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  • Open Access


    Mechanics of Elastomer--Shim Laminates

    A. H. Muhr1

    CMC-Computers, Materials & Continua, Vol.5, No.1, pp. 11-30, 2007, DOI:10.3970/cmc.2007.005.011

    Abstract The mechanics of laminates of elastomer and shims of high modulus material are reviewed. Such structures are often built to provide engineering components with specified, and quite different, stiffnesses in different modes of deformation. The shims may either be rigid or flexible, flat or curved, but are usually close to inextensible, being made of a high modulus material such as steel. On the other hand, rubber has an exceptionally low shear modulus, about one thousandth of its bulk modulus, so that shear of the rubber layers and flexure of the high modulus layers (if thin)… More >

  • Open Access


    An Empirical Comparison on Multi-Target Regression Learning

    Xuefeng Xi1, Victor S. Sheng1,2,*, Binqi Sun2, Lei Wang1, Fuyuan Hu1

    CMC-Computers, Materials & Continua, Vol.56, No.2, pp. 185-198, 2018, DOI:10.3970/cmc.2018.03694

    Abstract Multi-target regression is concerned with the simultaneous prediction of multiple continuous target variables based on the same set of input variables. It has received relatively small attention from the Machine Learning community. However, multi-target regression exists in many real-world applications. In this paper we conduct extensive experiments to investigate the performance of three representative multi-target regression learning algorithms (i.e. Multi-Target Stacking (MTS), Random Linear Target Combination (RLTC), and Multi-Objective Random Forest (MORF)), comparing the baseline single-target learning. Our experimental results show that all three multi-target regression learning algorithms do improve the performance of the single-target More >

  • Open Access


    The Spring-Damping Regularization Method and the Lie-Group Shooting Method for Inverse Cauchy Problems

    Chein-Shan Liu1,2, Chung-Lun Kuo3, Dongjie Liu4

    CMC-Computers, Materials & Continua, Vol.24, No.2, pp. 105-124, 2011, DOI:10.3970/cmc.2011.024.105

    Abstract The inverse Cauchy problems for elliptic equations, such as the Laplace equation, the Poisson equation, the Helmholtz equation and the modified Helmholtz equation, defined in annular domains are investigated. The outer boundary of the annulus is imposed by overspecified boundary data, and we seek unknown data on the inner boundary through the numerical solution by a spring-damping regularization method and its Lie-group shooting method (LGSM). Several numerical examples are examined to show that the LGSM can overcome the ill-posed behavior of inverse Cauchy problem against the disturbance from random noise, and the computational cost is More >

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