• Journal of Biomedical Engineering Research
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• v.15 no.4
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• pp.481-488
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• 1994

In this paper a study of neuro-pathway estimation based on visual and somatosensory evoked potential is given. The evoked potentials which are caused by visual and somatosensory stimulation are detected by an average method. The forward problem that is estimating a scalp potential from a given electrical source in the brain is solved by using a triple concentric spherical shell model of the head and a single current dipole model of the neuron activity. The inverse problem which calculates a source position is solved by a least square fit between the model predicted potential and a given evoked potential measurement. The similarities between estimated sensory neuro-pathways and physiological brain function regions are verified.

• Communications for Statistical Applications and Methods
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• v.15 no.4
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• pp.633-642
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• 2008

Variable selection algorithm for partial least square regression using penalty function is proposed. We use the fact that usual partial least square regression problem can be expressed as a maximization problem with appropriate constraints and we will add penalty function to this maximization problem. Then simulated annealing algorithm can be used in searching for optimal solutions of above maximization problem with penalty functions added. The HARD penalty function would be suggested as the best in several aspects. Illustrations with real and simulated examples are provided.

• Journal of the Korean Statistical Society
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• v.9 no.1
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• pp.61-77
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• 1980

In response surface experiments, a polynomial model is often used to fit the response surface by the method of least squares. However, if the vectors of predictor variables are multicollinear, least squares estimates of the regression parameters have a high probability of being unsatisfactory. Hoerland Kennard have demonstrated that these undesirable effects of multicollinearity can be reduced by using "ridge" estimates in place of the least squares estimates. Ridge regrssion theory in literature has been mainly concerned with selection of k for the first order polynomial regression model and the precision of $\hat{\beta}(k)$, the ridge estimator of regression parameters. The problem considered in this paper is that of selecting k of ridge regression for a given polynomial regression model with an arbitrary order. A criterion is proposed for selection of k in the context of integrated mean square error of fitted responses, and illustrated with an example. Also, a type of admissibility condition is established and proved for the propose criterion.criterion.

• Journal of computational fluids engineering
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• v.14 no.4
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• pp.93-100
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• 2009

The gridless (or meshfree) methods, such as MPS, SPH, FPM an so forth, are feasible and robust for the problems with moving boundary and/or complicated boundary shapes, because these methods do not need to generate a grid system. In this study, a gridless solver, which is based on the combination of moving least square interpolations on a cloud of points with point collocation for evaluating the derivatives of governing equations, is presented for two-dimensional unsteady incompressible Navier-Stokes problem in the low Reynolds number. A MAC-type algorithm was adopted and the Poission equation for the pressure was solved successively in the moving least square sense. Some typical problems were solved by the presented solver for the validation and the results obtained were compared with analytic solutions and the numerical results by conventional CFD methods, such as a FVM.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.7
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• pp.1321-1327
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• 2009

In this paper, as the mobile communication service is widely used and the demand for wireless repeaters is rapidly increasing because of the easiness of extending service areas. But a wireless repeater has a problem the oscillation due to feedback signal. We proposed a new hybrid interference canceller using the adaptive filter with CMA(Constant Modulus Algorithm)-Grouped LMS(Least Mean Square) algorithm in the adaptive interference canceller. The proposed interference canceller has better channel adaptive performance and a lower MSE(Mean Square Error) than conventional structure because it uses the cancellation method of Grouped LMS algorithm. The proposed detector uses the LMS algorithms with two different step size to reduce mean square error and to obtain fast convergence. This structure reduces the number of iterations for the same MSE performance and hardware complexity compared to conventional nonlinear interference canceller.

• Journal of applied mathematics & informatics
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• v.41 no.3
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• pp.647-656
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• 2023

In this work, we are concerned by the problem of identification of noisy terms which arise in singular problem as for remote sensing problems, and which are modeled by a linear singular parabolic equation. For the reason of missing some data that could be arisen when using the traditional sentinel method, the later will be changed by a new sentinel method for attaining the same purpose. Such new method is a particular least square-like method which permits one to distinguish between the missing terms and the pollution terms. In particular, a sentinel method will be given here in its more realistic setting for singular parabolic problems, where in this case, the observation and the control have their support in different open sets. The problem of finding a new sentinel is equivalent to finding singular optimality system of the least square control for the parabolic equation that we solve.

• Journal of Korea Multimedia Society
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• v.16 no.12
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• pp.1465-1474
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• 2013

In this paper, air pollutant tracing models using perceptron neural network(PNN) and non-negative least square(NNLS) are proposed. When the measured values of the air pollution and the contribution concentration of each source by chemical transport modeling are given, they estimate and trace the amount of the air pollutants emission from each source. Two kinds of emissions data are used in the experiments : CH4 and N2O of Geumgo-dong landfill greenhouse gas, and PM10 of 17 areas in Northeast Asia and eight regions of the Korean Peninsula. Emission values were calculated using pseudo inverse method, PNN and NNLS. Pseudo inverse method could be used for the model, but it may have negative emission values. In order to deal with the problem, we used the PNN and NNLS methods. As a result, the estimation using the NNLS is closer to the measured values than that using PNN. The proposed tracing models have better utilization and generalization than those of conventional pseudo inverse model. It could be used more efficiently for air quality management and air pollution reduction.

• The Transactions of the Korea Information Processing Society
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• v.4 no.12
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• pp.3200-3210
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• 1997

In image processing we encounter some tasks to detect ellipse or to discriminate the curves. LSM is well used to fit curves to ellipse but it can fail to fit correctly when fitting to defected one. To overcome this problem, we propose Modified LSM. Only 2-parameters among 5-paramaters are to be determined by LSM, while 3-parameters are to be calculated by the constrain that the curve must pass 3 given points. Those 3 points are selected by operator so as to have elliptic feature. Such proposed MLSM shows better result than genunal LSM in case when the ellipse is severely defected. and is proved to be good method for determing the human dentition.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.5
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• pp.467-473
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• 2007

In this paper, we present a position-based robot hand tracking scheme where a pan-tilt camera is controlled such that a robot hand is always shown in the center of an image frame. We calculate the rotation angles of a pan-tilt camera by transforming the coordinate systems. In order to identify the model parameters, we applied two optimization techniques: a nonlinear least square optimizer and a particle swarm optimizer. From the simulation results, it is shown that the considered parameter identification problem is characterized by a highly multimodal landscape; thus, a global optimization technique such as a particle swarm optimization could be a promising tool to identify the model parameters of a robot hand tracking system, whereas the nonlinear least square optimizer often failed to find an optimal solution even when the initial candidate solutions were selected close to the true optimum.

• Journal of the Korean Statistical Society
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• v.33 no.3
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• pp.313-321
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• 2004

We consider the problem of testing cell probabilities in sparse multinomial data. Aerts et al. (2000) presented T=${{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2$ as a test statistic with the local least square polynomial estimator ${{p}_{i}}^{*}$, and derived its asymptotic distribution. The local least square estimator may produce negative estimates for cell probabilities. The local maximum likelihood polynomial estimator ${{\hat{p}}_{i}}$, however, guarantees positive estimates for cell probabilities and has the same asymptotic performance as the local least square estimator (Baek and Park, 2003). When there are cell probabilities with relatively much different sizes, the same contribution of the difference between the estimator and the hypothetical probability at each cell in their test statistic would not be proper to measure the total goodness-of-fit. We consider a Pearson type of goodness-of-fit test statistic, $T_1={{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2/p_{i}$ instead, and show it follows an asymptotic normal distribution. Also we investigate the asymptotic normality of $T_2={{\Sigma}_{i=1}}^{k}{[{p_i}^{*}-E{(p_{i}}^{*})]^2/p_{i}$ where the minimum expected cell frequency is very small.