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The features of the asynchronous correlation between accident indices and the factors that influence accidents can provide an effective reference for warnings of coal mining accidents. However, what are the features of this correlation? To answer this question, data from the China coal price index and the number of deaths from coal mining accidents were selected as the sample data. The fluctuation modes of the asynchronous correlation between the two data sets were defined according to the asynchronous correlation coefficients, symbolization, and sliding windows. We then built several directed and weighted network models, within which the fluctuation modes and the transformations between modes were represented by nodes and edges. Then, the features of the asynchronous correlation between these two variables could be studied from a perspective of network topology. We found that the correlation between the price index and the accidental deaths was asynchronous and fluctuating. Certain aspects, such as the key fluctuation modes, the subgroups characteristics, the transmission medium, the periodicity and transmission path length in the network, were analyzed by using complex network theory, analytical methods and spectral analysis method. These results provide a scientific reference for generating warnings for coal mining accidents based on economic indices.

Abstract With the rapid development of the communication industry in the fifth generation and the advance towards the intelligent society of the sixth generation wireless networks, traditional methods are unable to meet the ever‐growing demands for higher data rates and improved quality of service. Deep learning (DL) has achieved unprecedented success in various fields such as computer vision, large language model processing, and speech recognition due to its powerful representation capabilities and computational convenience. It has also made significant progress in the communication field in meeting stringent demands and overcoming deficiencies in existing technologies. The main purpose of this article is to uncover the latest advancements in the field of DL‐based algorithm methods in the physical layer of wireless communication, introduce their potential applications in the next generation of communication mechanisms, and finally summarize the open research questions.

Abstract Pruning is showing huge potential for compressing and accelerating deep neural networks by eliminating redundant parameters. Along with more terminal chips integrated with AI accelerators for internet of things (IoT) devices, structured pruning is gaining popularity with the edge computing research area. Different from filter pruning and group-wise pruning, stripe-wise pruning (SWP) conducts pruning at the level of stripes in each filter. By introducing filter skeleton (FS) to each stripe, the existing SWP method sets an absolute threshold for the values in FS and removes the stripes whose corresponding values in FS could not meet the threshold. Starting with investigation into the process of stripe wise convolution, we use the statistical properties of the weights located on each stripe to learn the importance between those stripes in a filter and remove stripes with low importance. Our pruned VGG-16 achieves the existing results by a fourfold reduction in parameter with only 0.4% decrease in accuracy. Results from comprehensive experiments on IoT devices are also presented.