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

• The Transactions of the Korean Institute of Electrical Engineers D
• /
• v.51 no.4
• /
• pp.160-166
• /
• 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 KIEE Conference
• /
• 1998.07g
• /
• pp.2193-2196
• /
• 1998

Cellular automata are dynamical systems in which space and time are discrete, where each cell has a finite number of states and updates its states by interactive rules among the cell-neighborhood. From the characteristics of self-reproduction and self- organization, it is possible to create a neural network which has the specific patterns or structures dynamically. CAM-Brain is a kind of such neural network system which evolves its structure by adopting evolutionary computations like genetic algorithms (GA). In this paper, we suggest the evolution strategies for the structure of neural networks based on cellular automata.

• Journal of applied mathematics & informatics
• /
• v.29 no.5_6
• /
• pp.1327-1335
• /
• 2011

This paper demonstrates that there is a unique exponentially stable equilibrium state of a class of impulsive cellular neural network with delays. The analysis exploits M-matrix theory and generalized comparison principle to derive some easily verifiable sufficient conditions for the global exponential stability of the equilibrium state. The results extend and improve earlier publications. An example with its simulation is given for illustration of theoretical results.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.06a
• /
• pp.158-163
• /
• 1998

This paper is our first attempt to construct a information processing system such as the living creatures' brain based on artificial life technique. In this paper, we propose a method of constructing neural networks using bio-inspired emergent and evolutionary concept, Ontogeny of living things is realized by cellular automata model and Phylogeny that is living things adaptation ability themselves to given environment, are realized by evolutionary algorithms. Proposing evolving cellular automata neural systems are calledin a word ECANS. A basic component of ECANS is 'cell' which is modeled on chaotic neuron with complex characteristics, In our system, the states of cell are classified into eight by method of connection neighborhood cells. When a problem is given, ECANS adapt itself to the problem by evolutionary method. For fixed cells transition rule, the structure of neural network is adapted by change of initial cell' arrangement. This initial cell is to become a network b developmental process. The effectiveness and the capability of proposed scheme are verified by applying it to pattern classification and robot control problem.

• Journal of the Korean Institute of Telematics and Electronics B
• /
• v.30B no.10
• /
• pp.86-94
• /
• 1993

This paper presents the two-stage neural network method for efficient channel assignment of cellular mobile radio network. The first stage decomposes the region into non-adjacent groups of cells and the second stage assigns channels to the decomposed groups. The neural network model is tested with an experimental system of eighteen channels dedicated for nineteen hexagonal-cell region. When radom call requests of average density of 2 Erl/Cell to 8 Erl/Cell are presented, the real-time channel assignment method reduces the call-blocking rate up to 16% against the existing SCA(Static Channel Assignment) method.

• Journal of the Korean Institute of Intelligent Systems
• /
• v.12 no.5
• /
• pp.467-472
• /
• 2002

In this paper, we propose a Fuzzy Cellular Neural Network(FCNN) that is based on a-Least Trimmed Square Hausdorff distance(a-LTSHD) which applies Hausdorff distance(HD) to the FCNN structure in order to remove the impulse noise of images effectively and also improve the speed of operation. FCNN incorporates Fuzzy set theory to Cellular Neural Network(CNN) structure and HD is used as a scale which computes the distance between set or two pixels in binary images without confrontation of the feature object. This method has been widely used with the adjustment of the object. For performance evaluation, our proposed method is analyzed in comparison with the conventional FCNN, with the Opening-Closing(OC) method, and the LTSHD based FCNN by using Mean Square Error(MSE) and Signal to Noise Ratio(SNR). As a result, the performance of our proposed network structure is found to be superior to the other algorithms in the removal of impulse noise.

• Journal of the Korea Institute of Information and Communication Engineering
• /
• v.7 no.3
• /
• pp.532-543
• /
• 2003

In this paper, we proposed structure of a reversible discrete-time cellular neural network (DTCNN) for labeling digital images to protect copylight. First, we present the concept and the structure of reversible DTCNN, which can be used to generate 2D binary pseudo-random images sequences. We presented some, output examples of different kinds of reversible DTCNNs to show their complex behaviors. Then both the original image and the copyright label, which is often another binary image, are used to generate a binary random key image. The key image is then used to scramble the original image. Since the reversibility of a reversible DTCNN, the same reversible DTCNN can recover the copyright label from a labeled image. Due to the high speed of a DTCNN chip, our method can be used to label image sequences, e.g., video sequences, in real time. Computer simulation results are presented.

• The Journal of Korean Institute of Electromagnetic Engineering and Science
• /
• v.15 no.10 s.89
• /
• pp.969-976
• /
• 2004

The wide and growing use of cellular phones has raised the question about the possible health risks associated with radio-frequency electromagnetic fields. It would be helpful for phone users to blow the exposure levels during cellular phone use. But it is very difficult to recognize the amount of exposure, because measuring accurate level of RF is a difficult matter. In this study, we developed a model to estimate the exposure level and the individual risk of exposure by utilizing the available informations that we can get. We used such parameters as usage time a day, total using period, distance between cellular phone and head, slope of cellular phone, hands-free usage, antenna pulled out or not SAR(Specific Absorption Rate) of cellular phone, and flip or folder type. We proposed a model presenting individual risk of RF exposure from level 0 to 10 by using a neural network.

• Nuclear Engineering and Technology
• /
• v.43 no.3
• /
• pp.243-256
• /
• 2011

This paper considers the concept of analog computing based on a cellular neural network (CNN) paradigm to simulate nuclear reactor dynamics using a time-dependent second order form of the neutron transport equation. Instead of solving nuclear reactor dynamic equations numerically, which is time-consuming and suffers from such weaknesses as vulnerability to transient phenomena, accumulation of round-off errors and floating-point overflows, use is made of a new method based on a cellular neural network. The state-of-the-art shows the CNN as being an alternative solution to the conventional numerical computation method. Indeed CNN is an analog computing paradigm that performs ultra-fast calculations and provides accurate results. In this study use is made of the CNN model to simulate the space-time response of scalar flux distribution in steady state and transient conditions. The CNN model also is used to simulate step perturbation in the core. The accuracy and capability of the CNN model are examined in 2D Cartesian geometry for two fixed source problems, a mini-BWR assembly, and a TWIGL Seed/Blanket problem. We also use the CNN model concurrently for a typical small PWR assembly to simulate the effect of temperature feedback, poisons, and control rods on the scalar flux distribution.