• Journal of the Korean Institute of Intelligent Systems
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• v.19 no.6
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• pp.809-813
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• 2009

We want to show that the construct of best fuzzy tests for certain fuzzy situations of Poisson distribution. Due to Neyman and Pearson theorem, if we have ${\theta}_0$ and ${\theta}_1$ be distinct fuzzy values of ${\Omega}=\{{\theta}\;:\;{\theta}\;=\;{\theta}_0,\;{\theta}_1\}$ such that $L({\theta}_0\;:\;X)/L({\theta}_1\;:\;X)$ < k, then k is a fuzzy number. For each fuzzy random samples point $X\;{\subset}\;C$, we have most power test for fuzzy critical region C by agreement index.

• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.9
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• pp.76-87
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• 1992

A novel self-organizing fuzzy plus PD control algorithm is proposed, where the proposed controller consists of a typical fuzzy reasoning part and self organizing part in which both on-line and off-line algorithms are employed to modify the Look-Up Table(LUT) for the fuzzy control rules and to decide how much fuzzy rules are to be modifid after evaluating the control performance, respectively. And the fuzzy controller is replaced by a PD controller in a prespecified region nearby the set point for good settling actions, where gain parameters are determined by fuzzy rules based on the magnitude of error velocity at the instant when the output penetrates into the prespecified region. To show the effectiveness of the proposed controller, extensive computer simulation results as well as experimental results are illustrated for an inverted pendulum system.

• Journal of the Institute of Convergence Signal Processing
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• v.8 no.3
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• pp.222-226
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• 2007

In this paper we composed the dual fuzzy rules using each region of specific points and $e-{\Delta}e$ phase plane In order to make dual fuzzy rule base. We composed the fuzzy control rules which can decrease rise time, delay time, maximum overshoot than basic fuzzy control rules. proposed method is alternately use at specific points of $e-{\Delta}e$ phase plane with two fuzzy control rules that is one control rule occruing the steady state error in transient region and another fuzzy control rule use to decrease the steady state error and rapidly converge at the convergence region. Also, two fuzzy control rules in the $e-{\Delta}e$ phase plane decide the change time according to response characteristics of plants. In order to confirm thef proposed algorithm. As the results of experiments through the hydraulic servo motor control system with a DSP processor, We verified that proposed dual fuzzy control rules get the good response compare with the basic fuzzy control rule.

• Journal of Institute of Control, Robotics and Systems
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• v.9 no.3
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• pp.218-226
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• 2003

Due to the friction characteristics of pump, cylinder packing and passenger car, in the elevation system actuated with hydraulic inverter, there exist dead zones. which cannot be controlled by a PID controller. To overcome the drawbacks, in this paper, we propose a new hybrid fuzzy-sliding mode control scheme, which controls the controller output between a sliding mode control output and a PID control output by fuzzy control method. The proposed hybrid control scheme achieves an improved control performance by using both controllers. We first propose a design method of the hybrid controller far a hydraulic system controlled by inverters, then propose a design method of a hybrid fuzzy-sliding mode centre] scheme. The effectiveness of the proposed control scheme is shown by simulation results, in which the proposed hybrid control method yields better control performance then the PID controlled scheme, not only in the zero-crossing speed region but also in the overall control region including steady-state region.

• Korean Journal of Remote Sensing
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• v.20 no.5
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• pp.353-359
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• 2004

This study proposes an approach to unsupervisedly estimate the number of classes and the parameters of defining the classes in order to train the classifier. In the proposed method, the image is segmented using a spatial region growing based on hierarchical clustering, and fuzzy training is then employed to find the sample classes that well represent the ground truth. For cluster validation, this approach iteratively estimates the class-parameters in the fuzzy training for the sample classes and continuously computes the log-likelihood ratio of two consecutive class-numbers. The maximum ratio rule is applied to determine the optimal number of classes. The experimental results show that the new scheme proposed in this study could be used to select the regions with different characteristics existed on the scene of observed image as an alternative of field survey that is so expensive.

• Journal of the Korean Institute of Intelligent Systems
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• v.21 no.1
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• pp.25-28
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• 2011

In this paper, we study some properties of condition for fuzzy data, agrement index by ratio of area and the uniformly most powerful fuzzy test of hypothesis. Also, we suggest a confidence bound for uniformly most powerful fuzzy test. For illustration, we take the most powerful critical fuzzy region from exponential distribution by likelihood ratio and test the hypothesis of ${\chi}^2$-distribution by agreement index.

• 제어로봇시스템학회:학술대회논문집
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• 1991.10a
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• pp.979-983
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• 1991

A novel 2-dimensional matched filter of the parallel-jaw type using fuzzy logics is proposed for bin picking. Specifically, averaged pixel intensity of the windowed region for the filtering is considered to be fuzzy. Also membership function for darkness and brightness are designed by employing the intensite histogram of image. Then a rule is given to know how much a windowed region can be a possible holdsite. Furthermore eight rules are made to determine the part orientation, where Mamadi's resoning method is applied. To show the validities of our proposed technique. some experimental results are illustrated and compared with the results by conventional matched filter technique.

• Proceedings of KOSOMES biannual meeting
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• 2017.04a
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• pp.104-104
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• 2017

• Proceedings of the IEEK Conference
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• 1999.11a
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• pp.525-528
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• 1999

Abstract - In this paper, a segmentation method for brain Magnetic Resonance(MR) image using region clustering technique with statistical distribution of gradient image and fuzzy rules is described. The brain MRI consists of gray matter and white matter, cerebrospinal fluid. But due to noise, overlap, vagueness, and various parameters, segmentation of MR image is a very difficult task. We use gradient information rather than intensity directly from the MR images and find appropriate thresholds for region classification using gradient approximation, rayleigh distribution function, region clustering, and merging techniques. And then, we propose the adaptive fuzzy rules in order to extract anatomical structures and diseases from brain MR image data. The experimental results shows that the proposed segmentation algorithm given better performance than traditional segmentation techniques.

• Proceedings of the KIEE Conference
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• 1995.07b
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• pp.702-704
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• 1995

In this paper, using a new adaptive fuzzy controller we have designed a power system stabilizer. The adaptive fuzzy controller constitutes of several parallel fuzzy controller. Each of them can maintain the robust stability for a specified parametric uncertainty region. If the parametric variation is so large that a rule-base cannot cope with that parametric region, the other appropriate rule-base is selected to control. Applying adaptive fuzzy controller to single machine infinite bus system, we simulate the stability of the system and compare the performance with conventional PSS controller.