• 대한기계학회논문집A
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• 제20권3호
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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.

• KSII Transactions on Internet and Information Systems (TIIS)
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• 제9권6호
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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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• 제어로봇시스템학회 2000년도 제15차 학술회의논문집
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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 Biosystems Engineering
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• 제33권6호
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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.

• 제어로봇시스템학회논문지
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• 제20권4호
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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.

• 한국임상수의학회지
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• 제27권4호
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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.

• 한국통신학회논문지
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• 제41권12호
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• pp.1692-1699
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• 2016

This paper deals with the problem of time delay estimation using de-convolution. Two approaches, conjugate gradient method and the total lease square method have been presented to solve the de-convolution problem. Numerical simulation demonstrates the superior performance of the proposed methods over the conventional GCC based algorithms and FIR filter method.

• 한국음향학회:학술대회논문집
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• 한국음향학회 2004년도 추계학술발표대회논문집 제23권 2호
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• pp.547-550
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• 2004

본 논문은 시스템 인식에서 RLS의 성능을 높이기 위한 한 방법으로 UDU 행렬 분해법을 바탕으로 한 recursive total least squares (RTLS) algorithm을 제안한다. 기존의 RTLS는 Power Method에 의거해서 recursive하게 만든 형태이어서 RLS와 거의 같은 구조이다. 그러나 본 논문에서는 일반적인 Power Method가 rank-1 update를 이용하기 때문에 ill-condition에 빠질 가능성이 높은 점을 감안하여, UDU 행렬 분해법을 사용한 RTLS방법을 제안하고, 그를 시스템 인식에 적용한다.

• Journal of the Korean Data and Information Science Society
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• 제7권2호
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• pp.203-210
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• 1996

A linear neural unit with a modified anti-Hebbian learning rule has been shown to be able to optimally fit curves, surfaces, and hypersurfaces by adaptively extracting the minor component of the input data set. In this paper, we study how to use the robust version of this neural fitting method for linear regression analysis. Furthermore, we compare this method with other methods when data set is contaminated by outliers.

• 한국음향학회지
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• 제14권3호
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• pp.37-48
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• 1995

일반화된 완전최소자승법 (generalized total least squares method, GTLS)의 ARMA 시스템 식별에의 적용과 GTLS의 적응알고리듬에 대하여 논한다. 일반화된 완전최소자승법은 일별과 출력을 알고 있는 시스템식별 (system identification)문제에서, 출력이 잡음에 의하여 오염된 경우, 편이되지 않은 해를 구하기 위하여 사용되는 방법이다. 본 논문에서는 먼저 GTLS를 ARMA 시스템 식별에 적용하기 위한 formulation을 하고, 일반화된 완전최소자승법의 일반 해의 성질과 역행렬 정리 (matrix inverse lemma)를 이용하여 적응 GTLS 방법을 제안한다. 다음 제안된 방법을 통하여 시스템식별에 적용하여 그 성능을 평가한다. 또한 GTLS 알고리듬과 제안한 적응 GTLS 알고리듬의 성능을 수학적으로 해석하고 컴퓨터 시뮬레이션을 통하여 이를 검증한다.