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Aiming at the nonstationary characteristics of echo signal for a high-speed maneuvering target, a signal feature extraction method is proposed by combining the time-frequency analysis and convolution neural network, and then the automatic detection of radar moving target in a noisy environment is realized. Firstly, the echo signal is modelled as a more accurate Gaussian modulation-linear frequency modulation (GM-LFM) signal and converted into the time-frequency image by a second-order synchroextracting transform (SET2). Then, ridge extraction is applied to extract the maximum energy ridge from the time-frequency distribution, and the data set is constructed by the maximum energy ridge. Finally, the data set is input into AlexNet for training, and the deep-level features of echo signal are extracted to realize the automatic moving targets detection. Simulation results show that SET2 and RE can effectively enhance the time-frequency characteristics of echo signal under the noisy environment, and the detection accuracy and noise robustness of the proposed method are better than that of SET1 and smooth pseudo-Wigner–Ville distribution (SPWVD).

For subway connection channel projects built in coastal areas, freeze-thaw and compaction are two key factors that change the soil microstructure and affect engineering safety. Therefore, understanding the variation law of the microscopic pore structure of soft clay and freeze-thawed soft clay is crucial to the design and prediction of the serviceability of subway infrastructures. Considering this, to understand the effects of freeze-thaw and compaction coefficient on microscopic pore structures and fractal characteristics, a series of nuclear magnetic resonance (NMR) tests were performed on soft clay and freeze-thawed soft clay under different compaction coefficients. Based on the experimental data, a fractal theory was proposed to describe the fractal characteristics of the tested materials. Moreover, the effect of curing agents on the microscopic pore structure of soft clay was studied by adding two curing agents to the soft clay. The experimental results showed that compaction coefficients had a significant effect on the pore size distribution (PSD) of soft clay and freeze-thawed soft clay. As the compaction coefficient increased, the proportion of large pores decreased significantly, and the proportion of small and medium pores increased. Moreover, the compaction coefficient was found to have a significant effect on the fractal dimension (D) of soft clay and freeze-thawed soft clay whereby increases in compaction coefficients led to a trend of increased D and heterogeneity within soft clay and freeze-thawed soft clay. With the development of freezing front and the formation of ice lens, the pore structure of soft clay and the connection between soil particles are destroyed. The microscopic pore characteristics of the soil samples with an added solidifying agent were characterized by a significant increase in the number of small pores and a decrease in the number of large pores. This research can provide theoretical guidance for the construction of subway connection channel engineering projects.

Synthetic aperture radar automatic target recognition (SAR ATR) is a key technique of remote-sensing image recognition, which has many potential applications in the fields of military surveillance, national defense, civil application, and so on. With the development of science and technology, deep convolutional neural network (DCNN) has been widely applied for SAR ATR. However, it is difficult to use deep learning to train models with limited ray SAR images. To resolve this problem, we proposed an effectively lightweight attention mechanism CNN (AM-CNN) model for SAR ATR. Extensive experimental results on the Moving and Stationary Target Acquisition and Recognition (MSTAR) data set illustrate that the AM-CNN model can achieve a superior recognition performance, and the average recognition accuracy can reach 99.35% on the classification of 10 class targets. Compared with the traditional CNN and the state-of-the-art method, our model is significantly superior to improve performance and efficiency.