• Journal of the Korean Institute of Telematics and Electronics
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• v.20 no.1
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• pp.37-44
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• 1983

A class of adaptive control algorithms applied to discrete-time single-input single-output deterministic linear systems is analyzed by using direct model reference adaptive control. Controller parameters are identified with weighted least square Method. And computer simulations reveal that proposed weighted least square method in which the value of depends on the identification error can be used regardless of the sufficient condition of reference input signal.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.24 no.2
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• pp.219-224
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• 2020

Binary classification has been broadly investigated in machine learning. In addition, binary classification can be easily extended to multi class problems. To successfully utilize machine learning methods for classification tasks, preprocessing and feature extraction steps are essential. These are important steps to improve their classification performances. In this paper, we propose a new learning method based on weighted least squares. In the weighted least squares, designing weights has a significant role. Due to this necessity, we also propose a new technique to obtain weights that can achieve feature transformation. Based on this weighting technique, we also propose a method to combine the learning and feature extraction processes together to perform both processes simultaneously in one step. The proposed method shows the promising performance on five UCI machine learning data sets.

• The Transactions of the Korea Information Processing Society
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• v.5 no.9
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• pp.2345-2352
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• 1998

The hyper-geometric distribution software reliability growth model was recently developed and successfully applied Due to mathematical difficultv of the maximum likclihmd method, the least squares method has hem suggested for parameter estimation by the previous studies. We first summarize and compare the minimization criteria adopted by the previous studies. It is theo shown that the weighted least squares method is more appropriate hecause of the nonhomogeneous variability of the number of newly detected faults. The adequacy of the weighted least squares method is illustrated by two numerical examples. Finally, we propose a new method fur predicting the number of faults newly discovered by next test instances. The new prediction method can be used for determining the time to stop testing.

• Journal of Korean Society for Geospatial Information Science
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• v.20 no.3
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• pp.49-56
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• 2012

Performance analysis on RAIM, which is one of the techniques for monitoring integrity to ensure the reliability of GPS, was conducted in this study. RAIM is such a method which allows its user to monitor integrity in the stand-alone mode. Among the existing RAIM procedures, the representative methods including the RCM (Range Comparison Method), LSRM (Least Square Residual Method), Parity approach and WRAIM (Weighted RAIM) were evaluated, and their performance was analyzed. To validate the performance of the implemented algorithms, fault detection was tried on the clock malfunctioning event of PRN 23 occurred on January 1st, 2004. As a result, it was identified that the LSRM and the WRAIM detected all the faults happened in the event. In the case of RCM, all the states of fault were detected except for the error which occurred as a false alarm at one epoch. Furthermore, simulated biases were added for each satellite to analyze the sensitivity of each algorithm. Consequently, when biases of the 9-13 meters range were simulated for the RCM and LSRM algorithm, all the malfunctions were detected. For the WRAIM method, it could detect range biases greater than 15 meters.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.12 no.1
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• pp.47-56
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• 1999

최근 요소망의 구성없이 공학적인 문제의 해석이 가능한 무요소법이 많은 학자들에 의하여 제안되고 이에 관한 집중적인 연구가 이루어지고 있다. 본 연구에서는 갤러킨 정식화에 의한 무요소법을 고체역학적인 문제에 적용하여 이의 특성을 규명하고자 하였다. 특히 일반적으로 사용되고 있는 몇가지 가중 함수를 선정하여 이들이 해석결과에 미치는 특성과 절점 배치방법 및 가중 함수의 영향 영역 변화에 따른 해의 정확도 등을 서로 비교하고 검토하였다. 연구결과로 가중 함수의 형태와 영향 영역의 크기, 기정 함수의 차수와 절점 배치방법 등은 서로 상관관계를 갖고 해의 정확도에 크게 영향을 미침을 확인할 수 있었고 이의 적절한 선정은 무요소해석의 중요한 요건임을 알 수 있었다.

• Journal of IKEEE
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• v.4 no.2 s.7
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• pp.249-256
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• 2000

In EIT(electrical impedance tomography), the internal resistivity(or conductivity) distribution of the unknown object is estimated using the boundary voltage data induced by different current patterns using various reconstruction algorithms. In this paper, we present a regularized modified Newton-Raphson(mNR) scheme which employs additional a priori information in the cost functional as soft constraint and the weighting matrices in the cost functional are selected based on the exponentially weighted least square criterion. The computer simulation for the 32 channels synthetic data shows that the reconstruction performance of the proposed scheme is improved compared to that of the conventional regularized mNR at the expense of slightly increased computational burden.

• Journal of Korean Society of Steel Construction
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• v.17 no.6 s.79
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• pp.727-736
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• 2005

This study presents a method of determining the optimum cable tension forces for the proper initial equilibrium state of a cable-stayed bridge using the least square method. The proposed method minimizes the errors, i.e., the differences, such as the deflection and the moments of the girder and the tower, between the target values from a continuous beam by considering the cable anchor point as supports of the girder and the responses obtained from the analysis of the entire cable-stayed bridge system. Especially, the proposed method can selectively control the adjustment of the tower moment, the girder moment, and the deflections by introducing the weighing matrix. Through numerical analysis and comparisons with existing studies, the usefulness and validity of the proposed method was verified.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.129-136
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• 2005

The damped least-squares inversion has become a most popular method in finding the solution in geophysical problems. Generally, the least-squares inversion is to minimize the object function which consists of data misfits and model constraints. Although both the data misfit and the model constraint take an important part in the least-squares inversion, most of the studies are concentrated on what kind of model constraint is imposed and how to select an optimum regularization parameter. Despite that each datum is recommended to be weighted according to its uncertainty or error in the data acquisition, the uncertainty is usually not available. Thus, the data weighting matrix is inevitably regarded as the identity matrix in the inversion. We present a new inversion scheme, in which the data weighting matrix is automatically obtained from the analysis of the data resolution matrix and its spread function. This approach, named 'extended active constraint balancing (EACB)', assigns a great weighting on the datum having a high resolution and vice versa. We demonstrate that by applying EACB to a two-dimensional resistivity tomography problem, the EACB approach helps to enhance both the resolution and the stability of the inversion process.

• Geophysics and Geophysical Exploration
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• v.8 no.2
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• pp.163-169
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• 2005

Recently, the velocity stack domain is having an attention as a very useful domain for various processing in seismic data processing. In order to be used in many applications, the velocity stack should be obtained through an inversion method and the used inversion should have properties like the robustness to noise and the parsimony of velocity stack result. Iteratively Reweighted Least-Squares (IRLS) method is the one of the inversion methods that have such properties. This paper describes the theoretical background, implementation of the method, and examines the characteristics and limits of the IRLS method.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.1B
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• pp.9-20
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• 2012

Recently to overcome limitations of conceptual, hydrological and physics based models for flood stage forecasting, multiple linear regression model as one of data-driven models have been widely adopted for forecasting flood streamflow(stage). The objectives of this study are to compare performance of different multiple linear regression models according to regression coefficient estimation methods and determine most effective multiple linear regression flood stage forecasting models. To do this, the time scale was determined through the autocorrelation analysis of input data and different flood stage forecasting models developed using regression coefficient estimation methods such as LS(least square), WLS(weighted least square), SPW(stepwise) was applied to flood events in Jungrang stream. To evaluate performance of established models, fours statistical indices were used, namely; Root mean square error(RMSE), Nash Sutcliffe efficiency coefficient (NSEC), mean absolute error (MAE), adjusted coefficient of determination($R^{*2}$). The results show that the flood stage forecasting model using SPW(stepwise) parameter estimation can carry out the river flood stage prediction better in comparison with others, and the flood stage forecasting model using LS(least square) parameter estimation is also found to be slightly better than the flood stage forecasting model using WLS(weighted least square) parameter estimation.