• Transactions of the Korean Society of Mechanical Engineers A
• /
• v.26 no.9
• /
• pp.1849-1858
• /
• 2002

An h-adaptive scheme of first-order least-squares meshfree method is presented. A posteriori error estimates, which can be readily computed from the residual, are also presented. For elliptic problem the error indicators are further improved by applying the Aubin-Nitsche method. In the proposed refinement scheme, Voronoi cells are utilized to insert nodes at appropriate positions. Through numerical examples, it is demonstrated that the error indicators reveal good correlations with the actual errors and the adaptive first-order least-squares meshfree method is effectively applied to the localized problems such as the shock formation in fluid dynamics.

• The Korean Journal of Applied Statistics
• /
• v.29 no.3
• /
• pp.471-485
• /
• 2016

Least squares (LS) regression is a classic method for regression that is optimal under assumptions of regression and usual observations. However, the presence of unusual data in the LS method leads to seriously distorted estimates. Therefore, various robust estimation methods are proposed to circumvent the limitations of traditional LS regression. Among these, there are M-estimators based on maximum likelihood estimation (MLE), L-estimators based on linear combinations of order statistics and R-estimators based on a linear combinations of the ordered residuals. In this paper, robust regression estimators with high breakdown point and/or with high efficiency are compared under several simulated situations. The paper analyses and compares distributions of estimates as well as relative efficiencies calculated from mean squared errors (MSE) in the simulation study. We conclude that MM-estimators or GR-estimators are a good choice for the real data application.

• Proceedings of the Korean Society of Broadcast Engineers Conference
• /
• 2010.07a
• /
• pp.394-395
• /
• 2010

본 논문은 최소 평균 제곱 오차(minimum mean-square error: MMSE)에 기반한 비국소적 (nonlocal) 평균 영상 잡음 제거기법을 제안한다. 제안하는 기법에서는 기존의 비국소적 평균 기법에 추정 이론을 적용하여 잡음 제거에 사용되는 이웃 블록 또한 잡음을 포함하는 일반적인 경우로 확장하여 이웃 블록에 인가되는 가중치를 적응적으로 조절한다. 컴퓨터 모의실험을 통해 제안하는 알고리듬이 기존의 비국소적 기법에 비해 잡음 제거 성능이 향상됨을 확인한다.

• Communications for Statistical Applications and Methods
• /
• v.18 no.5
• /
• pp.625-636
• /
• 2011

This paper proposes two parametric entropy estimators, the minimum variance unbiased estimator and the maximum likelihood estimator, for the lognormal distribution for a comparison of the properties of the two estimators. The variances of both estimators are derived. The influence of the bias of the maximum likelihood estimator on estimation is analytically revealed. The distributions of the proposed estimators obtained by the delta approximation method are also presented. Performance comparisons are made with the two estimators. The following observations are made from the results. The MSE efficacy of the minimum variance unbiased estimator appears consistently high and increases rapidly as the sample size and variance, n and ${\sigma}^2$, become simultaneously small. To conclude, the minimum variance unbiased estimator outperforms the maximum likelihood estimator.

• The Journal of Korean Institute of Communications and Information Sciences
• /
• v.36 no.4C
• /
• pp.254-264
• /
• 2011

In this paper, we propose two algorithm for improving the performance of wireless localization(Trilateration and Least Square) based on the range based approach method in indoor environment using RSSI for ranging distance. we propose a method to discriminate the case that has relatively large estimation errors in trilateration using Heron''s formula for the volume of a tetrahedron. And we propose the algorithm to process the discriminated types of distance using the absolute value calculated by Heron''s formula. In addition, we propose another algorithm for the case of which the number of anchor nodes larger than three. In this case, Residual Weighting Factor(RWGH) improves the performance of Least Square. However, RWGH requires many number of calculations. In this paper, we propose Iterative Weighted Centroid Algorithm(IWCA) that has better performance and less calculation than RWGH. We show the improvement of performance for two algorithms and the combination of these algorithm by using simulation results.

• The Korean Journal of Applied Statistics
• /
• v.29 no.7
• /
• pp.1185-1199
• /
• 2016

Two phase sampling (double sampling) is often used when there is inadequate population information for proper stratification. Many recent papers have been devoted to the estimation method to improve the precision of the estimator using first phase information. In this study we suggested outlier weight adjustment methods to improve estimation precision based on the weight of the generalized ratio-cum-product estimator. Small simulation studies are conducted to compare the suggested methods and the usual method. Real data analysis is also performed.

• The KIPS Transactions:PartB
• /
• v.16B no.1
• /
• pp.47-54
• /
• 2009

Automatic object recognition in 3D measuring data is of great interest in many application fields e.g. computer vision, reverse engineering and digital factory. In this paper we present a software tool for a fully automatic object detection and parameter estimation in unordered and noisy point clouds with a large number of data points. The software consists of three interactive modules each for model selection, point segmentation and model fitting, in which the orthogonal distance fitting (ODF) plays an important role. The ODF algorithms estimate model parameters by minimizing the square sum of the shortest distances between model feature and measurement points. The local quadric surface fitted through ODF to a randomly touched small initial patch of the point cloud provides the necessary initial information for the overall procedures of model selection, point segmentation and model fitting. The performance of the presented software tool will be demonstrated by applying to point clouds.

• KSCE Journal of Civil and Environmental Engineering Research
• /
• v.29 no.2D
• /
• pp.319-327
• /
• 2009

In order to estimate accurate position by GPS observations, it is prerequisite to define both of the correct function model and the realistic stochastic model. In the case that un-modeled outliers exist in observations, estimates become biased, and their standard deviations are unable to be used as a measure which represents their accuracy. Hence, such outliers should be appropriately removed from the observations before estimating final solutions, so that the accuracy can be maximized with the improvement of the reliability. For this purpose, this research deals with quality control and quality measure computation algorithms for GPS stand-alone positioning. After theoretical studies, all the algorithms have been implemented and tested with real observations. Results of the tests indicate that the reliability of the estimated position is improved by increasing redundancy as well as using good satellite geometry and more realistic stochastic model. Moreover, the adaptation of the quality control procedure enable to improve positioning reliability and accuracy by appropriately excluding outlier in observations.

• Journal of the Korean Society for Aeronautical & Space Sciences
• /
• v.44 no.5
• /
• pp.416-422
• /
• 2016

This paper presents preliminary performance analysis of a satellite formation flying testbed, which is under development by Astrodynamics and Control Laboratory, Department of Astronomy, Yonsei University. A model reference adaptive controller (MRAC) with a first-order reference model is chosen to enhance the response of reaction wheel system which is subject to uncertainties caused by unmodelled dynamics and measurement noise. In addition, an on-line parameter estimation (OPE) technique based on the least square is combined to eliminate the effect of angular measurement noise by estimating the moment of inertia. Both numerical simulations and hardware experiments with MRAC support the effectiveness and applicability of the adaptive control scheme, which maintains the tracking error below $0.25^{\circ}$ for the entire time span. However, the high frequency control input generated in hardware experiment strongly suggests design modifications to reduce the effect of deadzone.

• Journal of Radiation Protection and Research
• /
• v.29 no.3
• /
• pp.157-163
• /
• 2004

Atmospheric dispersion modelling has been widely used to predict the fate and transport of radioactive or toxic materials released from nuclear facilities which is an unlikely accidental event. To improve the forecasting performance of the dispersion model, it is required that source rate and dispersion characteristics must be defined appropriately. Generally, source term of the radioactive materials is much uncertain at the early phase of an accidental event. In this study, we computed the source rate with the experimental field data monitored at the Yeoung-Kwang nuclear site and obtained the optimal source rate to minimize the errors between the measured concentrations and the computed ones by the Gaussian plume model. Computed source term showed a good result within 24% of the artificially released source rate.