• Journal of the Earthquake Engineering Society of Korea
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• v.1 no.2
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• pp.1-15
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• 1997

This paper presents substructural identification methods for the assessment of local damages in complex and large structural systems. For this purpose, an auto-regressive and moving average with stochastic input (ARMAX) model is derived for a substructure to process the measurement data impaired by noises. Using the substructural methods, the number of unknown parameters for each identification can be significantly reduced, hence the convergence and accuracy of estimation can be improved. Secondly, the damage index is defined as the ratio of the current stiffness to the baseline value at each element for the damage assessment. The indirect estimation method was performed using the estimated results from the identification of the system matrices from the substructural identification. To demonstrate the proposed techniques, several simulation and experimental example analyses are carried out for structural models of a 2-span truss structure, a 3-span continuous beam model and 3-story building model. The results indicate that the present substructural identification method and damage estimation methods are effective and efficient for local damage estimation of complex structures.

• Journal of Korean Society for Quality Management
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• v.36 no.4
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• pp.93-101
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• 2008

The rare metals, used for semiconductors, PDP-LCS and other specialized metal areas necessarily, has been playing a key role for the Korean economic development. Rare metals are influenced by exogenous variables, such as production quantity, price and supplied areas. Nowadays the supply base of rare metals is threatened by the sudden increase in price. For the stable supply of rare metals, a rational demand outlook is needed. In this study, focusing on the domestic demand for chromium, the uncertainty and probability materializing from demand and price is analyzed, further, a demand forecast model, which takes into account various exogenous variables, is suggested, differing from the previously static model. Also, through the OOS(out-of-sampling) method, comparing to the preexistence ARIMA model, ARMAX model, multiple regression analysis model and ECM(Error Correction Mode) model, we will verify the superiority of suggested model in this study.

• 제어로봇시스템학회:학술대회논문집
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• 1989.10a
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• pp.918-923
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• 1989

This paper presents a stability condition for Astrom's minimal variance control(MVC) with mismatch of time delay for a SISO ARMAX model containing time delay. The proof of the condition presented here is based on the characteristic equation in the feedback system and its magnitude. This condition, from easy numerical calculation, is able to find the stability of the feedback system without knowing the real time delay.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2305-2307
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• 2001

Generally, The modeling method for the mathematical model is mdeled by using the physical laws and the system identification. In this paper, The arago's disk system of the operating principle of induction motors is selected as an example for identification. The system transfer function is derived from input/output data through experiment. Model is estimated by using ARX, ARMAX, BJ, OE model structure and compared each other.

• Journal of the Korean Society for Precision Engineering
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• v.26 no.11
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• pp.70-76
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• 2009

Web tension control system recently have been applied to OLED(Organic Light-Emitting Diode), RFID of flexible material, e-Paper and PLED(Polymeric LED) and various web control algorithms have being developed for higher productivity and product quality These system need an accuracy model to design and implement controller. In this paper, the web tension control system with dancer roll is mathematically modeled. Mathematical model consists of 8 subsystems and each subsystems can be described as impedance structure which connected by velocity and tension. Mathematical model is different from the estimated model at high frequency range because of structure dynamics which is ignored on mathematical model. The estimated model is derived using ARMAX model. The controller is designed using the estimated model. The step response of the estimated model are compared with that of physical model for a validation of estimated model. The experimental results show a good match between them.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.410-418
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• 1999

This paper describes the use of residual and parameter identification methods for fault detection in an air handling unit. Faults can be detected by comparing expected condition with the measured faulty data using residuals. Faults can also be detected by examining unmeasurable parameter changes in a model of a controlled system using a system identification technique. In this study, AutoRegressive Moving Average with seXtrnal input(ARMAX) and AutoRegressive with eXternal input(ARX) models with both single-input/single-input and multi-input/single-input structures are examined. Model parameters are determined using the Kalman filter recursive identification method. Regression equations are calculated from normal experimental data and are used to compute expected operating variables. These approaches are tested using experimental data from a laboratory's variable-air-volume air-handling-unit.

• Smart Structures and Systems
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• v.32 no.1
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• pp.61-81
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• 2023

This study presents an ensemble learning based Bayesian model updating approach for structural damage diagnosis. In the developed framework, the structure is initially decomposed into a set of substructures. The autoregressive moving average (ARMAX) model is established first for structural damage localization based structural motion equation. The wavelet packet decomposition is utilized to extract the damage-sensitive node energy in different frequency bands for constructing structural surrogate models. Four methods, including Kriging predictor (KRG), radial basis function neural network (RBFNN), support vector regression (SVR), and multivariate adaptive regression splines (MARS), are selected as candidate structural surrogate models. These models are then resampled by bootstrapping and combined to obtain an ensemble model by probabilistic ensemble. Meanwhile, the maximum entropy principal is adopted to search for new design points for sample space updating, yielding a more robust ensemble model. Through the iterations, a framework of surrogate ensemble learning based model updating with high model construction efficiency and accuracy is proposed. The specificities of the method are discussed and investigated in a case study.

• Journal of Electrical Engineering and Technology
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• v.8 no.5
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• pp.975-983
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• 2013

This study proposes a low order dynamic model of a building system in order to predict thermal behavior within a building and its energy consumption. The building system includes a thermally well-insulated room and an electric heater. It is modeled by a second order lumped RC thermal network based on the thermal-electrical analogy. In order to identify unknown parameters of the model, an experimental procedure is firstly detailed. Then, the different linear parametric models (ARMA, ARX, ARMAX, BJ, and OE models) are recalled. The parameters of the parametric models are obtained by the least square approach. The obtained parameters are interpreted to the parameters of the physically based model in accordance with their relationship. Afterwards, the obtained models are implemented in Matlab/Simulink(R) and are evaluated by the mean of the sum of absolute error (MAE) and the mean of the sum of square error (MSE) with the variable of indoor temperature of the room. Quantities of electrical energy and converted thermal energy are also compared. This study will permit a further study on Model Predictive Control adapting to the proposed model in order to reduce energy consumption of the building.

• Journal of Korean Society on Water Environment
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• v.20 no.6
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• pp.685-691
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• 2004

Water quality forecasting with long term flow is important for management and operation of river environment. However, it is difficult to set up and operate a physical model for water quality forecasting due to large uncertainty in the data required for model setting. Therefore, relatively simpler stochastic approaches are adopted for this problem. In this study we try several multivariate time series models such as ARMAX models for the possible substitute for water quality forecasting. Those models are applied to the BOD and COD levels at Noryangin station, Han river, and also evaluated the effect of release from Paldang dam on them. Monthly BOD and COD data from 1985 to 1991 (7 years) are used for model building and another two year data for model testing. As a result of the study, the effect of improvement on water quality is much more effective combining with the water quality improvement of dam release than considering only increment of dam release in the downstream Han river.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.48 no.4
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• pp.395-401
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• 1999

In this paper, a temperature controller for a semiconductor test handler is proposed. First, a handware system for identification and control is established using RTD sensors, an A/D converter, solid state relays, a heater, and a computer system. Second, using ARMAX model and least square method, a chamber model for the design of a controller is identified through experiments. The identified model is verified to describe the real plant very well in the sense that it shows very similar input-output responses to those of the real system. With the identified model an LQG controller is designed. Frequency response of the designed controller shows that it has 15 dB of gainmargin and (-50˚, +50˚) of phase margin. Experiment with a real test handler demonstrates a good performance in the sense that its overshoot and steady state error are smaller and response time is faster, compared with those of a conventional PID controller.