Ghost imaging lidar via sparsity constraints

For remote sensing, high-resolution imaging techniques are helpful to catch more characteristic information of the target. We extend pseudo-thermal light ghost imaging to the area of remote imaging and propose a ghost imaging lidar system. For the first time, we demonstrate experimentally that the real-space image of a target at about 1.0 km range with 20 mm resolution is achieved by ghost imaging via sparsity constraints (GISC) technique. The characters of GISC technique compared to the existing lidar systems are also discussed.Comment: 4pages, 3figure

Predicting the Silent Majority on Graphs: Knowledge Transferable Graph Neural Network

Graphs consisting of vocal nodes ("the vocal minority") and silent nodes ("the silent majority"), namely VS-Graph, are ubiquitous in the real world. The vocal nodes tend to have abundant features and labels. In contrast, silent nodes only have incomplete features and rare labels, e.g., the description and political tendency of politicians (vocal) are abundant while not for ordinary people (silent) on the twitter's social network. Predicting the silent majority remains a crucial yet challenging problem. However, most existing message-passing based GNNs assume that all nodes belong to the same domain, without considering the missing features and distribution-shift between domains, leading to poor ability to deal with VS-Graph. To combat the above challenges, we propose Knowledge Transferable Graph Neural Network (KT-GNN), which models distribution shifts during message passing and representation learning by transferring knowledge from vocal nodes to silent nodes. Specifically, we design the domain-adapted "feature completion and message passing mechanism" for node representation learning while preserving domain difference. And a knowledge transferable classifier based on KL-divergence is followed. Comprehensive experiments on real-world scenarios (i.e., company financial risk assessment and political elections) demonstrate the superior performance of our method. Our source code has been open sourced.Comment: Paper was accepted by WWW202

A novel explicit design method for complex thin-walled structures based on embedded solid moving morphable components

In this article, a novel explicit approach for designing complex thin-walled structures based on the Moving Morphable Component (MMC) method is proposed, which provides a unified framework to systematically address various design issues, including topology optimization, reinforced-rib layout optimization, and sandwich structure design problems. The complexity of thin-walled structures mainly comes from flexible geometries and the variation of thickness. On the one hand, the geometric complexity of thin-walled structures leads to the difficulty in automatically describing material distribution (e.g., reinforced ribs). On the other hand, thin-walled structures with different thicknesses require various hypotheses (e.g., Kirchhoff-Love shell theory and Reissner-Mindlin shell theory) to ensure the precision of structural responses. Whereas for cases that do not fit the shell hypothesis, the precision loss of response solutions is nonnegligible in the optimization process since the accumulation of errors will cause entirely different designs. Hence, the current article proposes a novel embedded solid component to tackle these challenges. The geometric constraints that make the components fit to the curved thin-walled structure are whereby satisfied. Compared with traditional strategies, the proposed method is free from the limit of shell assumptions of structural analysis and can achieve optimized designs with clear load transmission paths at the cost of few design variables and degrees of freedom for finite element analysis (FEA). Finally, we apply the proposed method to several representative examples to demonstrate its effectiveness, efficiency, versatility, and potential to handle complex industrial structures

New potential carbon emission reduction enterprises in China: deep geological storage of CO2 emitted through industrial usage of coal in China

Deep geological storage of carbon dioxide (CO2) could offer an essential solution to mitigate greenhouse gas emissions from the continued use of fossil fuels. Currently, CO2 capture is both costly and energy intensive; it represents about 60% of the cost of the total carbon capture and storage (CCS) chain which is causing a bottleneck in advancement of CCS in China. This paper proposes capturing CO2 from coal chemical plants where the CO2 is high purity and relatively cheap to collect, thus offering an early opportunity for industrial-scale full-chain CCS implementation. The total amount of high concentration CO2 that will be emitted (or is being emitted) by the coal chemical factories approved by the National Development and Reform Commission described in this paper is 42 million tonnes. If all eight projects could utilize CCS, it would be of great significance for mitigating greenhouse gas emissions in China. Basins which may provide storage sites for captured CO2 in North China are characterized by large sedimentary thicknesses and several sets of reservoir-caprock strata. Some oil fields are nearing depletion and a sub-set of these are potentially suitable for CO2 enhanced oil recovery (EOR) and CCS demonstration but all these still require detailed geological characterization. The short distance between the high concentration CO2 sources and potential storage sites should reduce transport costs and complications. The authors believe these high purity sources coupled with EOR or aquifer storage could offer China an opportunity to lead development in this globally beneficial CCS optio