• Proceedings of the Korean Society for Emotion and Sensibility Conference
• /
• 2004.11a
• /
• pp.16-16
• /
• 2004

• Journal of Mechanical Science and Technology
• /
• v.20 no.11
• /
• pp.1823-1833
• /
• 2006

An immune algorithm is a kind of evolutional computation strategies, which is developed in the basis of a real immune mechanism in the human body. Recently, scientific or engineering applications using this scheme are remarkably increased due to its significant ability in terms of adaptation and robustness for external disturbances. Particularly, this algorithm is efficient to search optimal parameters against complicated dynamic systems with uncertainty and perturbation. In this paper, we investigate an immune algorithm embedded Proportional Integral Derivate (called I-PID) control, in which an optimal parameter vector of the controller is determined offline by using a cell-mediated immune response of the immunized mechanism. For evaluation, we apply the proposed control to mitigation of vibrations for nonlinear structural systems, cased by external environment load such as winds and earthquakes. Comparing to traditional controls under same simulation scenarios, we demonstrate the innovation control is superior especially in robustness aspect.

• East Asian mathematical journal
• /
• v.32 no.1
• /
• pp.139-152
• /
• 2016

Inflammatory Bowel Disease(IBD) is chronic, relapsing, immune mediated disorder. The exact cause of IBD is still unknown. The immune system is known to play important role in the dynamics of IBD. We focus on relation between T cells and cytokines in immune system that leads to IBD. In this paper, we propose a mathematical model describing IBD under considering immune mediated disorder by using ordinary differential equations. The existence and stability of the model are established, where an applicable basin of attraction are calculated and examined. Some numerical simulations are presented to verify the proposed results and as changing parameter values given by sensitivity analysis, we show how to change dynamic behaviors of the model.

• Smart Structures and Systems
• /
• v.8 no.4
• /
• pp.343-365
• /
• 2011

Structural system identification (SSI) is an inverse problem of difficult solution. Currently, difficulties lie in the development of algorithms which can cater to large size problems. In this paper, a parameter estimation technique based on evolutionary strategy is presented to overcome some of the difficulties encountered in using the traditional system identification methods in terms of convergence. In this paper, a non-traditional form of system identification technique employing evolutionary algorithms is proposed. In order to improve the convergence characteristics, it is proposed to employ immune algorithms which are proved to be built with superior diversification mechanism than the conventional evolutionary algorithms and are being used for several practical complex optimisation problems. In order to reduce the number of design variables, domain decomposition methods are used, where the identification process of the entire structure is carried out in multiple stages rather than in single step. The domain decomposition based methods also help in limiting the number of sensors to be employed during dynamic testing of the structure to be identified, as the process of system identification is carried out in multiple stages. A fifteen storey framed structure, truss bridge and 40 m tall microwave tower are considered as a numerical examples to demonstrate the effectiveness of the domain decomposition based structural system identification technique using immune algorithm.

• Journal of Institute of Control, Robotics and Systems
• /
• v.18 no.5
• /
• pp.465-470
• /
• 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.

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

• The Journal of the Society of Korean Medicine Diagnostics
• /
• v.19 no.2
• /
• pp.75-90
• /
• 2015

Objectives The purposes of this study was to develope a Qi gong exercise that suits characteristics of post mastectomy women, and to evaluate the effect of Qi gong exercise on immune response, blood circulation index, pulse wave parameter and heart rate variability. Methods This study was applied to total 35 post mastectomy women, including 17 for experiment group and 18 for control group. The Qi gong exercise was composed of total 24 times of 90 minutes per each time, twice a week, and 12 weeks and it was conducted by the oriental medicine professor who was an expert of Qi gong exercise. Results 1. Two group comparison revealed that the experimental group had significantly improved immune response(p<.021), HR(beats/min)(p<.001), ESV(ml/beat)(p=.038), ESI($ml/beat/m^2$)(p=.040), ECO (L/min)(p=.019), ECI($L/min/m^2$)(p=.023), ECRI($dyne^*sec/cm$)(p=.015), Left Kwan($div^3$)(p=.021), Right Kwan($div^3$)(p=.038), Mean HRV(cycle/min)(p<.001), SDNN(ms)(p=.043), RMSSD(ms)(p=.040), and TP(log $ms^2$)(p=.039). 2. Two group comparison revealed that the experimental group had significantly decreased ECR ($dyne^*sec^*cm^{-5}$) (p=.034), Left RAI(p=.044), Right RAI(p=.042), and pNN50(%)(p=.038). Conclusions These results from Qi gong exercise program can be used as basic data for development of health promotion program for Post Mastectomy Women.

• Journal of Power System Engineering
• /
• v.9 no.4
• /
• pp.194-201
• /
• 2005

In this paper, we propose an adaptive mechanism based on humoral immune algorithm and neural network identifier technique. It is also applied for an autonomous guided vehicle (AGV) system. 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 almost adjusted randomly. To slove this problem, we use the neural network identifier technique for modeling the plant humoral immune algorithm (HIA) which performs the parameter tuning of the considered model, respectively. Finally, the experimental results for control of steering and speed of AGV system illustrate the validity of the proposed control scheme. Also, these results for the proposed method show that it has better performance than other conventional controller design method such as PID controller.

• The Transactions of the Korean Institute of Electrical Engineers A
• /
• v.49 no.9
• /
• pp.433-445
• /
• 2000

In this paper, optimal tuning problem of power system stabilizer(PSS) using Immune Algorithm(IA) is investigated to improve power system dynamic stability. In proposed method, objective function is represented as antigens. An affinity calculation is embedded within the algorithm for determining the promotion or suppression of antibody. An antibody that most fits the antigen is considered as the solution to PSS tuning problem. The computaton performance by the proposed method is compared with Genetic Algorithm(GA). The porposed PSS using IA has been applied for two sample system, single-machine infinite bus system and multi-machine power system. The performance of the proposed PSS is compared with that of conventional PSS. It is shown that the proposed PSS tuned using immune algorithm is more robust than conventional PSS.

• Proceedings of the Korean Institute of Intelligent Systems Conference
• /
• 1998.03a
• /
• pp.127-130
• /
• 1998

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. Thus these features can be applied to decision making of optimal swarm behavior in dynamically changing environment. For the purpose of applying immune system to DARS, a robot is regarded as a B cell, each environmental condition as an antigen, a behavior strategy as an antibody and control parameter as a T-call respectively. The executing process of proposed method is as follows. When the environmental condition changes, a robot selects an appropriate behavior strategy. And its behavior strategy is stimulated and suppressed by other robot using communication. Finally much stimulated strategy is adopted as a swarm behavior strategy. This control scheme is based of clonal selection and idiotopic network hypothesis. And it is used for decision making of optimal swarm strategy. By T-cell modeling, adaptation ability of robot is enhanced in dynamic environments.