• 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.

• Journal of the Korean GEO-environmental Society
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• v.11 no.8
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• pp.5-14
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• 2010

Recent researches on the behavior of gravelly soils have been focused mainly on the relative density or on the gravel content. And some researchers presented the liquefaction behavior based on the relative density whereas others based on the gravel content of gravelly soil. However the relative densities vary with gravel content and relative density is not enough to fully express the behavior of gravelly soils. Therefore in this research state parameter which considers void ratio and effective confining pressure is introduced and Steady State Line(SSL) of gravelly soils for various gravel content are determined by undrained triaxial tests in order to express the behavior of gravelly soils. From the research the position of SSL moved downward with gravel content. And the same density of soil showed dense sand behavior or loose sand behavior depending upon the confining pressure. Especially relative density 80% of gravelly soil showed loose sand behavior under high confining pressure. However the gravelly soils with similar state parameters showed similar stress behaviors. It can bee seen that state parameter is useful tool to evaluate undrained behavior of gravelly soils. Also state parameter and undrained strength showed good correlations.

• International Journal of Concrete Structures and Materials
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• v.5 no.1
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• pp.57-64
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• 2011

An extensive database has been constructed of reinforced concrete (RC) beam-column connection tests subjected to cyclic lateral loading. All cases within the database experienced joint shear failure, either in conjunction with or without yielding of longitudinal beam reinforcement. Using the experimental database, envelope curves of joint shear stress vs. joint shear strain behavior have been created by connecting key points such as cracking, yielding, and peak loading. Various prediction approaches for RC joint shear behavior are discussed using the constructed experimental database. RC joint shear strength and deformation models are first presented using the database in conjunction with a Bayesian parameter estimation method, and then a complete model applicable to the full range of RC joint shear behavior is suggested. An RC joint shear prediction model following a U.S. standard is next summarized and evaluated. Finally, a particular joint shear prediction model using basic joint shear resistance mechanisms is described and for the first time critically assessed.

• Environmental Engineering Research
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• v.25 no.3
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• pp.281-289
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• 2020

Environmental fate of ionizable organic pollutants such as perfluoroalkyl acids (PFAAs) are of increasing interest but has not been well understood because of uncertain values for parameters related with atmospheric interphase partitioning behavior. In the present study, not only the values for air-water partition coefficient (K_AW) and dissociation constant (pKa) of PFAAs were induced by adjusting to in situ measurements of air-water distribution coefficient between vapor phase and rainwater but also gas-particle partition coefficients were also estimated using three-phase partitioning model of ionizable organic pollutants, in situ measurements of PFAAs in aerosol and air vapor phase, and obtained parameter values. The pKa values of PFAAs we obtained were close to the minimum values suggested in literature except for perfluorooctane sulfonic acids, and COSMOtherm-modeled K_AW values were assessed to more appropriate among suggested values. When applying parameter values we obtained, it was predicted that air particle-associated fate and transport of PFAAs could be negligible and PFAAs could distribute ubiquitously along the transection from urban to rural region by pH-dependent phase transfer in air. Our study is expected to have some implications in prediction of the environmental redistribution of other ionizable organic compounds.

• 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.

• Steel and Composite Structures
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• v.34 no.4
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• pp.511-524
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• 2020

In this research, a simple four-variable trigonometric integral shear deformation model is proposed for the static behavior of advanced functionally graded (AFG) ceramic-metal plates supported by a two-parameter elastic foundation and subjected to a nonlinear hygro-thermo-mechanical load. The elastic properties, including both the thermal expansion and moisture coefficients of the plate, are also supposed to be varied within thickness direction by following a power law distribution in terms of volume fractions of the components of the material. The interest of the current theory is seen in its kinematics that use only four independent unknowns, while first-order plate theory and other higher-order plate theories require at least five unknowns. The "in-plane displacement field" of the proposed theory utilizes cosine functions in terms of thickness coordinates to calculate out-of-plane shear deformations. The vertical displacement includes flexural and shear components. The elastic foundation is introduced in mathematical modeling as a two-parameter Winkler-Pasternak foundation. The virtual displacement principle is applied to obtain the basic equations and a Navier solution technique is used to determine an analytical solution. The numerical results predicted by the proposed formulation are compared with results already published in the literature to demonstrate the accuracy and efficiency of the proposed theory. The influences of "moisture concentration", temperature, stiffness of foundation, shear deformation, geometric ratios and volume fraction variation on the mechanical behavior of AFG plates are examined and discussed in detail.

• 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.

• 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.

• Computers and Concrete
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• v.17 no.6
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• pp.703-721
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• 2016

A novel approach to modeling concrete behavior at the stage of its maturing is presented in this paper. This approach assumes that at any point in the structure, concrete is composed of a set of layers that are activated in time layer by layer, based on amount of released heat that is produced during process of the concrete's maturing. This allows one to assume that each newly created layer has nominal stiffness moduli and tensile/compressive strengths. Hence introduction of explicit stiffness moduli and tensile/compressive strength dependencies on time, or equivalent time state parameter, is not needed. Analysis of plain concrete (PC) and reinforced concrete (RC) structures, especially massive ones, subjected to any kind of straining in their early stage of existence, mostly due to external loads but especially by thermal loading and shrinkage, is the goal of the approach. In this article a simple elasto-plastic softening model with creep is used for each layer and a general layered model behavior is illustrated on one-dimensional (1D) examples.

• Transactions of the Korean Society of Mechanical Engineers A
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• v.27 no.6
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• pp.923-929
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• 2003

In this paper, an analysis of fatigue crack growth behavior from a statistical point of view has been carried out. Fatigue crack growth tests were conducted on sixteen pre-cracked compact tension (CT) specimens of the pressure vessel (SPV50) steel in controlled identical load and environmental conditions. The assessment of the statistical distribution of fatigue crack growth experimental data obtained from SPV50 steel was studied and also the correlation of the parameter C and m in the Paris-Erdogan law was discussed. The probability distribution function of fatigue crack growth life seems to follow the 3-parameter Weibull. The fatigue crack growth rate seems to follow the 3-parameter Weibull and the log-normal distribution. The coefficient of variation (COV) of fatigue crack growth life was observed to decrease as the crack grows. Fatigue crack growth rate data shows a normal distribution for both m and logC. A strong negative linear correlation exists between the coefficient C and the exponent m.