Relationship between load/unload response ratio and damage variable and its application

The physics-based parameter: load/unload response ratio (LURR) was proposed to measure the proximity of a strong earthquake, which achieved good results in earthquake prediction. As LURR can be used to describe the damage degree of the focal media qualitatively, there must be a relationship between LURR and damage variable (D) which describes damaged materials quantitatively in damage mechanics. Hence, based on damage mechanics and LURR theory, taking Weibull distribution as the probability distribution function, the relationship between LURR and D is set up and analyzed. This relationship directs LURR applied in damage analysis of materials quantitatively from being qualitative earlier, which not only provides the LURR method with a more solid basis in physics, but may also give a new approach to the damage evaluation of big scale structures and prediction of engineering catastrophic failure. Copyright (c) 2009 John Wiley & Sons, Ltd.

Relationship between load/unload response ratio and damage variable and its application

The physics-based parameter: load/unload response ratio (LURR) was proposed to measure the proximity of a strong earthquake, which achieved good results in earthquake prediction. As LURR can be used to describe the damage degree of the focal media qualitatively, there must be a relationship between LURR and damage variable (D) which describes damaged materials quantitatively in damage mechanics. Hence, based on damage mechanics and LURR theory, taking Weibull distribution as the probability distribution function, the relationship between LURR and D is set up and analyzed. This relationship directs LURR applied in damage analysis of materials quantitatively from being qualitative earlier, which not only provides the LURR method with a more solid basis in physics, but may also give a new approach to the damage evaluation of big scale structures and prediction of engineering catastrophic failure. Copyright (c) 2009 John Wiley & Sons, Ltd.

Emittance estimation by an ion optical element with variable focusing strength and a viewing target

The emittance of an extracted ion beam can be estimated to first order by a series of three linear independent profile measurements. This estimation is restricted to the evaluation of an upper limit of the emittance value for a homogeneous, nonfilamented beam. The beam is assumed to be round, respectively elliptical, without any structure of the intensity distribution, no space charge has been assumed for the drifting beam, and the optics is assumed to be linear. Instead of using three different drift sections, a linear focusing element with three different focusing strengths can be used. Plotting the beam radius as function of focusing strength, three independent solutions can be used to calculate the Twiss parameters alpha, beta, and gamma and furthermore the emittance epsilon. Here we describe the measurements which have been performed with the SECRAL ion source at Institute of Modern Physics Lanzhou.

Supervised Gaussian Process Latent Variable Model for Dimensionality Reduction

The Gaussian process latent variable model (GP-LVM) has been identified to be an effective probabilistic approach for dimensionality reduction because it can obtain a low-dimensional manifold of a data set in an unsupervised fashion. Consequently, the GP-LVM is insufficient for supervised learning tasks (e. g., classification and regression) because it ignores the class label information for dimensionality reduction. In this paper, a supervised GP-LVM is developed for supervised learning tasks, and the maximum a posteriori algorithm is introduced to estimate positions of all samples in the latent variable space. We present experimental evidences suggesting that the supervised GP-LVM is able to use the class label information effectively, and thus, it outperforms the GP-LVM and the discriminative extension of the GP-LVM consistently. The comparison with some supervised classification methods, such as Gaussian process classification and support vector machines, is also given to illustrate the advantage of the proposed method.