• The Transactions of The Korean Institute of Electrical Engineers
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• v.59 no.10
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• pp.1874-1881
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• 2010

A recursive linear stochastic error compensation algorithm is newly proposed for target localization in sensor network which provides range difference of arrival(RDOA) measurements. Target localization with RDOA is a well-known nonlinear estimation problem. Since it can not solve with a closed-form solution, the numerical methods sensitive to initial guess are often used before. As an alternative solution, a pseudo-linear estimation scheme has been used but the auto-correlation of measurement noise still causes unacceptable estimation errors under low SNR conditions. To overcome these problems, a stochastic error compensation method is applied for the target localization problem under the assumption that a priori stochastic information of RDOA measurement noise is available. Apart from the existing methods, the proposed linear target localization scheme can recursively compute the target position estimate which converges to true position in probability. In addition, it is remarked that the suggested algorithm has a structural reconciliation with the existing one such as linear correction least squares(LCLS) estimator. Through the computer simulations, it is demonstrated that the proposed method shows better performance than the LCLS method and guarantees fast and reliable convergence characteristic compared to the nonlinear method.

• Journal of Institute of Control, Robotics and Systems
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• v.17 no.10
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• pp.1059-1066
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• 2011

This paper makes a close inquiry into ill-conditioning that may be occurred in wireless localization of the sensor nodes based on network signals in the wireless sensor network and provides the clue for solving the problem. In order to estimate the location of a node based on the range information calculated using the signal propagation time, LS (Least Squares) method is usually used. The LS method estimates the solution that makes the squared estimation error minimal. When a nonlinear function is used for the wireless localization, ILS (Iterative Least Squares) method is used. The ILS method process the LS method iteratively after linearizing the nonlinear function at the initial nominal point. This method, however, has a problem that the final solution may converge into a LM (Local Minimum) instead of a GM (Global Minimum) according to the deployment of the fixed nodes and the initial nominal point. The conditions that cause the problem are explained and an adaptive method is presented to solve it, in this paper. It can be expected that the stable location solution can be provided in implementation of the wireless localization methods based on the results of this paper.

• Journal of Korean Institute of Industrial Engineers
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• v.17 no.2
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• pp.131-142
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• 1991

The concept of criterion function is proposed as a framework for comparing the geometric and computational characteristics of various nonlinear programming approaches to linear programming such as the method of centers, Karmakar's algorithm and the gravitational method. Also, we discuss various computational issues involved in obtaining an efficient parallel implementation of these methods. Clearly, the most time consuming part in solving a linear programming problem is the direction finding procedure, where we obtain an improving direction. In most cases, finding an improving direction is equivalent to solving a simple optimization problem defined at the current feasible solution. Again, this simple optimization problem can be seen as a least squares problem, and the computational effort in solving the least squares problem is, in fact, same as the effort as in solving a system of linear equations. Hence, getting a solution to a system of linear equations fast is very important in solving a linear programming problem efficiently. For solving system of linear equations on parallel computing machines, an iterative method seems more adequate than direct methods. Therefore, we propose one possible strategy for getting an efficient parallel implementation of an iterative method for solving a system of equations and present the summary of computational experiment performed on transputer based parallel computing board installed on IBM PC.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.12
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• pp.2382-2390
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• 2007

A new polynomial-based predistortion method for linearizing nonlinear power amplifier is proposed. The proposed method finds the predistortion parameter directly without the help of postdistorter whereas most existing polynomial-based predistortion methods calculate the predistortion parameter indirectly from the prostdistorter. First, a new predistortion algorithm is derived based on the assumption that the characteristic of the amplifier is modeled by piecewise linear function. Then it is modified into a proposed method which does not require any assumption or prior knowledge of the amplifier. The proposed method is derived based on the RLS (recursive least squares) algorithm. The proposed technique is simpler to implement than the existing methods and the computer simulation demonstrates that the proposed method is more robust to the initial condition and the saturation region of the amplifier.

• YAKHAK HOEJI
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• v.57 no.1
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• pp.32-36
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• 2013

The purpose of this study was to determine the influence of assay error for improved pharmacokinetic modeling and simulation of vancomycin on the Bayesian and nonlinear least squares regression analysis in 24 Korean gastric cancer patients. Vancomycin 1.0 g was administered intravenously over 1 hr every 12 hr. Three specimens were collected at 72 hr after the first dose from all patients at the following times, at 0.5 hr before regularly scheduled infusion, at 0.5 hr and 2 hr after the end of 1 hr infusion. Serum vancomycin levels were analyzed by fluorescence polarization immunoassay technique with TDX-FLX. The standard deviation (SD) of the assay over its working range had been determined at the serum vancomycin concentrations of 0, 20, 40, 60, 80 and $120{\mu}g/ml$ in quadruplicate. The polynomial equation of vancomycin assay error was found to be SD $({\mu}g/ml)=0.0224+0.0540C+0.00173C^2$ ($R^2=0.935$). There were differences in the influence of weight with vancomycin assay error on pharmacokinetic parameters of vancomycin using the nonlinear least squares regression analysis but there were no differences on the Bayesian analysis. This polynomial equation can be used to improve the precision of fitting of pharmacokinetic models to optimize the process of model simulation both for population and for individualized pharmacokinetic models. The result suggests the improvement of dosage regimens for the better and safer care of patients receiving vancomycin.

• Journal of the Korean Data and Information Science Society
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• v.17 no.2
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• pp.609-617
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• 2006

A new fuzzy c-regression algorithm for switching regression analysis is presented, which combines fuzzy c-means clustering and least squares support vector machine. This algorithm can detect outliers in switching regression models while yielding the simultaneous estimates of the associated parameters together with a fuzzy c-partitions of data. It can be employed for the model-free nonlinear regression which does not assume the underlying form of the regression function. We illustrate the new approach with some numerical examples that show how it can be used to fit switching regression models to almost all types of mixed data.

• Journal of the Korean Society for Aviation and Aeronautics
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• v.18 no.3
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• pp.21-26
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• 2010

This paper deals with the precise parameter estimation of an air vehicle without a priori information. First, Recursive Least Squares technique, which is an equation error method and does not require any a priori information, is applied and then the extended Kalman filter is used to tune parameters more precisely. To show the performance, a nonlinear longitudinal missile model is simulated and the parameters are estimated. The results show that this consecutive application of the techniques gives a very good estimation performance.

• Journal of the Korean Data and Information Science Society
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• v.24 no.2
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• pp.391-399
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• 2013

Partially linear regression is capable of providing more complete description of the linear and nonlinear relationships among random variables. In support vector regression (SVR) the hyper-parameters are known to affect the performance of regression. In this paper we propose an iterative reweighted least squares (IRWLS) procedure to solve the quadratic problem of partially linear support vector regression with a modified loss function, which enables us to use the generalized approximate cross validation function to select the hyper-parameters. Experimental results are then presented which illustrate the performance of the partially linear SVR using IRWLS procedure.

• Journal of information and communication convergence engineering
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• v.16 no.1
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• pp.17-22
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• 2018

In massive multiple-input multiple-output (MIMO) systems, linear channel estimation algorithms are widely applied owing to their simple structures. However, they may cause pilot contamination, which affects the subsequent data detection performance. Therefore, herein, for an uplink multicell massive multiuser MIMO system, we consider using an alternating projection (AP) for channel estimation to eliminate the effect of pilot contamination and improve the performance of data detection in terms of the bit error rates as well. Even though the AP is nonlinear, it iteratively searches the best solution in only one dimension, and the computational complexity is thus modest. We have analyzed the mean square error with respect to the signal-to-interference ratios for both the cooperative and non-cooperative multicell scenarios. From the simulation results, we observed that the channel estimation results via the AP benefit the following signal detection more than that via the least squares for both the cooperative and non-cooperative multicell scenarios.

• Industrial Engineering and Management Systems
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• v.14 no.3
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• pp.312-317
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• 2015

The dynamic system approach in time series has been used in many real problems. Based on Taken's embedding theorem, we can build the predictive function where input is the time delay coordinates vector which consists of the lagged values of the observed series and output is the future values of the observed series. Although the time delay coordinates vector from multivariate time series brings more information than the one from univariate time series, it can exhibit statistical redundancy which disturbs the performance of the prediction function. We apply dimension reduction techniques to solve this problem and analyze the effect of this approach for prediction. Our experiment uses delayed Lorenz series; least squares support vector regression approximates the predictive function. The result shows that linearly preserving projection improves the prediction performance.