• Proceedings of the KIEE Conference
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• 2001.07a
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• pp.137-139
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• 2001

In this paper, we design a GA-fuzzy precompensator for enhancement of power system stability. Here, a fuzzy precompensator is designed as a fuzzy logic-based precompensation approach for Power System Stabilizer(PSS). This scheme is easily implemented simply by adding a fuzzy precompensator to an existing PSS. And we optimize the fuzzy precompensator with a genetic algorithm for complements the demerit such as the difficulty of the component selection of fuzzy controller, name1y, scaling factor, membership function and control rules. Simulation results show that the proposed control technique is superior to a conventional PSS in dynamic responses over the wide range of operating conditions and is convinced robustness and reliableness in view of structure.

• Journal of Computational Design and Engineering
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• v.2 no.3
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• pp.137-147
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• 2015

In this study, a new methodology in predicting a system output has been investigated by applying a data mining technique and a hybrid type II fuzzy system in CNC turning operations. The purpose was to generate a supplemental control function under the dynamic machining environment, where unforeseeable changes may occur frequently. Two different types of membership functions were developed for the fuzzy logic systems and also by combining the two types, a hybrid system was generated. Genetic algorithm was used for fuzzy adaptation in the control system. Fuzzy rules are automatically modified in the process of genetic algorithm training. The computational results showed that the hybrid system with a genetic adaptation generated a far better accuracy. The hybrid fuzzy system with genetic algorithm training demonstrated more effective prediction capability and a strong potential for the implementation into existing control functions.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.361-365
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• 2004

In this paper, we present the modeling of time series data which are corrupted by noise via nonsingleton fuzzy logic system. Nonsingleton fuzzy logic system (NFLS) is useful in cases where the available data are corrupted by noise. NFLS is a fuzzy system whose inputs are modeled as fuzzy number. The abilities of NFLS to approximate arbitrary functions, and to effectively deal with noise and uncertainty, are used to analyze corrupted time series data. In the simulation results, we compare the results of the NFLS approach with the results of using only a traditional fuzzy logic system.

• Proceedings of the Korean Society of Machine Tool Engineers Conference
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• 1999.10a
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• pp.142-147
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• 1999

This paper presents a new approach to the dynamic control technique for track vehicle system using neural network-fuzzy control method. The proposed control scheme uses a Gaussian function as a unit function in the neural network-fuzzy, and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized learning architecture. It is proposed a learning controller consisting of two neural network-fuzzy based on independent reasoning and a connection net with fixed weights to simply the neural networks-fuzzy. The performance of the proposed controller is shown by simulation for trajectory tracking of the speed and azimuth of a track vehicle.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.141.1-141
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• 2001

This paper describes an approach for synthesizing a modularized gain scheduled PD type fuzzy logic controller(FLC) for a high maneuverable aircraft system, where the gains of FLC are on-line adapted according to the flight condition. Specially, the systematic procedure via root locus technique is carried out for the sellection of the gains of FLC. Simulation results demonstrate that the proposed gain scheduled fuzzy logic controller yields better control performance than the normal (without gain scheduling) fuzzy controller.

• Journal of Korean Society of Industrial and Systems Engineering
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• v.23 no.54
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• pp.155-165
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• 2000

The fault diagnosis is a systematic and unified method to find based on the observing data resulting in noises. This paper presents the fault prediction and diagnosis using fuzzy expert system technique to manipulate the uncertainties efficiently in predictive perspective. We apply a fuzzy event tree analysis to the computer system, and build up the fault prediction and diagnosis using fuzzy expert system that predicts and diagnoses the error of the system in the advance of error.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.11 no.5
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• pp.1013-1018
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• 2007

In general, there are many sensors for fuel injection control such as an air flow sensor, an air intake temperature sensor, a cooling water temperature sensor, a throttle position sensor, and a motor position sensor. In this paper, we proposed a method for controlling the amount of fuel consumption in cars using fuzzy control technique by temperature change of an air intake temperature sensor and air-fuel ratio, the ratio of air and fuel mixture. In the proposed method, the amount of fuel injection is controlled by fuzzy membership functions and fuzzy inference rules established for air-fuel ratio, air intake temperature, and final fuel compensation, after computing air-fuel values using each amount of air intake and each amount of fuel injection. We verified that the proposed method is more efficient than conventional methods in fuel injection control from the results of the simulation program.

• IEMEK Journal of Embedded Systems and Applications
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• v.13 no.4
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• pp.205-214
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• 2018

The reliability test is a crucial step for ensuring robustness of high-cost and complex weapon systems. In this paper, we present a set of quantitative criteria to select critical parts or components in weapon systems for the reliability test, and implement a fuzzy inference system by applying developed criteria to fuzzy theory. We classify the selection criteria of critical parts or components into four fuzzy sets and membership functions. A fuzzy inference rule is proposed based on the AHP (Analytic Hierarchy Process) analysis technique so as to derive a convincing reliability test. The credibility of the fuzzy inference system is confirmed through a case study using actual equipment data exacted from an existent weapon system.

• Nuclear Engineering and Technology
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• v.33 no.5
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• pp.483-497
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• 2001

In this work, a neuro-fuzzy inference system combined with the wavelet denoising, PCA (principal component analysis) and SPRT (sequential probability ratio test) methods is developed to detect the relevant sensor failure using other sensor signals. The wavelet denoising technique is applied to remove noise components in input signals into the neuro-fuzzy system The PCA is used to reduce the dimension of an input space without losing a significant amount of information. The PCA makes easy the selection of the input signals into the neuro-fuzzy system. Also, a lower dimensional input space usually reduces the time necessary to train a neuro-fuzzy system. The parameters of the neuro-fuzzy inference system which estimates the relevant sensor signal are optimized by a genetic algorithm and a least-squares algorithm. The residuals between the estimated signals and the measured signals are used to detect whether the sensors are failed or not. The SPRT is used in this failure detection algorithm. The proposed sensor-monitoring algorithm was verified through applications to the pressurizer water level and the hot-leg flowrate sensors in pressurized water reactors.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.26 no.4
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• pp.587-595
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• 2002

Recent trend changes a criterion for evaluation of humors that environmental problems are raised as a global issue. Burners with higher thermal efficiency and lower oxygen in the exhaust gas, evaluated better. To comply with environmental regulations, burners must satisfy the NO/sub x/ and CO regulation. Consequently, 'good burner'means one whose thermal efficiency is high under the constraint of NO/sub x/ and CO consistency. To make existing burner satisfy recent criterion, it is highly recommended to develop a feedback control scheme whose output is the consistency of NO/sub x/ and CO. This paper describes the development of a real time flame diagnosis technique that evaluate and diagnose the combustion states, such as consistency of components in exhaust gas, stability of flame in the quantitative sense. In this paper, it was proposed on the flame diagnosis technique of burner using Neuro-Fuzzy algorithm. This study focuses on the relation of the color of the flame and the state of combustion. Neuro-Fuzzy loaming algorithm is used in obtaining the fuzzy membership function and rules. Using the constructed inference algorithm, the amount of NO/sub x/ and CO of the combustion gas was successfully inferred.