• Journal of Advanced Navigation Technology
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• v.4 no.1
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• pp.45-49
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• 2000

A GPS measurement solution, in general, is obtained as a least squares solution since the measurement includes errors such as clock errors, ionospheric and tropospheric delays, multipath effect etc. Because of the nonlinearity of the measurement equation, we utilize the nonlinear Newton algorithm to obtain a least squares solution, or mostly, use its linearized algorithm which is more convenient and effective. In this study we developed a fixed point algorithm and proved its availability to replace the nonlinear Newton algorithm and the linearized algorithm. A nonlinear Newton algorithm and a linearized algorithm have the advantage of fast convergence, while their initial values have to be near the unknown solution. On the contrary, the fixed point algorithm provides more reliable but slower convergence even if the initial values are quite far from the solution. Therefore, two types of algorithms may be combined to achieve better performance.

• Journal of Environmental Science International
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• v.29 no.5
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• pp.445-455
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• 2020

Fine dust is one of the most critical environmental issues in Korea, and the government recognizes the need to establish customized reduction policies based on regional characteristics. Several studies on air pollutants investigate whether factories affect the distribution of fine dust in a particular region. However, understanding the impact of the entire industry sector requires further investigation. This study identifies the impacts of industrial characteristics on fine dust levels of 141 municipalities across Korea in 2016. A total of 23 variables were used, of which 12 referred to industries and 11 to general characteristics of each city. Due to the high correlation between independent variables, partial least squares (PLS) regression models were used. The analysis identified 14 significant variables for PM10 and 13 for PM2.5. Therefore, the results suggest that local industrial characteristics can significantly influence fine dust levels and provide suggestions for establishing customized reduction policies based on local characteristics.

• The Korean Journal of Applied Statistics
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• v.5 no.2
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• pp.229-242
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• 1992

Bradley and Stewart(1991) considered a large class of experimental designs as multidimensional block designs(MBD's). The simplest MBD could be considered to be a row-column design(RCD). They presented the intrablock analysis of variance for a general row-column design. In this article, a generalized least squares solution for Bradley & Stewart's example is considered. In this case, the assumption is that row and column effects are random. This is an application of revised Paik(1990a,1990b)'s method. The Appendix is devoted to that revised method.

• The Journal of Asian Finance, Economics and Business
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• v.8 no.7
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• pp.195-201
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• 2021

This study aims to assess the impact of ownership structure of commercial banks on bank credit risk in Vietnam. The authors used the unbalanced table data of 28 commercial banks in the period from 2004 to 2020 with 439 observations. The ratio of loan loss provisioning to loans (CR) is selected as a dependent variable representing credit risk at commercial banks. The regression methods used include: least squares method (OLS), fixed-effect model (FEM), random-effect model (REM) and general least squares method (GLS). The results reveal that, with interaction variable between the ratio of equity to total assets and foreign ownership, the national GDP annual growth rate is negatively associated with credit risk. With the ratio of equity to total assets, the interaction variable between equity and state ownership, and bank size have a significant positive impact on credit risk. In addition, inflation has negligible impact on the credit risk of commercial banks in Vietnam over the research period. The findings of this study suggest that, if foreign-owned banks increase equity capital, there will be a stronger impact on reducing credit risk than other banks. On the other hand, when state-owned commercial banks in Vietnam increase equity, they will have higher credit risk.

• The Journal of Asian Finance, Economics and Business
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• v.9 no.5
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• pp.305-311
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• 2022

The Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is projected to provide several chances for Vietnam's banking industry to expand into the international market. This study examines the influence of foreign ownership on credit risk in Vietnamese commercial banks before the context of participation in the CPTPP. Using a sample of 28 commercial banks between 2009 and 2020, we find that foreign ownership has a negative relationship with bank credit risk. The regression methods used include the least-squares method, fixed-effects model, random effects model, and general least squares method. The research model adds interactive variables, which will help to reflect the role of intermediary factors more accurately such as listing on the stock market, capital ratio to the relationship between foreign ownership and bank credit risk. The test results reveal that increasing the foreign ownership ratio has a bigger impact on reducing credit risk for listed banks and banks with low capital than for other commercial banks. The government should flexibly adjust the foreign ownership ratio according to the capital size and role of each bank so that it can make good use of investment capital from abroad when Vietnam joins the CPTPP.

• Proceedings of the Korean Society of Broadcast Engineers Conference
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• 1996.06a
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• pp.209-212
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• 1996

In this paper, we propose to re-size images using an oblique projection operator instead of the orthogonal one in order to obtain faster, simpler, and more general algorithms. The main advantage is that it becomes perfectly feasible to use higher order models(e.g., splines of degree n 3). We develop the theoretical background and present a simple and practical implementation procedure that uses B-splines. Experiments show that the proposed algorithm consistently outperforms the standard interpolation method and that it essentially provides the same performance as the optimal procedure (least squares solution) with considerably less computations.

• Journal of the Korean Data and Information Science Society
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• v.18 no.2
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• pp.301-314
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• 2007

The aim of this paper is to deal with a method to construct a separate fuzzy regression model with crisp input and fuzzy output data using a best response function for the center and the width of the predicted output. Also we introduce the crisp mean and variance of the predicted fuzzy value and also give some examples to compare a performance of the proposed fuzzy model with various other fuzzy regression model.

• 제어로봇시스템학회:학술대회논문집
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• 1988.10a
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• pp.172-177
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• 1988

In this paper, a new fast algorithm of FIR least squares filter with linear phase is presented. The general unknown statistics case is considered, whereby only sample records of the data are available. Taking advantage of the near-to-Toeplitz+Hankel structure of the resulting normal equation, a fast algorithm which gurantees the linear phase constraint, is developed that recursively produces the filter coefficient of linear phase FIR filter for a single block of data.

• Journal of the Korean Society for Precision Engineering
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• v.11 no.5
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• pp.88-97
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• 1994

We persent a method for kinematic calibration of open chain mechanisms based on the product of exponentials (POE) formula. The POE formula represents the forward kinematics of an open chain as a product of matrix exponentials, and is based on a modern geometric interpretation of classical screw theory. Unlike the kinematic parameters in the POE formula vary smoothly with changes in the joint axes;ad hoc methods designed to address the inherent singularities in the D-H parameters are therefore are therefore unnecessary. After introducing the POE formula, we derive a least-squares kinematic calibration algorithm for general open chain mechanisms. Simulation results with a 6-axis open chain are presented.

• Communications for Statistical Applications and Methods
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• v.13 no.1
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• pp.125-134
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• 2006

This study confirms that the regression model of endpoint of interval outputs is not identical with that of the other endpoint of interval outputs in interval regression models proposed by Tanaka et al. (1987) and constructs interval regression models using the best regression model given by variable selection. Also, this paper suggests a method to minimize the sum of lengths of a symmetric difference among observed and predicted interval outputs in order to estimate interval regression coefficients in the proposed model. Some examples show that the interval regression model proposed in this study is more accuracy than that introduced by Inuiguchi et al. (2001).