• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.7
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• pp.1103-1109
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• 2003

An adaptive bone remodeling is simulated by using the cellular automata (CA) method. It is assumed that bone tissue consist of bone marrow, osteoclast, osteoblast cell or osteoprogenitor cell. Two types of local rule are adopted; those are the metabolism rule and adaptive bone formation rule. The metabolism rule is based on the interactions of cells and the bone formation rule is based on the adaptation against the mechanical stimulus. The history of load and memory of mechanical stimulus are also considered in the local rules. As a result, the pattern of distribution of the bone tissue is dynamically adequate and it is similar to intact cancellous bone.

• 제어로봇시스템학회:학술대회논문집
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• 1997.10a
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• pp.231-236
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• 1997

We propose the hybrid control for the path control of wheeled mobile robot system. To develop the hybrid control of mobile robot, the continuous dynamics of mobile robot are modeled by the switched systems. The abstract model and digital automata for the path control are developed. This hybrid control system has the 3-layered hierachical structure : digital automata as the higher process, mobile robot system as the lower process, and the interface as the interaction process between the continuous dynamics and the discrete dynamics. The control of following the desired-paths with edges are investigated as the applications by the computer simulation.

• KSII Transactions on Internet and Information Systems (TIIS)
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• v.11 no.10
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• pp.4804-4822
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• 2017

Coverage problem is a research hot spot in directional sensor networks (DSNs). However, the major problem affecting the performance of the current coverage-enhancing strategies is that they just optimize the coverage of networks, but ignore the maximum number of sleep sensors to save more energy. Aiming to find an approximate optimal method that can cover maximum area with minimum number of active sensors, in this paper, a new scheduling algorithm based on learning automata is proposed to enhance area coverage, and shut off redundant sensors as many as possible. To evaluate the performance of the proposed algorithm, several experiments are conducted. Simulation results indicate that the proposed algorithm have effective performance in terms of coverage enhancement and sleeping sensors compared to the existing algorithms.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 1998.06a
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• pp.181-184
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• 1998

CAM-Brain is a model to develop neural networks based in cellular automata by evolution, and finally aims at a model as and artificial brain,. In order to show the feasibility of evolutionary engineering to develop an artificial brain we have attempted to evolve a module of CAM-Brain for the problem to control a mobile robot, In this paper, we present some recent results obtained by analyzing the behaviors of the evolved neural module. Several experiments reveal a couple of problems that should be solved when CAM-Brain evolves to control a mobile robot. so that some modification of the original model is proposed to solve them. The modified CAM-Brain has evolved to behave well in a simulated environment, and a thorough analysis proves the power of evolution.

• 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.