• Journal of the Korea Academia-Industrial cooperation Society
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• v.7 no.3
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• pp.398-403
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• 2006

In this paper, we describe a fuzzy threshold selection technique for feature extraction in digital color images. this is achieved by the formulation a fuzzy inference system that evaluates threshold for feature configurations. The system uses two fuzzy measures. They capture desirable characteristics of features such as dependency of local intensity and continuity in an image. We give a graphical description of a nonlinear sketch feature extraction filter and design the fuzzy inference system in terms of the characteristics of the feature. Through the design, we provide selection method on the choice of a threshold to achieve certain characteristics of the extracted features. Experimental results show the usefulness of our fuzzy threshold inference approach which is able to extract features without human intervention.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.2
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• pp.310-316
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• 2003

One of the significant subject in the automatic arc welding is to establish control system of the welding parameters for controlling bead geometry as a criterion to evaluate the quality of arc welding. This paper proposes an inference algorithm for bead geometry in CMA Welding using Neuro-Fuzzy algorithm. The characteristic welding parameters are measured by the circuit composed of hall sensor, voltage divider tachometer, etc. and then the bead geometry of each weld pool is calculated and detected by an image processing with CCD camera and a measuring with microscope. The relationships between the characteristic welding parameters and the bead geometry have been arranged empirically. From the result of experiments, membership functions and fuzzy rules are tuned and determined by the learning of neural network, and then the relationship between actual bead geometry and inferred bead geometry are concluded by fuzzy logic controller. In the applied inference system of bead geometry using Neuro-Fuzzy algorithm, the inference error percent is within -5%∼+4% in case of bead width, -10%∼+10% in bead height, -5%∼+6% in bead area, -10%∼+10% in penetration. Use of the Neuro-Fuzzy algorithm allows the CMA Welding system to evaluate the quality in bead geometry in real time as the welding parameters change.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
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• v.24 no.2
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• pp.198-206
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• 2013

This paper presents the improvement of tracking performance using fuzzy inference system based task prioritizations for multi-function radars. The presented technique calculates elemental priorities using track information of a target and obtain the total priority from fuzzy inference system of each fuzzy set's membership function. In this paper, we proposed the task prioritization algorithms based on fuzzy inference system, and evaluated the tracking performance on multi-function radar scenario using it. As a result, we confirmed that excellent performance could be achieved when using the proposed algorithm.

• IE interfaces
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• v.3 no.1
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• pp.31-38
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• 1990

This paper deals with two topics which are vital in fuzzy expert systems; one is how to build fuzzy knowledge base by fuzzy expertise modeling for representing knowledge with imprecise characteristic and the other is how to draw an inference from fuzzy knowledge base using translating rules. The result of this study provides the basic principle for constructing the fuzzy knowledge base and the fuzzy inference system.

• Journal of Advanced Marine Engineering and Technology
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• v.28 no.2
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• pp.200-209
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• 2004

The fuzzy control based on $\alpha$-level fuzzy set decomposition. It is known to produce quick response and calculating time of fuzzy inference. This paper derived the embodiment computational algorithm for defuzzification by min-max fuzzy inference and the center of gravity method based on $\alpha$-level fuzzy set decomposition. It is easy to realize the fuzzy controller hardware. based on the calculation formula. In addition. this study proposed a circuit that generates PWM actual signals ranging from fuzzy inference to defuzzification. The fuzzy controller was implemented with mixed analog-digital logic circuit using the computational fuzzy inference algorithm by min-min-max and defuzzification by the center of gravity method. This study confirmed that the fuzzy controller worked satisfactorily when it was applied to the position control of a dc servo system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1993.06a
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• pp.1274-1276
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• 1993

We have designed a multiple-valued fuzzy Approximate Analogical-Reseaning system (AARS). The system uses a similarity measure of fuzzy sets and a threshold of similarity ST to determine whether a rule should be fired, with a Modification Function inferred from the Similarity Measure to deduce a consequent. Multiple-valued basic fuzzy blocks are used to construct the system. A description of the system is presented to illustrate the operation of the schema. The results of simulations show that the system can perform about 3.5 x 106 inferences per second. Finally, we compare the system with Yamakawa's chip which is based on the Compositional Rule of Inference (CRI) with Mamdani's implication.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.1
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• pp.50-55
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• 2004

We propose an intgrated Gill environment system having only integer fuzzy operations in the consequent part and the defuzzification stage. In this paper, we also propose an integrated Gill environment system with 4 parallel fuzzy processing units to be operated in parallel on the classification of the sensed image data. In this, we solve the problems of taking longer times as the fuzzy real computations of [0, 1] by using the integer pixel conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. This procedure is performed automatically in the GUI application program. As a Gill environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be operated in parallel manner for MIMO or MISO systems.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.10a
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• pp.532-538
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• 1998

In this paper, a new adaptive fuzzy inference method using neural network based fuzzy reasoning is proposed to make a fuzzy logic control system more adaptive and more effective. In most cases, the design of a fuzzy inference system rely on the method in which an expert or a skilled human operator would operate in that special domain. However, if he has not expert knowledge for any nonlinear environment, it is difficult to control in order to optimize. Thus, using the proposed adaptive structure for the fuzzy reasoning system can controled more adaptive and more effective in nonlinear environment for changing input membership functions and output membership functions. The proposed fuzzy inference algorithm is called adaptive neuro-fuzzy control(ANFC). ANFC can adapt a proper membership function for nonlinear plant, based upon a minimum number of rules and an initial approximate membership function. Nonlinear function approximation and rotary inverted pendulum control system ar employed to demonstrate the viability of the proposed ANFC.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.1
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• pp.7-12
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• 2005

This paper deals with the fuzzy modeling for the complex and uncertain nonlinear systems, in which conventional and mathematical models may fail to give satisfactory results. Finally, we provide numerical examples to evaluate the feasibility and generality of the proposed method in this paper. The expert system which introduces fuzzy logic in order to process uncertainties is called fuzzy expert system. The fuzzy expert system, however, has a potential problem which may lead to inappropriate results due to the ignorance of some information by applying fuzzy logic in reasoning process in addition to the knowledge acquisition problem. In order to overcome these problems, We construct fuzzy inference network by extending the concept of reasoning network in this paper. In the fuzzy inference network, the propositions which form fuzzy rules are represented by nodes. And these nodes have the truth values representing the belief values of each proposition. The logical operators between propositions of rules are represented by links. And the traditional propagation rule is modified.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.2
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• pp.133-138
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• 2002

In this paper, we propose an integrated GUI environment of parallel fuzzy inference system fur pattern classification of remote sensing data. In this, as 4 fuzzy variables in condition part and 104 fuzzy rules are used, a real time and parallel approach is required. For frost fuzzy computation, we use the scan line conversion algorithm to convert lines of each fuzzy linguistic term to the closest integer pixels. We design 4 fuzzy processor unit to be operated in parallel by using FPGA. As a GUI environment, PCI transmission, image data pre-processing, integer pixel mapping and fuzzy membership tuning are considered. This system can be used in a pattern classification system requiring a rapid inference time in a real-time.