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In order to solve the problem that steel surface defects are easily covered or submerged by other objects or noise, this paper proposes an open–closed transformation algorithm which can eliminate or weaken multiple noises. In the case of a small number of samples, this paper establishes a super-resolution generative adversarial neural network to achieve the enhancement of sample data. For avoiding unrealistic image defects caused by cuts or brightness variations, an enhancement method is given which incorporates the original defective high-frequency information into classical image fusion methods, such as rotation and error slicing. Experimental results show that the accuracy of the proposed denoising method reaches over 90%, which is more than 2.6% of that of the most primitive classification network. To compare with existing denoising methods, the denoising method proposed in this paper not only has higher accuracy, faster denoising speed, and stronger anti-interference ability, but also has better adaptation to the environment. This research will provide a new solution method for the denoising of multi-noise phenomena in multiple different environments.

Crack identification plays a vital role in preventive maintenance strategies during highway pavement maintenance. Therefore, accurate identification of cracks in highway pavement images is the key to highway maintenance work. In this paper, an improved U-Net network adopting multi-scale feature prediction fusion and the improved parallel attention module was put forward to better identify concrete cracks. Multiscale feature prediction fusion combines multiple U-Net features generated by intermediate layers for aggregated prediction, thus using global information from different scales. The improved parallel attention module is used to process the U-Net decoded output of multi-scale feature prediction fusion, which can give more weight to the target region in the image and further capture the global contextual information of the image to improve the recognition accuracy. Improving the bottleneck layer is used to improve the robustness of the model and prevent overfitting. Experiments show that the improved U-Net network in this paper has a significant improvement over the original U-Net network. The performance of the proposed method in this paper was investigated on two publicly available datasets (Crack500 and CFD) and compared with competing methods proposed in the literature. Using the Crack500 dataset, the method in this paper achieved the highest score in precision (89.60%), recall (95.83%), mIOU (83.80%), and F1-score (92.61%). Similarly, for the CFD dataset, the method in this paper achieved high values for precision (93.29%), mIOU (82.07%), recall (86.26%), and F1-score (89.64%). Thus, the method has several advantages for identifying cracks in highway pavements and is an ideal tool for practical work. In future work, identifying more crack types and model light-weighting are the key objectives. Meanwhile, this paper provides a new idea for road crack identification.

Path planning is an important aspect and component in the research of mobile-robot-related technologies. Many path planning algorithms are only applicable to static environments, while in practical tasks, the uncertainty in dynamic environments increases the difficulty of path planning and obstacle avoidance compared with static environments. To address this problem, this paper proposes an RRT*-FDWA algorithm. RRT* first generates a global optimal path, and then, when obstacles exist nearby, an FDWA algorithm fixes the local path in real time. Compared with other path planning algorithms, RRT*-FDWA can avoid local minima, rapidly perform path replanning, generate a smooth optimal route, and improve the robot’s maneuvering amplitude. In this paper, the effectiveness of the algorithm is verified through experiments in dynamic environments.

High thermal conductivity polymer matrix composites have become an urgent need for the thermal management of modern electronic devices. However, increasing the thermal conductivity of polymer-based composites typically results in loss of lightweight, flexibility and electrical insulation. Herein, the polyvinyl alcohol (PVA)/PVA-chitosan-adsorbed multi-walled carbon nanotubes/PVA (PVA/CS@MWCNTs) composite films with a sandwich structure were designed and fabricated by a self-construction strategy inspired by the surface film formation of milk. The obtained film simultaneously possesses high thermal conductivity, electrical insulation, and excellent flexibility. In this particular structure, the uniform intermediate layer of PVA-CS@MWCNTs contributed to improving the thermal conductivity of composite films, and the PVA distributed on both sides of the sandwich structure maintains the electrical insulation of the films (superior electrical resistivity above 1012 Ω·cm). It has been demonstrated that the fillers could be arranged in a horizontal direction during the scraping process. Thus, the obtained composite film exhibited high in-plane thermal conductivity of 5.312 W·m−1·K−1 at fairly low MWCNTs loading of 5 wt%, which increased by about 1190% compared with pure PVA (0.412 W·m−1·K−1). This work effectively realizes the combination of high thermal conductivity and excellent electrical insulation, which could greatly expand the application of polymer-based composite films in the area of thermal management.

Polarizer is a key component of the liquid crystal display panel, and the optical properties mainly include transmittance, degree of polarization and chromaticity. Polarizer is made of a multilayer optical film, in which the PVA polarizing film is the core structure for realizing the polarization of the whole polarizer. PVA polarizing film is commonly manufactured through a multi-step craft, including rinsing, dyeing, stretching and correcting. The correction process has a significant impact on the final apparent color and optical properties of the polarizer. In this study, the KI concentration in the correcting tank, ranging from 1% to 3%, was systematically investigated. With the increase in KI concentration, the Raman vibration peak at 160 cm−1 representing I5− ions gradually weakened, while the Raman vibration peak at 110 cm−1 representing I3− ions gradually increased, indicating that the KI in the correcting tank changed the chemical equilibrium of polyiodide ions in PVA. Then abundant chromophore I5− ions were consumed and I3− ions generated, so that the apparent color of PVA polarizing film gradually changed from dark blue to dark gray, and the chromaticity a-value and b-value gradually increased. The change in the concentration of dichroic species (I5− and I3−) in PVA polarizing film had directly affected its transmittance in the visible range. From the UV-Vis transmittance spectrum of PVA polarizing film, when the I5− ions were consumed and I3− ions generated, the transmittance of PVA polarizing film in the region of 675–525 nm wavelength increased gradually while the polarization degree also increased.