Plucked piezoelectric bimorphs for knee-joint energy harvesting: modelling and experimental validation

The modern drive towards mobility and wireless devices is motivating intensive research in energy harvesting technologies. To reduce the battery burden on people, we propose the adoption of a frequency up-conversion strategy for a new piezoelectric wearable energy harvester. Frequency up-conversion increases efficiency because the piezoelectric devices are permitted to vibrate at resonance even if the input excitation occurs at much lower frequency. Mechanical plucking-based frequency up-conversion is obtained by deflecting the piezoelectric bimorph via a plectrum, then rapidly releasing it so that it can vibrate unhindered; during the following oscillatory cycles, part of the mechanical energy is converted into electrical energy. In order to guide the design of such a harvester, we have modelled with finite element methods the response and power generation of a piezoelectric bimorph while it is plucked. The model permits the analysis of the effects of the speed of deflection as well as the prediction of the energy produced and its dependence on the electrical load. An experimental rig has been set up to observe the response of the bimorph in the harvester. A PZT-5H bimorph was used for the experiments. Measurements of tip velocity, voltage output and energy dissipated across a resistor are reported. Comparisons of the experimental results with the model predictions are very successful and prove the validity of the model

Pizzicato excitation for wearable energy harvesters

A new technique based on the plucking of flexible piezoelectric material can be used to boost energy harvested to power portable electronic devices

A Biologically Plausible Learning Rule for Deep Learning in the Brain

Researchers have proposed that deep learning, which is providing important progress in a wide range of high complexity tasks, might inspire new insights into learning in the brain. However, the methods used for deep learning by artificial neural networks are biologically unrealistic and would need to be replaced by biologically realistic counterparts. Previous biologically plausible reinforcement learning rules, like AGREL and AuGMEnT, showed promising results but focused on shallow networks with three layers. Will these learning rules also generalize to networks with more layers and can they handle tasks of higher complexity? We demonstrate the learning scheme on classical and hard image-classification benchmarks, namely MNIST, CIFAR10 and CIFAR100, cast as direct reward tasks, both for fully connected, convolutional and locally connected architectures. We show that our learning rule - Q-AGREL - performs comparably to supervised learning via error-backpropagation, with this type of trial-and-error reinforcement learning requiring only 1.5-2.5 times more epochs, even when classifying 100 different classes as in CIFAR100. Our results provide new insights into how deep learning may be implemented in the brain