• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.39C no.1
• /
• pp.9-16
• /
• 2014

The localization compensation algorithm for moving objects using the least-squares method is suggested and the performance of the algorithm is analyzed in this paper. The suggested compensation algorithm measures the distance values of the mobile object moving as a constant speed by the TMVS (TWR Minimum Value Selection) method, estimates the location of the mobile node by the trilateration scheme based on the values, and the estimated location is compensated using the least-squares method. By experiments, it is confirmed that the localization performance of the suggested compensation algorithm is largely improved to 58.84% and 40.28% compared with the conventional trilateration method in the scenario 1 and 2, respectively.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.22 no.3
• /
• pp.312-319
• /
• 2011

In this paper, a new linearization algorithm for DPD(Digital PreDistorter) is suggested. This new algorithm uses DFP(Davidon-Fletcher-Powell) method. This algorithm is more accurate than that of the existing algorithms, and this method renew the best-fit value in every routine with out setting the initial value of step-size. In modeling power amplifier, the memory polynomial model which can model the memory effect of the power amplifier is used. And the overall structure of linearizer is based on an indirect learning architecture. In order to verify for performance of proposed algorithm, we compared with LMS(Least Mean-Squares), RLS(Recursive Least squares) algorithm.

• Journal of Information Processing Systems
• /
• v.14 no.6
• /
• pp.1385-1397
• /
• 2018

There are many factors that affect the wind speed. In addition, the randomness of wind speed also leads to low prediction accuracy for wind speed. According to this situation, this paper constructs the short-time forecasting model based on the least squares support vector machines (LSSVM) to forecast the wind speed. The basis of the model used in this paper is support vector regression (SVR), which is used to calculate the regression relationships between the historical data and forecasting data of wind speed. In order to improve the forecast precision, historical data is clustered by cluster analysis so that the historical data whose changing trend is similar with the forecasting data can be filtered out. The filtered historical data is used as the training samples for SVR and the parameters would be optimized by particle swarm optimization (PSO). The forecasting model is tested by actual data and the forecast precision is more accurate than the industry standards. The results prove the feasibility and reliability of the model.

• The Journal of the Acoustical Society of Korea
• /
• v.38 no.3
• /
• pp.283-289
• /
• 2019

RLS (Recursive-least-squares) algorithm is known to have good convergence and excellent error level after convergence. However, there is a disadvantage that numerical instability is included in the algorithm due to inverse matrix calculation. In this paper, we propose an algorithm with no matrix inversion to avoid the instability aforementioned. The proposed algorithm still keeps the same convergence performance. In the proposed algorithm, we adopt an averaged gradient-based step size as a self-adjusted step size. In addition, a variable forgetting factor is introduced to provide superior performance for time-varying channel estimation. Through simulations, we compare performance with conventional RLS and show its equivalency. It also shows the merit of the variable forgetting factor in time-varying channels.

• ETRI Journal
• /
• v.42 no.6
• /
• pp.922-931
• /
• 2020

A hierarchical iterative algorithm for the canonical polyadic decomposition (CPD) of tensors is proposed by improving the traditional conjugate gradient least squares (CGLS) method. Methods based on algebraic operations are investigated with the objective of estimating the direction of arrival (DoA) and polarization parameters of signals impinging on an array with electromagnetic (EM) vector-sensors. The proposed algorithm adopts a hierarchical iterative strategy, which enables the algorithm to obtain a fast recovery for the highly collinear factor matrix. Moreover, considering the same accuracy threshold, the proposed algorithm can achieve faster convergence compared with the alternating least squares (ALS) algorithm wherein the highly collinear factor matrix is absent. The results reveal that the proposed algorithm can achieve better performance under the condition of fewer snapshots, compared with the ALS-based algorithm and the algorithm based on generalized eigenvalue decomposition (GEVD). Furthermore, with regard to an array with a small number of sensors, the observed advantage in estimating the DoA and polarization parameters of the signal is notable.

• Communications for Statistical Applications and Methods
• /
• v.30 no.1
• /
• pp.65-73
• /
• 2023

Contemporary biomedical data often involve an ill-posed problem owing to small sample size and large number of multi-collinear variables. Partial least squares (PLS) method could be a plausible alternative to an ill-conditioned ordinary least squares. However, in the case of a PLS model that includes a random-effect, how to deal with a random-effect or mixed effects remains a widely open question worth further investigation. In the present study, we propose a modified multivariate PLS method implementing mixed-effect model (PLSM). The advantage of PLSM is its versatility in handling serial longitudinal data or its ability for taking a randomeffect into account. We conduct simulations to investigate statistical properties of PLSM, and showcase its real clinical application to predict treatment outcome of esthetic surgical procedures of human faces. The proposed PLSM seemed to be particularly beneficial 1) when random-effect is conspicuous; 2) the number of predictors is relatively large compared to the sample size; 3) the multicollinearity is weak or moderate; and/or 4) the random error is considerable.

• Journal of the Korea Computer Graphics Society
• /
• v.30 no.2
• /
• pp.1-9
• /
• 2024

In this paper, we propose a new method to visualize different vector field patterns from density data. We use moving least squares (MLS), which is used in physics-based simulations and geometric processing. However, typical MLS does not take into account the nature of density, as it is interpolated to a higher order through vector-based constraints. In this paper, we design an algorithm that incorporates Monte Carlo-based weights into the MLS to efficiently account for the density characteristics implicit in the input data, allowing the algorithm to represent different forms of white noise. As a result, we experimentally demonstrate detailed vector fields that are difficult to represent using existing techniques such as naive MLS and divergence-constrained MLS.

• Asian-Australasian Journal of Animal Sciences
• /
• v.16 no.10
• /
• pp.1535-1539
• /
• 2003

Four microsatellite DNA loci BM1818, BM1258, BM1443 and BM1905 associated with the somatic cell counts (SCC) in cow milk were analyzed for genetic variation in 240 Beijing Holstein cows. The PCR amplified products of microsatellites DNA were detected by non-denatured polyacrylamide gel electrophoresis. The number of alleles for BM1818, BM1258, BM1443 and BM1905 were 4, 5, 8 and 6 in Beijing Holstein cows, respectively. The allele size ranges for BM1818, BM1258, BM1443 and BM1905 were 274 bp to 286 bp, 92 bp to 106 bp, 154 bp to 170 bp and 187 bp to 201 bp, respectively. The polymorphism information content/effective number of alleles/heterozygosity for BM1818, BM1258, BM1443 and BM1905 were 0.3869/1.7693/0.4348, 0.5923/2.9121/0.6566, 0.7114/3.9012/0.7437 and 0.5921/2.8244/0.6459. These data showed the microsatellite DNA locus BM1443 has the highest variability, followed by BM1258, BM1905 and BM1818. The results of the least squares means analysis showed as follows: the least squares mean of SCC for BM1818 284 bp/284 bp was significantly lower than that for BM1818 286 bp/286 bp (p<0.05). The least squares mean of SCC for BM1258 100 bp/100 bp was significantly lower than that for BM1258 102 bp/102 bp, 106 bp/106 bp, 106 bp/104 bp, 106 bp/102 bp, 106 bp/100 bp, 104 bp/100 bp (p<0.05). The least squares mean of SCC for BM1443 166 bp/160 bp and 166 bp/166 bp was significantly lower than that for BM1443 170 bp/160 bp, 160 bp/157 bp, 165 bp/160 bp (p<0.05). The least squares mean of SCC for BM1905 187 bp/187 bp was significantly lower than that for BM1905 197 bp/195 bp, 193 bp/187 bp (p<0.05).

• Journal of Broadcast Engineering
• /
• v.21 no.6
• /
• pp.920-928
• /
• 2016

Recently high dynamic range imaging technique is hot issue in computer graphic area. We present a progressive tone mapping algorithm, which is based on weighted least squares optimization framework. Our approach combines weighted least squares filtering with iCAM06 model. To show more perceptual high dynamic range images in conventional display, we decompose high dynamic range image into base layers and detail layers. The base layers are obtained by using weighted least squares filter. Then, we adopt chromatic adaption function and non-linear compression function to deal with base layers. Only the base layers reduce contrast, and preserving detail. The image quality assessment shows that our tone mapped image is more similar to original high dynamic range image. Moreover, the subjective result shows our algorithm produces more reliable and pleasing image.

• Journal of the Institute of Electronics and Information Engineers
• /
• v.50 no.6
• /
• pp.246-253
• /
• 2013

Based on the conventional 2-6GHz digital frequency discriminator (DFD) using the phase unwrapping and least-squares techniques, we propose a new 2-18GHz DFD. To compensate for lowered-precision frequency estimation due to the expanded bandwidth, the proposed DFD design employs more delay lines, accordingly accompanying high complexity. Thus, a new computationally efficient frequency estimation algorithm is also presented to overcome such high computational burden. More specifically, the proposed frequency estimation algorithm is basically based on the conventional phase unwrapping technique, along with a new candidates selection for the unwrapped phases under the condition that the phase margin is known. As a result, the computational burden required for the least-squares technique can be reduced. Finally, simulation results are provided to demonstrate the effectiveness of the proposed approach, compared with those of the conventional DFD's.