• Journal of Institute of Control, Robotics and Systems
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• v.6 no.12
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• pp.1079-1085
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• 2000

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). An immune system is the living bodys self-protection and self-maintenance system. these features can be applied to decision making of the optimal swarm behavior in a dynamically changing environment. For 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-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 robots using communication (immune network). Finally, much stimulated strategy 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 the optimal swarm strategy. Adaptation ability of the robot is enhanced by adding T-cell model as a control parameter in dynamic environments.

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

• Transactions of the Korean hydrogen and new energy society
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• v.24 no.4
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• pp.311-319
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• 2013

A clear understanding on cell voltage-reversal behavior due to local hydrogen starvation in a stack is of paramount importance to operate the fuel cell vehicle (FCV) stably since it affects significantly the cell performance and durability. In the present study, a novel experimental method to simulate the local cell voltage-reversal behavior caused by local hydrogen starvation, which typically occurs only one or several cells out of several hundred cells in a stack of FCV, has been proposed. Contrary to the conventional method of overall fuel starvation, the present method of local hydrogen starvation caused the local cell voltage-reversal behavior in a stack very well. Degradation of both membrane electrode assembly (i.e., pin-hole formation) and gas diffusion layer due to an excessive exothermic heat under voltage-reversal condition was also observed clearly.

• Journal of the Korean Institute of Intelligent Systems
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• v.11 no.7
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• pp.591-597
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• 2001

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 B-cell, each environmental condition 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, 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 school is based on 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.

• Proceedings of the KIEE Conference
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• 2005.07b
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• pp.1722-1724
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• 2005

The focus of this paper is to develop a mathematical model for investigating the dynamic performance of a polymer electrolyte membrane fuel cell. The model in this work is based on physical laws having clear significance in replicating the fuel cell system and can easily be used to set up different operational strategies. Simulation results display the transient behavior of the voltage within each single cell, and also within a number of such single cells combined into a fuel cell stack system. A linear as well as a nonlinear analysis of the polymer electrolyte membrane fuel cell system(PEMFC) has been discussed in order to present a complete and comprehensive view of this kind of modeling. Also, a comparison of the two kinds of analysis has been performed. Finally, the various characteristics of the fuel cell system are plotted in order to help us understand its dynamic behavior. Results indicate that there is a considerable amount of error in the modeling process if we use a linear model of the fuel cell. Thus, the nonlinearities present in the fuel cell system should be taken into account in order to obtain a better understanding of the dynamic behavior of the fuel cell system.

• 제어로봇시스템학회:학술대회논문집
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• 2000.10a
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• pp.386-386
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• 2000

Cell mapping is a powerful computational technique for analyzing the global behavior of nonlinear dynamic systems. It simplifies the task of analyzing a continuous phase space by partitioning it into a finite number of disjoint cells and approximating system trajectories as cell transitions. A cell map for the system is then constructed based on the allowable control actions. Next search algorithms are employed to identify the optimal or near-optimal sequence(s) of control actions required to drive the system from each cell to the target cell by an "unravelling algorithm." Errors resulting from the cell center-point approximation could be reduced and eliminated by fuzzifying the bonders of cells. The dynamic system control method based on the cell mapping has been demonstrated for a motor control problem.l problem.

• The Korean Journal of Physiology and Pharmacology
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• v.26 no.5
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• pp.307-312
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• 2022

It is well known that dopamine transmission from the ventral tegmental area (VTA) modulates motivated behavior and reinforcement learning. Although dopaminergic neurons are the major type of VTA neurons, recent studies show that a significant proportion of the VTA contains GABAergic and type 2 vesicular glutamate transporter (VGLUT2)-positive neurons. The non-dopaminergic neurons are also critically involved in regulating motivated behaviors. Some VTA neurons appear to co-release two different types of neurotransmitters. They are VGLUT2-DA neurons, VGLUT2-GABA neurons and GABA-DA neurons. These co-releasing neurons show distinct features compared to the neurons that release a single neurotransmitter. Here, we review how VTA cell populations wire to the other brain regions and how these projections differentially contribute to motivated behavior through the distinct molecular mechanism. We summarize the activities, projections and functions of VTA neurons concerning motivated behavior. This review article discriminates VTA cell populations related to the motivated behavior based on the neurotransmitters they release and extends the classical view of the dopamine-mediated reward system.

• Proceedings of the Korean Operations and Management Science Society Conference
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• 1996.04a
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• pp.543-546
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• 1996

Recently, some research has been done to analyze the asymptotic behavior of queue length distribution in ATM (Asynchronous Transfer Mode) multiplexer. In this paper, we relate this asymptotic behavior with the asymptotic behavior of decreasing cell loss probability when the buffer capacity is increased. We find with reasonable assumptions that the asymptotic rate of queue length distribution is the same as that of decreasing cell loss probability. Even under different priority control schemes and traffic classes, we find that this asymptotic rate of the individual cell loss probability of each traffic class does not change. As a consequence, we propose the upper bound of cell loss probability of each traffic class when a priority control scheme is employed. This bound is computationally feasible in a real-time. The numerical examples will be provided to validate this finding.

• Corrosion Science and Technology
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• v.2 no.3
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• pp.109-116
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• 2003

It is well known that SSCC (sulfide stress corrosion cracking) is caused by drastic ingression of hydrogen during the service and accumulation of hydrogen near the potential crack initiation site in the material. It is important to characterize the hydrogen trapping behavior to evaluate the service performance of the high strength pipeline steels. In this study. the relationship between the hydrogen trapping behavior and SSCC susceptibility is evaluated in terms of alloy composition, microstructure and carbide behavior. The hydrogen trapping behavior was measured by electrochemical hydrogen permeation test cell (Devanathan cell). The SSCC susceptibility is evaluated by constant extension rate test and constant strain lest method. The hydrogen trapping behavior is affected greatly by microstructure and nature of carbide particles. The fine TiC, and NbC in the matrix of ferritic structure acts as strong irreversible trap sites whereas the bainitic structure acts as reversible trap site. The SSCC susceptibility is closely related to not only the hydrogen trapping behavior but also the loading condition. As the activity of reversible trap site increases, SSCC susceptibility decreases under static loading condition below yield strength, whereas SSCC susceptibility increases under dynamic loading condition or above yield strength. As the activity of irreversible trap site increases. SSCC susceptibility increases regardless of loading condition. It is cased by the mixed effect of dislocation on hydrogen diffusion and trapping behavior.

• 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.