• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.05a
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• pp.142-145
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• 2000

A new robust load-frequency control (LFC) methodology is proposed for nonlinear power systems with the valve position limits of the governor in the presence of parametric uncertainties. The Takagi-Sugeno (TS) fuzzy model is adopted for fuzzy modeling of the nonlinear power system. A sufficient condition of the robust stability is presented in the sense of Lyapunov for the TS fuzzy model with parametric uncertainties. The intelligent digital redesign technique for the uncertain nonlinear power system is also studied. The effectiveness of the proposed robust fuzzy LFC controller design method is demonstrated through a numerical simulation.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.297-300
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• 1997

This paper uses genetic algorithm (GA) for optimal control. GA can find optimal control profile, but the profile may be oscillating feature. To make profile smooth, fuzzy genetic algorithm (FGA) is proposed. GA with fuzzy logic techniques for optimal control can make optimal control profile smooth. We describe the Fuzzy Genetic Algorithm that uses a fuzzy knowledge based system to control GA search. Result from the simulation example shows that GA can find optimal control profile and FGA makes a performance improvement over a simple GA.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1995.10b
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• pp.131-138
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• 1995

A new technique using integer programming based on fuzzy multi-criteria function is proposed for generator maintenance scheduling. Minimization maintenance delay cost and maximization reserve power are considered for fuzzy multi-criteria function. To obtain an optimal solution for generator maintenance scheduling under fuzzy environment, fuzzy multi-criteria integer programming is used. In the maintenance scheduling, a characteristic feature of the presented approach is that the crisp constraints with uncertainty can be taken into account by using fuzzy set theory and so more flexible solution can be obtained. The effectiveness of the proposed approach is demonstrated by the simulation results.

• Transactions of the Korean Society of Mechanical Engineers
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• v.16 no.10
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• pp.1833-1840
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• 1992

In the diagnostic process for automobile, Neuro-Fuzzy technique was compared with the conventional diagnostic method for the verification of performance, and proto-type system was developed. For the utilities of the system, 1/3 octave filter(band-pass filter) and A/D converter were used for data acquisition and then data were analyzed using octave band processing and pattern recognition using hamming network algorithm. In order to raise the reliability of the diagnostic results by considering many operating variables and condition of automobile to be diagnosed, fuzzy inference technique was applied in combining several information. The validation of this diagnostic system was examined through computer simulation and experiment, and it showed an acceptable performance for diagnostic process.

• The Transactions of the Korean Institute of Electrical Engineers A
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• v.55 no.12
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• pp.514-521
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• 2006

The existing ULTC operation control strategy based on the measured data deteriorates the voltage compensation capability making the efficient corresponding to the load variation difficult by following the fixed load center point voltage. Accordingly, this paper proposes a new on-line fuzzy ULTC controller based on the designed multiple load center points which can improve the voltage compensation capability of ULTC and minimize voltage deviation by moving in real-time the load center point according to the load variation to an adequate position among the multiple load center points designed using the clustering technique. The Max-Min distance technique is adopted as the clustering technique for the decision of multiple load points from measured MTr load current and PTr voltage, and the minimum distance classifier is adopted for the decision of fuzzy output membership function. To verify the effectiveness of the proposed strategy, Visual C++ MFC-based simulation environments is developed. Finally, the superiority the proposed strategy is proved by comparing the fuzzy ULTC operation control results based on multiple load center points with the existing ULTC operation control results based on fixed load center point using the data for three day.

• Journal of Power Electronics
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• v.18 no.2
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• pp.573-587
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• 2018

Recently evolved power electronics' based domestic/residential appliances have begun to behave as single phase non-linear loads. Performing as voltage/current harmonic sources, those loads when connected to a three phase distribution network contaminate the line current with harmonics in addition to creating a neutral wire current increase. In this paper, an enhanced performance three phase four leg shunt active power filter (SAPF) controller is presented as a solution for this problem. The presented control strategy incorporates a hybrid predictive fuzzy-logic based technique. The predictive part is responsible for the SAPF compensating current generation while the DC-link voltage control is performed by a fuzzy logic technique. Simulations at various loading conditions are carried out to validate the effectiveness of the proposed technique. In addition, an experimental test rig is implemented for practical validation of the of the enhanced performance of the proposed technique.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.13 no.7
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• pp.3794-3820
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• 2019

In recent years, a cloud environment with the ability to detect illegal behaviours along with a secured data storage capability is much needed. This study presents a cloud storage framework, wherein a 128-bit encryption key has been generated by combining deoxyribonucleic acid (DNA) cryptography and the Hill Cipher algorithm to make the framework unbreakable and ensure a better and secured distributed cloud storage environment. Moreover, the study proposes a DNA-based encryption technique, followed by a 256-bit secure socket layer (SSL) to secure data storage. The 256-bit SSL provides secured connections during data transmission. The data herein are classified based on different qualitative security parameters obtained using a specialized fuzzy-based classification technique. The model also has an additional advantage of being able to decide on selecting suitable storage servers from an existing pool of storage servers. A fuzzy-based technique for order of preference by similarity to ideal solution (TOPSIS) multi-criteria decision-making (MCDM) model has been employed for this, which can decide on the set of suitable storage servers on which the data must be stored and results in a reduction in execution time by keeping up the level of security to an improved grade.

• Journal of the Korean Institute of Intelligent Systems
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• v.26 no.1
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• pp.87-92
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• 2016

In this paper, optimization technique such as particle swarm optimization was used to optimize the parameters of fuzzy Extreme Learning Machine. While the learning speed of conventional neural networks is very slow, that of Extreme Learning Machine is very fast. Fuzzy Extreme Learning Machine is composed of the Extreme Learning Machine with very fast learning speed and fuzzy logic which can represent the linguistic information of the field experts. The general sigmoid function is used for the activation function of Extreme Learning Machine. However, the activation function of Fuzzy Extreme Learning Machine is the membership function which is defined in the procedure of fuzzy C-Means clustering algorithm. We optimize the parameters of the membership functions by using optimization technique such as Particle Swarm Optimization. In order to validate the classification capability of the proposed classifier, we make several experiments with the various machine learning datas.

• Journal of the Korean Institute of Intelligent Systems
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• v.27 no.1
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• pp.50-58
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• 2017

For nonlinear control system design, there are many studies based on TSK fuzzy model. However, TSK fuzzy modelling needs nonlinear dynamic equations of the object system or a data set fully distributed in input-output space. This paper proposes an modelling technique using only step input response data. The technique uses also the genetic algorithm. The object systems in this paper are nonlinear to control input variable or output variable. In the case of nonlinear to control input, response data obtained with several step input values are used. In the case of nonlinear to output, step input response data and zero input response data are used. This paper also presents a fuzzy controller design technique from TSK fuzzy model. The effectiveness of the proposed techniques is verified with numerical examples.

• Smart Structures and Systems
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• v.16 no.6
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• pp.1107-1132
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• 2015

A damage detection algorithm based on neuro fuzzy hybrid system is presented in this study for location and severity predictions of cracks in beam-like structures. A combination of eigenfrequencies and rotation deviation curves are utilized as input to the soft computing technique. Both single and multiple damage cases are considered. Theoretical expressions leading to modal properties of damaged beam elements are provided. The beam formulation is based on Euler-Bernoulli theory. The cracked section of beam is simulated employing discrete spring model whose compliance is computed from stress intensity factors of fracture mechanics. A hybrid neuro fuzzy technique is utilized to solve the inverse problem of crack identification. Two different neuro fuzzy systems including grid partitioning (GP) and subtractive clustering (SC) are investigated for the highlighted problem. Several error metrics are utilized for evaluating the accuracy of the hybrid algorithms. The study is the first in terms of 1) using the two models of neuro fuzzy systems in crack detection and 2) considering multiple damages in beam elements employing the fused neuro fuzzy procedures. At the end of the study, the developed hybrid models are tested by utilizing the noise-contaminated data. Considering the robustness of the models, they can be employed as damage identification algorithms in health monitoring of beam-like structures.