Development of artificial mechanoreceptors capable of sensing and pre‐processing external mechanical stimuli is a crucial step toward constructing neuromorphic perception systems that can learn and store information. Here, bio‐inspired artificial fast‐adaptive (FA) and slow‐adaptive (SA) mechanoreceptors with synapse‐like functions are demonstrated for tactile perception. These mechanoreceptors integrate self‐powered piezoelectric pressure sensors with synaptic electrolyte‐gated field‐effect transistors (EGFETs) featuring a reduced graphene oxide channel. The FA pressure sensor is based on a piezoelectric poly(vinylidene fluoride‐trifluoroethylene) (P(VDF‐TrFE)) thin film, while the SA pressure sensor is enabled by a piezoelectric ionogel with the piezoelectric‐ionic coupling effect based on P(VDF‐TrFE) and an ionic liquid. Changes in post‐synaptic current are achieved through the synaptic effect of the EGFET by regulating the amplitude, number, duration, and frequency of tactile stimuli (pre‐synaptic pulses). These devices have great potential to serve as artificial biological mechanoreceptors for future artificial neuromorphic perception systems.
A new hybrid arithmetic optimization algorithm (AOA) associated with differential evolution (DE) is developed for truss optimization. The development is named as ADE with the goal of maintaining a balance between low computational cost and good solution quality. Besides, several limitations of the AOA, which include the inefficiency of the exploration phase and the inconvenient use of two parameters MOA and MOP to find the optimal solution, as well as how to overcome them are also discussed. In terms of AOA in ADE, the exploration phase is removed, and both math optimizer accelerated (MOA) and math optimizer probability (MOP) parameters are adjusted to be independent of the maximum number of iterations. Moreover, the exploitation phase is modified to exploration which helps to limit local solutions and maintain a balance between exploitation and exploration in ADE algorithm. Through a probability parameter, the DE with DE/best/1 operator is executed in ADE to improve exploitation capability as well as convergence rate. Four truss structures with continuous design variables are considered to demonstrate the performance of the current algorithm. The obtained results show that the developed algorithm has a low computational cost, indicating its computational efficiency.
We study static mechanical behavior of non-uniform hexagonal cross-sections for thin-walled functionally graded beams using a non-traditional computational approach based on artificial neural network. One of the main objectives of our approach is to save the computational cost for the optimization process, which is usually time-consuming by using traditional methods such as finite element method (FEM). In this study, 1000 data sets randomly generated by the FEM through iterations are used for the training process to get optimal weights. Based on these obtained optimal weights, beam behaviors under the changes in material distribution through thickness could then be predicted. In this model, the ANN's inputs are the gradation index of the power-law distribution and thickness, while the outputs are compliance and beam displacements. The computed results are verified against those derived from the FEM.
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