Dynamic response of elastic beam to a moving pulse: finite element analysis of critical velocity

Dynamic behaviour of a semi-infinite elastic beam subjected to a moving single sinusoidal pulse was investigated by using finite element method associated with dimensionless analysis. The typical features of the equivalent stress and beam deflection were presented. It is found that the average value of maximal equivalent stress in the beam reaches its maximum value when the velocity of moving pulse is closed to a critical velocity. The critical velocity decreases as the pulse duration increases. The material, structural and load parameters influencing the critical velocity were analysed. An empirical formula of the critical velocity with respect to the speed of elastic wave, the gyration radius of the cross-section and the pulse duration was obtained

RGM: A Robust Generalist Matching Model

Finding corresponding pixels within a pair of images is a fundamental computer vision task with various applications. Due to the specific requirements of different tasks like optical flow estimation and local feature matching, previous works are primarily categorized into dense matching and sparse feature matching focusing on specialized architectures along with task-specific datasets, which may somewhat hinder the generalization performance of specialized models. In this paper, we propose a deep model for sparse and dense matching, termed RGM (Robust Generalist Matching). In particular, we elaborately design a cascaded GRU module for refinement by exploring the geometric similarity iteratively at multiple scales following an additional uncertainty estimation module for sparsification. To narrow the gap between synthetic training samples and real-world scenarios, we build a new, large-scale dataset with sparse correspondence ground truth by generating optical flow supervision with greater intervals. As such, we are able to mix up various dense and sparse matching datasets, significantly improving the training diversity. The generalization capacity of our proposed RGM is greatly improved by learning the matching and uncertainty estimation in a two-stage manner on the large, mixed data. Superior performance is achieved for zero-shot matching and downstream geometry estimation across multiple datasets, outperforming the previous methods by a large margin.Comment: 17 pages. Fixed typo in the first two equations. Code is available at: https://github.com/aim-uofa/RG

Shaping the future of application for information and communication technology in national economic analysis

Characterized by networking, digitization, and intellectualization, the new generation of Information and Communication Technologies (ICTs) has promoted integration of various national economic industries, with a more significant leading role in the socio-economic development. In order to describe the influence of ICT industries on economic growth, this paper has comprehensively analyzed the contribution mechanism of ICTs to current economy. Crucial indicators, such as Total Telecommunications Business (TTB), Data Traffic (DT), and Computing Power Development Index (CPDI), have been innovatively selected to sufficiently describe the data circulation scale and the processing capacity in the era of digital economy. Based on relevant statistics of 31 provinces (municipalities or autonomous regions) in China from 2011 to 2020, quantitative results have been put forward to interpret the impact of ICT industries on the national economy development by using correlation coefficient analysis and data regression analysis methods. The measurement results show that TTB, DT, and CPDI are positively correlated with the national economic development, indicating that each 1% increasement of TTB, DT, and CPDI increases Gross Domestic Product (GDP) by 0.08%, 0.09%, and 0.6%, respectively