• The Transactions of The Korean Institute of Electrical Engineers
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• v.68 no.1
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• pp.83-89
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• 2019

In this paper, beam forming using moving least squares method (MLSM) is studied. In the previous research, the least squares method (LSM), one of the data interpolation methods, was used to determine the desired beam pattern and obtain a beam pattern that minimizes the square of the error with the desired beam pattern. However, LSM has a disadvantage in that the beam pattern can not be formed to satisfy the exact steering angle of the desired beam pattern and the peak sidelobe level (PSLL) condition. To overcome this drawback, MLSM is used for beam forming. In order to verify, the proposed method is applied in beam forming of Bezier platform array antenna which is one of conformal array antenna platform.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.40 no.4
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• pp.723-730
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• 2015

In this paper, a location tracking compensation algorithm based on the Least-Squares Method ($LCA_{LSM}$) was proposed to improve the autonomous tracking efficiency for the docent robot in exhibition hall, and the performance of the $LCA_{LSM}$ is analyzed by several practical experiments. The proposed $LCA_{LSM}$ compensates the collected location coordinates for the robot using the Least-Squares Method (LSM) in order to reduce the cumulated errors that occur in the Encoder/Giro sensor (E/G) and to enhance the measured tracking accuracy rates in the autonomous tracking of the robot in exhibition hall. By experiments, it was confirmed that the average error reduction rates of the $LCA_{LSM}$ are higher as 4.85% than that of the $LCA_{KF}$ in Scenario 1 (S1) and Scenario 2 (S2), respectively on the location tracking. In addition, it was also confirmed that the standard deviation in the measured errors of the $LCA_{LSM}$ are much more low and constant compared to that of the E/G sensor and the $LCA_{KF}$ in S1 and S2 respectively. Finally, we see that the suggested $LCA_{LSM}$ can execute more the stabilized location tracking than the E/G sensors and the $LCA_{KF}$ on the straight lines of S1 and S2 for the docent robot.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.1
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• pp.67-74
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• 2023

A meshless technique using the geometric conservation least-squares method (GC-LSM) was devised to discretize the governing equation of linear elasticity. Although the finite-element method is widely used for structural analysis, a meshless method was developed because of its advantages in a moving grid system. This work is the preliminary phase for developing a fully meshless-based fluid-structure interaction solver. In this study, Cauchy's momentum equation was discretized in strong form using GC-LSM for the structural domain, and the Newmark beta method was used for time integration. The solver was validated in 1D, 2D, and 3D benchmarking problems. Static and dynamic results were obtained. The results are more accurate than those of analytic solutions.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.10 no.2
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• pp.38-45
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• 2011

The criteria for determining the elements are the minimum zone method(MZM) and the least squares method(LSM). The LSM is deterministic and simple but is limited at the measurements whose errors are significant compared with form errors. For the precise condition, minimum zone method(MZM) has been selected to determine the elements. The roundness is the fundamental problem in the evaluating form errors. In this paper, anew approach adapting the genius education concept is proposed to obtain an accurate results for the MZM and the LSM of the roundness. Its computational algorithm is studied on a set of measured sample data. To be of almost no account of the specification(the number and the standard deviation etc.) of the sanple data, the results shoqs excellent reliability and high accuracy in estimating the roundness.

• Journal of Advanced Marine Engineering and Technology
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• v.31 no.1
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• pp.58-66
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• 2007

Genetic Algorithms (GA) have some difficulties in practical applications because of too many function evaluations. To overcome these limitations, an approximated modeling method such as Response Surface Modeling(RSM) is coupled to GAs. Original RSM method predicts linear or convex problems well but it is not good for highly nonlinear problems cause of the average effect of the least square method(LSM). So the locally approximated methods. so called as moving least squares method(MLSM) have been used to reduce the error of LSM. In this study, the efficient evolutionary GAs tightly coupled with RSM with MLSM are constructed and then a 2-dimensional inviscid airfoil shape optimization is performed to show its efficiency.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.27 no.2
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• pp.138-146
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• 2016

This paper proposes an optimum beam forming of conformal array antenna by using LSM(Least Squares Method) and GA(Genetic Algorithm). The weights which approximate conformal array antenna beam pattern to linear array antenna beam pattern have been evaluated by applying LSM. Also, the optimum locations of conformal array antenna which form wideband optimum beam pattern have been obtained by using GA. The proposed method is applied to a problem of Bezier platform array antenna for a verification purpose.

• Journal of the Korean Society for Precision Engineering
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• v.16 no.8
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• pp.130-137
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• 1999

The criteria for determining the elements are the minimum zone method(MZM) and the least squares method(LSM). The LSM is deterministic and simple but is limited at the measurements whose errors are significant compared with form errors. For the precise condition, minimum zone method(MZM) has been selected to determine the elements. It is not deterministic and nonlinear so that a optimizing procedure is needed. The Straightness is the fundamental problem in the evaluating form error. In this paper, a new approach adapting the genius education concept is proposed to obtain an accurate results for the minimum zone problem of the straightness. Its computational algorithm is studied on a set of randomly generated data. To be of almost no account of the specification(the number and the standard devistion etc.) of the sample data, the results shows excellent reliability and high accuracy in estimating the straightness.

• Wind and Structures
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• v.27 no.3
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• pp.199-211
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• 2018

This paper deals with wind fragility and risk analysis of high rise buildings subjected to stochastic wind load. Conventionally, such problems are dealt in full Monte Carlo Simulation framework, which requires extensive computational time. Thus, to make the procedure computationally efficient, application of metamodelling technique in fragility analysis is explored in the present study. Since, accuracy by the conventional Least Squares Method (LSM) based metamodelling is often challenged, an efficient Moving Least Squares Method based adaptive metamodelling technique is proposed for wind fragility analysis. In doing so, artificial time history of wind load is generated by three wind field models: i.e., a simple one based on alongwind component of wind speed; a more detailed one considering coherence and wind directionality effect, and a third one considering nonstationary effect of mean wind. The results show that the proposed approach is more accurate than the conventional LSM based metamodelling approach when compared to full simulation approach as reference. At the same time, the proposed approach drastically reduces computational time in comparison to the full simulation approach. The results by the three wind field models are compared. The importance of non-linear structural analysis in fragility evaluation has been also demonstrated.

• Proceedings of the Korea Society for Energy Engineering kosee Conference
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• 2000.11a
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• pp.111-114
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• 2000

본 논문은 선형모델의 모델 계수의 결정방법으로 사용되는 최소자승법 (Least Squares Method, LSM)의 단점을 해결하기 위해 해석적으로 계산된 자료를 함께 적용하는 방법과 원자로의 출력분포 측정을 위한 SAM (Shape Annealing Matrix) 결정에 적용한 결과를 기술하고 있다. 해석적 자료를 함께 적용할 경우 연료 연소에 따른 원자로 특성변화를 적절히 반영하여 LSM 추정치의 정확도를 크게 개선할 수 있음을 확인하였다.

• Proceedings of the KSME Conference
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• 2003.11a
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• pp.1882-1887
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• 2003

The response surface method (RSM) became one of famous meta modeling techniques, however its approximation errors give designers several restrictions. Classical RSM uses the least squares method (LSM) to find the best fitting approximation models from the all given data. This paper discusses how to construct RSM efficiently and accurately using moving least squares method (MLSM) with sensitivity information. In this method, several parameters should be determined during the construction of RSM. Parametric study and optimization for these parameters are performed. Several difficulties during approximation processes are described and numerical examples are demonstrated to verify the efficiency of this method.