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In this paper, we propose a novel method to annotate the image of power grid objects (i.e., the electric equipment, the workers with different behaviors). This method is based on the convolutional neural networks (CNN). First, we obtain the attribute list of the image under the multi-label networks. Second, we employ the attribute-specific segmentation model to annotate the image. In this paper, we build an image database for power grid objects which consists of a large number of images, such as the electric equipment and the workers with different behavior. The experimental results demonstrate the good performance of the proposed method.

Nowadays, Device-to-Device (D2D) communication in LTE system has been proposed as a possible candidate for vehicle-to-vehicle(V2V) communication. As V2V communications share the same licensed frequency spectrum pool with typical UEs communications in one LTE cell, efficient radio resource allocation is a challenging issue. In this paper, we proposed a traffic flow based dynamic resource allocation algorithm for vehicular communication networks, which could make radio resource to be utilized in a more efficient way with a flexible node-grouping algorithm. The simulation results show, compared with random allocation and greedy allocation algorithms, the proposed algorithm can significantly reduce running time and significantly improve spectrum utilization efficiency.