Home / Journals / CMES / Vol.108, No.1, 2015
Table of Content
  • Open Access

    ARTICLE

    Modular Model Library for Energy System in Lunar Vehicle

    Chen Chang1, Su Shaohui1, Chen Guojin1
    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 1-20, 2015, DOI:10.3970/cmes.2015.108.001
    Abstract For modeling and simulation of energy system in lunar vehicle, there are many special purpose tools along with their models, such as PSIM, EMTP/ATP, could be used. But the models in these tools lack of flexibility and are not open to the end-user. Models developed in one tool can’t be conveniently used in others because of the barriers among these simulators. Usually these models are expressed in an explicit state-space form and their topology gets lost and future extension and reuse of the model is almost impossible. In order to solve those problems, a flexible and extensible energy system model… More >

  • Open Access

    ARTICLE

    Elasto-Plastic MLPG Method for Micromechanical Modeling of Heterogeneous Materials

    Isa Ahmadi1, M.M. Aghdam2
    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 21-48, 2015, DOI:10.3970/cmes.2015.108.021
    Abstract In this study, a truly meshless method based on the meshless local Petrov-Galerkin method is formulated for analysis of the elastic-plastic behavior of heterogeneous solid materials. The incremental theory of plasticity is employed for modeling the nonlinearity of the material behavior due to plastic strains. The well-known Prandtl-Reuss flow rule of plasticity is used as the constitutive equation of the material. In the presented method, the computational cost is reduced due to elimination of the domain integration from the formulation. As a practical example, the presented elastic-plastic meshless formulation is employed for micromechanical analysis of the unidirectional composite material. A… More >

  • Open Access

    ARTICLE

    Extreme Learning Machines Based on Least Absolute Deviation and Their Applications in Analysis Hard Rate of Licorice Seeds

    Liming Yang1,2, Junjian Bai1, Qun Sun3
    CMES-Computer Modeling in Engineering & Sciences, Vol.108, No.1, pp. 49-65, 2015, DOI:10.3970/cmes.2015.108.049
    Abstract Extreme learning machine (ELM) has demonstrated great potential in machine learning and data mining fields owing to its simplicity, rapidity and good generalization performance. In this work, a general framework for ELM regression is first investigated based on least absolute deviation (LAD) estimation (called LADELM), and then we develop two regularized LADELM formulations with the l2-norm and l1-norm regularization, respectively. Moreover, the proposed models are posed as simple linear programming or quadratic programming problems. Furthermore, the proposed models are used directly to analyze the hard rate of licorice seeds using near-infrared spectroscopy data. Experimental results on eight different spectral regions… More >

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