Microstructural optimization of cellular acoustic materials

A microstructure based acoustic model is introduced, which can be used to optimize the microstructure of cellular materials and thus to obtain their optimal acoustic property. This acoustic model is an unsteady one which is appropriate in the limit of low Reynolds numbers. The model involves three elements. This first involves the propagation of acoustic waves passing the cylinders whose axes are aligned parallel to the direction of propagation. The second model relates to the propagation of acoustic waves passing the cylinders whose axes are aligned perpendicular to the direction of propagation. In both cases the interaction between adjacent cylinders is taken into account by considering the effect of polygonal periodic boundary conditions. As these two models are linear they are combined to give the characteristics of propagation at arbitrary incidence. The third model involves propagation passing spheres in order to represent the joints. Heat transfer is also included. These three models are then used to expand the design space and calculate the optimum cell structure for desired acoustic performance in a number of different applications. Moreover, the application fields are also analyzed.

Structure learning in action.

'Learning to learn' phenomena have been widely investigated in cognition, perception and more recently also in action. During concept learning tasks, for example, it has been suggested that characteristic features are abstracted from a set of examples with the consequence that learning of similar tasks is facilitated-a process termed 'learning to learn'. From a computational point of view such an extraction of invariants can be regarded as learning of an underlying structure. Here we review the evidence for structure learning as a 'learning to learn' mechanism, especially in sensorimotor control where the motor system has to adapt to variable environments. We review studies demonstrating that common features of variable environments are extracted during sensorimotor learning and exploited for efficient adaptation in novel tasks. We conclude that structure learning plays a fundamental role in skill learning and may underlie the unsurpassed flexibility and adaptability of the motor system.