• 한국윤활학회:학술대회논문집
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• 한국윤활학회 2002년도 proceedings of the second asia international conference on tribology
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• pp.249-250
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• 2002

In this paper, the relationship among the 3-D surface topography parameters are studied. Several surface topography parameters that are important in tribology are calculated against various surface topography data. 3-D surface data with desired properties are generated by using the non-causal 2-D auto-regressive (AR) model. The non-causal 2-D AR model is a random 3-D surface topography model that can generate 3-D surface topography data with specified parameters.

• 해양환경안전학회:학술대회논문집
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• 해양환경안전학회 2018년도 공동국제학술발표회
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• pp.171-171
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• 2018

• 산업경영시스템학회지
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• 제19권40호
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• pp.1-14
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• 1996

We develop a dynamic demand forecasting model compared to regression analysis model and AutoRegressive Integrated Moving Average(ARIMA) model. The dynamic model can apply to the current dynamic data to forecasts through introducing state equation. A multiple regression model and ARIMA model using given data are designed via the model analysis. The forecasting fitness evaluation between the designed models and the dynamic model is compared with the criterion of sum of squared error.

• 한국자동차공학회논문집
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• 제8권6호
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• pp.156-164
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• 2000

In this study, a discrete time model for a simplified front wheel suspension system which has nonlinear dampling and stiffness property is introduced. The model is estimated from the discrete data which are generated based on the real car parameter. The performance of the proposed method is evaluated through numerical simulation, and the simulation results show that the proposed method can estimate the nonlinear behavior of the suspension system very well.

• 한국건축시공학회:학술대회논문집
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• 한국건축시공학회 2014년도 춘계 학술논문 발표대회
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• pp.14-15
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• 2014

The modular construction is as yet early stage of market in Korea. So It is have difficulty of market demand forecast of the modular building. Therefore, this study was done analysis for market trends of the modular building using ARIMA(Auto Regressive Integrated Moving Average) model by time series data.

• 한국공작기계학회:학술대회논문집
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• 한국공작기계학회 1998년도 춘계학술대회 논문집
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• pp.40-52
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• 1998

Among the non-traditional machining methods, Abrasive waterjet machining process shows big promise in drilling difficult-to-machine materials due to its numerous advantages such as absence of heat affect zone and thermal distortion. Acoustic emission signal technique is used to understand about material removal mechanisms during abrasive waterjet drilling process. More information about the drilling process is derived through frequency decomposition of auto regressive moving average modeling representing acoustic emission signals.

• 한국전산구조공학회:학술대회논문집
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• 한국전산구조공학회 1989년도 가을 학술발표회 논문집
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• pp.46-50
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• 1989

Methods for the estimation of the coefficient matrices in the equation of motion for a linear multi-degree-of-freedom structure arc studied. For this purpose, the equation of motion is transformed into an auto-regressive and moving average with auxiliary input (ARMAX) model. The ARMAX parameters are evaluated using several methods of parameter estimation; such as toe least squares, the instrumental variable, the maximum likelihood and the limited Information maximum likelihood methods. Then the parameters of the equation of motion are recovered therefrom. Numerical example is given for a 3-story building model subjected to an earthquake exitation.

• Journal of Advanced Marine Engineering and Technology
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• 제22권6호
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• pp.928-934
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• 1998

This paper describes a fully digitalized driver for BLDC motors which is realized by a single chip microprocessor. The speed change can be done by using the signal obtained from the position detecting sensor and adjusting the pulse width at the input channel of power module. In order to establish a speed control system a repetitive control method is adopted to track a periodic refer-ence change in the BLDC motor system. The experimental results show accurate reference track-ing performance under the given periodic reference in the repetitive controller design.

• Journal of Electrical Engineering and information Science
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• 제2권3호
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• pp.61-65
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• 1997

This paper proposes an intelligent controller for brushless DC motor and load with unknown nonlinear dynamics. The proposed intelligent control system consists of a plant identifier and PID controller with varying gains. The identifier is constructed using an Auto Regressive Moving Average (ARMA) model. In order to tune the parameters of the identifier and the gains of the PID controller efficiently, e also propose a modified Evolution Strategy. Experimental results show that the proposed intelligent controller for brushless DC motor has good control performance under unknown disturbance.

• Journal of the Korean Data and Information Science Society
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• 제15권2호
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• pp.355-365
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• 2004

Kim et al.. (2003) developed an Economic Instability Index (EII) by using mean squared error (MSE) from the neural network (NN) trained on the 1995 KOSPI. In this paper we study validity of the NN. For this we compare the NN with the well known Box-Jenkins linear auto-regressive processes. Our conclusive understanding of the problem is that the NN provides quite effective EII because it tends to overfit.