Study on distribution of sidewall earth pressure on open caissons considering soil arching effect.

Based on the soil arching effect theory, the magnitude and distribution of sidewall earth pressure on open caissons when the embedded depth is large was analyzed by using theory of non-limit state earth pressure theory and horizontal differential element method. The theoretical formula was deduced. The theoretical calculation results are compared with the field test results and centrifugal model test results respectively. The results show that when the embedded depth of the open caisson is large, the distribution of earth pressure on the side wall of the open caisson first increases with the increase of embedded depth, reaches a peak value, and then sharply decreases. The peak point is located at 2/3 ~ 4/5 of the embedded depth. In engineering practice, when the embedded depth of the open caisson is 40 m, the relative error between the field test value and the theoretical calculation value is - 55.8% ~ 1.2%, with an average error of 13.8%. When the equivalent embedded depth of the open caisson in the centrifugal model test is 36 m, the relative error between the centrifugal model test value and the theoretical calculation value is - 20.1% ~ 68.0%, with an average error of 10.6%, The results are consistent well. The results of this article provides reference for the design and construction of open caisson.

Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation.

High-resolution medical images can be effectively used for clinical diagnosis. However, the acquisition of high-resolution images is difficult and often limited by medical instruments. Super-resolution (SR) methods provide a solution, where high-resolution (HR) images can be reconstructed from low-resolution (LR) ones. Most of existing deep neural networks for 3D SR medical images trained in a non-blind process, where LR images are directly degraded from HR data via a pre-determined downscale method. Such approaches rely heavily on the assumed degradation model, resulting in inevitable deviations in real clinical practice. Blind super-resolution, as a more attractive research line for this field, aims to generate HR images from LR inputs containing unknown degradation. Towards generalizing SR models for diverse types of degradation, we propose a robust blind SR of 3D medical images in an unsupervised manner with domain correction and upscaling treatment. First, a CycleGAN-based architecture is implemented to generate the LR data from the source domain to the target one for domain correction. Then, an upscaling network is learned via pre-determined HR-LR couples for reconstruction. The proposed framework is able to automatically learn noisy and blurry correction kernels for unpaired 3D SR magnetic resonance images (MRI). Our method achieves better and more robust performances in reconstruction of HR images from LR MRI with multiple unknown degradation processes, and show its superiority to other state-of-the-art supervised models and cycle-consistency based methods, especially in severe distortion cases.