Search

Carbon fiber reinforced polymer（CFRP）composites has small and hidden damage after low-speed impact，and the existence of damage significantly reduces the bearing capacity and service life of CFRP materials. C-scan represents a conventional ultrasonic imaging method. To address the issue of low imaging precision in C-scan detection of internal damage caused by low-velocity impact in CFRP，gradient operators were employed to process the original images，and transfer learning methodology was utilized to conduct damage classification training on ResNet18 and ResNet50 architectures. To enhance the classification model’s performance，an image reconstruction model（IRM）based on convolutional neural networks was proposed to improve imaging precision. Additionally，a performance metric σEOL，based on the structural similarity index（SSIM），was introduced to validate the level of image quality enhancement. The iterative training results demonstrate that when the iteration count reaches 200，the σEOL of different types of impact damage is greater than 1. To further improve imaging precision，the ResNet residual connection concept is incorporated，leading to the development of the ResIRM network. Compared to IRM，ResIRM exhibits enhanced detection precision for different types of impact damage，with an average σEOL improvement of 0.85% across all impact types. Furthermore，the gradient saliency heat maps of the classification model processed by ResIRM indicate that ResIRM effectively reinforces the features in damaged regions.

Aimed at the prevailing issues such as unclear construction mechanisms, insufficient sensing capability and integration compatibility of fabric-based sensor, recent advances in the sensing principles,construction method and response characteristic of mechanical, thermal, and humidity fabric-based sensors were systematically reviewed. The typical material application, structural construction and fabrication method of various sensors along with the challenges faced at the current stage were discussed. Moreover, the characteristic application of these sensors in various application such as motion monitoring, healthcare, e-skin, human-computer interaction and so on were analyzed. It is considered that improving the signal stability, sensitivity, environmental adaptability and scalability of fabric-based flexible sensors based on material property, hierarchical structure and manufacturing process to study specific application environment is the key development direction. [ABSTRACT FROM AUTHOR]

Cone-beam CT (CBCT) of Elekta can be used to analyze the setup errors in intensity modulated radiotherapy. A total of 240 patients with head and neck cancer were chosen in the period from October, 2009 to October, 2010. Their CBCT images were obtained after initial setup, and registered to the planning CT images, and then the setup errors on translational directions (X, Y, Z) and on rotational directions (U, V, W) were obtained and analyzed. The results showed that the setup errors on translational directions in Y-axis and Z-axis were larger than in X-axis, and the setup errors on rotational directions in Y-axis was much more obvious than those on the other two directions, which should be taken care during the patient daily setup. It may be concluded that the CBCT system can improve the precision for radiotherapy.