During the beam current calibration process, accurate guidance of the beam current to the metal target is a challenging issue for proton accelerators. To address this challenge, we propose the use of beam orbital parameters combined with reinforcement learning algorithms to achieve automatic beam calibration. This study introduces a system architecture that employs edge intelligent acceleration nodes based on deep learning acceleration techniques. We designed a system to predict BPM parameters using a cascaded bac…

Deep convolutional neural networks have made some progress in skin lesion classification and cancer diagnosis, but there are still some problems to be solved, such as the challenge of small inter-class feature differences and large intra-class feature differences, which might limit the classification performance of the model as high-level and low-level features are not properly utilized. This paper proposes a multi-scale skin cancer image identification network using self-interactive attention pyramid and cross-la…

MRI is often influenced by many factors, and single image super‐resolution (SISR) based on a neural network is an effective and cost‐effective alternative technique for the high‐resolution restoration of low‐resolution images. However, deep neural networks can easily lead to overfitting and make the test results worse. The network with a shallow training network is difficult to fit quickly and cannot completely learn training samples. To solve the above problems, a new end‐to‐end super‐resolution (SR) method is pr…

The surface defects of a hot-rolled strip will adversely affect the appearance and quality of industrial products. Therefore, the timely identification of hot-rolled strip surface defects is of great significance. In order to improve the efficiency and accuracy of surface defect detection, a lightweight network based on coordinate attention and self-interaction (CASI-Net), which integrates channel domain, spatial information, and a self-interaction module, is proposed to automatically identify six kinds of hot-rol…

The classification method of steel surface defects based on deep learning provides a basis for quality control of industrial steel manufacturing. Due to a large number of interference in the steel production area and the limited computing resources of the edge equipment deployed in the production area, it is a challenge to develop a lightweight model to achieve rapid and accurate classification in the case of limited computing resources. In this article, an improved lightweight convolution structure (LCS) is propo…