• 로봇학회논문지
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• 제7권4호
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• pp.231-242
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• 2012

This paper proposes particle filter(PF) method using acoustic signal for localization of an underwater robot. The method uses time of arrival(TOA) or time difference of arrival(TDOA) of acoustic signals from beacons whose locations are known. An experiment in towing tank uses TOA information. Simulation uses TDOA information and it reveals dependency of the localization performance on the uncertainty of robot motion and senor data. Also, comparison of the PF method with the least squares method of spherical interpolation(SI) and spherical intersection(SX) is provided. Since PF uses TOA or TDOA which comes from measurement of external information as well as internal motion information, its estimation is more accurate and robust to the sensor and motion uncertainty than the least squares methods.

• International Journal of Control, Automation, and Systems
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• 제6권5호
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• pp.713-721
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• 2008

This paper presents the robust velocity estimation of an omnidirectional mobile robot using a polygonal array of optical mice that are installed at the bottom of the mobile robot. First, the velocity kinematics from a mobile robot to an array of optical mice is derived as an overdetermined linear system. The least squares velocity estimate of a mobile robot is then obtained, which becomes the same as the simple average for a regular polygonal arrangement of optical mice. Next, several practical issues that need be addressed for the use of the least squares mobile robot velocity estimation using optical mice are investigated, which include measurement noises, partial malfunctions, and imperfect installation. Finally, experimental results with different number of optical mice and under different floor surface conditions are given to demonstrate the validity and performance of the proposed least squares mobile robot velocity estimation method.

• Journal of applied mathematics & informatics
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• 제32권5_6호
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• pp.761-781
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• 2014

An exponentially accurate nonconforming spectral element method for elasticity systems with discontinuities in the coefficients and the flux across the interface is proposed in this paper. The method is least-squares spectral element method. The jump in the flux across the interface is incorporated (in appropriate Sobolev norm) in the functional to be minimized. The interface is resolved exactly using blending elements. The solution is obtained by the preconditioned conjugate gradient method. The numerical solution for different examples with discontinuous coefficients and non-homogeneous jump in the flux across the interface are presented to show the efficiency of the proposed method.

• 한국컴퓨터그래픽스학회논문지
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• 제30권2호
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• pp.1-9
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• 2024

본 논문에서는 밀도 데이터로부터 다양한 벡터장 패턴을 시각화하는 새로운 방법을 제안한다. 이를 위해 물리 기반 시뮬레이션과 기하학적 처리에서 사용되는 이동최소제곱(Moving least squares, MLS)을 이용한다. 하지만 일반적인 MLS는 벡터기반의 제약조건을 통해 고차 보간되기 때문에 밀도의 특성을 고려하지 못한다. 본 논문에서는 입력 데이터에 내포되어 있는 밀도의 특성을 효율적으로 고려하기 위해 몬테카를로 기반의 가중치를 MLS에 통합하여 다양한 형태의 백터장을 표현할 수 있도록 알고리즘을 설계한다. 결과적으로 일반적인 MLS와 발산제약 기반의 MLS 같은 기존 기법으로는 표현하기 힘든 디테일한 벡터장을 실험을 통해 보여준다.

• Communications for Statistical Applications and Methods
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• 제28권1호
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• pp.81-88
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• 2021

k-least quantile of squares (k-LQS) estimates are a generalization of least median of squares (LMS) estimates. They have not been used as much as LMS because their breakdown points become small as k increases. But if the size of outliers is assumed to be fixed LQS estimates yield a good fit to the majority of data and residuals calculated from LQS estimates can be a reliable tool to detect outliers. We propose to use LQS estimates for separating a clean set from the data in the context of outlyingness of the cases. Three procedures are suggested for the identification of outliers using LQS estimates. Examples are provided to illustrate the methods. A Monte Carlo study show that proposed methods are effective.

• 한국시뮬레이션학회:학술대회논문집
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• 한국시뮬레이션학회 2000년도 추계학술대회 논문집
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• pp.6-10
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• 2000

In regression model we estimate the unknown parameters using various methods. There are the least squares method which is the most general, the least absolute deviation, the regression quantile and the asymmetric least squares method. In this paper, we will compare each others with two case: to begin with the theoretical comparison in the asymptotic sense, and then the practical comparison using Monte Carlo simulation for a small sample size.

• 한국군사과학기술학회지
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• 제14권4호
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• pp.694-699
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• 2011

A closed-form technique is presented for estimating a single source location from a set of noisy time delay measurements between distributed sensors. The localization formula is derived from nonlinear least squares minimization over the unknowns of target range and bearing in polar coordinates. Computer simulation results are provided for the purpose of performance analysis. Constrained least squares minimization method with prior source location information is also discussed.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 2007년도 정기 학술대회 논문집
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• pp.501-506
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• 2007

A generalized finite difference method for solving solid mechanics problems such as elasticity and crack problems is presented. The method is constructed in framework of Taylor polynomial based on the Moving Least Squares method and collocation scheme based on the diffuse derivative approximation. The governing equations are discretized into the difference equations and the nodal solutions are obtained by solving the system of equations. Numerical examples successfully demonstrate the robustness and efficiency of the proposed method.

• Journal of the Korean Statistical Society
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• 제33권4호
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• pp.401-410
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• 2004

We address the problem of parameter estimation in multivariate distributions under ignorable non-monotone missing data. The factoring likelihood method for monotone missing data, termed by Rubin (1974), is extended to a more general case of non-monotone missing data. The proposed method is algebraically equivalent to the Newton-Raphson method for the observed likelihood, but avoids the burden of computing the first and the second partial derivatives of the observed likelihood. Instead, the maximum likelihood estimates and their information matrices for each partition of the data set are computed separately and combined naturally using the generalized least squares method.

• Journal of the Korean Data and Information Science Society
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• 제11권2호
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• pp.201-215
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• 2000

The classical method for regression analysis is the least squares method. However, if the data contain significant outliers, the least squares estimator can be broken down by outliers. To remedy this problem, the robust methods are important complement to the least squares method. Robust methods down weighs or completely ignore the outliers. This is not always best because the outliers can contain some very important information about the population. If they can be detected, the outliers can be further inspected and appropriate action can be taken based on the results. In this paper, I propose a sequential outlier test to identify outliers. It is based on the nonrobust estimate and the robust estimate of scatter of a robust regression residuals and is applied in forward procedure, removing the most extreme data at each step, until the test fails to detect outliers. Unlike other forward procedures, the present one is unaffected by swamping or masking effects because the statistics is based on the robust regression residuals. I show the asymptotic distribution of the test statistics and apply the test to several real data and simulated data for the test to be shown to perform fairly well.