Advanced Research on Computer Education, Simulation and by Jin Li (auth.), Song Lin, Xiong Huang (eds.)

By Jin Li (auth.), Song Lin, Xiong Huang (eds.)

This two-volume set (CCIS one hundred seventy five and CCIS 176) constitutes the refereed complaints of the foreign convention on machine schooling, Simulation and Modeling, CSEM 2011, held in Wuhan, China, in June 2011. The 148 revised complete papers awarded in either volumes have been conscientiously reviewed and chosen from a good number of submissions. The papers hide concerns akin to multimedia and its program, robotization and automation, mechatronics, computing device schooling, glossy schooling study, keep an eye on platforms, facts mining, wisdom administration, picture processing, conversation software program, database know-how, man made intelligence, computational intelligence, simulation and modeling, agent dependent simulation, biomedical visualization, gadget simulation & modeling, object-oriented simulation, net and safeguard visualization, imaginative and prescient and visualization, coupling dynamic modeling idea, discretization approach , and modeling strategy research.

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Extra resources for Advanced Research on Computer Education, Simulation and Modeling: International Conference, CESM 2011, Wuhan, China, June 18-19, 2011. Proceedings, Part I

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We can consider the boxes in Dots-and-Boxes as the coins in the Strings-and-Coins, so we get the corresponding relationship of boxes and coins. 16 z D. Li, S. Li, and H. Bao Edges correspond with strings The initial chess board of Dots-and-Boxes has no connected edges at all, but in Stings-and-Coins chess board, each coin is connected with four strings, we can consider the edges of the Dots-and-Boxes game as strings, but it is seen that placing an edge in the Dots game position separates two boxes, so this has exactly the effect of cutting a string in the corresponding Strings-and-Coins position.

The combination of GA and EM is known as genetic EM algorithm [6]. The procedure of the genetic EM algorithm is shown as following. Initial: oldChrom, Emrate,bestFit,oldFit; while (bestFit-oldFit) > EMRate fitV = Evaluation (oldChrom,R); newChrom = Selection(oldChrom, fitV,ps); newChrom = Crossover(newChrom, k, pc); newChrom = Mutation(newChrom, pm); newChrom = EM(newChrom,R); oldFit = bestFit; bestFit = max(fitV); newChrom = sortByMiu(newChrom); oldChrom = newChrom; end The fitness function used in the genetic EM algorithm is the log likelihood function of Θ defined in equation (5) and calculation stops when improvement of the fitness function value decreases below a given threshold.

When it comes to using DWT to Multi-scale decomposition, Mallat fast algorithm is most people's first choice. This algorithm requires a two-dimensional scaling function ϕ ( x, y ) and three-dimensional wavelet functions ψ 1 ( x , y ) , ψ 2 ( x, y ) and ψ 3 ( x, y ) . The Fourier Transform of scaling function has low-pass filter characteristic, while the Fourier Transform of wavelet function has high-pass filter characteristic. Constructing low-pass filter and high-pass filter by the scaling function and wavelet function[7], and then you can decompose the image.

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