• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.48-51
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• 2010

본 논문에서는 자유경계문제 해석을 위해 정확도가 향상된 implicit 이동최소제곱 차분법을 제시한다. 계면경계에 대한 implicit 정의로 인해 비선형 시스템이 구성되고, 매 해석단계마다 절점해와 계면경계의 위치를 반복계산을 통해 찾는다. 계면경계 결정시 속도항을 한 단계 뒤로 지연시켜 explicit하게 근사적으로 계산하던 기존 방법에 비해 계면경계의 위치를 더 정확하게 계산할 수 있고, 결과적으로 해의 정확도가 향상되었다. 계면경계 위치값이 비교적 빠른 속도로 수렴하기 때문에 많은 반복계산이 필요치 않다. 수치예제를 통해 기존의 방법으로 계산한 결과와 비교하여 새롭게 개발한 implicit 방법의 향상된 정확도를 보였다.

• The Korean Journal of Applied Statistics
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• v.7 no.2
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• pp.239-248
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• 1994

The Yule-Walker estimator and the approximate conditional least squares estimator of the parameter of the EARMA(1, 1) model are obtained. These two estimators are compared by simulation study. It is shown that the approximate conditional least squares estimator is better in the sense of the mean square error than the Yul-Walker estimator.

• The Korean Journal of Applied Statistics
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• v.31 no.3
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• pp.315-328
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• 2018

In this paper, we examine the methodology to predict consumer preferences using several groups of attributes of products and application to real data. In the food industry, studies are in progress to investigate the relationship between product attributes and consumer preferences; consequently, various methodologies are proposed. Among these methodologies, we consider multiple factor analysis (MFA). The result of the MFA enable the division of consumers into four clusters with similar liking and the defining of preference characteristics for each cluster. Also, using the results of multiple factor analysis, we find the partial least squares path model to predict consumer preferences through the characteristics of the product and the characteristics evaluated by consumers. We can understand the relationship between the cluster of consumers and the preferred/undesirable characteristics of products through the partial least squares path model applied to two clusters with different liking. When multiple factor analysis is used in the partial least squares path model, it is possible to investigate relationships between products and consumers by analyzing product characteristics and consumer preferences simultaneously. The results can be applied to product developments and sales which makes this methodology important and useful.

• Journal of the Korean Data and Information Science Society
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• v.11 no.1
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• pp.1-18
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• 2000

In this paper, we study several parameter estimation methods used for autoregressive processes and compare them in view of forecasting. The least square estimation, least absolute deviation estimation, robust estimation are compared through Monte Carlo simulations.

• The Korean Journal of Applied Statistics
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• v.19 no.3
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• pp.587-598
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• 2006

The number of variables exceeds the number of samples in microarray data. We propose a nonparametric local linear logistic classification procedure using orthogonal components for classifying high-dimensional microarray data. The proposed method is based on the local likelihood and can be applied to multi-class classification. We applied the local linear logistic classification method using PCA, PLS, and factor analysis components as new features to Leukemia data and colon data, and compare the performance of the proposed method with the conventional statistical classification procedures. The proposed method outperforms the conventional ones for each component, and PLS has shown best performance when it is embedded in the proposed method among the three orthogonal components.

• Proceedings of the Korean Society of Surveying, Geodesy, Photogrammetry, and Cartography Conference
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• 2003.10a
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• pp.15-19
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• 2003

The Total Least Squares (TLS) method is introduced in comparison with the conventional Least Squares (LS) method. The principles and mathematical models for both methods are summarized and the comparison results from their applications to a simple geometric example, fitting a straight line to a set of 2D points are presented. As conceptually reasoned, the results clearly indicate that LS is more susceptible of producing wrong parameters with worse precision rather than TLS. For many applications in surveying, can adjustment computation and parameter estimation based on TLS provide better results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2010.04a
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• pp.179-182
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• 2010

본 연구에서는 MLS 차분법을 이용하여 동역학 문제를 해석하기 위한 explicit 동적해석 알고리즘을 제시한다. 격자망이 없는 장점을 부각시키기 위해 이동최소제곱법에 근거한 Taylor 전개로부터 미분근사를 얻고 차분식을 구성했다. 지배 미분방정식의 시간항을 CDM(Central difference Method) 차분하여 빠른 속도로 동적해석을 수행하였다. 수치결과를 통해 본 연구에서 제시한 알고리즘의 정확성과 안정성을 확인할 수 있었다.

• Proceedings of the KIPE Conference
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• 2020.08a
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• pp.209-211
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• 2020

운송기관의 온실가스 저감을 위해 배터리-수소연료전지 하이브리드 철도추진시스템에 대한 연구가 활발히 진행되고 있다. 이 중 배터리는 빠른 응답 특성으로 하이브리드 철도추진 시스템의 효율을 극대화 시키기 위해 주요 전원으로 사용되고 있어, 시스템의 안전성 및 신뢰성을 높이기 위해 정확한 열화추정이 요구되고 있다. 본 논문에서는 사전 모델의 수립이 필요하지 않고 미소 용량 및 폐회로 제어가 가능한 재귀 최소제곱 추정 기법을 이용한 리튬이온 배터리의 SOH 추정 기법을 제안하였으며, 1S18P 배터리 모듈을 통해 열화 추정결과를 검증하였다.

• Journal of Digital Convergence
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• v.13 no.12
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• pp.117-124
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• 2015

As the acceptance of statistical analysis has been increased because of Big Data, the needs for an advanced second generation of statistical analysis method like Structural Equation Model are also increasing. This study suggests how R-Program, as open software, can be utilized when Partial Least Square Model, one of the SEMs, is applied to statistical analysis. R is a free software as a part of GNU projects as well as a powerful and useful tool for statistical analysis including Big Data. The study utilized R and SmartPLS, a representative statistical package of PLS-SEM, and analyzed internal consistency reliability, convergent validity, and discriminant validity of the measurement model. The study also analyzed path coefficients and moderator effects of the structural model and compared the results, respectively. The results indicated that R showed the same results with SmartPLS on the measurement model and the structural model. Therefore, the study confirmed that R could be a powerful tool that is alternative to a commercial statistical package in the future.

• The Journal of the Korea institute of electronic communication sciences
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• v.9 no.12
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• pp.1337-1344
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• 2014

The objective of an echo canceller connected to any end of a communication line such as digital subscriber line (DSL) is to compensate the outgoing transmit signal in the receiving path that the hybrid circuit leaks. The echo canceller working in a full duplex environment is an adaptive system driven by the local signal. Conventional echo canceller that implement the least mean square (LMS) algorithm provides a low computational burden but poor convergence properties. The length of the echo canceller will directly affect both the degree of performance and the convergence speed of the adaptation process. To cancel long time-varying echoes, the number of tap coefficients of a conventional echo canceller must be large, which decreases the convergence speed of the adaptive filter. This paper proposes an alternative technique for the echo cancellation in a telecommunication channel. The new technique employs the bi-directional least mean square (LMS) algorithm for adaptively computing the optimal set of the coefficients of the echo canceller, which is composed of weighted combination of both feedforward and feedback algorithms. Finally, Simulation results as well as mathematical analysis demonstrates that the proposed echo canceller has faster convergence speed than the conventional LMS echo canceller with nearly equivalent complexity of computation.