• Proceedings of the Korean Nuclear Society Conference
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• 2005.05a
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• pp.5-6
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• 2005

• Proceedings of the KSME Conference
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• 2007.05a
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• pp.1149-1154
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• 2007

Fluctuation of control rod was experienced when plant was operating in normal operation mode in WH type NPPs. In order to cope with increased control rod fluctuation, the lead-lag controller setpoint for rod control system was optimized and resulted in increasing the margin of operation and minimizing unnecessary control rod movement. By optimization of the time constant, the margin of operation was increased by $1.5^{\circ}F$ and the control rod movement was not occurred due to mitigation of temperature fluctuation in loop. According to the mitigation of time constant, the margin of operation was increased but safety margin can be affected badly, so that the influences to FSAR design reference was evaluated. As the result of this evaluation, it satisfied the design reference of the existing safety analysis and was applied to NPP after obtaining the approval.

• Proceedings of the SAREK Conference
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• 2009.06a
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• pp.435-440
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• 2009

For the effective control of a $CO_2$ air-conditioning system, the system high-side pressure algorithm, the indoor temperature algorithm, and the outdoor fan algorithm were developed. The system high-side pressure algorithm was composed of the setpoint algorithm, the reset algorithm, and the electronic expansion valve control algorithm. The indoor temperature algorithm was composed of the compressor control algorithm and the indoor fan control algorithm. These algorithms were tested by using mathematical models developed from the previous study. Results from the setpoint step change test and the disturbance test showed good control performances. Therefore, algorithms developed in this study may practically used for the control of a $CO_2$ air-conditioning system.

• Proceedings of the KIEE Conference
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• 1998.07b
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• pp.401-403
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• 1998

This paper describes the neural network control method for the identification and control of chaotic nonlinear dynamical systems effectively. In our control method, the controlled system is modeled by an unknown NARMA model, and a feedforward neural network is used for identifying the chaotic system. The control signals are directly obtained by minimizing the difference between a setpoint and the output of the neural network model. Since learning algorithm guarantees that the output of the neural network model approaches that of the actual system, it is shown that the control signals obtained can also make the real system output close to the setpoint.

• Korean Journal of Air-Conditioning and Refrigeration Engineering
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• v.17 no.2
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• pp.147-155
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• 2005

For a long road tunnel, a tunnel ventilation system may be used in order to reduce the pollution level below the required level. To control the tunnel pollution level, a closed loop control algorithm may be used. The cascade control algorithm, which composed of a jet fan control algorithm and an air velocity setpoint algorithm, was developed to regulate the CO level in a tunnel. The verification of control algorithms was carried out by dynamic models developed from real tunnel data sets. The simulation results showed that control algorithms developed for this study were effective to control the tunnel ventilation system.

• Proceedings of the SAREK Conference
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• 2007.11a
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• pp.29-33
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• 2007

In order to protect the environment from the refrigerant pollution, the $CO_2$ may be regarded as one of the most attractive alternative refrigerants for an automotive air-conditioning system. Control methods for a $CO_2$ system should be different because of $CO_2$'s unique properties as a refrigerant. Especially, the high-side pressure of a $CO_2$ system should be controlled for the effective operation of the system. In this study, the high-side pressure setpoint algorithm was developed by using a neural network and a Lagrange interpolation method. These methods were compared. Simulation results showed that a Lagrange interpolation method was more effective than a neural network in the respect of its easiness of programming and shorter execution time.

• Nuclear Engineering and Technology
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• v.30 no.6
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• pp.617-627
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• 1998

A technically more direct Statistical Combination of Uncertainties (SCU) method, extended SCU (XSCU), was developed to statistically combine the uncertainties associated with the DNBR alarm setpoint and the DNBR trip setpoint of digital nuclear power plants. The Modified SCU (MSCU) method is currently used as the USNRC approved design method to perform the same function. In this study, the MSCU and XSCU methods were compared in terms of the total uncertainties, and the thermal margins to the DNBR alarm and trip setpoints. The MSCU method resulted in small total uncertainties due to large negative biases which are unphysical. The XSCU method gives virtually unbiased total uncertainties which are physically meaningful in order to represent the actual magnitude of the total uncertainties associated with the DNBR alarm and trip setpoints. But the thermal margins to the DNBR alarm and trip setpoints by the MSCU method agree with those by the XSCU method within allowable statistical Variations.

• Proceedings of the Korean Nuclear Society Conference
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• 2004.10a
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• pp.653-654
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• 2004

• Proceedings of the SAREK Conference
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• 2006.11a
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• pp.8-8
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• 2006

• Proceedings of the Korean Society for Agricultural Machinery Conference
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• 1999.12a
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• pp.292-301
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• 1999