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With the rapid development of the railway industry, railway safety has received increasing attention. However, traditional methods for signal detection are limited by high cost and energy requirements. Data-driven methods are becoming increasingly popular for railway signal detection. In this paper, we propose a wavelet-based network model for railway track stiffness signal detection by combining deep neural networks and wavelet transform. In the training phase, we propose a wavelet-based convolutional neural network. We use wavelet coefficients to enhance the input features to improve the convolutional neural network. In the detection phase, we combine the sliding window algorithm and the voting algorithm to detect anomalous signals. Extensive experiments on general metrics demonstrate the effectiveness of our proposed model. The classification performance still outperforms the general network by 30–50% in terms of accuracy, precision and F1 score, which is a huge improvement. In addition, we test the model classification performance under different wavelet functions to validate the superiority of neural networks using wavelets. [ABSTRACT FROM AUTHOR]

Among various network compression methods, network pruning has developed rapidly due to its superior compression performance. However, the trivial pruning threshold limits the compression performance of pruning. Most conventional pruning threshold methods are based on well-known hard or soft techniques that rely on time-consuming handcrafted tests or domain experience. To mitigate these issues, we propose a simple yet effective general pruning threshold method from an optimization point of view. Specifically, the pruning threshold problem is formulated as a constrained optimization program that minimizes the size of each layer. More importantly, our pruning threshold method together with conventional pruning works achieves a better performance across various pruning scenarios on many advanced benchmarks. Notably, for the L 1 -norm pruning algorithm with VGG-16, our method achieves higher FLOPs reductions without utilizing time-consuming sensibility analysis. The compression ratio boosts from 34% to 53%, which is a huge improvement. Similar experiments with ResNet-56 reveal that, even for compact networks, our method achieves competitive compression performance even without skipping any sensitive layers. [ABSTRACT FROM AUTHOR]

Compressive sensing SAR imaging can significantly reduce the sampling rate and the amount of data,but it is essential only in the case where the reflection coefficients of SAR scene are sparse. This paper proposed a compressive sensing SAR imaging method based on wavelet packet sparse representation. The wavelet packet algorithm is used to choose the most sparse representation of the SAR scene by training the same type of SAR images. By solving for the minimum 1 l norm optimization, the SAR scene reflection coefficients can be reconstructed. Unambiguous SAR image can be produced with the proposed method even with fewer samples. SAR data simulation experiments demonstrate the efficiency of the proposed method.