Electromagnetic torque ripple and copper losses reduction in permanent magnet synchronous machines

This paper presents a method for electromagnetic torque ripple and copper losses reduction in (non-sinusoidal or trapezoidal) surface-mount permanent magnet synchronous machines (SM-PMSM). The method is based on an extension of classical dq transformation that makes it possible to write a vectorial model for this kind of machine (with a non-sinusoidal back-EMF waveform). This model is obtained by the application of that transformation in the classical machine per-phase model. That transformation can be applied to machines that have any type of back-EMF waveform, and not only trapezoidal or square-wave back-EMF waveforms. Implementation results are shown for an electrical converter, using the proposed vectorial model, feeding a non-sinusoidal synchronous machine (brushless DC motor). They show that the use of this vectorial mode is a way to achieve improvements in the performance of this kind of machine, considering the electromagnetic torque ripple and copper losses, if compared to a drive system that employs a classical six-step mode as a converter. Copyright (C) 2011 John Wiley & Sons, Ltd.

Relationship between body size and flying-related structures in Neotropical social wasps (Polistinae, Vespidae, Hymenoptera)

Body size influences wing shape and associated muscles in flying animals which is a conspicuous phenomenon in insects, given their wide range in body size. Despite the significance of this, to date, no detailed study has been conducted across a group of species with similar biology allowing a look at specific relationship between body size and flying structures. Neotropical social vespids are a model group to study this problem as they are strong predators that rely heavily on flight while exhibiting a wide range in body size. In this paper we describe the variation in both wing shape, as wing planform, and mesosoma muscle size along the body size gradient of the Neotropical social wasps and discuss the potential factors affecting these changes. Analyses of 56 species were conducted using geometric morphometrics for the wings and lineal morphometrics for the body; independent contrast method regressions were used to correct for the phylogenetic effect. Smaller vespid species exhibit rounded wings, veins that are more concentrated in the proximal region, larger stigmata and the mesosoma is proportionally larger than in larger species. Meanwhile, larger species have more elongated wings, more distally extended venation, smaller stigmata and a proportionally smaller mesosoma. The differences in wing shape and other traits could be related to differences in flight demands caused by smaller and larger body sizes. Species around the extremes of body size distribution may invest more in flight muscle mass than species of intermediate sizes.

Combining meta-learning and search techniques to select parameters for support vector machines

Support Vector Machines (SVMs) have achieved very good performance on different learning problems. However, the success of SVMs depends on the adequate choice of the values of a number of parameters (e.g., the kernel and regularization parameters). In the current work, we propose the combination of meta-learning and search algorithms to deal with the problem of SVM parameter selection. In this combination, given a new problem to be solved, meta-learning is employed to recommend SVM parameter values based on parameter configurations that have been successfully adopted in previous similar problems. The parameter values returned by meta-learning are then used as initial search points by a search technique, which will further explore the parameter space. In this proposal, we envisioned that the initial solutions provided by meta-learning are located in good regions of the search space (i.e. they are closer to optimum solutions). Hence, the search algorithm would need to evaluate a lower number of candidate solutions when looking for an adequate solution. In this work, we investigate the combination of meta-learning with two search algorithms: Particle Swarm Optimization and Tabu Search. The implemented hybrid algorithms were used to select the values of two SVM parameters in the regression domain. These combinations were compared with the use of the search algorithms without meta-learning. The experimental results on a set of 40 regression problems showed that, on average, the proposed hybrid methods obtained lower error rates when compared to their components applied in isolation.