• Title, Summary, Keyword: artificial immune system

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Introduction to a Novel Optimization Method : Artificial Immune Systems (새로운 최적화 기법 소개 : 인공면역시스템)

  • Yang, Byung-Hak
    • IE interfaces
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    • v.20 no.4
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    • pp.458-468
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    • 2007
  • Artificial immune systems (AIS) are one of natural computing inspired by the natural immune system. The fault detection, the pattern recognition, the system control and the optimization are major application area of artificial immune systems. This paper gives a concept of artificial immune systems and useful techniques as like the clonal selection, the immune network theory and the negative selection. A concise survey on the optimization problem based on artificial immune systems is generated. The overall performance of artificial immune systems for the optimization problem is discussed.

Optimization of Distributed Autonomous Robotic Systems Based on Artificial Immune Systems

  • Hwang, Chul-Min;Park, Chang-Hyun;Sim, Kwee-Bo
    • Proceedings of the Korean Institute of Intelligent Systems Conference
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    • pp.220-223
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    • 2003
  • In this paper, we optimize distributed autonomous robotic system based on artificial immune system. Immune system has B-cell and T-cell that are two major types of lymphocytes. B-cells take part in humoral responses that secrete antibodies and T-cells take part in cellular responses that stimulate or suppress cells connected to the immune system. They have communicating network equation, which have many parameters. The distributed autonomous robotics system based on this artificial immune system is modeled on the B-cells and T-cells system. So performance of system is influenced by parameters of immune network equation. We can improve performance of Distributed autonomous robotics system based on artificial immune system.

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Design of Autonomous Mobile Robot System Based on Artificial Immune Network and Internet (인공 면역망과 인터넷에 의한 자율이동로봇 시스템 설계)

  • Lee, Dong-Je;Lee, Min-Jung;Choi, Young-Kiu
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.50 no.11
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    • pp.522-531
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    • 2001
  • Recently conventional artificial intelligence(AI) approaches have been employed to build action selectors for the autonomous mobile robot(AMR). However, in these approaches, the decision making process to choose an action from multiple competence modules is still an open question. Many researches have been focused on the reactive planning systems such as the biological immune system. In this paper, we attempt to construct an action selector for an AMR based on the artificial immune network and internet. The information from vision sensors is used for antibody. We propose a learning method for artificial immune network using evolutionary algorithm to produce antibody automatically. The internet environment for an AMR action selector shows the usefulness of the proposed learning artificial immune network application.

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A New Artificial Immune Approach to Hardware Test Based on The Principle of Antibody Diversity (하드웨어 테스트를 위한 새로운 인공 면역 시스템)

  • 이상형;김은태;박민용
    • Proceedings of the IEEK Conference
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    • pp.2673-2676
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    • 2003
  • This Paper proposes a new artificial immune approach to hardware test. A Novel Algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity. Tolerance conditions in artificial immune system correspond to the antibody in biological immune system. The suggested method is applied to the on-line monitoring of a typical FSM (a decade counter) and its effectiveness is demonstrated by the computer simulation.

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An Artificial Immune system using Memory Cell for the Inventory Routing Problem (기억 세포를 이용한 재고-차량 경로 문제의 인공면역시스템)

  • Yang, Byoung-Hak
    • Proceedings of the Korean Operations and Management Science Society Conference
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    • pp.236-246
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    • 2008
  • We consider the Inventory Routing problem(IRP) for the vending machine operating system. An artificial immune system(AIS) is introduced to solve the IRP. The IPR is an rolling wave planning. The previous solution of IRP is one of good initial solution of current IRP. We introduce an Artificial Immune system with memory cell (AISM) which store previous solution in memory cell and use an initial solution for current problem. Experiment results shows that AISM reduced calculations time in relatively less demand uncertainty.

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A New Artificial Immune Approach to Hardware Test Based on the Principle of Antibody Diversity

  • Lee, Sanghyung;Kim, Euntai;Park, Mignon
    • International Journal of Fuzzy Logic and Intelligent Systems
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    • v.3 no.1
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    • pp.23-26
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    • 2003
  • This paper proposes a new artificial immune approach to hardware test. A novel algorithm of generating tolerance conditions is suggested based on the principle of the antibody diversity. Tolerance conditions in artificial immune system correspond to the antibody in biological immune system. The suggested method is applied to the on-line monitoring of a typical FSM (a decade counter) and its effectiveness is demonstrated by the computer simulation.

Autonomous Mobile Robots Navigation Using Artificial Immune Networks and Neural Networks (인공 면역망과 신경회로망을 이용한 자율이동로봇 주행)

  • 이동제;김인식;이민중;최영규
    • The Transactions of the Korean Institute of Electrical Engineers D
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    • v.52 no.8
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    • pp.471-481
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    • 2003
  • The acts of biological immune system are similar to the navigation for autonomous mobile robots under dynamically changing environments. In recent years, many researchers have studied navigation algorithms using artificial immune networks. Conventional artificial immune algorithms consist of an obstacle-avoidance behavior and a goal-reaching behavior. To select a proper action, the navigation algorithm should combine the obstacle-avoidance behavior with the goal-reaching behavior. In this paper, the neural network is employed to combine the behaviors. The neural network is trained with the surrounding information. the outputs of the neural network are proper combinational weights of the behaviors in real-time. Also, a velocity control algorithm is constructed with the artificial immune network. Through a simulation study and experimental results for a autonomous mobile robot, we have shown the validity of the proposed navigation algorithm.

Swarm Control of Distributed Autonomous Robot System based on Artificial Immune System using PSO (PSO를 이용한 인공면역계 기반 자율분산로봇시스템의 군 제어)

  • Kim, Jun-Yeup;Ko, Kwang-Eun;Park, Seung-Min;Sim, Kwee-Bo
    • Journal of Institute of Control, Robotics and Systems
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    • v.18 no.5
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    • pp.465-470
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    • 2012
  • This paper proposes a distributed autonomous control method of swarm robot behavior strategy based on artificial immune system and an optimization strategy for artificial immune system. The behavior strategies of swarm robot in the system are depend on the task distribution in environment and we have to consider the dynamics of the system environment. In this paper, the behavior strategies divided into dispersion and aggregation. For applying to artificial immune system, an individual of swarm is regarded as a B-cell, each task distribution in environment as an antigen, a behavior strategy as an antibody and control parameter as a T-cell respectively. The executing process of proposed method is as follows: When the environmental condition changes, the agent selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other agent using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. In order to decide more accurately select the behavior strategy, the optimized parameter learning procedure that is represented by stimulus function of antigen to antibody in artificial immune system is required. In this paper, particle swarm optimization algorithm is applied to this learning procedure. The proposed method shows more adaptive and robustness results than the existing system at the viewpoint that the swarm robots learning and adaptation degree associated with the changing of tasks.

Hybrid Artificial Immune System Approach for Profit Based Unit Commitment Problem

  • Lakshmi, K.;Vasantharathna, S.
    • Journal of Electrical Engineering and Technology
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    • v.8 no.5
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    • pp.959-968
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    • 2013
  • This paper presents a new approach with artificial immune system algorithm to solve the profit based unit commitment problem. The objective of this work is to find the optimal generation scheduling and to maximize the profit of generation companies (Gencos) when subjected to various constraints such as power balance, spinning reserve, minimum up/down time and ramp rate limits. The proposed hybrid method is developed through adaptive search which is inspired from artificial immune system and genetic algorithm to carry out profit maximization of generation companies. The effectiveness of the proposed approach has been tested for different Gencos consists of 3, 10 and 36 generating units and the results are compared with the existing methods.

Negative Selection within an Artificial Immune System for Network Intrusion Detection (네트워크 침입 탐지를 위한 인공 면역 시스템에서의 부정적 선택( Negative Selection) 알고리즘)

  • Kim, Jung-Won;Bentley, Peter J.;Choi, Jong-Uk
    • Proceedings of the Korea Information Processing Society Conference
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    • pp.273-276
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    • 2000
  • This paper describes on-going research, applying an artificial immune system to the problem of network intrusion detection. The paper starts by introducing the motivation and rationale of this research. After describing the overall architecture of the proposed artificial immune system fur network intrusion detection, the real network traffic data and its profile features used in this research are explained. As the first step of this effort, the negative selection algorithm, which is one of three significant evolutionary stages comprising an overall artificial immune system, is investigated and initial results are briefly discussed. Finally, the direction of future work is discussed based on this initial result and the contribution of this research is addressed.

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