• Proceedings of the Korean Nuclear Society Conference
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• 1996.11a
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• pp.263-269
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• 1996

A comprehensive scaling method is proposed for a scaled-down facility simulating SBLOCA in the CARR passive reactor (CP-1300). The present method consists of two stages: scaling methodology, and validation of scaling methodology and code. The present scaling methodology is based on the integral response scaling method. Through sensitivity study, the condensation of the top of the CMT is identified as one of the little-known phenomenon with high importance which should be addressed for the applicability of the code. Using the similarity of the derived scaling parameters, the major component geometries of the scaled-down facility are determined. In the case of 1/4 height and 1/100 area ratio scaling, it is found out that the power ratio is the same as the area ratio, and the present scaling methodology generates the design parameters of the scaled-down facility without any distortion.

• 제어로봇시스템학회:학술대회논문집
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• 1996.10b
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• pp.192-194
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• 1996

This paper propose an optimal scaling gain tuning method of the fuzzy PI controller using Genetic Algorithm(GA). Scaling gains can reflect the control resolution and fuzziness of input/output variables. By the scaling gain method, the design of a fuzzy logic controller(FLC) can be simplified without affecting the system performance in comparison with multi-decision table method. In designing a fuzzy logic controller, the analytic approach method for the optimization is unavailable. Therefore GA is excellent optimization algorithms for scaling gain tuning. Using this optimal scaling gain tuning method, a good performance can be achieved both in transient and steady state.

• Proceedings of the IEEK Conference
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• 2003.07c
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• pp.2807-2810
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• 2003

In this paper, we propose a fuzzy control method for improving the control performance by automatically tuning the scaling factor. The proposed method is that automatically tune the input scaling factor and the output scaling factor of fuzzy logic system through neural network. Used neural network is ADALINE (ADAptive Linear NEron) neural network with delayed input. ADALINE neural network has simple construct, superior learning capacity and small computation time. In order to verify the effectiveness of the proposed control method, we performed simulation. The results showed that the proposed control method improves considerably on the environment of the disturbance.

• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.8 no.2
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• pp.15-21
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• 2004

A Conjugate Gradiant (CG) method is proposed for unconstained optimization which is invariant to a nonlinear scaling of a strictly convex quadratic function. The technique has the same properties as the classical CG-method when applied to a quadratic function. The algorithm derived here is based on a logarithmic model and is compared to the standard CG method of Fletcher and Reeves [3]. Numerical results are encouraging and indicate that nonlinear scaling is promising and deserves further investigation.

• Proceedings of the Korean Society of Propulsion Engineers Conference
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• 2003.05a
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• pp.105-109
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• 2003

A new scaling method for the prediction of gas turbine components characteristics using experimental data of gas turbine test unit has been proposed. In order to minimize the analyzed performance error in the this study, firstly component maps were constructed by real experimental performance data at some operating conditions and a polynomial obtained from scaling factors at given conditions, and then the simulated performance using the identified maps was compared with the performance result using the currently used traditional scaling method. In comparison, the performance analysis result by the currently used traditional scaling method was met well agreed with the real engine performance at most off-design points except for the design point. However the performance analysis result using the newly proposed scaling method had good agreement with the experimental results within maximum 5％ error.

• Earthquakes and Structures
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• v.5 no.2
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• pp.129-142
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• 2013

Linear and nonlinear time history analyses have been becoming more common in seismic analysis and design of structures with advances in computer technology and earthquake engineering. One of the most important issues for such analyses is the selection of appropriate acceleration time histories and matching these histories to a code design acceleration spectrum. In literature, there are three sources of acceleration time histories: artificial records, synthetic records obtained from seismological models and accelerograms recorded in real earthquakes. Because of the increase of the number of strong ground motion database, using and scaling real earthquake records for seismic analysis has been becoming one of the most popular research issues in earthquake engineering. In general, two methods are used for scaling actual earthquake records: scaling in time domain and frequency domain. The objective of this study is twofold: the first is to discuss and summarize basic methodologies and criteria for selecting and scaling ground motion time histories. The second is to analyze scaling results of time domain method according to ASCE 7-05 and Eurocode 8 (1998-1:2004) criteria. Differences between time domain method and frequency domain method are mentioned briefly. The time domain scaling procedure is utilized to scale the available real records obtained from near fault motions and far fault motions to match the proposed elastic design acceleration spectrum given in the Eurocode 8. Why the time domain method is preferred in this study is stated. The best fitted ground motion time histories are selected and these histories are analyzed according to Eurocode 8 (1998-1:2004) and ASCE 7-05 criteria. Also, characteristics of both near fault ground motions and far fault ground motions are presented by the help of figures. Hence, we can compare the effects of near fault ground motions on structures with far fault ground motions' effects.

• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.30 no.7
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• pp.36-43
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• 2002

A new scaling method for the prediction of gas turbine components characteristics using experimental data or partially given data from engine manufacturers has been proposed. In order to minimize the analyzed performance error in the this study, firstly component maps were constructed by identifying performance data given by engine manufacturers at some operating conditions, then the simulated performance using the identified maps was compared with the performance result using the currently used traditional scaling method. In comparison, the performance result by the currently used traditional scaling method was well agreed with the real engine performance at on-design point but it had maximum 12% error at off-design points within the flight envelope of a study turboprop engine. However because the analysed performance by the newly proposed scaling method had maximum 6% reasonable error even within all flight envelope.

• International Journal of Fluid Machinery and Systems
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• v.6 no.4
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• pp.177-187
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• 2013

A scaling method valid for most turbomachines based on first principles is derived. It accounts for axial and centrifugal turbomachines with respect to relative gap width/tip clearance, relative roughness, Reynolds number and/or Mach number for design and off-design operation as well. The scaling method has been successfully validated by a variety of experimental data obtained at TU Darmstadt. The physically based, hence reliable and universal method is compared with previous, empirical scaling methods.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.3
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• pp.133-138
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• 2006

This paper presents an Optimal Power Flow (OPF) algorithm using Interior Point Method (IPM) to swiftly and precisely perform the five minute dispatch. This newly suggested methodology is based on Affine Scaling Interior Point Method which is favorable for large-scale problems involving many constraints. It is also eligible for OPF problems in order to improve the calculation speed and the preciseness of its resultant solutions. Big-M Method is also used to improve the solution stability. Finally, this paper provides relevant case studies to confirm the efficiency of the proposed methodology.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.474-478
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• 1998

This paper proposes a method which can resolve the problem of exisiting fuzzy PI controller using optimal scaling gains obtained by genetic algorithm. The new method adapt a fuzzy logic controller as a high level controller to perform scaling gain algorithm between two pre-determined sets.