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Monopole time reversal is an advanced noise source localization technique, but its sidelobe level in localizing sound sources is greatly affected by noise, leading to a decrease in the accuracy of sound source localization under a low signal-to-noise ratio. To address this issue, this paper proposes a time-domain monopole time reversal technique that excludes autocorrelation to reduce the impact of noise on sound source localization results. Numerical simulation and experimental results show that, compared to conventional monopole time reversal techniques, the proposed method can effectively reduce the sidelobes in sound source localization results and achieve accurate sound source localization under −20 dB low signal-to-noise ratio conditions.

Infrared thermography technology, leveraging its unique ability to capture temperature features, has significantly improved the precision of high-temperature target localization. However, infrared imaging technology is limited by issues such as low image contrast, difficulty in distinguishing object categories, and limited image clarity. To enable intelligent detection of high-temperature objects that may cause fires in warehouses, this paper proposes an innovative method that integrates deep learning image segmentation with infrared and visible light image technology. We developed a new image segmentation model based on improved Fully Convolutional Networks and Deconvolutional Networks, introducing a batch normalization layer to accelerate convergence and employing the PReLU activation function to prevent neuron death, thereby enhancing convergence speed and accuracy. Through a feature dynamic image registration method combining a joint model and a cross-modulation strategy, we achieved efficient image fusion. In addition, a game theory-based strategy was adopted to correct localization results, ensuring accuracy. Experimental results demonstrate that the improved model achieves localization accuracy and precision rates of up to 89.30% and 88.00%, respectively, in real-world warehouse heat source scenarios, representing a significant improvement of 9.90% and 2.85% compared to the pre-improvement model, fully validating its advancement and effectiveness.

Ultrasonic non-destructive testing can effectively detect damage in aircraft composite materials, but traditional manual testing is time-consuming and labor-intensive. To realize the intelligent recognition of aircraft composite material damage, this paper proposes a 1D-YOLO network, in which intelligent fusion recognizes both the ultrasonic C-scan image and ultrasonic A-scan signal of composite material damage. Through training and testing the composite material damage data on aircraft skin, the accuracy of the model is 94.5%, the mean average precision is 80.0%, and the kappa value is 97.5%. The use of dilated convolution and a recursive feature pyramid effectively improves the feature extraction ability of the model. The effectively used Cascade R-CNN (Cascade Region-Convolutional Neural Network) improves the recognition effect of the model, and the effectively used one-dimensional convolutional neural network excludes non-damaged objects. Comparing our network with YOLOv3, YOLOv4, cascade R-CNN, and other networks, the results show that our network can identify the damage of composite materials more accurately.

In this work, the laser cleaning process of the surface coating for the Ti-6Al-4V titanium alloy was investigated using a pulsed laser, including cleaning time, laser power, and scanning speed. Meanwhile, the corresponding mechanism of laser cleaning was analyzed using the surface morphology, element content change, coating fracture cross section, and phase change. Results demonstrated that the quality of the coating removed by laser cleaning increased first and then decreased with the increase in the three key process parameters of laser cleaning time, laser power, and scanning speed, respectively. The superior laser cleaning efficiency was achieved when the laser power was 15 W, the cleaning time was 8, and the scanning speed was 400 mm/s. It is concluded that the removal of the titanium alloy surface coating is the result of the coupling of the cleaning mechanisms such as ablation and thermal expansion.

The aircraft skin is an important component of the aircraft, and its integrity affects the flight performance and safety performance of the aircraft. Damage detection technology with ultrasonic nondestructive testing as the core has played an important role in aircraft skin damage detection. Due to the difficulty in aircraft skin detection, relying solely on the ultrasonic A-scan equipment has very low detection efficiency. The introduction of artificial intelligence can effectively improve the detection efficiency. This paper establishes the one-dimensional convolutional neural network and single shot multibox detector network, which is based on the SSD network and uses dilated convolution to improve the real-time tracking of the ultrasonic probe. At the same time, 1DCNN is introduced to classify the ultrasonic A-scan signal. Finally, the detection results of the two are combined to achieve the reflection of the internal conditions of the aircraft skin. After testing, the algorithm can identify skin simulation specimens, and its recognition accuracy is 96.5%, AP is 90.9%, and kappa is 0.996. Comparing the improved SSD network with networks such as SSD, YOLOv3, and Faster R-CNN, the results show that the improved network used in this paper is more excellent and effective. At the same time, the detection effects of four types of optimization algorithms and five learning rates are studied, and finally, the corresponding signal classification model adopts Adam and the learning rate of 0.0001 has the best effect.

Infrared stealth is an important guarantee for weapon equipment to survive on the battlefield. Emissivity is an important index to measure the infrared stealth characteristics, and the emissivity is closely related to the surface roughness of objects. Therefore, it is an important work to study the relationship between emissivity and roughness. In this paper, the correlation between emissivity and roughness is studied, and the fitting curve and specific relationship are obtained. It is found that the correlation between the emissivity in the 8–14 µm band and roughness is stronger. The cast iron surface roughness dataset is constructed, and a new convolution neural network (CNN) is designed by the feature fusion method, which is the strengthen CNN. The network can effectively extract the detail features in the image, and the model is optimized by the Adam method. Finally, the deep learning model for measuring emissivity based on the optical image is obtained. The effects of different learning rate decay methods, such as piecewise constant decay, exponential decay, cosine annealing, and cosine annealing with warm restart, on the model optimization are studied. The results show that the cosine annealing with warm restart has the best effect, the error of the model is the smallest, and its mean square error is only 0.0014. This paper presents a new idea for the emissivity measurement, which is of great significance to emissivity measurement, infrared stealth, and infrared detection.

The basis of plasma stealth technology is the attenuation of electromagnetic waves by plasma. In this study, the calculation principle of the finite-difference time-domain (FDTD) method is introduced and a FDTD time–space coupling model of electromagnetic wave propagation in plasma is established. The time-domain variation characteristics of electromagnetic waves entering the plasma are analyzed. The plasma parameter distribution under different conditions obtained using the COMSOL fluid mechanics model is introduced into the FDTD model. The plasma reflectivity measurement experiment was carried out in a microwave anechoic chamber, and the influence of different experimental conditions on the plasma reflectivity was analyzed. The variation of reflectivity under different plasma parameter distributions is obtained. The results show that increasing electron density and plasma thickness and enhanced plasma distribution uniformity are beneficial for improving the attenuation effect of plasma on electromagnetic waves. These results provide a reference for the inductively coupled plasma parameter distribution in a closed quartz cavity, which provides a basis for the plasma to attenuate the electromagnetic waves.

The use of low-emissivity coatings as an infrared (IR) stealth method for weaponry is an important guarantee for the survival of weaponry in the battlefield. The damage on low-emissivity coatings leads to a significant drop in the IR stealth performance of weaponry. Therefore, this paper carries out a research on the intelligent damage identification of low-emissivity coatings based on convolutional neural networks (CNNs). A low-emissivity coating damage dataset with the characteristics of large changes in brightness, distortion of images, and large changes in the target scale is constructed. A composite network (CompoNet) based on feature fusion is designed to extract the detailed features of damage and identify damage types. Compared with VGG16, the damage identification accuracy of CompoNet is improved by 5.49% points. The idea of specific feature extraction layers (SFELs) is proposed. Three SFELs are designed to extract the color, texture, and contour features, which are important basis for judging damage types. The SFEL accelerates the convergence of the model and increases the identification accuracy of the model by 2.02% points. The generative adversarial network is used to generate low-emissivity coating damage images, which solves the problem that the number of damage images collected is small due to practical difficulties. The final damage identification accuracy of the CompoNet fused with SFELs (CompoNet with SFELs) constructed in this paper reaches 99.17%, which is much higher than other generic CNNs.

To study the effects of an axial magnetic field on the discharge characteristics of Ar inductively coupled plasma, a set of discharge plasma generators was designed. The plasma parameters such as electron temperature and electron density were diagnosed with a Langmuir probe. The research showed that as the air pressure was 10 Pa, with the increase in axial magnetic field intensity, the electron temperature and electron density reduced continuously in the central discharge region, while the threshold power of discharge mode transition increased constantly. The analysis suggested that due to the circumnutation of charged particles acted upon by Lorentz force, the introduction of the axial magnetic field had a constraint effect on the particle movement and energy transfer and decreased the collision between the high-energy electron in the discharge sheath and the electron in the central region, thereby reducing the electron density and inductive coupling efficiency. From further analysis of the electron energy probability function, it could be found that in the E mode, the constraint effect of the axial magnetic field on electron motion was more obvious. The proportion of the high-energy electron (>27 eV) was apparently higher than that in the H mode, and the electron energy distribution was more even. This was caused by less electron collision.

Setting the thickness and pressure for an absorbing plasma is difficult in plasma stealth engineering. In this study we established a numerical model of electromagnetic wave propagation in radiofrequency plasma using a z-transformed, finite-difference, time-domain method. We studied the effects of thickness and pressure on the reflectance, transmittance and attenuation of the plasma under three typical electron density distributions: uniform, axially symmetric and monotonic. The results show that the electron density distribution has a significant influence on the electromagnetic wave transmission characteristics. The attenuation effect reaches a maximum when the electron density is increased monotonically along the wave propagation direction. An increase in thickness can significantly increase the attenuation rate of the incident wave and reduce the transmittance, but has little effect on the reflectance. An increase in air pressure reduces the reflectance of the incident wave and increases the transmittance and the attenuation rate. However, once the air pressure exceeds a certain threshold, any further increase in air pressure will no longer enhance the attenuation rate.