• Proceedings of the KSR Conference
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• 2011.10a
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• pp.849-852
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• 2011

Dynamic response of high-speed railway bridges is important due to the possibility of resonance from continuous and repeated action of high-speed train running. Acceleration of bridge deck is important parameter in the evaluation of dynamic stability of bridges. This study investigated acceleration characteristics according to type of tracks.

• The Transactions of The Korean Institute of Electrical Engineers
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• v.67 no.10
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• pp.1303-1307
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• 2018

A improving sensorless compensator for the IPMSM(Interior Permanent Magnet Synchronous Motor) drive system is proposed. Generally, the motor drive system is required the robust parameter variation and disturbance. The speed estimation methods of the conventional IRP(Instantaneous Reactive Power) compensator is improved by the speed estimation techniques of the current model observer with the proposed instantaneous reactive power compensator. Performance evaluations of the novel speed error compensator and sensorless control system are carried out by the experiments.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.5 no.1
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• pp.150-156
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• 2001

This paper proposed a speed estimator using MRAC(Model Reference Adaptive Control) for sensorless vector control. It is robust for parameter variation or disturbance and the estimated speed is used as feedback in a vector control system. Experiment is presented to confirm the theoretical analysis.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.1141-1146
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• 1993

The problem of tension control in metal-strip processing line is discussed. A new mathematical dynamic model which relates tension change, motor-speed change and roll-gap change is developed. Through the computer simulation of this model, parameter sensitivity, the tension transfer phenominon, and static and dynamic characteristics of strip tension were studied. Guidelines are developed to help one selecting locations of the master-speed drive in multi-drive speed control for tension adjustment and reducing the effect of interaction between tension and roll gap control.

• Proceedings of the KIPE Conference
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• 1998.07a
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• pp.230-233
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• 1998

The conventional PI controller are fragile in parameter variation and load-variation. Therefore, in this paper, a speed control algorithm based on the Fuzzy PI controller is proposed for the high performance speed control of a voltage-source inverter to drive 3-phase induction motors. The computer simulation results show that the proposed controller are more excellent control characteristics than conventional PI controller in transient-state and steady-state response.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.135-135
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• 2000

In this Paper. the DSP implementation of induction motor drive is Presented on the viewpoint of the design and experiment. The speed estimation of control system for induction motor drive is designed on the base of neural network speed estimator. This neural network speed estimator is experimentally applied to the induction motor system. This system Provides the satisfactory results.

• Proceedings of the KIEE Conference
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• 2002.07d
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• pp.2149-2151
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• 2002

In this paper, a novel speed estimation method of an induction motor using neural networks(NNs) is presented. The NN speed estimator is trained online by using the error backpropagation algorithm, and the training starts simultaneously with the induction motor working. The neural network based vector controller has the advantage of robustness against machine parameter variation. The simulation results using Matlab/Simulink verify the useful of the proposed method.

• Journal of the Korean Society of Safety
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• v.12 no.2
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• pp.22-26
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• 1997

A small scale centrifugal oil separator consisted of two concentric tubes was fabricated for spilt oil recovery. With speed control of the inner tube, its performance of oil separation was investigated. Oil-water mixture is separated by forced vortex motion with the rotating inner tube. Velocity and pressure distributions in the tubes were calculated. Control of rotating speed, which is the most influencing parameter, showed an optimum value 946rpm corresponding to the acceleration of 20g at the inner tube surface. Separation performance was suddenly deteriorated at rotating speed higher than 1200rpm.

• Journal of Power Electronics
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• v.5 no.2
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• pp.129-141
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• 2005

A high-performance robust hybrid speed controller for a permanent-magnet synchronous motor (PMSM) drive with an on-line trained neural-network model-following controller (NNMFC) is proposed. The robust hybrid controller is a two-degrees-of-freedom (2DOF) integral plus proportional & rate feedback (I-PD) with neural-network model-following (NNMF) speed controller (2DOF I-PD NNMFC). The robust controller combines the merits of the 2DOF I-PD controller and the NNMF controller to regulate the speed of a PMSM drive. First, a systematic mathematical procedure is derived to calculate the parameters of the synchronous d-q axes PI current controllers and the 2DOF I-PD speed controller according to the required specifications for the PMSM drive system. Then, the resulting closed loop transfer function of the PMSM drive system including the current control loop is used as the reference model. In addition to the 200F I-PD controller, a neural-network model-following controller whose weights are trained on-line is designed to realize high dynamic performance in disturbance rejection and tracking characteristics. According to the model-following error between the outputs of the reference model and the PMSM drive system, the NNMFC generates an adaptive control signal which is added to the 2DOF I-PD speed controller output to attain robust model-following characteristics under different operating conditions regardless of parameter variations and load disturbances. A computer simulation is developed to demonstrate the effectiveness of the proposed 200F I-PD NNMF controller. The results confirm that the proposed 2DOF I-PO NNMF speed controller produces rapid, robust performance and accurate response to the reference model regardless of load disturbances or PMSM parameter variations.

• Smart Structures and Systems
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• v.21 no.5
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• pp.591-600
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• 2018

The probabilistic characterization of wind field characteristics is a significant task for fatigue reliability assessment of long-span railway bridges in wind-prone regions. In consideration of the effect of wind direction, the stochastic properties of wind field should be represented by a bivariate statistical model of wind speed and direction. This paper presents the construction of the bivariate model of wind speed and direction at the site of a railway arch bridge by use of the long-term structural health monitoring (SHM) data. The wind characteristics are derived by analyzing the real-time wind monitoring data, such as the mean wind speed and direction, turbulence intensity, turbulence integral scale, and power spectral density. A sequential quadratic programming (SQP) algorithm-based finite mixture modeling method is proposed to formulate the joint distribution model of wind speed and direction. For the probability density function (PDF) of wind speed, a double-parameter Weibull distribution function is utilized, and a von Mises distribution function is applied to represent the PDF of wind direction. The SQP algorithm with multi-start points is used to estimate the parameters in the bivariate model, namely Weibull-von Mises mixture model. One-year wind monitoring data are selected to validate the effectiveness of the proposed modeling method. The optimal model is jointly evaluated by the Bayesian information criterion (BIC) and coefficient of determination, $R^2$. The obtained results indicate that the proposed SQP algorithm-based finite mixture modeling method can effectively establish the bivariate model of wind speed and direction. The established bivariate model of wind speed and direction will facilitate the wind-induced fatigue reliability assessment of long-span bridges.