Various hyperplane classifiers using kernel feature spaces

In this paper we introduce a new family of hyperplane classifiers. But, in contrast to Support Vector Machines (SVM) - where a constrained quadratic optimization is used - some of the proposed methods lead to the unconstrained minimization of convex functions while others merely require solving a linear system of equations. So that the efficiency of these methods could be checked, classification tests were conducted on standard databases. In our evaluation, classification results of SVM were of course used as a general point of reference, which we found were outperformed in many cases

Modelling and simulation of a series wound DC motor with ANSYS

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Classification using a sparse combination of basis functions

Combinations of basis functions are applied here to generate and solve a convex reformulation of several well-known machine learning algorithms like certain variants of boosting methods and Support Vector Machines. We call such a reformulation a Convex Networks (CN) approach. The nonlinear Gauss-Seidel iteration process for solving the CN problem converges globally and fast as we prove. A major property of CN solution is the sparsity, the number of basis functions with nonzero coefficients. The sparsity of the method can effectively be controlled by heuristics where our techniques are inspired by the methods from linear algebra. Numerical results and comparisons demonstrate the effectiveness of the proposed methods on publicly available datasets. As a consequence, the CN approach can perform learning tasks using far fewer basis functions and generate sparse solutions

Improvement of Biogas Production by Bioaugmentation

Biogas production technologies commonly involve the use of natural anaerobic consortia of microbes. The objective of this study was to elucidate the importance of hydrogen in this complex microbial food chain. Novel laboratory biogas reactor prototypes were designed and constructed. The fates of pure hydrogen-producing cultures of Caldicellulosiruptor saccharolyticus and Enterobacter cloacae were followed in time in thermophilic and mesophilic natural biogas-producing communities, respectively. Molecular biological techniques were applied to study the altered ecosystems. A systematic study in 5-litre CSTR digesters revealed that a key fermentation parameter in the maintenance of an altered population balance is the loading rate of total organic solids. Intensification of the biogas production was observed and the results corroborate that the enhanced biogas productivity is associated with the increased abundance of the hydrogen producers. Fermentation parameters did not indicate signs of failure in the biogas production process. Rational construction of more efficient and sustainable biogas-producing microbial consortia is proposed

Asymptotic safety in the sine-Gordon model

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The Evaluation of the Parallel Bond’s Properties in DEM Modeling of Soils

In this study we conducted the sensitivity analysis of the parallel bond used in the discrete element method (DEM, 3D) based soil model. We researched those parameters which simulate real soil physique attributes. In our investigations we modeled the inhomogeneity by the differentiation in particle size, the soil moisture condition by the parallel bond’s radius R, the cracking tendency by the bonding stiffnesses kn, ks and the air phase by the pore volume of the particle block. We based the validation of the simulation on the results of the simple direct shear box test which were performed in laboratory environment. We analyzed the effect of micromechanical and macromechanical parameters, used in the modeled particle block, with the use of direct shear box and triaxial shear simulations. After the recalculation of micromechanical parameters (EC, ĒC, γ) we analyzed the effect of the adjustable macromechanical parameters (kn, ks ,λ, kn, ks, σC, τC) used in the block with triaxial shear simulation. Based on the comparison of the shear simulations’ results the accuracy of the recalculation of the parallel bond’s micro-macro parameters is proved by the good correlation of the Coulomb failure criterion lines (σ-τ)