Non-contact handling in microassembly: Acoustical levitation

Microassembly is currently of the utmost importance in industry. Nevertheless, the classical assembly processes are no longer usable for very small components, typically ranging from 10  m to 10 mm, since usually neglected surface forces disturb the handling task by inducing adhesion between the component and the gripper. A promising alternative to tackle surface forces consists in levitating the handled component. The various advantages of this contactless handling method are reviewed here and justify the choice of this approach. Consequently, the numerous physical principles suitable for contactless handling are briefly described together with their limitations. The evaluation shows that acoustic levitation is best fitted in the case of microassembly. A classification of literature applications is presented hereafter with special focus on acoustic levitation. Finally, the most common models of acoustical levitation are inspected in a general way. The described models come within the scope of non-linear acoustics.     

Analysis of electrocardiograms during atrial fibrillation

The research discussed in this article is motivated by the search for an optimal classification of the different types of atrial fibrillation (AF) on the basis of recorded atrial signals. This would facilitate the selection of an optimal therapy. This article focuses on the biophysical models of the genesis of ECG waveforms during AF. The model of the electric activity of the atria was found to have sufficient realism to be used to describe the electric sources during AF. The inclusion of the volume conduction model resulted in electrocardiographic signals that are in all aspects similar to those observed clinically. The model is currently applied to solve various problems related to optimal signal preprocessing and extraction of features in AF signals for the classification of AF abnormalities. The biophysical model of the atrial activity is an essential element in this research, since it is capable of describing the electric source specifications derived from different hypotheses relating to the etiology of AF     

A theoretical framework for gain scheduling

The weighted incremental norm approach was originally introduced as a natural framework for extending well‐known H_∞ linear control concepts into the nonlinear context. In this paper, we investigate the numerous links between this new approach and the classical gain‐scheduling technique. Although based on heuristic rules, gain‐scheduled control is probably the most widespread nonlinear technique. In this paper, we point out that the control objectives of the gain‐scheduled controller design can be expressed as the weighted incremental norm minimization of a nonlinear operator. The result interest is twofold: it first provides a rigorous mathematical formulation of the gain‐scheduling problem. Furthermore, existing gain‐scheduling techniques can be interpreted as approximate solutions to the weighted incremental norm minimization of a nonlinear operator. Copyright © 2003 John Wiley & Sons, Ltd.     

Richard Lester Solomon (1918–1995): Obituary.

Memorializes Richard Solomon, one of the major learning theorists of the 2nd half of the 20th century. Solomon was named the 1st James M. Skinner University Professor of Science in 1975, was elected to the National Academy of Sciences, and was awarded, among other honors, the Warren Medal for research by the Society of Experimental Psychologists, the American Psychological Association Distinguished Scientific Contribution Award, and the American Psychological Foundation's Award for Distinguished Teaching in Experimental Psychology. He conducted research on a broad array of topics such as word frequency and perceptual defense, children's estimation of the size of tokens, hoarding behaviors in rats, and personality ratings and sociometric patterns. His work made a major contribution to the exploration of Pavlovian conditioning. Other topics of research focused on in Solomon's laboratories included avoidance learning, learned helplessness, punishment, and the opponent-process theory of acquired motivation. (PsycINFO Database Record (c) 2010 APA, all rights reserved)     

Effect of magnesium on the composition,microstructure and mechanical properties of carbon fibres

This work was undertaken in order to provide more detailed information on the chemical and mechanical behaviour of carbon fibres during the elaboration of graphite-magnesium composite materials. For this purpose, PAN-based T300, pitch-based P55 and P100 carbon fibres were isothermally heat treated, at temperatures ranging from 450 to 700 °C, under a saturated vapour pressure of magnesium. The composition, microstructure and tensile strength of the resulting samples were characterized by chemical and electron probe microanalysis, Raman spectrometry, X-ray diffraction and mechanical test of single filaments. From the results obtained, it has been concluded that highly graphitized fibres such as pitch-based P55 or P100 are not affected by long-time annealing in the presence of magnesium vapour, whereas impure and disorded fibres such as PAN-based T300 undergo some chemical and microstructural modifications decreasing their mechanical properties.     

Signal classification through multifractal analysis and complex domain neural networks

This paper describes a system capable of classifying stochastic self-affine nonstationary signals produced by nonlinear systems. The classification and the analysis of these signals are important because these are generated by many real-world processes. The first stage of the signal classification process entails the transformation of the signal into the multifractal dimension domain, through the computation of the variance fractal dimension trajectory (VFDT). Features can then be extracted from the VFDT using a Kohonen self-organizing feature map. The second stage involves the use of a complex domain neural network and a probabilistic neural network to determine the class of a signal based on these extracted features. The results of this paper show that these techniques can be successful in creating a classification system which can obtain correct classification rates of about 87% when performing classification of such signals without knowing the number of classes.     

Branching and pruning: An optimal temporal POCL planner based on constraint programming

A key feature of modern optimal planners such as graphplan and blackbox is their ability to prune large parts of the search space. Previous Partial Order Causal Link (POCL) planners provide an alternative branching scheme but lacking comparable pruning mechanisms do not perform as well. In this paper, a domain-independent formulation of temporal planning based on Constraint Programming is introduced that successfully combines a POCL branching scheme with powerful and sound pruning rules. The key novelty in the formulation is the ability to reason about supports, precedences, and causal links involving actions that are not in the plan. Experiments over a wide range of benchmarks show that the resulting optimal temporal planner is much faster than current ones and is competitive with the best parallel planners in the special case in which actions have all the same duration.¹