• Journal of the Korean Institute of Telematics and Electronics B
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• v.29B no.11
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• pp.130-137
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• 1992

This paper presents the word recognition method using a neural-fuzzy pattern matching, in order to make a proper speech pattern for a spectrum sequence and to improve a recognition rate. In this method, a frequency variation is reduced by generating binary spectrum patterns through associative memory using a neural network, and a time variation is decreased by measuring the simillarity using a fuzzy pattern matching. For this method using binary spectrum patterns and logic algebraic operations to measure the simillarity, memory capacity and computation requirements are far less than those of DTW using a conventional distortion measure. To show the validity of the recognition performance for this method, word recognition experiments are carried out using 28 DDD city names and compared with DTW and a fuzzy pattern matching. The results show that our presented method is more excellent in the recognition performance than the other methods.

• Journal of the Korean Institute of Telematics and Electronics
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• v.26 no.1
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• pp.98-103
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• 1989

A new system for logic optimization at gate-level is proposed in this paper. Ths system is rule-based, i which the rules represent the local trnsformation replacing a portion of circuits with the simplified equivalent circuits. In this system, 'rule generalization' and 'local optimization' are proposed for effective pattern matching. Rule generalization is used to reduce the circuit-search for pattern matching, and local optimization, to exclude unnecessary circuit-search. In additionk, in order to reduce unnecessary trial of pattern matching, the matching order of circuit patern is included in the rule descriptions. The effectiveness of this system is shown by its application ot the circuits which are generated by a hardware compiler.

• Proceedings of the KIEE Conference
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• 2008.07a
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• pp.1959-1960
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• 2008

The Logic Built In Self Test (LBIST) technique is substantially applied in chip design in most many semiconductor company in despite of unavoidable overhead like an increase in dimension and time delay occurred as it used. Currently common LBIST software uses the MISR (Multiple Input Shift Register) However, it has many considerations like defining the X-value (Unknown Value), length and number of Scan Chain, Scan Chain and so on for analysis of result occurred in the process. So, to solve these problems, common LBIST software provides the solution method automated. Nevertheless, these problems haven't been solved automatically by Tri-state Bus in logic circuit yet. This paper studies the algorithm that it also suggest algorithm that reduce additional circuits and time delay as matching of pattern about 2-type circuits which are CUT(circuit Under Test) and additional circuits so that the designer can detect the wrong location in CUT: Circuit Under Test.

• Journal of the Korean Institute of Intelligent Systems
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• v.1 no.1
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• pp.9-25
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• 1991

This tutorial paper has been written for biologists, physicians or beginners in fuzzy sets theory and applications. This field is introduced in the framework of medical diagnosis problems. The paper describes and illustrates with practical examples, a general methodology of special interest in the processing of borderline cases, that allows a graded assignment of diagnoses to patients. A pattern of medical knowledge consists of a tableau with linguistic entries or of fuzzy propositions. Relationships between symptoms and diagnoses are interpreted as labels of fuzzy sets. It is shown how possibility measures (soft matching) can be used and combined to derive diagnoses after measurements on collected data. The concepts and methods are illustrated in a biomedical application on inflammatory protein variations. In the case of poor diagnostic classifications, it is introduced appropriate ponderations, acting on the characterizations of proteins, in order to decrease their relative influence. As a consequence, when pattern matching is achieved, the final ranking of inflammatory syndromes assigned to a given patient might change to better fit the actual classification. Defuzzification of results (i.e. diagnostic groups assigned to patients) is performed as a non fuzzy sets partition issued from a "separating power", and not as the center of gravity method commonly employed in fuzzy control. It is then introduced a model of fuzzy connectionist expert system, in which an artificial neural network is designed to build the knowledge base of an expert system, from training examples (this model can also be used for specifications of rules in fuzzy logic control). Two types of weights are associated with the connections: primary linguistic weights, interpreted as labels of fuzzy sets, and secondary numerical weights. Cell activation is computed through MIN-MAX fuzzy equations of the weights. Learning consists in finding the (numerical) weights and the network topology. This feed forward network is described and illustrated in the same biomedical domain as in the first part.

• Journal of the Korea Society of Computer and Information
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• v.16 no.5
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• pp.13-22
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• 2011

Network Intrusion Detection Systems use regular expression to represent malicious packets and hardware-based pattern matching is required for fast deep packet inspection. Although hardware architectures for implementing constraint repetition operators such as {10} were recently proposed, they have some limitation. In this paper, we propose hardware architecture supporting constraint repetitions of general regular expression sub-patterns with lower logic complexity. The subpatterns supported by the proposed contraint repetition architecture include general regular expression patterns as well as a single character and fixed length patterns. With the proposed building block, we can implement more efficiently regular expression pattern matching hardwares.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1997.11a
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• pp.228-233
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• 1997

An efficient corner matching algorithm is developed to minimize the amount of calculation. To reduce the amount of calculation, all available information from a corner detector is used to make model. This information has uncertainties due to discretization noise and geometric distortion, and this is represented by fuzzy rule base which can represent and handle the uncertainties. Form fuzzy inference procedure, a matched segment list is extracted, and resulted segment list is used to calculate the transformation between object of model and scene. To reduce the false hypotheses, a vote and re-vote method is developed. Also an auto tuning scheme of the fuzzy rule base is developed to find out the uncertainties of features from recognized results automatically. To show the effectiveness of the developed algorithm, experiments are conducted for images of real electronic components.

• Journal of Institute of Control, Robotics and Systems
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• v.2 no.3
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• pp.229-235
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• 1996

CAD diagrams generally consists of many basic components: symbols, character, and connection lines. Thus, to recognize the diagrams, it is necessary to extract each components, and understand their meanings and relation among them. This paper describes a method for linking basic components extracted efficiently from hand-down diagrams to OrCAD data format. Experimental results with a hand-drawn diagrams of electronic and logic circuit show utility of the proposed method.

• The Transactions of the Korean Institute of Electrical Engineers
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• v.35 no.11
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• pp.487-494
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• 1986

This paper is concerned with the techniques of error check and reduction of state diagram using logic programming. Error check program aims to check not only syntax errors but also semantic errors. And reduction program optimizes the state diagram by finding the redundant equivalence states and removing those from the set of states. The input of both program is state diagram represented as state table form. The output of error check program is error comment. The output of reduction program is equivalence reduced state table. Both programs are implemented using prolog. Prolog has very powerful pattern matching, and its automatic back-tracking capabilities facilitate easy-to-write error check and reduction programs.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.5 no.4
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• pp.310-315
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• 2005

This paper proposes a fast optimization method by combining queen-bee evolution and derivative evaluation in genetic algorithms. These two operations make it possible for genetic algorithms to focus on highly fitted individuals and rapidly evolved individuals, respectively. Even though the two operations can also increase the probability that genetic algorithms fall into premature convergence phenomenon, that can be controlled by strong mutation rates. That is, the two operations and the strong mutation strengthen exploitation and exploration of the genetic algorithms, respectively. As a result, the genetic algorithm employing queen-bee evolution and derivative evaluation finds optimum solutions more quickly than those employing one of them. This was proved by experiments with one pattern matching problem and two function optimization problems.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.4 no.3
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• pp.347-352
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• 2004

We generated blocks from the direction-extracted fingerprint during the pre-process of the fingerprint recognition algorithm and performed training by using the direction minutiae of each block as the input pattern of the neural network, so that we extracted the core points to use in the matching. Based on this, we designed the fingerprint recognition embedded system and tested it by using the control board and the serial communication to utilize it for a variety of application systems. As a result, we can verify the reliance satisfactorily.