Aiming at the characteristics of the periodic stacking structure of a lithium-ion battery core and the corresponding relationship between the air-coupled ultrasonic transmission initial wave and the wave propagation mode in each layer medium of a lithium-ion battery, the homogenized finite element model of a lithium-ion battery was developed based on the theory of pressure acoustics and solid mechanics. This model provided a reliable method and basis for solving the visualization of ultrasonic propagation in a lithium-ion battery and the analysis of ultrasonic time-frequency domain characteristics. The finite element simulation analysis and experimental verification of a lithium-ion battery with a near-surface stomata defect, near-bottom stomata defect and middle-layer stomata defect were performed. The results showed that the air-coupled ultrasonic transmission signal can effectively characterize the stomata defect inside a lithium-ion battery. The energy of an air-coupled ultrasonic transmission signal is concentrated between 350–450 kHz, and the acoustic diffraction effect has an important influence on the effect of the ultrasonic and stomata defect. Based on the amplitude response characteristics of the air-coupled ultrasonic transmission wave in the stomata defect area, a C-scan of the lithium-ion battery was performed. The C-scan result verified that air-coupled ultrasonic testing technology can accurately and effectively detect the pre-embedded stomata defect and natural stomata defect in a lithium-ion battery, which is able to promote and expand the application of the technology in the field of electric energy security.

The finite models of honeycomb sandwich composite with intact and embedded debonding defects are constructed. The sound pressure in fluid domain and the stress strain problem in solid domain are related by acoustic-structure coupling method, which visually shows the propagation process and modal characteristics of the acoustic wave inside the honeycomb sandwich composite. The simulation results show that the transmission longitudinal wave T1 (transmission initial wave) can effectively characterize debonding defects of honeycomb sandwich composite. However, in the actual detection of honeycomb sandwich composite, there are some problems, such as poor Signal-to-noise ratio (SNR) of received signal, incognizable transmission initial wave. In order to solve these problems, this paper proposes to apply polyphase coded pulse compression technique to air-coupled ultrasonic testing system. The actual test results show that the SNR of received signal is effectively improved, the transmission initial wave can be effectively identified, and the compressed signal has a good response to debonding defect. The air-coupled ultrasonic testing C scan result of honeycomb sandwich composite verifies the rationality and correctness of the theoretical simulation and signal processing technique, which promotes industrial application of air-coupled ultrasonic testing technique in the aerospace field.

Air-coupled ultrasonic testing (ACUT) technique has been viewed as a viable solution in defect detection of advanced composites used in aerospace and aviation industries. However, the giant mismatch of acoustic impedance in air–solid interface makes the transmission efficiency of ultrasound low, and leads to poor signal-to-noise (SNR) ratio of received signal. The utilisation of signal-processing techniques in non-destructive testing is highly appreciated. This paper presents a wavelet filtering and phase-coded pulse compression hybrid method to improve the SNR and output power of received signal. The wavelet transform is utilised to filter insignificant components from noisy ultrasonic signal, and pulse compression process is used to improve the power of correlated signal based on cross-correction algorithm. For the purpose of reasonable parameter selection, different families of wavelets (Daubechies, Symlet and Coiflet) and decomposition level in discrete wavelet transform are analysed, different Barker codes (5–13 bits) are also analysed to acquire higher main-to-side lobe ratio. The performance of the hybrid method was verified in a honeycomb composite sample. Experimental results demonstrated that the proposed method is very efficient in improving the SNR and signal strength. The applicability of the proposed method seems to be a very promising tool to evaluate the integrity of high ultrasound attenuation composite materials using the ACUT.

Dicentric chromosome analysis is the gold standard for biological dose assessment. To enhance the efficiency of biological dose assessment in large-scale radiation catastrophes, automatic identification of dicentric chromosome images is a promising and objective method. In this paper, an automatic identification method for dicentric chromosome images using two-stage convolutional neural network is proposed based on Giemsa-stained automatic microscopic imaging. To automatically segment the adhesive chromosome masses, ak-means based adaptive image segmentation and watershed segmentation algorithm is applied. The first-stage CNN is used to identify the dicentric chromosome images from all the images and the second-stage CNN works to specifically identify the dicentric chromosome images. This two-stage CNN identification method can effectively detects chromosome images with concealed centromeres, poorly expanded and long-armed entangled chromosomes, and tricentric chromosomes. The novel two-stage CNN method has a chromosome identification accuracy of 99.4%, a sensitivity of 85.8% sensitivity, and a specificity of 99.6%, effectively reducing the false positive rate of dicentric chromosome. The analysis speed of this automatic identification method can be 20 times quicker than manual detection, providing a valuable reference for other image identification situations with small target rates.

Aiming at the problem of low efficiency of dicentric chromosome identification counting under the microscope, this paper presents a joint processing algorithm combining clustering and watershed. The method first uses clustering and watershed algorithm to segment the original chromosome image, and then identifies the individual chromosomes. The results show that when the equivalent width Y parameter is selected m = 1, n = 1, the true positive rate of dicentric chromosome identification is 76.6%, and positive predictive value is 76.6% in high dose, which is higher than the threshold algorithm for the true positive rate (63.9%) and positive predictive value (63.5%). The number of identified dicentric chromosomes can be used for dose estimation. When 500 cells are used for identification and dose estimation, the dose estimation pass rate can reach 80% in high dose. But for low dose, more cells should be used to identify to increase the dose estimation pass rate.