The large range of deformations of ionic polymer metal composites (IPMCs) has often been proposed as a key advantage of these soft active materials. Nevertheless, many applications in soft robotics still cannot be addressed by current IPMC technology, demanding an even wider deformation range. Here, we empirically demonstrate the feasibility of integrating electrostatic actuation to enhance IPMC deformations. Through the use of external contactless electrodes, an electrostatic pressure is generated on the IPMC, thereby magnifying the deformation elicited by the small voltage applied across its electrodes. A mathematical model is established to predict the onset of the pull-in instability, which defines when electrostatic actuation can be effectively utilized to enhance IPMC performance.
Flexural elastic waves and sound in solids are of great interest in wide-ranging contexts such as ultrasound in plates, geophysics, ocean engineering, aerospace and automotive structures, and musical acoustics. Despite bending waves being the most important elastic waves for such surface structures, their propagation in the presence of the inevitable non-uniformity is poorly understood. Here we show the branching and focusing behaviour of highly dispersive flexural waves travelling in elastic plates of non-uniform thickness. The thickness profile has isotropically correlated spatial randomness. The correlation length is much larger than the wavelength. The location of wave focusing shows a scaling relationship with randomness, which is consistent with those previously reported in other random media. We show this analytically and numerically. This suggests a universality in the scaling between the location of wave focusing with randomness and the correlation length, regardless of the physics of the waves in question.
The vibration of a homogeneous circular membrane backed by two taut strings is shown to yield several harmonic overtones for a wide range of physical and geometric parameters. Such a membrane is present at each end of the barrel of an idakkā, an Indian snare drum well known for its rich musicality. The audio recordings of the musical drum are analyzed and a case is made for the strong sense of pitch associated with the drum. A computationally inexpensive model of the string-membrane interaction is proposed assuming the strings to be without inertia. The interaction essentially entails wrapping/unwrapping of the string around a curve on the deforming membrane unlike the colliding strings in Western snare drums. The range of parameters for which harmonicity is achieved is examined and is found to be conforming with what is used in actual drum playing and construction.
Unsupervised word alignments offer a lightweight and interpretable method to transfer labels from high-to low-resource languages, as long as semantically related words have the same label across languages. But such an assumption is often not true in industrial NLP pipelines, where multilingual annotation guidelines are complex and deviate from semantic consistency due to various factors (such as annotation difficulty, conflicting ontology, upcoming feature launches etc.); We address this difficulty by constraining the alignment model to remain consistent with both source and target annotation guidelines, leveraging posterior regularization and labeled examples. We illustrate the overall approach using IBM 2 (fast_align) as a base model, and report results on both internal and external annotated datasets. We measure consistent accuracy improvements on the MultiATIS++ dataset over AWESoME, a popular transformer-based alignment model, in the label projection task (+2.7% at word-level and +15% at sentence-level), and show how even a small amount of target language annotations helps substantially.
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