In this paper, we designed a genetic algorithm based on a new charging path estimation model to obtain an efficient charging scheduling in a wireless rechargeable sensor network. Specifically, we first proposed a charging path estimation model, through which an expected cost of a scheduling charging path can be obtained. Based on this model, a genetic algorithm, which includes a traditional design of chromosome structure, selection, cross-over and mutation operation, supporting the charging scheduling for wireless charging vehicles is devised at the same time. We finally evaluate the performance of the proposed algorithm by extensive simulations. Simulation results show that the proposed algorithm is promising, can improve the performance of wireless rechargeable sensor network.
In recent years increasingly complex architectures for deep convolutional networks (DCNs) have been proposed to boost the performance on image recognition tasks. However, the gains in performance have come at a cost of substantial increase in computation and model storage resources. Implementation of quantized DCNs has the potential to alleviate some of these complexities and facilitate potential deployment on embedded hardware. In this paper, we experiment with three different quantizers for the implementation of DCNs. We denote them by min-max quantizer (MMQ), average quantizer (AQ) and histogram average quantizer (HAQ). We used a set of 8 different bit-widths (i.e one, two, …, eight bits) to quantize each DCN’s weight to run our experiments. Experimental results show that due to the non-destructive effect on the original distribution of HAQ, it outperforms both MMQ and AQ.
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