• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.29 no.1
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• pp.399-404
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• 2005

In this paper, An immunized PID(I-PID) controller based on cell mediated immune response is proposed to improve the control performance of the controller with PID scheme. And it is applied to the vibration of the building structure in the port with active damper systems. The immune system of organism in the real body regulates the antibody and T-cells to protect the attack from the foreign materials which are virus, germ cell, and other antigens. It has similar characteristics that are the adaptation and robustness to overcome disturbances and to control the plant of engineering application. At firstly, we build a model of the T-cell regulated immune response mechanism. We have also designed an I-PID controller focusing on the T-cell regulated immune response of biological immune system. Finally, we show that some computer simulations of the vibraton control for the building structure system with wind force excitation. These results for the proposed method also show that is has performance than other conventional controller design method.

• Proceedings of the Korean Information Science Society Conference
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• 2004.04b
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• pp.598-600
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• 2004

기존의 연구 논문 중 비결정론적인 알고리즘인 유전자 알고리즘이나 인공신경망 등을 오델로 게임에 적용하여 자동학습을 시킨 예는 많으나 면역알고리즘을 모델로 게임에 적용한 예는 찾기가 어렵다 본 논문에서는 생리학의 면역시스템의 특징을 그대로 적용한 면역알고리즘을 모델로 게임에 적용하여 게임전략 생성에 관하여 연구한다. 생리학의 면역시스템은 자기조절능력이 있다는 외과 재 감염시 빠르게 대응할 수 있다는 특징이 있다. 면역알고리즘을 이용하여 탐색된 전략을 유전자알고리즘 그리고 기존에 연구되어진 게임전략 등과 실험하여 그 결과를 비교.연구한 결과 면역알고리즘을 적용하여 탐색된 모델로 게임전략이 가장 높은 승률을 보인다.

• Journal of the Korean Institute of Intelligent Systems
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• v.14 no.6
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• pp.699-704
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• 2004

This paper proposes a Distributed Autonomous Robotic System(DARS) based on an Artificial Immune System(AIS) and a Classifier System(CS). The behaviors of robots in the system are divided into global behaviors and local behaviors. The global behaviors are actions to search tasks in environment. These actions are composed of two types: aggregation and dispersion. AIS decides one among these two actions, which robot should select and act on in the global. The local behaviors are actions to execute searched tasks. The robots learn the cooperative actions in these behaviors by the CS in the local. The proposed system is more adaptive than the existing system at the viewpoint that the robots learn and adapt the changing of tasks.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.9
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• pp.801-806
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• 2001

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users A computer virus is one of program in computer and has abilities of self reproduction ad destruction like a virus of biology. And hacking is to rob a person's data in a intruded computer and to delete data in a person s computer from the outside. To block hacking that is intrusion of a person s computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive selection and negative selection of self-recognition process that is ability of T-cytotoxic cell that plays an important part in biological immune system. So we embody a self-nonself distinction algorithm in computer, which is an important part when we detect an infected data by computer virus and a modified data by intrusion from the outside. The composed self-recognition process distinguishes self-file from the changed files. To prove the efficacy of self-recognition algorithm, we use simulation by a cell change and a string change of self file.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.4
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• pp.320-324
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• 2001

According to the rapid growth of computer and internet recently, A hacking to steal infonnations and the computer vinls to destroy the data in computer are now prevailing in the whole world. A study of methods to protect the data of computer is in progress. One of the study is constmction of computer immune system using biological immune system tbat has ability of removal and protection from extemal invasion. In this paper, we make a change detection algorithm which is based on ability of distinction between self and nonself in T-cytotoxic cell that is one of biological immune cell. In algorithm, MHC receptors are composed of a part of self-file that is recognized as itself and those shall distinguish self-file from the changed file. As a result of applying this algorithm to the changed self-files, we prove the efficacy of detection of the self-files changed by computer virus and hacking.

• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.1
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• pp.52-60
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• 2002

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. A computer virus is one of program in computer and has abilities of self reproduction and destruction like a virus of biology. And hacking is to rob a person's data in a intruded computer and to delete data in a Person s computer from the outside. To block hacking that is intrusion of a person's computer and the computer virus that destroys data, a study for intrusion detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive and negative selection for self recognition which have a similar function like T-cytotoxic cell that plays an important role in biological immune system. We embody a self-nonself distinction algorithm in computer, which is an important part when we detect an infected data by computer virus and a modified data by intrusion from the outside. And we showed the validity and effectiveness of the proposed self recognition algorithm by computer simulation about various infected data obtained from the cell change and string change in the self file.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.12a
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• pp.185-188
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• 2001

According as many people use a computer newly, damage of computer virus and hacking is rapidly increasing by the crucial users. To block hacking that is intrusion of a person's computer and the computer virus that destroys data, a study for intrusion-detection of system and virus detection using a biological immune system is in progress. In this paper, we make a model of positive selection and negative selection of self-recognition process that is ability of T-cytotoxic cell that plays an important part in biological immune system. So we embody a self-nonself distinction algorithm in computer. To prove the efficacy of self-recognition algorithm, we use simulations by a cell change and a string change of self file.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2001.05a
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• pp.167-170
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• 2001

인터넷 보안문제 해결을 위해서 침입탐지 시스템 및 바이러스 백신 등이 연구되어 왔고, 생체면역을 응용한 보안체계가 연구되고 있으나, 컴퓨터 자원소비와 비실시간대응의 문제점을 가지고 있다. 이에 Antibody Layer[1]는 인공면역과 Host alliance를 기반으로 하여 보안문제 해결에 효율성과 정확성을 제공하였다. 본 논문에서는 Antibody Layer의 Host alliance를 위한 Cooperation Protocol에 대하여 논하였다.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.05a
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• pp.173-176
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• 2003

지문은 종생불변성, 만인부동성, 그리고 사용상의 편리함 때문에 신원인증을 위한 생체인식에 많이 사용되고 있다. 최근에는 기하학구조에 기반한 특이점 매칭방식이 제안되어 인식성능이 매우 높고 잡음에 강한 특성이 있으나 매칭 회수가 많아 인식속도가 느린 단점이 있다. 따라서 기존의 방식은 소수의 지문에 대한 1:다 매칭이나 1:1매칭에 주로 사용된다. 본 논문에서는 기존의 문제점들을 개선하기 위하여 생체 면역계의 자기-비자기 인식 능력에 주목하였다. 생체 면역계는 자기-비자기의 구별 능력을 바탕으로 바이러스나병원균 등의 낮선 외부침입자로부터 자신을 보호하고 침입자를 식별, 제거하는 시스템이다. 본 논문에서는 생체 면역계를 이루는 면역세포 중의 하나인 세포독성 T세포의 생성과정에서 자기, 비자기를 구별하기 위한 MHC 인식부를 형성하는 과정에 착안한 빠르고 신뢰성 있는 지문 인식 알고리즘을 제안한다. 제안한 방식은 지문에 존재하는 특이점(minutiae)인식을 통해 1단계로 global 패턴을 생성하고 2단계로 기하학적인 구조를 만들며, 인식시 global 패턴을 인식한 MHC 인식부에 대해서만 2차 local 매칭을 수행함으로써 매칭 속도가 매우 빠르며 지문의 비틀림이나 회전 등에 대하여 강인하게 인식된다.

• Journal of the Korean Institute of Intelligent Systems
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• v.9 no.6
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• pp.627-633
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• 1999

In this paper, we propose a method of cooperative control (T-cell modeling) and selection of group behavior strategy (B-cell modeling) based on immune system in distributed autonomous robotic system (DARS). Immune system is living body's self-protection and self-maintenance system. These features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For applying immune system to DARS, a robot is regarded as a ？3-cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. When the environmental condition (antigen) changes, a robot selects an appropriate behavior strategy (antibody). And its behavior strategy is stimulated and suppressed by other robot using communication (immune network). Finally much stimulated strateby is adopted as a swarm behavior strategy. This control scheme is based on clonal selection and immune network hypothesis, and it is used for decision making of optimal swarm strategy. Adaptation ability of robot is enhanced by adding T-cell model as a control parameter in dynamic environments.