In today’s era when the original data is huge, useful information can only be extracted from the data through calculations and corresponding processing. Nowadays, the computing power of wireless access points, laptops, and mobile phones is comparable to that of computers more than a decade ago. However, most of the time, these computing resources are often idle, which greatly causes a waste of resources. In the Internet of Vehicles scenario, this problem has always been the focus of attention by scholars. The V2V (Vehicle to Vehicle) technology has solved this problem to a certain extent. This article considers the link failure time LET (Link Expiration Time) and the limit of the task allowable delay. Most of the equipment on the road uses external power supply and user equipment is powered by energy. If the transmission of the task cannot be completed within the effective time of the link, it will cause more energy consumption. This article aims at link failure time and task allowable delay Under the limitation of V2V multi-hop task, the total energy loss in the unloading process of the V2V multi-hop task is minimized.
Neural networks have the problems of excessive number of parameters and computation due to data noise and redundant filters, which limits the application of neural networks. A feature map pruning method using channel attention (FPC) is proposed to address the problem of excessive accuracy loss of existing pruning methods. Experiments show that the feature graph pruning method using channel attention has better pruning effect with the same accuracy loss.
In view of the low recognition accuracy of traditional weather recognition methods and the serious imbalance in the number of weather images in various categories in the weather image data set, a weather image classification algorithm based on generative adversarial network and transfer learning is proposed to solve the above problems. The proposed method mainly includes two parts: data equalization based on generative adversarial network and image classification based on transfer learning. This paper uses generative adversarial network to amplify the data of a few categories of weather images, so as to obtain a relatively balanced weather image data set.The method of transfer learning is used to fine-tune the model to realize the classification of weather images. The experimental results show that the method proposed in this paper is better than the traditional method, effectively solving the problem of low model classification accuracy caused by the imbalance of training samples, and realizing the recognition and classification of four types of weather images: sunny, foggy, rainy, and snowy.
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