• Journal of the Korean Institute of Intelligent Systems
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• v.17 no.2
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• pp.270-275
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• 2007

Motion capture technology is widely used for manufacturing animation since it produces high quality character motion similar to the actual motion of the human body. However, motion capture has a significant weakness due to the lack of an industry wide standard for archiving and retrieving motion capture data. In this paper, we propose a framework to integrate, store and retrieve heterogeneous motion capture data files effectively. We define a standard format for integrating different motion capture file formats. Our standard format is called MCML (Motion Capture Markup Language). It is a markup language based on XML (eXtensible Markup Language). The purpose of MCML is not only to facilitate the conversion or integration of different formats, but also to allow for greater reusability of motion capture data, through the construction of a motion database storing the MCML documents. We propose a fuzzy string searching method to retrieve certain MCML documents including strings approximately matched with keywords. The method can be used to retrieve desired series of frames included in MCML documents not entire MCML documents.

• International Journal of Fuzzy Logic and Intelligent Systems
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• v.2 no.1
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• pp.53-58
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• 2002

In the construction of successful fuzzy models for nonlinear systems, the identification of an optimal fuzzy model system is an important and difficult problem. Traditionally, sGA(simple genetic algorithm) has been used to identify structures and parameters of fuzzy model because it has the ability to search the optimal solution somewhat globally. But SGA optimization process may be the reason of the premature local convergence when the appearance of the superior individual at the population evolution. Therefore, in this paper we propose a new method that can yield a successful fuzzy model using VmGA(virus messy genetic algorithms). The proposed method not only can be the countermeasure of premature convergence through the local information changed in population, but also has more effective and adaptive structure with respect to using changeable length string. In order to demonstrate the superiority and generality of the fuzzy modeling using VmGA, we finally applied the proposed fuzzy modeling methodof a complex nonlinear system.

• Proceedings of the Korean Institute of Intelligent Systems Conference
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• 2000.11a
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• pp.157-160
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• 2000

This paper deals with the fuzzy modeling for the complex and uncertain system in which conventional and mathematical models may fail to give satisfactory results. mGA(messy Genetic Algorithm) has more effective and adaptive structure than sGA with respect to using changeable-length string and VEGA(Virus Evolution Genetic) Algorithm) can search the global and local optimal solution simultaneously with reverse transcription operator and transduction operator. Therefore in this paper, the optimal fuzzy model is obtained using Virus-messy Genetic Algorithm(Virus-mGA). In this method local information is exchanged in population so that population may sustain genetic divergence. To prove the surperioty of the proposed approach, we provide the numerical example.