• The Transactions of the Korean Institute of Power Electronics
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• v.18 no.4
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• pp.349-358
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• 2013

In this paper, the sensorless MPPT algorithm is proposed where the performance of varied duty ratio change has been improved using multi-layer neuro-fuzzy that aligns with neuro-fuzzy based optimized membership function. Since the change of duty ratio of sensorless MPPT is varied by using the neuro-fuzzy, the MPPT response speed is faster than the convectional method and is able to reduce the steady-state ripple. The neuro fuzzy controller has the response characteristics which is superior to the existing fuzzy controller, because of the usage of the optimal width of the fuzzy membership function. The effectiveness of the proposed method has been verified by simulations and experimental results.

• Journal of the Korean Institute of Intelligent Systems
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• v.5 no.3
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• pp.52-63
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• 1995

A neuro-fuzzy algorithm is presented for nuclear reactor power control in a pressurized water reactor. Automatic reacotr power control is complicated by the use of control rods because of highly nonlinear dynamics in the axial power shape. Thus, manual shaped controls are usually employed even for the limited capability during the power maneuvers. In an attempt to achieve automatic shape control, a neuro-fuzzy approach is considered because fuzzy algorithms are good at various aspects of operator's knowledge representation while neural networks are efficinet structures capable of learning from experience and adaptation to a changing nuclear core state. In the proposed neuro-fuzzy control scheme, the rule base is formulated based ona multi-input multi-output system and the dynamic back-propagation is used for learning. The neuro-fuzzy powere control algorithm has been tested using simulation fesponses of a Korean standard pressurized water reactor. The results illustrate that the proposed control algorithm would be a parctical strategy for automatic nuclear reactor power control.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2003.04a
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• pp.403-410
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• 2003

A new semiactive control strategy for seismic response reduction using a neuro-controller and a magnetorheological (MR) fluid damper is proposed. The proposed control system adopts a clipped algorithm which induces the MR damper to generate approximately the desired force. The improved neuro - controller, which was developed by employing the training algorithm based on a cost function and the sensitivity evaluation algorithm replacing an emulator neural network, produces the desired active control force, and then by using the clipped algorithm the appropriate command voltage is selected in order to cause the MR damper to generate the desired control force. The simulation results show that the proposed semiactive neuro-control algorithm is quite effective to reduce seismic responses. In addition, the semi-active control system using MR fluid dampers has many attractive features, such as the bounded-input, bounded-output stability and small energy requirements. The results of this investigation, therefore, indicate that the proposed semi-active neuro-control strategy using MR fluid dampers could be effectively used for control of seismically excited structures.

• Proceedings of the Earthquake Engineering Society of Korea Conference
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• 2003.03a
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• pp.473-480
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• 2003

A new semiactive control strategy for seismic response reduction using a neuro-controller and a magnetorheological (MR) fluid damper is proposed. The proposed control system adopts a clipped algorithm which induces the MR damper to generate approximately the desired force. The improved neuro-controller, which was developed by employing the training algorithm based on a cost function and the sensitivity evaluation algorithm replacing an emulator neural network, produces the desired active control force, and then by using the clipped algorithm the appropriate command voltage is selected in order to cause the MR damper to generate the desired control force. The simulation results show that the proposed semiactive neuro-control algorithm is quite effective to reduce seismic responses. In addition, the semiactive control system using MR fluid dampers has many attractive features, such as bounded-input, bounded-output stability and small energy requirements. The results of this investigation, therefore, indicate that the proposed semiactive neuro-control strategy using MR fluid dampers could be effective used for control seismically excited structures.

• Journal of Magnetics
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• v.17 no.1
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• pp.36-41
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• 2012

The transcranial magnetic stimulation recharges the energy storing condenser, and sends the stored energy in the condenser to the pulse shaping circuit, which then delivers it to the stimulating coil. The previous types of transcranial magnetic stimulation required a booster transformer, secondary rectifier for high voltages and a condenser for smooth type. The energy storing condenser is recharged by switching the high-voltage direct current power. Loss occurs due to the resistance in the recharging circuit, and the single-pulse output energy in the transcranial magnetic stimulation can be changed because the recharging voltage cannot be adjusted. In this study a booster transformer, which decreases the volume and weight, was not used. Instead, a current resonance inverter was applied to cut down the switching loss. A transcranial magnetic stimulation, which can simultaneously alter the recharging voltage and pulse repeats, was used to examine the output characteristics.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.25 no.11
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• pp.1495-1504
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• 2021

An ultra-compact binary power generation plant converts thermal energy into electric power using temperature difference between heat source and cooling source. In the actual power generation environment, the characteristic value of the plant changes due to any negative effects such as environmental condition or corrosion of related equipment. If the characteristic value of the plant changes, it may lead to unstable output of the turbine in a conventional PID control system with fixed PID parameters. A Neuro PID control system based on Neural Network adaptively to adjust the PID parameters according to the change in the characteristic value of the plant is proposed in this paper. Discrete-time transfer function models to represent the dynamic characteristics near the operating point of the investigated plant are deduced, and a design strategy of the proposed control system is described. The proposed Neuro PID control system is compared with the conventional PID control system, and its effectiveness is demonstrated through the simulation results.

• Proceedings of the Computational Structural Engineering Institute Conference
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• 2002.10a
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• pp.313-320
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• 2002

A new semi-active control strategy for seismic response reduction using a neuro-controller and a magnetorheological (MR) fluid damper is proposed. The proposed control system consists of the improved neuro-controller and the bang-bang-type controller. The improved neuro-controller, which was developed by employing the training algorithm based on a cost function and the sensitivity evaluation algorithm replacing an emulator neural network, produces the desired active control force, and then the bang-bang-type controller causes the MR fluid damper to generate the desired control force, so long as this force is dissipative. In numerical simulation, a three-story building structure is semi-actively controlled by the trained neural network under the historical earthquake records. The simulation results show that the proposed semi-active neuro-control algorithm is quite effective to reduce seismic responses. In addition, the semi-active control system using MR fluid dampers has many attractive features, such as the bounded-input, bounded-output stability and small energy requirements. The results of this investigation, therefore, indicate that the proposed semi-active neuro-control strategy using MR fluid dampers could be effectively used for control of seismically excited structures.

• The Transactions of the Korean Institute of Power Electronics
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• v.16 no.5
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• pp.484-493
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• 2011

The MPPT algorithm using neuro-fuzzy controller is proposed to improve the performance of fuzzy controller in this paper. The width of membership function and fuzzy rule have an effect on the performance of fuzzy controller. The neuro-fuzzy controller has the response characteristic which is superior to the existing fuzzy controller, because of using the optimal width of the fuzzy membership function through the neural learning. The superior control characteristic of a proposed algorithm is confirmed through simulation and experiment results.

• The Transactions of the Korean Institute of Electrical Engineers P
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• v.64 no.4
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• pp.246-250
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• 2015

Solar energy will be an increasingly important part of power generation because of its ubiquity abundance, and sustainability. To manage effectively solar energy to power system, it is essential part In this paper, we develop the PV power prediction algorithm using adaptive neuro-fuzzy model considering various input factors such as temperature, solar irradiance, sunshine hours, and cloudiness. To evaluate performance of the proposed model according to input factors, we performed various experiments by using real data.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.15 no.4
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• pp.2436-2442
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• 2014

In this study, EEG, HRV, and Vibra image were compared and analyzed in the environmental test room due to variation of temperature. The condition of the environmental test room was in relative humidity 50[RH%], air current speed 0.02[m/s] and illuminance 1000[lux] with setting up different temperatures from $18[^{\circ}C]$ to $31[^{\circ}C]$. At temperature $25[^{\circ}C]$, relative $M{\alpha}$ wave, relative $M{\beta}$ wave, $\frac{SMR}{\theta}$, and SDNN were revitalized, and both sides ${\alpha}$ wave asymmetry index $A_2$, HRT, stress index, and fatigue degree were decreased. Therefore, it was found that temperature $25[^{\circ}C]$ effects to increase the Neuro-energy like amenity, productivity, and concentration.