• Journal of the Korean Society for Industrial and Applied Mathematics
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• v.3 no.2
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• pp.137-145
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• 1999

In this paper, We consider two-dimensional Maximum Length Cellular Automata (2-D MLCA) as an extension of the 1-D MLCA. 2-D MLCA can display much better random patterns than those generated by 1-D CA and LFSR. To generate random pattern, a CA should have a maximum length cycle. So, it is necessary to find MLCA that the characteristic polynomial of the transition matrix is primitive. New boundary conditions of 3 types are proposed and some rules having primitive polynomials of 2-D MLCA are found.

• 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.14 no.7
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• pp.1597-1602
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• 2010

Sarkar et al. proposed a new Cellular Automata(CA) based security scheme providing both authentication and confidentiality. The application of CA for designing the scheme makes it suitable for hardware implementation. But the proposed method by Sakar dt al. has some problems. In this paper, we analyze CA and give a method for detecting secret key.

• Proceedings of the Korean System Dynamics Society
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• 1999.08a
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• pp.133-157
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• 1999

시스템 다이나믹스는 전통적인 단선론적 인과론을 극복하고 시스템의 동태적 특성을 파악하는데 상당한 공헌을 하였다. 그러나 이러한 시스템의 특성을 방정식에 의존하는 시뮬레이션 기법(Equation-Based Simulation:EBS)을 이용하여 분석하는 경우 방정식으로 묘사하기 어려운 복잡적응시스템(Complex Adaptive System)에서는 한계를 갖고 있다. 따라서 시스템의 동태적 특성을 좀더 정확히 파악을 하기 위해서는 시스템을 구성하는 개체(agent)들의 행동이 시스템 전체에 미치는 영향들을 모형화 함으로써 파악하는 것이 바람직하다고 할 수 있다. 본 연구는 이러한 개체지향 시뮬레이션(Agent-eased Simulation: ABS)의 기법을 복잡성과학의 패러다임을 소개함으로써 그 중요성을 설명하고자 한다. 특히 카오스 이론으로부터 복잡성과학으로의 발전 과정을 개념을 중심으로 논의함으로서 복잡적응시스템의 이해를 돕고자 한다. 또한 ABS가 실제로 전통적인 EBS가 묘사하는 생태계 시스템의 변화를 잘 묘사할 수 있다는 사실을 보여주기 위하여 3개체가 있는 Lotka-Voltera 모형을 Cellular Automata 라는 ABS에 기반 한 시뮬레이션 기법을 활용하여 그 사용가능성을 제시하고자 한다.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 2005.10a
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• pp.3-9
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• 2005

The recent notion of Network Centric Warfare (NCW) emphasizes the ability to distribute right information at the right time to maximize the combat effectiveness. Accordingly, in the modern combat system the importance of non-physical element, such as communication system is increasing. However, an NCW-support communication network system is expensive. Therefore, it is essential to develop a proper combat system evaluation method to establish an efficient NCW-support combat system. Traditionally, combat system effectiveness is measured in terms of physical elements such as men and fire power. Obviously, such method is hardly applicable to a modern combat system To overcome this difficulty, we propose an evaluation model based on CA (Cellular Automata) simulation. A set of preliminary combat simulations show that CA simulation may be promising in evaluating non-physical element of a modem combat system.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.51 no.4
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• pp.160-166
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• 2002

Since conventional mobile robot control with one module has limitation to solve complex problems, there have been a variety of works on combining multiple modules for solving them. Recently, many researchers attempt to develop mobile robot controllers using artificial life techniques. In this paper, we develop a mobile robot controller using cellular automata based neural networks, where complex tasks are divided to simple sub-tasks and optimal neural structure of each sub-task is explored by genetic algorithm. Neural network modules are combined dynamically using the action selection mechanism, where basic behavior modules compete each other by inhibition and cooperation. Khepera mobile robot simulator is used to verify the proposed model. Experimental results show that complex behaviors emerge from the combination of low-level behavior modules.

• Proceedings of the KSRS Conference
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• 2007.03a
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• pp.55-60
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• 2007

The purpose of this study is to evaluate the temporal land cover change by gradual urbanization of Gyeongan-cheon watershed. This study used the five land use of Landsat TM satellite images(l987, 1991, 2001, 2004) which were classified by maximum likelihood method. The five land use maps examine its accuracy by error matrix and administrative district statistics. This study analyze land use patterns in the past using time.series Landsat satellite images, and predict 2004 year land use using a CA-Markov combined CA(Cellular Automata) and Markov process, and examine its appropriateness. Finally, predict 2030, 2060 year land use maps by CA-Markov model were constructed from the classified images.

• Journal of Korea Multimedia Society
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• v.5 no.2
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• pp.191-197
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• 2002

In this paper, we propose a stream cipher using a cellular automata. For the security evaluation, we use the statistical tests suggested on the report of FIPS PUB 140-2 and additionally, we apply entropy test, linear complexity test and auto-correlation test which are popular statistical tests for the output sequences of stream ciphers.

• Proceedings of the Korean Institute of Information and Commucation Sciences Conference
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• 2003.10a
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• pp.247-250
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• 2003

This paper describes the extraction method for brain region using cellular automata from the brain MR image. In the first removing the background from the brain MR image, and then extracting the brain region by applying the cellular automata rule obtained from histogram analysis information. The results on some experimental results showed that the PSNR is 42.11(dB) on image quality and also the correlation factor is estimated 98.46%. The result of this study can be used as the auto-diagnostics system.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.10 no.7
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• pp.3397-3413
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• 2016

Considering that the layered cellular automata (LCA) are naturally fit for representing image data in various applications, a novel reverse iterative image encryption scheme based on LCA is proposed. Specifically, the plain image is set as the final configuration of an 8-layer CA, and some sequences derived from a random sequence are set as the pre-final configuration, which ensure that the same plain image will never be encrypted in the same way when encrypted many times. Then, this LCA is backward evolved by following some reversible two order rules, which are generated with the aid of a newly defined T-shaped neighborhood. The cipher image is obtained from the recovered initial configuration. Several analyses and experimental results show that the proposed scheme possesses a high security level and executive performance.