• Transactions of the Korean Society of Mechanical Engineers A
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• v.20 no.3
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• pp.843-849
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• 1996

The least square estimation is used frequently in experimental modal analysis techinque to eliminate noise signals. However, identified modal parameters are sometimes inaccurate, since the least squre estimation is sensitive to noise. In this paper, a new total least squre estimation, which is robust to noise signals, is developed and applied to experimental modal analysis technique such as Prony method and Circle Fit method. Several simulated results show that the proposed method is robuster to noise than conventional method.

• Wind and Structures
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• v.26 no.6
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• pp.383-395
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• 2018

An accurate determination of wind speed distribution is the basis for an evaluation of the wind energy potential required to design a wind turbine, so it is important to estimate unknown parameters of wind speed distribution. In this paper, Gumbel distribution is used in modelling wind speed data, and alternative robust estimation methods to estimate its parameters are considered. The methodologies used to obtain the estimators of the parameters are least absolute deviation, weighted least absolute deviation, median/MAD and least median of squares. The performances of the estimators are compared with traditional estimation methods (i.e., maximum likelihood and least squares) according to bias, mean square deviation and total mean square deviation criteria using a Monte-Carlo simulation study for the data with and without outliers. The simulation results show that least median of squares and median/MAD estimators are more efficient than others for data with outliers in many cases. However, median/MAD estimator is not consistent for location parameter of Gumbel distribution in all cases. In real data application, it is firstly demonstrated that Gumbel distribution fits the daily mean wind speed data well and is also better one to model the data than Weibull distribution with respect to the root mean square error and coefficient of determination criteria. Next, the wind data modified by outliers is analysed to show the performance of the proposed estimators by using numerical and graphical methods.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.48 no.3
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• pp.139-143
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• 1999

This paper is on realization of the speed-sensorless vector control of an induction motor using the RLS(Recursive Least Square) algorithm. The speed estimator is including the RLS algorithm and a rotor flux observer. The RLS algorithm has speed and rotor time constant as parameter vectors and rotor flux observer is designed to have robustness to stator resistance variation and through the IP(Integral and Proportional) speed controller stable performance is obtained for estimating rotor speed. Finally the total algorithm are realized in induction motor drive system and its effectiveness is verified.

• Proceedings of the Acoustical Society of Korea Conference
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• 1991.06a
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• pp.23-27
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• 1991

RLS(Recursive Least Squares)나 LMS(Least mean square)등은 알고리듬 고유의 성질상 잡음이 섞인 시스템에 있어서는 올바른 역 모델링을 할 수 없다. 따라서, 잡음의 영향을 받지않는 견실한(robust) 모델 추정 알고리듬이 필요하다. 본 논문에서는 잡음환경하에 있는 시스템을역 모델링하는데 있어서, 잡음의 영향을 줄이기위해 완전최소자승법을 도입하고 기존의 최소자승법과 비교 실험하였다. 그리고, 이 방법의 적응 알고리듬을 제안하였으며, RLS(Recursive least squares)와 그 성능을 비교하여 타당성을 검토하였다.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.9 no.6
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• pp.2302-2316
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• 2015

We propose an efficient method of extracting targets within a region of interest in non-homogeneous infrared images by using a principal component analysis (PCA) plane and adaptive Gaussian kernel. Existing approaches for extracting targets have been limited to using only the intensity values of the pixels in a target region. However, it is difficult to extract the target regions effectively because the intensity values of the target region are mixed with the background intensity values. To overcome this problem, we propose a novel PCA based approach consisting of three steps. In the first step, we apply a PCA technique minimizing the total least-square errors of an IR image. In the second step, we generate a binary image that consists of pixels with higher values than the plane, and then calculate the second derivative of the sum of the square errors (SDSSE). In the final step, an iteration is performed until the convergence criteria is met, including the SDSSE, angle and labeling value. Therefore, a Gaussian kernel is weighted in addition to the PCA plane with the non-removed data from the previous step. Experimental results show that the proposed method achieves better segmentation performance than the existing method.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.366-366
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• 2000

In this paper, an adaptive neuro-fuzzy filter using the conditional fuzzy c-means(CFCM) methods is proposed. Usualy, the number of fuzzy rules exponentially increases by applying the grid partitioning of the input space, in conventional adaptive neuro-fuzzy inference system(ANFIS) approaches. In order to solve this problem, CFCM method is adopted to render the clusters which represent the given input and output data. Parameter identification is performed by hybrid learning using back-propagation algorithm and total least square(TLS) method. Finally, we applied the proposed method to the nonlinear channel equalization problem and obtained a better performance than previous works.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.4
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• pp.414-421
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• 2014

This paper presents a method of ballistic missile launch point estimation using phase division least squares. The proposed algorithm employs smoothing to enhance estimation accuracy and generates functions of time for total velocity, flight path angle and heading angle, allowing extrapolation to estimate the launch point. Performance of the proposed algorithm is tested in conjunction with the extended Kalman filter and the Kalman filter.

• Journal of Veterinary Clinics
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• v.27 no.4
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• pp.427-434
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• 2010

Correlation is a technique used to measure the strength or the degree of closeness of the linear association between two quantitative variables. Common misuses of this technique are highlighted. Linear regression is a technique used to identify a relationship between two continuous variables in mathematical equations, which could be used for comparison or estimation purposes. Specifically, regression analysis can provide answers for questions such as how much does one variable change for a given change in the other, how accurately can the value of one variable be predicted from the knowledge of the other. Regression does not give any indication of how good the association is while correlation provides a measure of how well a least-squares regression line fits the given set of data. The better the correlation, the closer the data points are to the regression line. In this tutorial article, the process of obtaining a linear regression relationship for a given set of bivariate data was described. The least square method to obtain the line which minimizes the total error between the data points and the regression line was employed and illustrated. The coefficient of determination, the ratio of the explained variation of the values of the independent variable to total variation, was described. Finally, the process of calculating confidence and prediction interval was reviewed and demonstrated.

• Journal of Biosystems Engineering
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• v.33 no.6
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• pp.438-445
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• 2008

The purpose of this study was to develop a measurement model based on PLS (Partial least square) method for blood pressures. Measurement system for blood pressure signals consisted of pressure sensor, va interface and embedded module. A mercury sphygmomanometer was connected with the measurement system through 3-way stopcock and used as reference of blood pressures. The blood pressure signals of 20 subjects were measured and tests were repeated 5 times per each subject. Total of 100 data were divided into a calibration set and a prediction set. The PLS models were developed to determine the systolic and the diastolic blood pressures. The PLS models were evaluated by the standard methods of the British Hypertension Society (BHS) protocol and the American Association for the Advancement of Medical Instrumentation (AAMI). The results of the PLS models were compared with those of MAA (maximum amplitude algorithm). The measured blood pressures with PLS method were highly correlated to those with a mercury sphygmomanometer in the systolic ($R^2=0.85$) and the diastolic blood pressure ($R^2=0.84$). The results showed that the PLS models were the effective tools for blood pressure measurements with high accuracy, and satisfied the standards of the BHS protocol and the AAMI.

• 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.