• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2008.05a
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• pp.264-267
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• 2008

Many researchers have studied synthesis method of 90/150 group CA. However, there is a lack of researches for synthesis method of 90/150 nongroup CA. In this paper we report some interesting properties of 90/150 multiple-attractor CA in which all of the cycles are of unit length. 90/150 multiple-attractor CA is a class of nongroup CA. And we propose a construction of 90/150 single-attractor CA. Also we construct 90/150 multiple-attractor CA derived from 90/150 single-attractor CA.

• The Journal of the Korea institute of electronic communication sciences
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• v.5 no.3
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• pp.315-320
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• 2010

Classifying multi-class pattern plays an important role in grouping of records in database systems, detection of faults in the VLSI circuits and so on. In this paper, we propose an algorithm for the construction of multi-class pattern classifier with minimum memory capacity using MACA(Multiple Attractor Cellular Automata) and the subspace concept for given multi-class patterns.

• Proceedings of the Korea Information Processing Society Conference
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• 2001.04a
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• pp.375-378
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• 2001

본 논문에서는 선형 MACA의 0-트리의 한 경로를 기본 경로로 하여 나머지 다른 트리를 구성하고 여원벡터를 선형 MACA의 하나의 상태로 해석하였을 때 상태전이그래프에서 놓이는 위치에 따라 선형 MACA로부터 유도된 여원을 갖는 CA의 모든 트리를 구성하는 알고리즘을 제안한다. 주어진 전이행렬과 여원벡터를 가지는 additive CA의 트리를 구성함에 있어 보다 빠르게 구성할 수 있는 알고리즘을 제시한다.

• Journal of Korea Multimedia Society
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• v.18 no.11
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• pp.1281-1288
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• 2015

Nongroup Cellular Automata(CA) having two trees in the state transition diagram of a CA is suitable for pattern classifier which divides pattern set into two classes. Maji et al. [1] classified patterns by using multiple attractor cellular automata as a pattern classifier with dependency vector. In this paper we propose a method of generation of a pattern classifier using feature vector which is the extension of dependency vector. In addition, we propose methods for finding nonreachable states in the 0-tree of the state transition diagram of TPMACA corresponding to the given feature vector for the analysis of the state transition behavior of the generated pattern classifier.

• Journal of the Korea Institute of Information and Communication Engineering
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• v.13 no.6
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• pp.1099-1104
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• 2009

Cellular Automata(CA) has been used as modeling and computing paradigm for a long time. While studying the models of systems, it is seen that as the complexity of the physical system increase, the CA based model becomes very complex and becomes to difficult to track analytically. Also such models fail to recognize the presence of inherent hierarchical nature of a physical system. In this paper we analyze the properties of GF($2^p$) multiplue attractor cellular automata(GF($2^p$) MACA) C and give a method of synthesis of C which is a special class of hierarchical cellular automata proposed as an alternative to solve the problem.