• Journal of the Korean Society for Precision Engineering
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• v.14 no.5
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• pp.66-75
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• 1997

A fault diagnosis system for a variable speed rotating shaft probably demands a huge database, which makes it diffcult to be realized. This stuydy presents an effective method of fault diagnosis for variable speed rotating shafts. The proposed method is based upon a fuzzy reasoning and it includes a stepwize strategy to reduce the size of database in a diagnosis system. A computer program is developed to show the procedure of the diagnosis, and four cases of faults are applied to the program to illustarate the effectiveness of the proposed method. The propsed method is found to be useful in reducing the size of database from observation of the data files of the dianosis system. The case studies show that the proposed method can be useful for the diagnosis of variable speed rotating shafts.

• Proceedings of the IEEK Conference
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• 2001.06c
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• pp.237-240
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• 2001

An image processing and decision making method for the Automatic Surveillance System is proposed. The aim of our Automatic Surveillance System is to detect a moving object and make a decision on whether it is human or not. Various object features such as the ratio of the width and the length of the moving object, the distance dispersion between the principal axis and the object contour, the eigenvectors, the symmetric axes, and the areas if the segmented region are used in this paper. These features are not the unique and decisive characteristics for representing human Also, due to the outdoor image property, the object feature information is unavoidably vague and inaccurate. In order to make an efficient decision from the information, we use a fuzzy rules base system ai an approximate reasoning method. The fuzzy rules, combining various object features, are able to describe the conditions for making an intelligent decision. The fuzzy rule base system is initially constructed by heuristic approach and then, trained and tasted with input/output data Experimental result are shown, demonstrating the validity of our system.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.41 no.8
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• pp.938-946
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• 1992

A novel 2-dimensional matched filter of the parallel-jaw type using fuzzy logic is proposed for bin picking. Specifically, the averaged pixel intensity of the windowed region for the filtering is considered to be fuzzy. Also membership functions for darkness and brightness are designed by employing the intensity histogram of the 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 Mamdani's reasoning method is applied. The proposed technique shows better performances than that of the conventional matched filtering technique in the following senses` 1) most of holdsites determined by the proposed technique are not concentrated at the locations nearly the end of part and 2) our filter is rather insensitive to noises than the conventional method. 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 the Korean Society for Technology of Plasticity Conference
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• 1995.06a
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• pp.1-25
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• 1995

This paper deals with the development of an intelligent CAD system for specifying the operation sequence in cold forging. Cold forging technology is facing with various new design requirements. Therefore, it is very important to develop a decision method for the operation sequence, with comparatively high adaptability to the new requirements. An intelligent CAD system which is the uncertain factors in human knowledge into consideration by applying fuzzy theory is established. Various actual design data about were organized, and these organized data were applied to the system as the case base. The system automatically generates the design data of operation sequence such as the forming method and the geometric data of products in each operation stage by the reasoning method applied the fuzzy pattern matching. By comparing the design results in the above system with the actual design data of a human expert, this paper presents that our method is useful for practical application.

• Transactions of the Korean Society of Machine Tool Engineers
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• v.11 no.4
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• pp.62-67
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• 2002

This paper presents a new approach to the design of cruise control system of a mobile robot with two drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network and back propagation algorithm to train the fuzzy-neural network controller in the framework of the specialized loaming 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 performing the computer simulation for trajectory tucking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Journal of Power System Engineering
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• v.2 no.3
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• pp.74-82
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• 1998

This paper presents a new approach to the design of speed and azimuth control of a mobile robot with two drive wheels. The proposed control scheme uses a Gaussian function as a unit function in the neural-fuzzy network and back propagation algorithm to train the neural-fuzzy network controller in the framework of the specialized learning architecture. It is proposed to a learned controller with two neural-fuzzy networks based on an independent reasoning and a connection net with fixed weights to simplify the neural-fuzzy network. The performance of the proposed controller can be seen by the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• Proceedings of the Korean Society for Emotion and Sensibility Conference
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• 2000.04a
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• pp.253-257
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• 2000

the objective of this study is to design and evaluate a methodology for diagnosing the appendicitis in a fuzzy neural network that integrates the partition of input space by fuzzy entropy and the generation of fuzzy control rules and learning algorithm. In particular the diagnosis of appendicitis depends on the rule of thumb of the experts such that it associates with the region, the characteristics, the degree of the ache and the potential symptoms. In this scheme the basic idea is to realize the fuzzy rle base and the process of reasoning by neural network and to make the corresponding parameters of the fuzzy control rules be adapted by back propagation learning rule. To eliminate the number of the parameters of the rules, the output of the consequences of the control rules is expressed by the network's connection weights. As a result we obtain a method for reducing the system's complexities. Through computer simulations the effectiveness of the proposed strategy is verified for the diagnosis of appendicitis.

• Journal of Ocean Engineering and Technology
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• v.11 no.1
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• pp.84-95
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• 1997

This paper presents a new approach to the design speed and azimuth control of a mobile robot with drive wheel. The proposed control scheme uses a Gaussian function as a unit function in the fuzzy-neural network, and back propagation algorithm to train the fuzzy-neural network controller in the frmework 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 simple the neural networks-fuzzy. The performance of the proposed controller is shown by performing the computer simulation for trajectory tracking of the speed and azimuth of a mobile robot driven by two independent wheels.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.70-70
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• 2000

Recently, a neuro-fuzzy approach, a combination of neural networks and fuzzy reasoning, has been playing an important role in the motor control. In this paper, a novel method of fiction compensation using neuro-fuzzy architecture has been shown to significantly improve the performance of a DC motor system with nonlinear friction characteristics. The structure of the controller is the neuro-fuzzy network with the TS(Takagi-Sugeno) model. A back-propagation neural network based on a gradient descent algorithm is employed, and all of its parameters can be on-line trained. The performance of the proposed controller is compared with both a conventional neuro-controller and a PI controller.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.120-123
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• 1997

In this paper, we propose the Fuzzy Inference-based Reinforcement Learning Algorithm. We offer more similar learning scheme to the psychological learning of the higher animal's including human, by using Fuzzy Inference in Reinforcement Learning. The proposed method follows the way linguistic and conceptional expression have an effect on human's behavior by reasoning reinforcement based on fuzzy rule. The intervals of fuzzy membership functions are found optimally by genetic algorithms. And using Recurrent state is considered to make an action in dynamical environment. We show the validity of the proposed learning algorithm by applying to the inverted pendulum control problem.