• Structural Engineering and Mechanics
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• v.51 no.3
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• pp.447-470
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• 2014

Numerical simulation of the non-linear behavior of (RC) structural walls subjected to severe earthquake ground motions requires a reliable modeling approach that includes important material characteristics and behavioral response features. The objective of this paper is to optimize a simplified method for the assessment of the seismic response and damage development analyses of an RC structural wall building using macro-element model. The first stage of this study investigates effectiveness and ability of the macro-element model in predicting the flexural nonlinear response of the specimen based on previous experimental test results conducted in UCLA. The sensitivity of the predicted wall responses to changes in model parameters is also assessed. The macro-element model is next used to examine the dynamic behavior of the structural wall building-all the way from elastic behavior to global instability, by applying an approximate Incremental Dynamic Analysis (IDA), based on Uncoupled Modal Response History Analysis (UMRHA), setting up nonlinear single degree of freedom systems. Finally, the identification of the global stiffness decrease as a function of a damage variable is carried out by means of this simplified methodology. Responses are compared at various locations on the structural wall by conducting static and dynamic pushover analyses for accurate estimation of seismic performance of the structure using macro-element model. Results obtained with the numerical model for rectangular wall cross sections compare favorably with experimental responses for flexural capacity, stiffness, and deformability. Overall, the model is qualified for safety assessment and design of earthquake resistant structures with structural walls.

• 제어로봇시스템학회:학술대회논문집
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• 2004.08a
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• pp.524-529
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• 2004

In this paper, We described the method which developed the optimal control system for air separation unit to change production rates frequently and rapidly. Control models of the process were developed from actual plant data using subspace identification method which is developed by many researchers in resent years. The model consist of a series connection of linear dynamic block and static nonlinear block (Wiener model). The model is controlled by model based predictive controller. In MPC the input is calculated by on-line optimization of a performance index based on predictions by the model, subject to possible constraints. To calculate the optimal the performance index, conditions are expressed by LMI(Linear Matrix Inequalities).In order to access at the Bailey DCS system, we applied the OPC server and developed the Client program. The OPC sever is a device which can access Bailey DCS system.The Client program is developed based on the Matlab language for easy calculation,data simulation and data logging. Using this program, we can apply the optimal input to the DCS system at real time.

• 제어로봇시스템학회:학술대회논문집
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• 2003.10a
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• pp.122-127
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• 2003

In this paper, an adaptive predistortion scheme is proposed to compensate nonlinear distortions caused by high power amplifiers (HPA) in OFDM systems. A complex Wiener-Hammerstein model (WHM) is used to describe input-output relationship of HPA with linear dynamics. The predistorter is directly identified by complex power series model with memory, which is an approximate inverse of the HPA expressed by the WHM. The effectiveness of the proposed adaptive compensation scheme is validated by numerical simulation for 64QAM-OFDM systems.

• Proceedings of the Korean Society of Precision Engineering Conference
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• 1993.10a
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• pp.321-327
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• 1993

In order to maintain the cutting force at a desired level during peripheral end milling processes in spite of variation of the depth of cut and other machining conditions, a feedrate override. Apaptive Control Constraint (ACC) system are developed. Feedrate override was accomplished by a developed MMC board and PMC interface techniques. Nonlinear model of the cutting process was linearized as an adaptive model with time varying paramrters. Integral type estimators were introduced for on-line identification of cutting and control parameters in peripheral and milling processes. Zero Order Jold (ZOH) type degital control methodology which uses pole-placement concepts was applied for the ACC system. Performance of the developed ACC system was confirmed on the vertical machining center equipped with FANUC OMC for a large amount of experiment

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2007.11a
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• pp.183-189
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• 2007

In this paper, we developed the fuzzy group method data handling-type (GMDH) Model and applied it to demand forecasting of educational expenses. At present, GMDH family of modeling algorithms discovers the structure of empirical models and it gives only the way to get the most accurate identification and demand forecasts in case of noised and short input sampling. In distinction to fuzzy system, the results are explicit mathematical models, obtained in a relative short time. In this paper, an adaptive learning network is proposed as a kind of fuzzy GMDH. The proposed method can be reinterpreted as a multi-stage fuzzy decision rule which is called as the fuzzy GMDH. The fuzzy GMDH-type networks have several advantages compared with conventional multi-layered GMDH models. Therefore, many types of nonlinear systems can be automatically modeled by using the fuzzy GMDH. A computer program is developed and successful applications are shown in the field of demand forecasting problem of educational expenses with the number of factors considered.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2004.04a
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• pp.381-384
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• 2004

본 논문에서는 변조 함수법을 이용하여 비선형 연속시스템의 퍼지모델 파라미터 인식을 위한 새로운 알고리즘을 제시하였다. 동력학 미분방정식은 미분항을 가지고 있기 때문에 입출력 데이터를 이용하여 퍼지모델 파라미터를 인식하는 경우 외란의 영향을 무시할 수 없으므로 퍼지모델 파라미터 인식이 어렵다. 그러나 변조 함수법을 이용하면 미분항을 소거할 수 있어 미분항이 없는 연립방정식으로부터 쉽게 퍼지모델 파라미터 인식이 가능하다 몇 개의 시뮬레이션을 통해 제안한 변조 함수법을 이용한 퍼지모델 파라미터 인식의 정확성과 유효성을 확인할 수 있었다.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.101.5-101
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• 2001

A linear input/output data-based predictive control with integral action is developed. The control input is obtained directly from the input/output data in a single step. However, the state estimation in subspace identification gives a biased estimate and there is model mismatch when the controller is applied to a nonlinear process. To overcome such difficulties, we add integral action to a linear input/output data-based predictive controller by augmenting the integrated white noise disturbance model and use each of best linear unbiased estimation(BLUE) filter and Kalman filter as a stochastic observer for the unmeasured disturbance. When applied to a continuous styrene polymerization reactor the proposed controller demonstrates.

• Journal of Institute of Control, Robotics and Systems
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• v.20 no.7
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• pp.695-700
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• 2014

In this paper, we utilize the CMG's momentum bias to control the roll/pitch attitude of the Quad-rotor. While the previous control approaches have used the thrust control approach, we design and add a new momentum controller (using CMG) in order to improve the transient response over the existing methods. The focal point of this paper is the design of a controller for a Quad-rotor's attitude using CMG. This leads to other tasks such as an identification of the model's parameters and mathematical nonlinear modeling. Then, the previous thrust controller is designed based on the linearized model. Finally, the overall system with our designed controller is implemented and tested in real time to show that the Quad-rotor is kept in a good balanced position faster than the traditional thrust-only control approach.

• The Journal of the Acoustical Society of Korea
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• v.15 no.4
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• pp.77-82
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• 1996

In this paper an identification method is presented to obtain the equivalent electric model of a sandwitch type piezoelectric transducer. Unknown parameters related to the equivalent circuit are identified by solving a nonlinear optimization problem which can minimize an error between the experimental and analytical admittances in air. The proposed method is applied to an example transducer. The validity of equivalent circuit model is demonstrated by the comparison between the experimental measurements and analytical calculations of transmitting voltage response(TVR) and receiving voltage response(RVS).

• Proceedings of the KIEE Conference
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• 1996.07b
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• pp.717-719
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• 1996

An artificial neural network(ANN) modeling is presented using the Input-output power characteristics of SMES. When using the ANN which functions as a model-free system, network construction and determination of learning parameters are carefully chosen to represent the complicated nonlinear input-output relation from the black-boxed SMES system. The proposed ANN-based SMES model is applied to analyse the power system stability and the simulation results provide the property of this approach.