• Journal of the Society of Cosmetic Scientists of Korea
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• v.38 no.3
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• pp.219-224
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• 2012

European Union prohibited the marketing of cosmetic products containing constituents that have been examined through animal experiments. Thus, non-animal test models are needed to replace animal experiments. The reconstructed skin models are important as a test system for cosmetic, pharmaceutical, and medical device safety testing. In the present study, we tried to develop an optimal skin equivalent model containing basement membrane and epidermis. For this purpose, we used mesenchymal stem cells (MSCs) and/or preadipocytes as well as fibroblasts as the dermal matrix cells. The formation of basement membrane and epidermis was verified by immunohistochemical stains. Among various models, the epidermis was thickest when MSCs were used in the dermal matrix. Furthermore, PCNA and involucrin distribution showed that dermal matrix with MSCs resembled human skin. Therefore, skin equivalents with MSCs could be developed as a non-animal test model to replace animal experiments.

• 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.