• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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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.

• 대한전기학회논문지:시스템및제어부문D
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• 제51권4호
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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.

• 대한전기학회:학술대회논문집
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• 대한전기학회 1998년도 하계학술대회 논문집 G
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• pp.2193-2196
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• 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
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• 제29권5_6호
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• pp.1327-1335
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• 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.

• 한국지능시스템학회:학술대회논문집
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• 한국퍼지및지능시스템학회 1998년도 The Third Asian Fuzzy Systems Symposium
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• pp.158-163
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• 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.

• 전자공학회논문지B
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• 제30B권10호
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• pp.86-94
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• 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.

• 한국지능시스템학회논문지
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• 제12권5호
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• pp.467-472
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• 2002

본 논문에서, 영상에서 임펄스 잡음을 효과적으로 제거하고, 연산 속도를 개선하기 위해 Fuzzy Cellular Neural Network(FCNN)구조에 Hausdorff distance(HD)를 적용한 $\alpha$-Least Trimmed Square HD($\alpha$-LTSHD) 기반 FCNN 구조를 제안한다. FCNN는 Cellular Neural Network(CNN) 구조에 퍼지 이론을 적용한 것이고, HD는 특징 대상의 대응 없이 이진 영상의 두 픽셀 집합 사이의 거리를 구하는 척도로 물체의 정합에 널리 사용한다. 성능 평가를 위해, 제안된 방법을 MSE와 SNR을 이용하여 기존 FCNN, Opening-Closing(OC) 그리고 LTSHD 연산자를 적용한 FCNN과 비교 분석하였다. 그 결과, 본 논문에서 제안된 망(network) 구조의 성능이 다른 필터보다 임펄스 잡음 제거에 우수함을 확인하였다.

• 한국정보통신학회논문지
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• 제7권3호
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• pp.532-543
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• 2003

본 논문은 저작권보호를 위해 디지털영상의 라벨링을 위한 reversible DTCNN(discrete-time cellular neural network) 구조를 제안한다. 이러한 저작권보호 라벨링을 위해서 2차원 이진 pseudo 랜덤 영상열에 사용할 수 있는 새로운 reversible DTCNN의 구조와 개념을 설명하고 이에 대한 복잡행위를 보여주기 위해 reversible DTCNN의 서로 다른 방법들의 예시를 들어 설명한다. 또한 서로 다른 2진영상인 원영상과 복사된 영상은 서로 다른 2진 랜덤 영상키를 사용한다. 이 영상키는 원영상을 스크램블하는데 사용된다. 따라서 reversible DTCNN를 다시 역변환시켜서 저작권보호가 라벨링된 영상으로부터 복사된 영상임을 찾아낼 수 있다. 그러나 이러한 동영상을 처리하는 데는 S/W에서는 많은 시간이 소요되므로 고속 DTCNN 칩을 사용하여 실시간에서 동영상이나 비디오영상을 저작권보호를 위한 라벨링에 사용할 수 있으며, 이러한 결과를 컴퓨터에서 시뮬레이션됨을 보인다.

• 한국전자파학회논문지
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• 제15권10호
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• pp.969-976
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• 2004

이동통신 가입자가 증가함으로 인하여 휴대전화로부터 유발되는 전자파 노출 유해성 여부에 대한 논란이 끊임없이 제기되면서 이와 관련한 연구가 국제적으로 활발하게 이루어지고 있다. 휴대전화 사용에 관한 정보로 사용자 본인이 정량적인 전자파 노출량을 직접 알 수 있다면 매우 이상적일 것이다. 그러나 인체에 노출되는 전자파의 양을 직접적으로 측정하는 것은 매우 어렵기 때문에 정확한 노출량을 아는 것은 쉽지 않다. 따라서 본 연구에서는 국내외에서 선행된 연구 결과 및 쉽게 알 수 있는 휴대전화의 모델에 관한 간단한 정보, 사용경향을 이용하여 개인의 휴대전화 전자파의 노출 정도를 제시해 보고자 한다. 휴대전화 사용에 따른 노출 정도를 제시하기 위하여 1일 평균 통화시간, 총 사용기간에 관한 정보와 선행된 연구 결과들을 기반으로 휴대전화 사용시 이격거리와 기울기, 핸즈프리와 안테나의 사용 여부, 휴대전화의 SAR(Specific Absorption Rate), 플립 혹은 폴더형인지 등에 관한 인자들을 이용하였다. 이 인자들을 신경망 회로를 이용하여 노출 정도를 간접적으로 평가하여 사용자에게 제시해 보고자 하였다.

• Nuclear Engineering and Technology
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• 제43권3호
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• pp.243-256
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• 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.