Motor task variation induces structural learning.

When we have learned a motor skill, such as cycling or ice-skating, we can rapidly generalize to novel tasks, such as motorcycling or rollerblading [1-8]. Such facilitation of learning could arise through two distinct mechanisms by which the motor system might adjust its control parameters. First, fast learning could simply be a consequence of the proximity of the original and final settings of the control parameters. Second, by structural learning [9-14], the motor system could constrain the parameter adjustments to conform to the control parameters' covariance structure. Thus, facilitation of learning would rely on the novel task parameters' lying on the structure of a lower-dimensional subspace that can be explored more efficiently. To test between these two hypotheses, we exposed subjects to randomly varying visuomotor tasks of fixed structure. Although such randomly varying tasks are thought to prevent learning, we show that when subsequently presented with novel tasks, subjects exhibit three key features of structural learning: facilitated learning of tasks with the same structure, strong reduction in interference normally observed when switching between tasks that require opposite control strategies, and preferential exploration along the learned structure. These results suggest that skill generalization relies on task variation and structural learning.

Shallow water equations: Magic and limitations

D Liang from Cambridge University explains the shallow water equations and their applications to the dam-break and other steep-fronted flow modeling. They assume that the horizontal scale of the flow is much greater than the vertical scale, which means the flow is restricted within a thin layer, thus the vertical momentum is insignificant and the pressure distribution is hydrostatic. The left hand sides of the two momentum equations represent the acceleration of the fluid particle in the horizontal plane. If the fluid acceleration is ignored, then the two momentum equations are simplified into the so-called diffusion wave equations. In contrast to the SWEs approach, it is much less convenient to model floods with the Navier-Stokes equations. In conventional computational fluid dynamics (CFD), cumbersome treatments are needed to accurately capture the shape of the free surface. The SWEs are derived using the assumptions of small vertical velocity component, smooth water surface, gradual variation and hydrostatic pressure distribution.

Fast O2 measurement using modified UEGO sensors in the intake and exhaust of a diesel engine

Recent work has investigated the use of O2 concentration in the intake manifold as a control variable for diesel engines. It has been recognised as a very good indicator of NOX emissions especially during transient operation, however, much of the work is concentrated on estimating the O2 concentration as opposed to measuring it. This work investigates Universal Exhaust Gas Oxygen (UEGO) sensors and their potential to be used for such measurements. In previous work it was shown that these sensors can be operated in a controlled pressure environment such that their response time is of the order 10ms. In this paper, it is shown how the key causes of variation (and therefore potential sources of error) in sensor output, namely, pressure and temperature are largely mitigated by operating the sensors in such an environment. Experiments were undertaken on a representative light duty diesel engine using modified UEGO sensors in the intake and exhaust system. Results from other fast emissions measuring equipment are also shown and it is seen that the UEGO sensors are capable of giving an accurate measurement of O2 and EGR. Copyright © 2013 SAE International.