• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.28 no.11
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• pp.51-57
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• 2014

This paper presents an algorithmic type computing technique of process coefficient in predicting model of temperature for reheating furnace and also suggests a design method of neural network model to find an adequate value of process coefficient for arbitrary operating conditions including test conditons. The proposed neural network use furnace temperature, line speed and slab information as input variables, and process coefficient is output variable. Reasonable process coefficients can be obtained by an algorithmic procedure proposed in this paper using process data gathered at test conditons. Also, neural network model output equal process coefficient under same input conditions. This means that adquate process coefficients can be found by only computing neural network model without additive test even if operating conditions vary.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.37 no.10
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• pp.727-732
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• 1988

In this paper, a parameter estimation method of linear systems with bounded output disturbances is studied. The bound of the disturbances is assumed to known Weighting factors are proposed to modify LS(Least Square) algorithm in the parameter estimation method. The conditions of weighting factors are given so that the estimation method has good convergence properties. This condition is more relaxed form than other known conditions. The compensation term in the estimation equations is represented by a function of the output prediction error and this function should lie in a specified region on x-y plane to satisfy these conditions of weighting factors. A set of weighting factor is selected and an algorithm is proposed using this set of weighting factor. The proposed algorithm is compared with another existing algorithm by simulation and its performance in parameter estimation id discussed.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.16 no.6
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• pp.125-132
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• 2017

Generally, in machining operations, the required machining performance can be obtained by properly combining several machining parameters properly. In this research, we construct a simulation model, which that predicts the relationship between the input variables and output variables in the turning operation. Input variables necessary for the turning operation include cutting speed, feed, and depth of cut. Surface roughness and electrical current consumption are used as the output variables. To construct the simulation model, an Artificial Neural Network (ANN) is employed. With theIn ANN, training is necessary to find appropriate weights, and the Ant Colony Optimization (ACO) technique is used as a training tool. EspeciallyIn particular, for the continuous domain, ACOR is adopted and athe related algorithm is developed. Finally, the effects of the algorithm on the results are identified and analyzsed.

• Journal of Institute of Control, Robotics and Systems
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• v.7 no.12
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• pp.967-972
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• 2001

In this paper, an adaptive robust nonlinear predictive controller is developed for the continuous time nonlinear systems whose control objective is composed of the system output and its desired value. The basic control law is derived from the continuous time prediction model and its feedback dynamcis shows another from if input and output linearization. In order to cope with the parameter uncertainty, robust control is incorporated into the basic control law and the asymptotic convergence of tracking error to a certain bounded region is guaranteed. For stability and performance improvement within the bounded region, an adaptive control is introduced. Simulation tests for the motion control of an underwater wall-ranging robot confirm the performance improvement and the robustness of this controller.

• Journal of Institute of Control, Robotics and Systems
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• v.13 no.6
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• pp.589-595
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• 2007

This paper presents a new method on controller design of brushless dc motors. In such drives the current ripples are generated by motor inductance in stator windings and the back EMF. To suppress the current ripples the current controller is generally used. To minimize the size and the cost of the drives it is desirable to control motors without the current controller and the current sensing circuits. To estimate the motor CUlTent it is modeled by a neural network that is contigured as an output-error dynamic system. The identified model is essentially a one step ahead prediction structure in which past inputs and outputs are used to calculate the current output. Using the model, a state feedback controller to compensate the effects of disturbance has been designed. The controller is implemented by a 16-bit microprocessor and the effectiveness of the proposed control method is verified through experiments.

• Journal of Communications and Networks
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• v.9 no.2
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• pp.141-149
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• 2007

Multiple-input multiple-output (MIMO) transmit pre-coding/beamforming can significantly improve system spectral efficiency. However, several obstacles prevent precoding from wide deployment in early wireless networks: The significant feedback overhead, performance degradation due to feedback delay, and the large storage requirement at the mobile devices. In this paper, we propose a precoding method that addresses these issues. In this approach, only 3 or 6 bits feedback is needed to select a precoding matrix from a codebook. There are fifteen codebooks, each corresponding to a unique combination of antenna configuration (up to 4 antennas) and codebook size. Small codebooks are prestored and large codebooks are efficiently computed from the prestored codebook, modified Hochwald method and Householder reflection. Finally, the feedback delay is compensated by channel prediction. The scheme is validated by simulations and we have observed significant gains comparing to space-time coding and antenna selection. This solution was adopted as a part of the IEEE 802.16e specification in 2005.

• The Transactions of the Korean Institute of Electrical Engineers B
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• v.49 no.4
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• pp.246-255
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• 2000

This paper deals with a novel full digital control method of the three-phase PWM inverter for UPS. The voltage and current of output filter capacitor as state variables are the feedback control input. In addition, a double deadbeat control consisting of a d-q current minor loop and a d-q voltage major loop, both with precise decoupling, have been developed. The switching pulse width modulation based on SVM is adopted so that the capacitor current should be exactly equal to its reference current. In order to compensate the calculation time delay, the predictive control is achieved by the current·voltage observer. The load prediction is used to compensate the load disturbance by disturbance observer with deadbeat response. The experimental results show that the proposed system offers an output voltage with THD less than 2% at a full nonlinear load.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2005.11a
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• pp.750-755
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• 2005

This paper proposes a small VCM (Voice coil motor) type actuator using curved suspensions for auto-focusing and zoom motions for mobile information devices. 1'he proposed focusing actuator adopts a nontraditional type of suspension using curved beams in order to extend output displacement within small height restriction. The curved beam is similar to the leaf spring type which is usually used in optical disk drives. In addition, three different materials are considered for the curved suspension model, and Aluminum shows the best dynamic characteristics. The proposed zoom actuator does not use a suspension supporting bobbin but a moving rail and a sloper mechanism by generating rotational force at lens holder. The sensitivity of design parameters on output performance is studied using ANSYS (3D FEM tool). Experiments using a prototype of the proposed actuator model verified the analytical prediction and performance.

• Current Optics and Photonics
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• v.2 no.5
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• pp.395-399
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• 2018

This article investigates the mechanism of electronic signal phase noise degradation induced by laser phase noise in optical links comprising optical resonators. Through theoretical derivation, we find that the phase noise of the output electronic signal has the same spectral shape of optical intensity noise as the output of the optical resonator. We propose that the optical resonator transfers laser phase noise to light intensity fluctuation and then the intensity fluctuation is converted to electric phase noise through AM-PM conversion mechanism in the photodiode. An optical link comprising a Fabry-Perot resonator was constructed to verify the proposed mechanism. The experimental results agree with our theoretical prediction verifying that the supposition is correct.

• Computers and Concrete
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• v.29 no.3
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• pp.145-159
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• 2022

In this study, the extreme learning machine and deep learning models were devised to estimate the bond strength of corroded reinforcement in concrete. The six inputs and one output were used in this study. The compressive strength, concrete cover, bond length, steel type, diameter of steel bar, and corrosion level were selected as the input variables. The results of bond strength were used as the output variable. Moreover, the Analysis of variance (Anova) was used to find the effect of input variables on the bond strength of corroded reinforcement in concrete. The prediction results were compared to the experimental results and each other. The extreme learning machine and the deep learning models estimated the bond strength by 99.81% and 99.99% accuracy, respectively. This study found that the deep learning model can be estimated the bond strength of corroded reinforcement with higher accuracy than the extreme learning machine model. The Anova results found that the corrosion level was found to be the input variable that most affects the bond strength of corroded reinforcement in concrete.