Curvature analysis and visualization for functions defined on Euclidean spaces or surfaces

Visualization of a scalar-valued function ƒ defined on Euclidean n-space ⁿ is often based on its graph-hypersurface . Particularly for curvature interrogation, it is natural to equip ^n-1 with a so-called isotropic metric and use isotropic measures of the graph instead of Euclidean invariants. The ideas are extended to functions defined on surfaces in ³. We present the central formulae for a curvature analysis of functions defined on surfaces. It is shown how to use them for visualization purposes and as a mathematical basis for the construction of interpolating or approximating functions on surfaces.     

Fixed bed reactor kalman filtering and optimal control—II Experimental investigation of discrete time case with stochastic disturbances

Discrete time linear quadraticoptimal control is investigated experimentally on a catalytic fixed-bed reactor with stochastic upstream disturbances. It is demonstrated that the discrete time variance evaluated with time intervals equal to the control intervals oscillates along the reactor axis due to the time discretization of the control action. However, if the variance is evaluated with much shorter time intervals than used for control, the oscillations are not present. The control system behaviour is demonstrated with both Gaussian and step disturbances.     

Application of a new automatic lattice orientation measurement technique to polycrystalline aluminum

The power of a fully automatic system for measuring lattice orientation in the scanning electron microscope is demonstrated. The system couples automatic analysis of electron backscattering diffraction patterns with precise movement of a computer-controlled stage. Over 100 000 lattice orientations were measured on 40% channel-die compressed aluminum. The texture calculated from these measurements closely matched the expected rolling texture. The value of this technique for exploring the relationships between lattice orientation and morphology in polycrystalline microstructures is demonstrated by reconstructing grain boundary maps from the measurements and using color to visualize particular orientations. It is also shown that the average grain shape for a particular orientation can be recovered from the measurements using statistical measures of spatial orientation correlation.     

Industrielle aspekte der suspensionspolymerisation

Suspension polymerization is one of the most important processes for the production of high polymers. Great demands are made on the technical applicability of the polymers. In order to meet these requirements the distribution of the particle sizes and the morphology of the particles are of great importance. The size and the structure of the particles depend in a complicated interaction on factors such as the chemical structure of the suspending agent, the concentration of the suspending agent and on the agitation conditions. The interdependence of these factors have been and still are explained differently. Important mechanistic and process engineering aspects of the suspension polymerization are demonstrated by the polymerization of vinylchloride and styrene.     

Ventilation of passenger car cabins: comparison of experimental data and numerical calculations

The complex flow in the interior of a passenger car cabin is measured and calculated in a Reynolds number range of practical importance. It turns out that the mean flow field compares quite well, even in minor details. But, experiments and calculations deviate considerably as far as turbulent quantities are concerned. The situation is analysed by a closer look to the physics of the flow field. From this, conclusions are drawn why the k−ɛ model applied here might be inappropriate for the flow field under consideration.     

Rayleigh Mixture Model-Based Hidden Markov Modeling and Estimation of Noise in Noisy Speech Signals

In this paper, we propose a new statistical model for noise periodogram modeling and estimation. The proposed model is a hidden Markov model (HMM) with a Rayleigh mixture model (RMM) in each state. For this new model, we derive an expectation-maximization (EM) training algorithm and a minimum mean-square error (MMSE) noise periodogram estimator. It is shown that when compared to the Gaussian mixture model (GMM)-based HMM, the RMM-based HMM has less computationally complex EM iterations and gives a better fit of the noise periodograms when the mixture models has a low number of components. Furthermore, we propose a specialization of the proposed model, which is shown to provide better MMSE noise periodogram estimates than any other of the tested HMM initializations for cyclo-stationary noise types