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Time-frequency aggregation is an important criterion for evaluating the performance of time-frequency analysis methods, and traditional time-frequency analysis methods do not match the requirements of time-frequency aggregation. Synchroqueezing Wavelet Transform (SWT) method, as a new method proposed recently, can improve the time-frequency aggregation effectively by compressing the Wavelet coefficients in frequency domain. This paper applies the SWT method to extract the time-frequency characteristics of the analog signal and compare the analysis results with that of the traditional methods. The bolt anchorage damage detection signals based on guided wave technology of magnetostrictive effect are analyzed as the measured data to verify the effect of SWT method. The results show that SWT method can accurately describe the frequency composition of signals and obtain a relatively concentrated time-frequency energy distribution, providing a new time-frequency analysis method for bolt anchoring quality NDT (Non Destructive Testing).

The problem of “Jingri (Chinese mad about Japanese Militarism psychologically)” among a few students reflects the lack of awareness of the history around the Anti-Japanese War, the absence of the history education about the War and the prominent historical identity crisis of the War. Through literature research, group discussion and expert interviews, a questionnaire on the historical cognition status of the War among vocational college students is preliminarily compiled. Through EFA, three factors are extracted, including 20 items, with a cumulative variance contribution rate of 67.069%. Under $\text{CFA},\ 2 0.9,\ \text{NFI} > 0.9, \text{TLI} > 0.9, \text{IFI} > 0.9$ , the fitting indexes are up to the standard, and the load of each factor is greater than 0.7. The Cronbach Alpha coefficient about three dimensions is 0.928, 0.906 and 0.908, and the Combined Reliability is 0.929, 0.907 and 0.909 respectively. The correlation coefficients of each dimension, dimension and the scale meet the requirements. The AVE is more than 0.5, which is greater than the correlation coefficients. Therefore, the scale has high reliability and validity. It has guiding significance for vocational colleges to carry out a survey of the status quo of students' historical cognition about the War and take targeted historical education of it.

Deep learning is a hot topic in the field of machine learning, which provides a new method for the nondestructive testing of bolt anchorage. Aiming at the importance of identifying the type of bolt defects, this paper proposes a DS-DBN-SVM(Differential Search-Deep belief network-Support vector machine) model for identifying the type of bolt anchorage defects. The DS algorithm is used to optimize the weights and thresholds of the DBN network. The original acceleration signal of the anchor is used as the input of the DS-DBN model to extract the high-level features of the signal. Finally, the SVM classffier is used for defect identification. To assess the effectiveness of the method, and compare it with the traditional classification methods. The results show that the defect recognition effect of this method is better than the traditional defect recognition method, and the recognition rate reaches 95.45%.

For the complexity and concealment of the construction, the bolt anchoring system has great difficulty in quality detection. Due to various reasons the traditional detection methods of quality are difficult to obtain effective results. In recent years, stress wave reflection has been the main method in quality nondestructive testing of rock bolts, but the reflection signal contains noise which brings great inconvenience for the signal analysis. Harmonic wavelet has a strict "box" spectrum characteristics, and by using the harmonic wavelet packet, signal can be decomposed into different frequency bands and the unwanted ingredients can be effectively eliminated, so the denoised signal could be obtained. In this paper, a reflection signal is decomposed by using the harmonic wavelet packet, and the appropriate decomposition layers and the frequency band width are chosen, the effective frequency band is extracted and reconstructed. Compared with the classical wavelet transform, experiment results show that the bolt parameters accuracy using harmonic wavelet packet is improved.