposition (SVD) is employed to solve the normal equation. Then, the duality theory is used to newly develop the conservative least squares fitting (CONFIT) method. This can directly control the ever- or the under-estimation behavior of the approximate functions. Finally, the performance of CONFIT is numerically shown by comparing its'conservativeness with that of conventional fitting method. Also, optimizing one practical design problem numerically shows the effectiveness of the sequential approximate optimization (SAO) combined with the proposed ISCD and CONFIT.

• Journal of Advanced Marine Engineering and Technology
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• 제20권2호
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• pp.108-108
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• 1996

This paper describes a new approach to solve balancing of a rigid rotor. In this paper, the balancing of the rigid rotor using genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics is proposed. Under the assumption that the initial vibration values used to calculate correction masses contain errors, the influence coefficient method, the least squares method and a genetic algorithm are compared. The results show that the vibration amplitude obtained with the least squares method and the genetic algorithm is smaller than that obtained with the influence coefficient method.

• Journal of Advanced Marine Engineering and Technology
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• 제20권2호
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• pp.40-47
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• 1996

This paper describes a new approach to solve balancing of a rigid rotor. In this paper, the balancing of the rigid rotor using genetic algorithms, which are search algorithms based on the mechanics of natural selection and natural genetics is proposed. Under the assumption that the initial vibration values used to calculate correction masses contain errors, the influence coefficient method, the least squares method and a genetic algorithm are compared. The results show that the vibration amplitude obtained with the least squares method and the genetic algorithm is smaller than that obtained with the influence coefficient method.