• Journal of the Korean Institute of Intelligent Systems
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• v.12 no.3
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• pp.261-268
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• 2002

This paper represents that auto tunings of membership functions and weights in the fuzzy neural networks are effectively performed by immune algorithm. A number of hybrid methods in fuzzy-neural networks are considered in the context of tuning of learning method, a general view is provided that they are the special cases of either the membership functions or the gain modification in the neural networks by genetic algorithms. On the other hand, since the immune network system possesses a self organizing and distributed memory, it is thus adaptive to its external environment and allows a PDP (parallel distributed processing) network to complete patterns against the environmental situation. Also, it can provide optimal solution. Simulation results reveal that immune algorithms are effective approaches to search for optimal or near optimal fuzzy rules and weights.

• Proceedings of the Korea Inteligent Information System Society Conference
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• 2001.01a
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• pp.298-309
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• 2001

This research aims to unravel the significant features of the human immune system, which would be successfully employed for a novel network intrusion detection model. Several salient features of the human immune system, which detects intruding pathogens, are carefully studied and the possibility and the advantages of adopting these features for network intrusion detection are reviewed and assessed.

• 제어로봇시스템학회:학술대회논문집
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• 2001.10a
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• pp.91.5-91
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• 2001

This paper suggests that the immune algorithm can effectively be used in tuning of a PID controller. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that form a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. That is, the artificial immune network flexibly self-organizes accord Eng to dynamic changes of external environment (meta-dynamics function). However, up to the present time, models based on the conventional crisp approach have been used to describe dynamic model relationship between antibody and antigen. Therefore, there are some problems ...

• Annual Conference of KIPS
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• 2000.10a
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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.

• Proceedings of the KIEE Conference
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• 2001.07d
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• pp.2126-2128
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• 2001

This paper suggests that the immune network algorithm based on fuzzy set can effectively be used in tuning of a PID controller for multivariable process or nonlinear process. The artificial immune network always has a new parallel decentralized processing mechanism for various situations, since antibodies communicate to each other among different species of antibodies/B-cells through the stimulation and suppression chains among antibodies that from a large-scaled network. In addition to that, the structure of the network is not fixed, but varies continuously. On the other hand, a number of tuning technologies have been considered for the tuning of a PID controller. As a less common method, the fuzzy and neural network or its combined techniques are applied. However, in the case of the latter, yet, it is not applied in the practical field, in the former, a higher experience and technology is required during tuning procedure. Along with these, this paper used the fuzzy set in order that the stimulation and suppression relationship between antibody and antigen can be more adaptable controlled against the external condition, including noise or disturbance of plant. The immune network based on fuzzy set suggested here is applied for the PID controller tuning of multivariable process with two inputs and one output and is simulated.

• 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.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2003.09a
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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.

• IMMUNE NETWORK
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• v.16 no.1
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• pp.26-32
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• 2016

Aortic valve stenosis is a heart disease prevalent in the elderly characterized by valvular calcification, fibrosis, and inflammation, but its exact pathogenesis remains unclear. Previously, aortic valve stenosis was thought to be caused by chronic passive and degenerative changes associated with aging. However, recent studies have demonstrated that atherosclerotic processes and inflammation can induce valvular calcification and bone deposition, leading to valvular stenosis. In particular, the most abundant cell type in cardiac valves, valvular interstitial cells, can differentiate into myofibroblasts and osteoblast-like cells, leading to valvular calcification and stenosis. Differentiation of valvular interstitial cells can be trigged by inflammatory stimuli from several immune cell types, including macrophages, dendritic cells, T cells, B cells, and mast cells. This review indicates that crosstalk between immune cells and valvular interstitial cells plays an important role in the development of aortic valve stenosis.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• 2000.04a
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• pp.56-63
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• 2000

Abstract - In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the case that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.

• The Transactions of the Korean Institute of Electrical Engineers D
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• v.49 no.4
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• pp.201-212
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• 2000

In this paper, an adaptive mechanism based on immune algorithm is designed and it is applied for the autonomous guided vehicle(AGV) driving. When the immune algorithm is applied to the PID controller, there exists the cast that the plant is damaged due to the abrupt change of PID parameters since the parameters are adjusted almost randomly. To solve this problem, a neural network is used to model the plant and the parameter tuning of the model is performed by the immune algorithm. After the PID parameters are determined in this off-line manner, these gains are then applied to the plant for the on-line control using immune adaptive algorithm. Moreover, even though the neural network model may not be accurate enough intially, the weighting parameters are adjusted to be accurate through the on-line fine tuning. The computer simulation for the control of steering and speed of AGV is performed. The results show that the proposed controller has better performances than other conventional controllers.