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Molybdenum (IV) sulfide is widely employed in electronic device applications. Recognized as one of the most efficacious methods for fabricating high-quality molybdenum (IV) sulfide, the chemical vapor deposition (CVD) method's application necessitates the development of a straightforward, swift and nondestructive characterization tool. Thus, this paper presents a rapid and reliable differential reflectance spectroscopy (DRS) instrumentation, grounded on the optical microscope, capable of characterizing two-dimensional materials prepared by CVD. This universal nondestructive identification tool facilitates the investigation of two-dimensional materials on both opaque and transparent substrates. MoS2 samples prepared by CVD were characterized on SiO2/Si and sapphire substrate. The results indicate that the exciton absorption peak can be accurately identified, validating the instrumentation's utility for material detection and monolayer identification. The characterization results, corroborated by the Raman spectra with a wavenumber difference of approximately 20 cm−1 and the triangular appearance of the optical image, affirm the application of this instrumentation. Ultimately, the DRS instrumentation provides a swift and nondestructive route to characterize physical properties such as bandgap and optical properties for two-dimensional materials. [ABSTRACT FROM AUTHOR]

Anomaly detection of internal defects in underground engineering structures is critical. This paper proposes an unsupervised deep learning image-to-image translation method tailored for ground penetrating radar (GPR) images. The proposed model can translate real-world GPR images to simulated ones. In this manner, labeling real GPR images is not necessary, and only the target detection model trained on simulated GPR images is required to directly identify defects in real GPR images. The unsupervised deep learning network introduces geometry-consistency constraints into the CycleGAN, which largely prevents the problem of semantic distortion in translation. Validation of the proposed method was performed using GPR data collected in various scenarios using GPR of different center frequencies and manufacturers. Moreover, to verify its adaptability and feasibility for defect recognition, commonly used deep learning-based defect recognition methods, which were trained only on simulated GPR images, were used to detect the translated GPR images. The findings indicate that the type and location of internal defects in translated GPR images can be accurately identified using the proposed method. [ABSTRACT FROM AUTHOR]

In the field of structural health monitoring, Lamb Wave has become one of the most widely used inspection tools due to its advantages of wide detection range and high sensitivity. In this paper, a new damage detection method for honeycomb sandwich structures based on frequency spectrum and Lamb Wave Tomography is proposed. By means of simulation and experiment, a certain number of sensors were placed on the honeycomb sandwich plate to stimulate and receive the signals in both undamaged and damaged cases. By Lamb Wave Tomography, the differences of signals before and after damage were compared, and the damage indexes were calculated. Furthermore, the probability of each sensor path containing damage was analyzed, and the damage image was finally realized. The technology does not require analysis of the complex multimode propagation properties of Lamb Wave, nor does it require understanding and modeling of the properties of materials or structures. In both simulation and experiment, the localization errors of the damage conform to the detection requirements, thus verifying that the method has certain feasibility in damage detection. [ABSTRACT FROM AUTHOR]

With long-term operation of tunnels, the surface of tunnel lining is prone to crack, spalling, water leakage and other defects. Therefore, the detection of tunnel surface defects has become an important process to ensure the safety of tunnel operation. Linear scan cameras have become the main way of tunnel surface defect detection because of their ultrahigh resolution and ultrahigh scanning frequency. However, in the process of collecting tunnel surface images at high speed, the linear scan cameras are easy to cause images distortion, which leads to reduction of tunnel defect identification accuracy. Thus, this paper proposes a tunnel surface distortion image restoration method based on a supervised generative adversarial network, which introduces an attention mechanism to guide the method to calibrate the weights of feature channels. It solves the problem of distortion in the process of images acquisition of the linear scan cameras. Then, to deal with the difficulty in identifying small-size tunnel defects in complex tunnel environments, this paper combines a backbone network with a multi-scale feature fusion module based on the You Only Look At CoefficienTs (YOLACT) network, which enhances the feature extraction ability of the network and improves the accuracy of tunnel surface defect identification. [ABSTRACT FROM AUTHOR]

In nonlinear ultrasonic damage detection, the appropriate Lamb wave mode to accurately characterise the nonlinearity attributed to the material damage should be selected important. However, an effective mode is more difficult to select practically as impacted by the dispersion and multi-mode characteristic of the Lamb wave. In this study, based on the dispersion curve of Lamb waves, the modes meeting the second harmonics and third harmonics phase velocity matching were given, and the nonlinear cumulative effects exerted by different modes on fatigue cracks were compared. In addition, an investigation was conducted on the amplitude change of the second and third harmonics signal of the identical fatigue damage. The results show that the harmonic amplitude decreases with the increase of the fatigue crack length. The third harmonics under the two modes are more sensitive to fatigue damage than the second harmonics. Moreover, the amplitude of the third harmonic excited by S1 mode exceeds that of the other mode. As the fatigue crack length increases, the relative second- and third-order nonlinear coefficients generated by S1 mode have approximately the same trend, first increasing and then decreasing. The results reveal that the third harmonic under S1 mode could more effectively characterise fatigue damage. [ABSTRACT FROM AUTHOR]