• Proceedings of the Computational Structural Engineering Institute Conference
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• 1990.10a
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• pp.79-84
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• 1990

The modal parameter estimations of linear multi-degree-of-freedom structural dynamic systems are carried out in time domain. For this purpose, the equation of motion is transformed into the autoregressive and moving average model with auxiliary stochastic input (ARMAX) model. The parameters of the ARMAX model are estimated by using the sequential prediction error method. Then, the modal parameters of the system are obtained thereafter. Experimental results are given for a 3-story building model subject to ground exitations.

• The Journal of Fisheries Business Administration
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• v.52 no.4
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• pp.13-28
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• 2021

The purpose of this study is to compare short-term price predictive power among ARMA ARMAX and VAR forecasting models based on the MDM test using monthly consumer price data of frozen mackerel. This study also aims to help policymakers and economic actors make reasonable choices in the market on monthly consumer price of frozen mackerel. To analyze this study, the frozen wholesale prices and new consumer prices were used as variables while the price time series data were used from December 2013 to July 2021. Through the unit root test, it was confirmed that the time series variables employed in the models were stable while the level variables were used for analysis. As a result of conducting information standards and Granger causality tests, it was found that the wholesale prices and fresh consumer prices from the previous month have affected the frozen consumer prices. Then, the model with the highest predictive power was selected by RMSE, RMSPE, MAE, MAPE, and Theil's inequality coefficient criteria where the predictive power was compared by the MDM test in order to examine which model is superior. As a result of the analysis, ARMAX(1,1) with the frozen wholesale, ARMAX(1,1) with the fresh consumer model and VAR model were selected. Through the five criteria and MDM tests, the VAR model was selected as the superior model in predicting the monthly consumer price of frozen mackerel.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1989.10a
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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.

• Structural Engineering and Mechanics
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• v.72 no.6
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• pp.689-712
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• 2019

Time-series models like AR-ARX and ARMAX, provide a robust way to capture the dynamic properties of structures, and their residuals can be effectively used as features for damage detection. Even though several research papers discuss the implementation of AR-ARX and ARMAX models for damage diagnosis, they are basically been exploited so far for detecting the time instant of damage and also the spatial location of the damage. However, the inverse problem associated with damage quantification i.e. extent of damage using time series models is not been reported in the literature. In this paper, an approach to detect the extent of damage by combining the ARMAX model by formulating the inverse problem as a multi-constrained optimization problem and solving using a newly developed hybrid adaptive differential search with dynamic interaction is presented. The proposed variant of the differential search technique employs small multiple populations which perform the search independently and exchange the information with the dynamic neighborhood. The adaptive features and local search ability features are built into the algorithm in order to improve the convergence characteristics and also the overall performance of the technique. The multi-constrained optimization formulations of the inverse problem, associated with damage quantification using time series models, attempted here for the first time, can considerably improve the robustness of the search process. Numerical simulation studies have been carried out by considering three numerical examples to demonstrate the effectiveness of the proposed technique in robustly identifying the extent of the damage. Issues related to modeling errors and also measurement noise are also addressed in this paper.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.170.1-170
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• 2001

This paper developed the model for electro hydraulic force control system by identification method via ARMAX model. Implementation of Identification is performed on Pin-On-Disk type tribotester. The wear mechanism is an important mechanic property to select a material´s life and a optimum work condition. Pin-on-disk type tribotester is popular wear analysis experimental equipment and its mechanism is that adding a force on a rotating disk to simplify two surface contact´s wear experimental condition. Material´s rotating velocity and eccentricity rotation makes disturbance and it affects adding constant force. To get a high performance of force adding part, DDV(Direct Drive Valve) which has pressure control loop is used. To obtain a tribotester´ s ARMAX model, prediction error method(PEM) is used in case force adding part and rotating part is ...

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.669-673
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• 2006

수자원시스템의 설계, 계획, 운영에 있어서 핵심적인 수문변수의 미래거동에 대한 보다 나은 추정치가 필요하다. 예를 들어, 수력발전, 레크리에이션 이용과 하류지역의 오염희석과 같은 다중목적 기능을 유지하기 위하여 다목적댐을 운영할 때에, 다가오는 미래시간에 대한 계획된 유량의 예측이 요구된다. 예측의 목적은 미래에 발생할 정확한 예상치를 제공하는 것이다(Keith W. Hipel, 1994). 본 연구의 주요 목적은 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN, ARMAX모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우인 상관된 입력을 설명할 수 있도록 개발된다. 일반적인 모형을 만드는 전략이며 실제유역시스템에 적용하여 검토된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.394-398
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• 2011

수자원시스템의 설계, 계획, 운영에 있어 핵심적인 수문변수의 미래거동에 대한 보다 나은 추정치가 필요하다. 예를 들어, 수력발전, 레크리에이션 이용과 하류지역의 오염희석과 같은 다중 목적 기능을 유지하기 위하여 다목적댐을 운영할 때에, 다가오는 미래시간에 대한 계획된 유량의 예측이 요구된다. 예측의 목적은 미래에 발생할 정확한 예상치를 제공하는 것이다(Keith W. Hipel, 1994). 본 연구의 주요 목적은 금강수계인 대청댐에서 다변량 추계학적 시스템의 해석을 위한 모형의 추정과 등정을 위한 과정을 개발하는데 있다. 일반적 추계학적 시스템 모형이 표현되며 그것으로부터 수문학적 시스템의 모형을 매우 적절하게 유도하기 위한 다중 입력-단일 출력 TF, TFN, ARMAX모형을 유도하는데 있다. 이 모형은 수문학적 시스템을 위한 경우인 상관된 입력을 설명할 수 있도록 개발된다. 일반적인 모형을 만드는 전략이 사용되며 실제유역시스템에 적용하여 검토해 보고자 한다.

• Proceedings of the KIEE Conference
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• 2004.11c
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• pp.678-680
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• 2004

In many cases the systems are so complex that it is not possible to obtain reasonable models using physical laws. Also a model based on physical laws contains a number of unknown parameters even if the structure is derived from physical laws. These problems can be solved by system identification. In this paper, plate activity vibration is selected as an example for system identification. The transfer functions of this system is derived by using ARMAX based on input/output data through experiment.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 1992.04a
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• pp.13-18
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• 1992

구조물의 동적실험을 통하여 얻은 하중과 거동에 대한 시간기록을 분석하여, 구조계의 동 특성계수들을 추정하는 기법에 대하여 연구하였다. 실험과정 및 해석모형과정의 오차를 고려하기 위하여, 하중기록과 구조거동기록간의 관계를 추계론적 자동회기 및 이동평균모형(Stochastic Auto-Regressive and Moving-Average (ARMAX) Model)음 사용하여 모형화하였다. 미지의 ARMAX 계수행렬들은 순차적 예측오차기법을 사용하여 추정하였으며, 계수추정기법의 효율성을 증진시키기 위하여, Exponential Data Weighting, Global Data Weighting 및 Square Root Estimation 기법을 활용하였다. 다중거동측정계의 예제해석을 통하여 이의 효율성을 분석하였다.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.5 no.3
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• pp.36-42
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• 2006

The analytical realization of uncoupled system was introduced in this study using times series and its spectrum analysis. The ARMAX spectra of time series methods were compared with the conventional FFT spectrum. Also, the response of second order system uncoupled was solved using the Runge-Kutta Gill method. In this numerical analysis, the displacement, velocity and acceleration were calculated. The displacement response among them was used for the power spectrum analysis. The ARMAX algorithm in time series was proved to be appropriate for the mode estimation and spectrum analysis. Using the separate response of first and second mode, each modes were calculated separately and the response of mixed modes was also analyzed for the mode estimation using several time series methods.