An Adaptive Vehicle Clustering Algorithm Based on Power Minimization in Vehicular Ad-Hoc Networks

In this paper, we propose an adaptive vehicle clustering algorithm based on fuzzy C-means algorithm, which aims at minimizing power consumption of the vehicles. Specifically, the proposed algorithm firstly dynamically allocates the computing resources of each virtual machine in the vehicle, according to the popularity of different virtualized network functions. The optimal clustering number to minimize the total energy consumption of vehicles is determined using the fuzzy C-means algorithm and the clustering head is selected based on vehicles moving direction, weighted mobility, and entropy. Simulation results are provided to confirm that the proposed algorithm can decrease the power consumption of vehicles while satisfying the vehicle delay requirement

Experimental investigation on the effect of singeing on cotton yarn properties (+Erratum)

In this study, combed ring spun and compact spun cotton yarns with different counts were selected from different textile mills to conduct singeing treatment. Yarn properties including hairiness, fineness, unevenness and tensile properties were tested after the treatment and the results were compared with those before treatment. Hairiness was greatly removed after singeing, especially for short hairs with length less than 3 mm. Compact ring spun cotton yarns showed higher tex values and lower weight loss than combed ring spun cotton yarns under the same counts. Coefficient of variation of yarns increased slightly after singeing treatment. The unevenness of yarn was divided into two parts: basic unevenness of yarn body and hairiness unevenness, to explain the worse yarn evenness after singeing treatment. In this way, the effect of singeing on yarn properties was investigated thoroughly so as to improve yarn quality with less hairiness and good evenness