• Journal of the Ergonomics Society of Korea
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• v.20 no.1
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• pp.45-62
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• 2001

This study suggests a genetic algorithm-based partial least squares (GA-based PLS) method to select the design variables for building a usability model. The GA-based PLS uses a genetic algorithm to minimize the root-mean-squared error of a partial least square regression model. A multiple linear regression method is applied to build a usability model that contains the variables seleded by the GA-based PLS. The performance of the usability model turned out to be generally better than that of the previous usability models using other variable selection methods such as expert rating, principal component analysis, cluster analysis, and partial least squares. Furthermore, the model performance was drastically improved by supplementing the category type variables selected by the GA-based PLS in the usability model. It is recommended that the GA-based PLS be applied to the variable selection for developing a usability model.

• Journal of KIISE
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• v.45 no.1
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• pp.69-75
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• 2018

In the streaming data where data samples arrive sequentially in time, it is difficult to apply the dimension reduction method based on batch learning. Therefore an incremental dimension reduction method for the application to streaming data has been studied. In this paper, we propose an incremental linear discriminant analysis method using the least squared error solution. Instead of computing scatter matrices directly, the proposed method incrementally updates the projective direction for dimension reduction by using the information of a new incoming sample. The experimental results demonstrate that the proposed method is more efficient compared with previously proposed incremental dimension reduction methods.

• The Pure and Applied Mathematics
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• v.11 no.2
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• pp.103-115
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• 2004

A computational algorithm is developed for fitting given data in the plane or in 3-space by implicitly defined quadrics. Implicity implies that the type of the quadric is part of the model and need not be known in advance. Starting with some estimate for the coefficients of the quadric the method will alternatively determine the shortest distances from the given points onto the quadric and adapt the coefficients such as to reduce the sum of those squared distances. Numerical examples are given.

• Proceedings of the IEEK Conference
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• 1998.10a
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• pp.943-946
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• 1998

In this paper, we applied FFT to PCB Images, cutting unnecessary singals and noise, moving the starting point to center of image and used rotaion transform. from the detected edge Hough Transform identify the length, but not the angle, so we matched PCB images with using rotation transform to identify length and angle. After rotation transform we employ Least Squared Method to exact stereo matching.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.42 no.3
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• pp.661-672
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• 2017

This paper introduces the NLSE(Nonlinear Least Squared Estimator) using curved LOBs(Line Of Bearings) considering the earth curvature based on sphere to avoid the map conversion distortion and minimize the estimation error. This paper suggests a method improving a performance of the NLSE using curved LOBs by using an ellipsoid model. The analysis of the simulation results shows that the NLSE using curved LOBs has better performance than the conventional triangulation method and can improve its performance using a suggested method.

• Journal of Korean Society of Water and Wastewater
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• v.12 no.3
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• pp.99-106
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• 1998

This paper presents a procedure for determining the design rainfall depth and the design rainfall intensity at Incheon city area in Korea. In this study the eight probability distributions are considered to estimate the probable rainfall depths for 11 different durations. The Kolmogorov - Smirnov test and the Chi-square test are adopted to test each distribution. The probable rainfall intensity formulas are then determined by i) the least squares (LS) method, ii) the least median squares (LMS) method, iii) the reweighted least squares method based on the LMS (RLS), and iv) the constrained regression (CR) model. The Talbot, the Sherman, the Japanese, and the Unified type are considered to determine the best type for the Incheon station. The root mean squared (RMS) errors are computed to test the formulas derived by four methods. It is found that the Unified type is the most reliable and that all methods presented herein are acceptable for determining the coefficients of rainfall intensity formulas from an engineering point of view.

• Ocean Science Journal
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• v.43 no.2
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• pp.81-90
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• 2008

A study of estuarine flows during a neap tide was performed using 13-hour roving acoustic Doppler current profiles (ADCP) and conductivity-temperature-depth (CTD) profiles in the Altamaha River estuary, Georgia, U.S.A. The least-squared harmonic analysis method was used to fit the tidal ($M_2$) component and separate the flow into two components: the tidal and residual ($M_2$-removed) flows. We applied this method to depth-averaged data. Results show that the $M_2$ component demonstrates over 95% of the variability of observation data. As the flow was dominated by the $M_2$ tidal component in a narrow channel, the tidal ellipse distribution was essentially a back-and-forth motion. The amplitude of $M_2$ velocity component increased slightly from the river mouth (0.45 m/sec) to land (0.6 m/sec) and the phase showed fairly constant values in the center of the channel and rapidly decreasing values near the northern and southern shoaling areas. The residual flow and transport calculated from depth-averaged flow shows temporal variability over the tidal time scale. Strong landward flows appeared during slack waters which may be attributed to increased baroclinic forcing when turbulent mixing decreases.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2006.05a
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• pp.1788-1792
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• 2006

The ordered weighted averaging (OWA) operator by Yager has received more and more attention since its appearance. One key point in the OWA operator is to determine its associated weights. Among numerous methods that have appeared in the literature, we notice the maximum entropy OWA (MEOWA) weights that are determined by taking into account two appealing measures characterizing the OWA weights. Instead of maximizing the entropy in the formulation for determining the MEOWA weights, the new method in the article tries to obtain the OWA weights which are evenly spread out around equal weights as much as possible while strictly satisfying the orness value provided in the program. This consideration leads to the least squared OWA (LSOWA) weighting method in which the program tries to obtain the weights that minimize the sum of deviations from the equal weights since entropy is maximized when the weights are equal. Above all, the LSOWA weights display symmetric allocations of weights on the basis of equal weights. The positive or negative allocations of weights from the median as a basis depend on the magnitude of orness specified. Further interval LSOWA weights are constructed when a decision-maker specifies his or her value of orness in uncertain numerical bounds.

• Journal of the Korea Society of Computer and Information
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• v.16 no.8
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• pp.39-47
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• 2011

Ergezinger's method, one of the layer-by-layer algorithms used for multilyer perceptrons, consists of an output node and can make premature saturations in the output's weight because of using linear least squared method in the output layer. These saturations are obstacles to learning time and covergence. Therefore, this paper expands Ergezinger's method to be able to use an output vector instead of an output node and introduces a learning rate to improve learning time and convergence. The learning rate is a variable rate that reflects the correlation coefficient between new weight and previous weight while updating hidden's weight. To compare the proposed method with Ergezinger's method, we tested iris recognition and nonlinear approximation. It was found that the proposed method showed better results than Ergezinger's method in learning convergence. In the CPU time considering correlation coefficient computation, the proposed method saved about 35% time than the previous method.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.11 no.4
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• pp.55-68
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• 2007

First-order least-squares method of a distributed optimal control problem for the incompressible Navier-Stokes equations is considered. An optimality system for the optimal solution are reformulated to the equivalent first-order system by introducing velocity-flux variables and then the least-squares functional corresponding to the system is defined in terms of the sum of the squared $L^2$ norm of the residual equations of the system. The optimal error estimates for least-squares finite element approximations are obtained.