• Transactions on Control, Automation and Systems Engineering
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• v.1 no.1
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• pp.62-68
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• 1999

FCM(Fuzzy Cognitive Map) is proposed for representing causal reasoning. Its structure allows systematic causal reasoning through a forward inference. Authors have already proposed a diagnostic system based on FCM to utilized to identify the true origin of fault by on-line pattern diagnosis. In FCM based fault diagnosis, Temporal Associative Memories (TAM) recall of FCM is utilized to identify the true origin of fault by on-line pattern match where predicted pattern sequences obtained from TAM recall of fault FCM models are compared with actually observed ones. In engineering processes, the propagation delays are induced by the dynamics of processes and may vary with variables involved. However, disregarding such propagation delays in FCM-based fault diagnosis may lead to erroneous diagnostic results. To solve the problem, a concept of FTCM(Fuzzy Time Cognitive Map) is introduced into FCM-based fault diagnosis in this work. Expecially, translation method of FTCM makes it possible to diagnose the fault for some discrete time. Simulation studies through two-tank system is carried out to verify the effectiveness of the proposed diagnostic scheme.

• Proceedings of the IEEK Conference
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• 2006.06a
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• pp.321-322
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• 2006

In this paper, we propose a method that extracts invariant iris codes from user's iris pattern in order to apply these codes to a new cryptographic construct called fuzzy vault. The fuzzy vault, proposed by Juels and Sudan, has been used to manage cryptographic key safely by merging with biometrics. Generally, iris data has intra-variation of iris pattern according to sensed environmental changes, but cryptography requires correctness. Therefore, to combine iris data and fuzzy vault, we have to extract an invariant iris feature from iris pattern. In this paper, we obtain invariant iris codes by clustering iris features extracted by independent component analysis(ICA) transform. From experimental results, we proved that the iris codes extracted by our method are invariant to sensed environmental changes and can be used in fuzzy vault.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.3
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• pp.4-19
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• 1991

In this paper, we propose a fuzzy neural network pattern classifier utilizing fuzzy information. This system works without any a priori information about the number of clusters or cluster centers. It classifies each input according to the distance between the weights and the normalized input using Bezdek's [1] fuzzy membership value equation. This model returns the correct membership value for each input vector and find several cluster centers. Some experimental studies of comparison with other algorithms will be presented for sample data sets.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.28 no.5C
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• pp.486-492
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• 2003

The vigilance threshold of conventional fuzzy ART algorithm decide whether to permit the mismatch between any input pattern and stored pattern. If the vigilance threshold was large, despite of little difference among input and stored patterns, the input pattern may be classified to new category. On the other hand, if the vigilance threshold was small, the similarity between two patterns may be accepted in spite of lots of difference and the input pattern are classified to category of the stored pattern. Therefore, the vigilance threshold for the image recognition must be experientially set for the good result. Moreover, it may occur in the fuzzy ART algorithm that the information of stored patterns is lost in the weight-adjusting process and the rate of pattern recognition is dropped. In this paper, I proposed the enhanced fuzzy ART algorithm that supports the dynamical setting of the vigilance threshold using the generalized intersection operator of fuzzy logic and the weight value being adaptively set in proportional to the current weight change and the previous weight by reflecting the frequency of the selection of winner node. For the performance evaluation of the proposed method, we applied to the recognition of container identifiers from shipping container images. The experiment showed that the proposed method produced fewer clusters than conventional ART2 and fuzzy ART algorithm. and had tile higher recognition rate.

• Journal of Electrical Engineering and Technology
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• v.11 no.2
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• pp.309-319
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• 2016

This paper presents a new recognition and classification method for power quality (PQ) disturbances on the basis of pattern linguistic values. This method solves the difficulty of recognizing disturbances rapidly and accurately by using fuzzy logic. This method uses classification disturbance patterns to define the linguistic values of fuzzy input variables and used the input variables of corresponding disturbance pattern to set membership functions. This method also sets the fuzzy rules by analyzing the distribution regularities of the input variable values. One characteristic of this method is that the linguistic values of fuzzy input variables and the setting of membership functions are not only related to the input variables but also to the character of classification disturbance and the classification results. Furthermore, the number of fuzzy rules is equal to the number of disturbance patterns. By using this method for disturbance classification, the membership function and design of fuzzy rules are directly related to the objective of classification, thus effectively reducing the complexity of the design process and yielding accurate classification results. The classification results of the simulation and measured data verify the feasibility and effectiveness of this method.

• Journal of the Korean Institute of Intelligent Systems
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• v.15 no.7
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• pp.805-812
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• 2005

Smart home is one of the ubiquitous environment platforms with various complex sensor-and-control network. In this paper, a now learning methodology for learning user's behavior preference pattern is proposed in the sense of reductive user's cognitive load to access complex interfaces and providing personalized services. We propose a fuzzy inductive learning methodology based on life-long learning paradigm for knowledge discovery, which tries to construct efficient fuzzy partition for each input space and to extract fuzzy association rules from the numerical data pattern.

• Journal of KIISE:Software and Applications
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• v.29 no.8
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• pp.573-583
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• 2002

Pattern recognition process is made up of the feature extraction in the pre-processing, the pattern clustering by training and the recognition process. This paper presents the F-LVQ (Fuzzy Learning Vector Quantization) pattern classification model which is optimized by the fuzzy membership function for the OCR(Optical Character Recognition) system. We trained 220 numeric patterns of 22 Hangul and English fonts and tested 4840 patterns whose forms are changed variously. As a result of this experiment, it is proved that the proposed model is more effective and robust than other typical LVQ models.

• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.238-243
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• 2007

The TAM(Topographic Attentive Mapping) network neural network model is an especially effective one for pattern analysis. It is composed of of Input layer, category layer, and output layer. Fuzzy rule, lot input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of leadership type for organizational leader and show its usefulness. Here, criteria of input layer and target value of output layer are the value and leadership related personality type variables of the Egogram and Enneagram, respectively.

• Journal of the Korean Institute of Intelligent Systems
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• v.16 no.1
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• pp.86-93
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• 2006

The TAM(Topographic Attentive Mapping) network based on a biologically-motivated neural network model is an especially effective one for pattern analysis. It is composed of of input layer, category layer, and output layer. Fuzzy rule, for input and output data are acquired from it. The TAM network with three pruning rules for reducing links and nodes at the layer is called fuzzy TAM network. In this paper, we apply fuzzy TAM network to pattern analysis of core competency model for subcontractors of construction companies and show its usefulness.

• Journal of the Korean Institute of Telematics and Electronics S
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• v.36S no.1
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• pp.115-122
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• 1999

In this paper, we confirmed that a speech signal is a chaos signal, and in order to use it as a speaker recognition parameter, analyzed chaos dimension. In order to raise speaker identification and pattern recognition, by making up the strange attractor involving an individual's vocal tract characteristics very well and applying fuzzy membership function to correlation dimension, we proposed fuzzy correlation dimension. By estimating the correlation of the points making up an attractor are limited according space dimension value, fuzzy correlation dimension absorbed the variation of the reference pattern attractor and test pattern attractor. Concerning fuzzy correlation dimension, by estimating the distance according to the average value of discrimination error per each speaker and reference pattern, investigated the validity of speaker recognition parameter.