Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Computer science & Control theory. The organization has 67002 authors who have published 73507 publications receiving 975691 citations. The organization is also known as: Beijing University of Aeronautics and Astronautics.

Unsteady aerodynamic force generation by a model fruit fly wing in flapping motion.

Abstract: A computational fluid-dynamic analysis was conducted to study the unsteady aerodynamics of a model fruit fly wing. The wing performs an idealized flapping motion that emulates the wing motion of a fruit fly in normal hovering flight. The Navier-Stokes equations are solved numerically. The solution provides the flow and pressure fields, from which the aerodynamic forces and vorticity wake structure are obtained. Insights into the unsteady aerodynamic force generation process are gained from the force and flow-structure information. Considerable lift can be produced when the majority of the wing rotation is conducted near the end of a stroke or wing rotation precedes stroke reversal (rotation advanced), and the mean lift coefficient can be more than twice the quasi-steady value. Three mechanisms are responsible for the large lift: the rapid acceleration of the wing at the beginning of a stroke, the absence of stall during the stroke and the fast pitching-up rotation of the wing near the end of the stroke. When half the wing rotation is conducted near the end of a stroke and half at the beginning of the next stroke (symmetrical rotation), the lift at the beginning and near the end of a stroke becomes smaller because the effects of the first and third mechanisms above are reduced. The mean lift coefficient is smaller than that of the rotation-advanced case, but is still 80 % larger than the quasi-steady value. When the majority of the rotation is delayed until the beginning of the next stroke (rotation delayed), the lift at the beginning and near the end of a stroke becomes very small or even negative because the effect of the first mechanism above is cancelled and the third mechanism does not apply in this case. The mean lift coefficient is much smaller than in the other two cases.

HRank: Filter Pruning Using High-Rank Feature Map

Abstract: Neural network pruning offers a promising prospect to facilitate deploying deep neural networks on resource-limited devices. However, existing methods are still challenged by the training inefficiency and labor cost in pruning designs, due to missing theoretical guidance of non-salient network components. In this paper, we propose a novel filter pruning method by exploring the High Rank of feature maps (HRank). Our HRank is inspired by the discovery that the average rank of multiple feature maps generated by a single filter is always the same, regardless of the number of image batches CNNs receive. Based on HRank, we develop a method that is mathematically formulated to prune filters with low-rank feature maps. The principle behind our pruning is that low-rank feature maps contain less information, and thus pruned results can be easily reproduced. Besides, we experimentally show that weights with high-rank feature maps contain more important information, such that even when a portion is not updated, very little damage would be done to the model performance. Without introducing any additional constraints, HRank leads to significant improvements over the state-of-the-arts in terms of FLOPs and parameters reduction, with similar accuracies. For example, with ResNet-110, we achieve a 58.2%-FLOPs reduction by removing 59.2% of the parameters, with only a small loss of $0.14\%$ in top-1 accuracy on CIFAR-10. With Res-50, we achieve a 43.8%-FLOPs reduction by removing 36.7% of the parameters, with only a loss of 1.17% in the top-1 accuracy on ImageNet. The codes can be available at https://github.com/lmbxmu/HRank.

IoT-Based Intelligent Perception and Access of Manufacturing Resource Toward Cloud Manufacturing

Abstract: Recently, cloud manufacturing (CMfg) as a new service-oriented manufacturing mode has been paid wide attention around the world. However, one of the key technologies for implementing CMfg is how to realize manufacturing resource intelligent perception and access. In order to achieve intelligent perception and access of various manufacturing resources, the applications of IoT technologies in CMfg has been investigated in this paper. The classification of manufacturing resources and services, as well as their relationships, are presented. A five-layered structure (i.e., resource layer, perception layer, network layer, service layer, and application layer) resource intelligent perception and access system based on IoT is designed and presented. The key technologies for intelligent perception and access of various resources (i.e., hard manufacturing resources, computational resources, and intellectual resources) in CMfg are described. A prototype application system is developed to valid the proposed method.

Graphene and graphene-like layered transition metal dichalcogenides in energy conversion and storage.

Abstract: Being confronted with the energy crisis and environmental problems, the exploration of clean and renewable energy materials as well as their devices are urgently demanded. Two-dimensional (2D) atomically-thick materials, graphene and grpahene-like layered transition metal dichalcogenides (TMDs), have showed vast potential as novel energy materials due to their unique physicochemical properties. In this Review, we outline the typical application of graphene and grpahene-like TMDs in energy conversion and storage fields, and hope to promote the development of 2D TMDs in this field through the analysis and comparisons with the relatively natural graphene. First, a brief introduction of electronic structures and basic properties of graphene and TMDs are presented. Then, we summarize the exciting progress of these materials made in both energy conversion and storage field including solar cells, electrocatalysis, supercapacitors and lithium ions batteries. Finally, the prospects and further developments in these exciting fields of graphene and graphene-like TMDs materials are also suggested.