Mathematical Analysis of Some Typical Problems in Geodesy by Means of Computer Algebra

There are many complicated and fussy mathematical analysis processes in geodesy, such as the power series expansions of the ellipsoid’s eccentricity, high order derivation of complex and implicit functions, operation of trigonometric function, expansions of special functions and integral transformation. Taking some typical mathematical analysis processes in geodesy as research objects, the computer algebra analysis are systematically carried out to bread, deep and detailed extent with the help of computer algebra analysis method and the powerful ability of mathematical analysis of computer algebra system. The forward and inverse expansions of the meridian arc in geometric geodesy, the nonsingular expressions of singular integration in physical geodesy and the series expansions of direct transformations between three anomalies in satellite geodesy are established, which have more concise form, stricter theory basis and higher accuracy compared to traditional ones. The breakthrough and innovation of some mathematical analysis problems in the special field of geodesy are realized, which will further enrich and perfect the theoretical system of geodesy

Self-Domain Adaptation for Face Anti-Spoofing

Although current face anti-spoofing methods achieve promising results under intra-dataset testing, they suffer from poor generalization to unseen attacks. Most existing works adopt domain adaptation (DA) or domain generalization (DG) techniques to address this problem. However, the target domain is often unknown during training which limits the utilization of DA methods. DG methods can conquer this by learning domain invariant features without seeing any target data. However, they fail in utilizing the information of target data. In this paper, we propose a self-domain adaptation framework to leverage the unlabeled test domain data at inference. Specifically, a domain adaptor is designed to adapt the model for test domain. In order to learn a better adaptor, a meta-learning based adaptor learning algorithm is proposed using the data of multiple source domains at the training step. At test time, the adaptor is updated using only the test domain data according to the proposed unsupervised adaptor loss to further improve the performance. Extensive experiments on four public datasets validate the effectiveness of the proposed method.Comment: Camera Ready, AAAI 202

Effect of dry-wet cycles on dynamic properties and microstructures of sandstone: Experiments and modelling

Underground pumped storage power plant (UPSP) is an innovative concept for space recycling of abandoned mines. Its realization requires better understanding of the dynamic performance and durability of reservoir rock. This paper conducted ultrasonic detection, split Hopkinson pressure bar (SHPB) impact, mercury intrusion porosimetry (MIP), and backscatter electron observation (BSE) tests to investigate the dynamical behaviour and microstructure of sandstone with cyclical dry-wet damage. A coupling FEM-DEM model was constructed for reappearing mesoscopic structure damage. The results show that dry-wet cycles decrease the dynamic compressive strength (DCS) with a maximum reduction of 39.40%, the elastic limit strength is reduced from 41.75 to 25.62 MPa. The sieved fragments obtain the highest crack growth rate during the 23rd dry-wet cycle with a predictable life of 25 cycles for each rock particle. The pore fractal features of the macropores and micro-meso pores show great differences between the early and late cycles, which verifies the computational statistics analysis of particle deterioration. The numerical results show that the failure patterns are governed by the strain in pre-peak stage and the shear cracks are dominant. The dry-wet cycles reduce the energy transfer efficiency and lead to the discretization of force chain and crack fields