• Proceedings of the KIPE Conference
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• 1998.10a
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• pp.330-335
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• 1998

In this paper the robust vector control method of induction motor for the purpose of improving the system performance deterioration caused by parameter variations is proposed. The estimations of the stator current and the rotor flux are obtained by the full order state observer with corrective prediction error feedback. and the adaptive scheme is constructed to estimate the rotor speed with the error signal between real and estimation value of the stator current. Adaptive sliding observer based on the variable structure control is applied to parameter identification. Consequently predictive current control and speed sensorless vector control can be obtained simultaneously regardless of the parameter variations.

• Journal of Power Electronics
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• v.19 no.2
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• pp.497-508
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• 2019

The predictive speed control (PSC) strategy can realize the simultaneous control of speed and current by using one cost function. As a model-based control method, the performance of the PSC is vulnerable to model mismatches such as load torque disturbances and parameter uncertainties. To solve this problem, this paper presents a robust predictive speed control (RPSC) strategy for surface-mounted permanent magnet synchronous motor (SPMSM) drives. The proposed RPSC uses extended state observers (ESOs) to estimate the lumped disturbances caused by load torque changes and parameter mismatches. The observer-based prediction model is then compensated by using the estimated disturbances. The introduction of ESOs can achieve robustness against predictive model uncertainties. In addition, a modified cost function is designed to further suppress load torque disturbances. The performance of the proposed RPSC scheme has been corroborated by experimental results under the condition of load torque changes and parameter mismatches.

• Proceedings of the Korea Concrete Institute Conference
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• 2005.11a
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• pp.531-534
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• 2005

The basic prediction model was constructed to obtain optimal maintenance method for concrete structure under marine environment by exploring the mechanism of mono and combined deterioration in lab. This model was planned to be upgraded with data acquired from several exposure specimens under same environment as structures. The computer program developed to give useful guidance observer would be improved. Several repair materials and repair construction methods applied to exposure specimens will be tested for its performance of prohibit salt attack and freezing & thawing action during experimental period about ten years. All of these data could be available to complete the prediction system. The manager will be able to use the system for optimal maintenance of marine concrete structures.

• Proceedings of the Korea Concrete Institute Conference
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• 2006.05b
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• pp.241-244
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• 2006

The basic prediction model was constructed to obtain optimal maintenance method for concrete structure under marine environment by exploring the mechanism of mono and combined deterioration in lab. This model was planned to be upgraded with data acquired from several exposure specimens under same environment as structures. The computer program developed to give useful guidance observer would be improved. Several repair materials and repair construction methods applied to exposure specimens will be tested for its performance of prohibit salt attack and freezing & thawing action during experimental period about ten years. All of these data could be available to complete the prediction system. The manager will be able to use the system for optimal maintenance of marine concrete structures.

• Transactions of the Korean Society for Noise and Vibration Engineering
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• v.12 no.1
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• pp.5-11
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• 2002

Active vibration control of intelligent laminated composite plates is performed experimental1y Laminated composite place is modeled by the system identification method. For the system identification process, the laminated composite place is excited by two piezoelectric actuators with PRBS signals. At the same time, the displacement of the laminated composite plate is measured by a gap sensor. From these excited PRBS signals and the measured displacement sequence, system parameters of the laminated composite plate are estimated using a recursive prediction error method. Model of the laminated composite plate with two piezoeletric actuators is assumed to be the form of ARMAX. From the estimated ARHMAX model, a state space equation of the observable canonical form is obtained. With this state space equation, a controller and an observer for active vibration control is designed using the optimal control method. Controller and observer are implemented on a digital system. Experiments on the vibration control are Performed with changing the outer layer fiber orientation of intelligent composite plates.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.33 no.2
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• pp.39-45
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• 2005

The aim of this research is to predict the thickness and loading noise of the round-tip shaped Hartzell propeller currently used in the general aviation aircraft. Before implementing the noise analysis, the pressure distribution on the propeller was obtained by using the free wake panel method and unsteady Bernoulli's equation. The noise signal at observer position can be obtained by using the FW-H equation. The noise prediction results for the propeller indicates that the thickness noise has s symmetric directivity pattern with respect to the tip path plane, while the noise due to loading shows higher noise directivity toward downstream than the upstream direction from the rotor plane. The loading noise is dominant rather than the thickness noise in normal operating condition.

• Journal of Advanced Marine Engineering and Technology
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• v.24 no.1
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• pp.94-103
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• 2000

This paper describes the model identification and the discrete-time sliding mode control of electro-hydraulic servo systems which are composed of servo valves, double-rod cylinder and load mass. The controlled plant is identified as a 3th-order discrete-time ARMAX model obtained from the prediction error algorithm, where a nominal model and modeling errors are zuantitatively constructed. The discrete sliding mode controller for 3th-order ARMAX model is designed in discrete-time domain, where all states are observed from Kalman filter. The discrete sliding mode controller has better tracking performance than that obtained from continuous-time sliding mode controller, in experiment.

• Proceedings of the KIPE Conference
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• 1999.07a
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• pp.350-353
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• 1999

In this paper, a new simple control method for active power filter which can realized the complete compensation of the harmonic currents is proposed. In the proposed scheme, a compensating current reference generator employing lossless resonato implemented by a DSP(Digital Signal Processor) is introduced. Deadbeat control is employed to contro the active power filter. The switching pulse width based SVM(Space Vector Modulation) is adopted so that the current of active power filter is been exactly equal to its reference at the next sampling instant. To compensate the computation delay of digital controller, the prediction of current is achieved by the current observer with deadbeat response.

• The Journal of Korea Robotics Society
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• v.19 no.1
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• pp.8-15
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• 2024

This paper proposes a linear model predictive control of 6-DOF remotely operated underwater vehicles using nonlinear robust internal-loop compensator (NRIC). First, we design a integrator embedded linear model prediction controller for a linear nominal model, and then let the real model follow the values calculated through forward dynamics. This work is carried out through an NRIC and in this process, modeling errors and external disturbance are compensated. This concept is similar to disturbance observer-based control, but it has the difference that H_∞ optimality is guaranteed. Finally, tracking results at trajectory containing the velocity discontinuity point and the position tracking performance in the disturbance environment is confirmed through the comparative study with a traditional inverse dynamics PD controller.

• Imaging Science in Dentistry
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• v.52 no.3
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• pp.239-244
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• 2022

Purpose: Despite the proliferation of numerous morphometric and anthropometric methods for sex identification based on linear, angular, and regional measurements of various parts of the body, these methods are subject to error due to the observer's knowledge and expertise. This study aimed to explore the possibility of automated sex determination using convolutional neural networks(CNNs) based on lateral cephalometric radiographs. Materials and Methods: Lateral cephalometric radiographs of 1,476 Iranian subjects (794 women and 682 men) from 18 to 49 years of age were included. Lateral cephalometric radiographs were considered as a network input and output layer including 2 classes(male and female). Eighty percent of the data was used as a training set and the rest as a test set. Hyperparameter tuning of each network was done after preprocessing and data augmentation steps. The predictive performance of different architectures (DenseNet, ResNet, and VGG) was evaluated based on their accuracy in test sets. Results: The CNN based on the DenseNet121 architecture, with an overall accuracy of 90%, had the best predictive power in sex determination. The prediction accuracy of this model was almost equal for men and women. Furthermore, with all architectures, the use of transfer learning improved predictive performance. Conclusion: The results confirmed that a CNN could predict a person's sex with high accuracy. This prediction was independent of human bias because feature extraction was done automatically. However, for more accurate sex determination on a wider scale, further studies with larger sample sizes are desirable.