• Journal of the Korean Institute of Telematics and Electronics T
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• v.35T no.2
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• pp.56-65
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• 1998

In this paper, a neural network(NN) control system is proposed and practically implemented, which is adequate to the induction motor speed control system with frequent load variation. The back propagation neural network technique is used to provide a real adaptive estimation of the motor parameter. The error between the desired state variable and the actual one is back-propagated to adjust the motor parameter, so that the actual state variable will coincide with the desired one. Designed control system is based on PC-DSP structure for the purposed of easiness of applying NN algorithm. Through computer simulation and experimental results, it is verified that proposed control system is robust to the load variation and practical implementation is possible.

• Proceedings of the KIEE Conference
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• 1988.11a
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• pp.56-59
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• 1988

The main purpose of Set-up control of hot strip mill plant is to obtain the most regular thickness. Then the learning or adaptive computer control in hot strip rolling mill has been developed. But it is very difficult to keep the inter-stands load distribution ratio uniform; so that the deviation of strip flatness is not avoidable. This leads to the degradation of quality of the products. In this report, an improved method base on the steepest descent method including the computation of optimum step size. This method is applied to the off-line simulation. In consequence, the better balances of inter-stands load distribution is achieved in addition to improvements of output thickness of hot strip mill in POSCO.

• Proceedings of the KIEE Conference
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• 1999.07c
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• pp.1314-1317
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• 1999

In this paper, we design an Adaptive Neuro-Fuzzy Inference System(ANFIS) Precompensator for the performance improvement of conventional proportional integral derivative (PID) controller that the governor system of power plant constantly maintains the load frequency of two-area power system. The ANFIS Precompensator is expressed as the membership functions of premise parameters and the linear combination of consequent parameters by Sugeno's fuzzy if-then rules using nonlinear input-output relation for the set point automatic modification maintaining conventional PID controller. The proposed compensation design technique is hoped to be satisfactory method overcome difficulty of exact modelling and arising problems by the complex nonlinearities of power system, and our design shows merit that is easily implemented by adding an ANFIS precompenastor to an existing PID controller without replacement.

• Wind and Structures
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• v.27 no.3
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• pp.199-211
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• 2018

This paper deals with wind fragility and risk analysis of high rise buildings subjected to stochastic wind load. Conventionally, such problems are dealt in full Monte Carlo Simulation framework, which requires extensive computational time. Thus, to make the procedure computationally efficient, application of metamodelling technique in fragility analysis is explored in the present study. Since, accuracy by the conventional Least Squares Method (LSM) based metamodelling is often challenged, an efficient Moving Least Squares Method based adaptive metamodelling technique is proposed for wind fragility analysis. In doing so, artificial time history of wind load is generated by three wind field models: i.e., a simple one based on alongwind component of wind speed; a more detailed one considering coherence and wind directionality effect, and a third one considering nonstationary effect of mean wind. The results show that the proposed approach is more accurate than the conventional LSM based metamodelling approach when compared to full simulation approach as reference. At the same time, the proposed approach drastically reduces computational time in comparison to the full simulation approach. The results by the three wind field models are compared. The importance of non-linear structural analysis in fragility evaluation has been also demonstrated.

• The Proceedings of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.10 no.5
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• pp.83-93
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• 1996

In this paper, a minimum variance control system is proposed and practically implemented, which is adequate to the induction motor speed control system with frequent load variation. Minimum variance control method is used as a control law and recursive least square method with selective forgetting factor is proposed and analyzed with general forgetting algorithm as an estimation method. Designed control system is based on PC-DSP structure for the purposed of easiness of applying adaptive algorithm. Through computer simulation and experimental results, it is verified that proposed control system is robust to the load variation and practical implementation is possible.

• Proceedings of the KIEE Conference
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• 2005.04a
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• pp.191-193
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• 2005

This paper presents Hybrid PI controller of IPMSM drive using fuzzy adaptive mechanism(FAM) control. To increase the robustness, fixed gam PI controller, Hybrid PI controller proposes a new method based self tuning PI controller. Hybrid PI controller is developed to minimize overshoot and settling time following sudden parameter changes such as speed, load torque, inertia, rotor resistance and self inductance. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fixed gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Proceedings of the Korean Institute of IIIuminating and Electrical Installation Engineers Conference
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• 2006.05a
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• pp.315-320
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• 2006

This paper presents speed control of IPMSM drive using neural network(NN) PI controller. In general, PI controller in computer numerically controlled machine process fixed gain. They may perform well under some operating conditions, but not all. To increase the robustness of fixed gain PI controller, NNPI controller proposes a new method based neural network. NNPI controller is developed to minimize overshoot, rise time and settling time following sudden parameter changes such as speed, load torque and inertia. Also, this paper is proposed speed control of IPMSM using neural network and estimation of speed using artificial neural network(ANN) controller. The back propagation neural network technique is used to provide a real time adaptive estimation of the motor speed. The results on a speed controller of IPMSM are presented to show the effectiveness of the proposed gain tuner. And this controller is better than the fired gains one in terms of robustness, even under great variations of operating conditions and load disturbance.

• Journal of Power Electronics
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• v.19 no.3
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• pp.771-783
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• 2019

Due to nonlinear-synthetic uncertainty including the total unknown nonlinear load torque, the total parameter variation and the fixed load torque, a synchronous reluctance motor (SynRM) driving a continuously variable transmission (CVT) system causes a lot of nonlinear effects. Linear control methods make it hard to achieve good control performance. To increase the control performance and reduce the influence of nonlinear time-synthetic uncertainty, an admixed recurrent Gegenbauer orthogonal polynomials neural network (ARGOPNN) with a modified particle swarm optimization (MPSO) control system is proposed to achieve better control performance. The ARGOPNN with a MPSO control system is composed of an observer controller, a recurrent Gegenbauer orthogonal polynomial neural network (RGOPNN) controller and a remunerated controller. To insure the stability of the control system, the RGOPNN controller with an adaptive law and the remunerated controller with a reckoned law are derived according to the Lyapunov stability theorem. In addition, the two learning rates of the weights in the RGOPNN are regulating by using the MPSO algorithm to enhance convergence. Finally, three types of experimental results with comparative studies are presented to confirm the usefulness of the proposed ARGOPNN with a MPSO control system.

• The Transactions of the Korean Institute of Power Electronics
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• v.12 no.4
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• pp.291-299
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• 2007

To operate a permanent magnet synchronous motor (PMSM) at a maximum torque per ampere (MTPA) operation point, the exact values of machine parameters such as inductances and back-EMF constant, which are sensitive to motor phase currents and temperature respectively, should be blown. An adaptive estimation method for on-line estimation of the machine parameters is not suitable for practical applications since it has difficulties in estimating exact values and requires complex mathematical calculations. The purpose of this paper is to present a simple MTPA operation point tracking control strategy for vector controlled PMSM drives with slow dynamic loads. The proposed method searches MTPA operation points by modulating current phase angle and observing the variation in command power. The current angle modulation strategy is designed to sense the effect of load variations in the command power. Therefore, the proposed method can track the MTPA operation points of the PMSM regardless of load variations. Computer simulation and experimental study is also presented to show the effectiveness of the proposed method.

• Science of Emotion and Sensibility
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• v.23 no.2
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• pp.103-120
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• 2020

Using driving simulation, this study investigated the effects of driver's trust in the adaptive cruise control (ACC) system and road density on driver's workload and situation awareness. The drivers were allocated into one of four experimental conditions manipulated by ACC system trust level (trust-increased vs. trust-decreased) and road congestion (high vs. low). The workload and situational awareness of the participants were measured as dependent variables. The results showed followings. First, trust-decreased group for the ACC system had significantly lower trust scores for the system in all of the measurement items, including reducing the driving load and securing safe driving due to the use of this system, than the trust-increased group. Second, the trust-decreased group showed a slower reaction time in the secondary tasks and higher subjective workload than trust-increased group. Third, in contrast, the situational awareness for the driving situation was significantly higher in the trust-decreased group than trust-increased group. The results of this study showed that the driver's trust in the ACC system can affect the various information processing performed while driving. Also, these results suggest that trust in the user's system should be considered as an important variable in the design of an automated driving assistance system.