• Proceedings of the IEEK Conference
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• 2000.11c
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• pp.13-16
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• 2000

This paper is to compare DTCNN(Discrete-time Cellular Neural Networks) applying the fuzzy morphology operators with the conventional FCNN(Fuzzy CNN) using the general morphology operators. These methods are to the image filtering, and are compared as MSE. Also the main goal of this paper is to compare the fuzzy morphology operators with the general morphology operators through image input. In a result of computer simulation, we could know that the error of DTCNN applying the fuzzy morphology operators is less about 6.1809 than FCNN using the general morphology operators in the image included 10% noise, also the error of the former is less about 5.5922 than the latter in the image included 20% noise. And the image of DTCNN applying the fuzzy morphology operators is superior to FCNN using the general morphology operators.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1033-1036
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• 1993

Most of the today realized fuzzy logic control applications has been designed using different heuristic approaches for synthesis and implemented with conventional programming languages on general purpose microcontrollers. This paper aims to present a new methodology to design a fuzzy controller. The methodology is based on the Cell-to-Cell approach to extract the control law. A set of fuzzy rules is then found by using a FAM (Fuzzy associative memories) approach. The proposed procedure was implemented to control the rotor position of a DC motor.

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.20 no.4
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• pp.98-106
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• 2006

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the cower and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and f-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. It is shown that the current components which directly govern the torque production have been very well regulated by the efficiency optimization control scheme. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent(HAI) controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm

• Journal of the Korean Institute of Illuminating and Electrical Installation Engineers
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• v.21 no.5
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• pp.1-9
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• 2007

This paper is proposed an efficiency optimization control algorithm for a synchronous reluctance motor which minimizes the coner and iron losses. The design of the speed controller based on adaptive fuzzy-neural networks(AFNN) controller that is implemented using fuzzy control and neural networks. There exists a variety of combinations of d and q-axis current which provide a specific motor torque. The objective of the efficiency optimization controller is to seek a combination of d and q-axis current components, which provides minimum losses at a certain operating point in steady state. The proposed algorithm allows the electromagnetic losses in variable speed and torque drives to be reduced while keeping good torque control dynamics. The control performance of the hybrid artificial intelligent controller is evaluated by analysis for various operating conditions. Analysis results are presented to show the validity of the proposed algorithm.

• Proceedings of the Korean Statistical Society Conference
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• 2004.11a
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• pp.65-66
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• 2004

The aim of this article is to illustrate what soft computing is and how important it is.

• Proceedings of the Korean Society for Noise and Vibration Engineering Conference
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• 2010.05a
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• pp.457-458
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• 2010

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.39 no.3
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• pp.18-31
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• 2002

In this study, we introduce a concept of advanced neurofuzzy polynomial networks(ANFPN), a hybrid modeling architecture combining neurofuzzy networks(NFN) and polynomial neural networks(PNN). These networks are highly nonlinear rule-based models. The development of the ANFPN dwells on the technologies of Computational Intelligence(Cl), namely fuzzy sets, neural networks and genetic algorithms. NFN contributes to the formation of the premise part of the rule-based structure of the ANFPN. The consequence part of the ANFPN is designed using PNN. At the premise part of the ANFPN, NFN uses both the simplified fuzzy inference and error back-propagation learning rule. The parameters of the membership functions, learning rates and momentum coefficients are adjusted with the use of genetic optimization. As the consequence structure of ANFPN, PNN is a flexible network architecture whose structure(topology) is developed through learning. In particular, the number of layers and nodes of the PNN are not fixed in advance but is generated in a dynamic way. In this study, we introduce two kinds of ANFPN architectures, namely the basic and the modified one. Here the basic and the modified architecture depend on the number of input variables and the order of polynomial in each layer of PNN structure. Owing to the specific features of two combined architectures, it is possible to consider the nonlinear characteristics of process system and to obtain the better output performance with superb predictive ability. The availability and feasibility of the ANFPN are discussed and illustrated with the aid of two representative numerical examples. The results show that the proposed ANFPN can produce the model with higher accuracy and predictive ability than any other method presented previously.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.156-161
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• 2001

Currently, agricultural facilities are evaluated using either basic inspections or detailed analysis. However, conventional analyses as well as methods based on fuzzy logic and rule of thumb have not been very successful in providing a clear relationship between rating and real state of agricultural facilities, because they can't provide exactly acceptable reliability of degraded structures with manager or supervisor. Therefore, in this stage, we must define probabilistic variables for representing degradation of structures being given damages during a survival time. This paper describes the application of neural network system in developing the relation between subjective ratings and parameters of agricultural reservoir as well as that between subjective and analytical ratings. It is shown that neural networks can be trained and used successfully in estimating a rating based on several parameters. The specific application problem for agricultural reservoir in the rural area of Korea is presented and database is constructed to maintain training data set, the information of inspection and facilities. This study showed that a successful training of a neural network could be useful, especially if the input data set for target problem contains parameters with a diverse combination of inter-correlation coefficients. And the networks had a prediction rating of about $^{\ast}^{\ast}^{\ast}%$. The neural network system is expected to show high performance fairly in estimate than statistical method to use equation that is consisted of very lowly interrelated variables.

• 제어로봇시스템학회:학술대회논문집
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• 1993.10a
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• pp.953-958
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• 1993

We develop a neural network control algorithm for the ACS (Advanced Control System). The ACS is an extended version of the DCS (Distributed Control System) to which functions of fault detection and diagnosis and advanced control algorithms are added such as neural networks, fuzzy logics, and so on. In spite of its usefulness proven by computer simulations, the neural network control algorithm, as far as we know, has no tool which makes it applicable to process control. It is necessary that the neural network controller should be turned into the function code for its application to the ACS. So we develop a general method to implement the neural network control systems for the ACS. By simulations using the simulator for the boiler of 'Seoul fire power plant unit 4', the methodology proposed in this paper is validated to have the applicability to process control.

• Journal of IKEEE
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• v.12 no.3
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• pp.158-166
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• 2008

Medical ultrasound has benefits in mobility and safety than any other medical techniques such as X-ray, CT and MRI but has speckle noise which decrease the ability of an observer to distinguish the fine details in diagnostic examination. But simple removing of speckle often causes losing boundary information. Then, in this paper, we presented a novel neuro-fuzzy method which could remove speckle efficiently without loss of boundary information. Proposed method consists of image clustering by fuzzy algorithm and image processingby neural networks which was learned by back propagation. From the experiments for simulation image and real ultrasound image, we could verify the proposed method.