Beihang University

About: Beihang University is a education organization based out in Beijing, China. It is known for research contribution in the topics: Control theory & Microstructure. 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.

R-VCANet: A New Deep-Learning-Based Hyperspectral Image Classification Method

Abstract: Deep-learning-based methods have displayed promising performance for hyperspectral image (HSI) classification, due to their capacity of extracting deep features from HSI. However, these methods usually require a large number of training samples. It is quite difficult for deep-learning model to provide representative feature expression for HSI data when the number of samples are limited. In this paper, a novel simplified deep-learning model, rolling guidance filter (RGF) and vertex component analysis network (R-VCANet), is proposed, which achieves higher accuracy when the number of training samples is not abundant. In R-VCANet, the inherent properties of HSI data, spatial information and spectral characteristics, are utilized to construct the network. And by this means the obtained model could generate more powerful feature expression with less samples. First, spectral and spatial information are combined via the RGF, which could explore the contextual structure features and remove small details from HSI. More importantly, we have designed a new network called vertex component analysis network for deep features extraction from the smoothed HSI. Experiments on three popular datasets indicate that the proposed R-VCANet based method reveals better performance than some state-of-the-art methods, especially when the training samples available are not abundant.

TTSA: An Effective Scheduling Approach for Delay Bounded Tasks in Hybrid Clouds

Abstract: The economy of scale provided by cloud attracts a growing number of organizations and industrial companies to deploy their applications in cloud data centers (CDCs) and to provide services to users around the world. The uncertainty of arriving tasks makes it a big challenge for private CDC to cost-effectively schedule delay bounded tasks without exceeding their delay bounds. Unlike previous studies, this paper takes into account the cost minimization problem for private CDC in hybrid clouds, where the energy price of private CDC and execution price of public clouds both show the temporal diversity. Then, this paper proposes a temporal task scheduling algorithm (TTSA) to effectively dispatch all arriving tasks to private CDC and public clouds. In each iteration of TTSA, the cost minimization problem is modeled as a mixed integer linear program and solved by a hybrid simulated-annealing particle-swarm-optimization. The experimental results demonstrate that compared with the existing methods, the optimal or suboptimal scheduling strategy produced by TTSA can efficiently increase the throughput and reduce the cost of private CDC while meeting the delay bounds of all the tasks.

Bioinspired Graphene-Based Nanocomposites and Their Application in Flexible Energy Devices.

Abstract: Graphene is the strongest and stiffest material ever identified and the best electrical conductor known to date, making it an ideal candidate for constructing nanocomposites used in flexible energy devices. However, it remains a great challenge to assemble graphene nanosheets into macro-sized high-performance nanocomposites in practical applications of flexible energy devices using traditional approaches. Nacre, the gold standard for biomimicry, provides an excellent example and guideline for assembling two-dimensional nanosheets into high-performance nanocomposites. This review summarizes recent research on the bioinspired graphene-based nanocomposites (BGBNs), and discusses different bioinspired assembly strategies for constructing integrated high-strength and -toughness graphene-based nanocomposites through various synergistic effects. Fundamental properties of graphene-based nanocomposites, such as strength, toughness, and electrical conductivities, are highlighted. Applications of the BGBNs in flexible energy devices, as well as potential challenges, are addressed. Inspired from the past work done by the community a roadmap for the future of the BGBNs in flexible energy device applications is depicted.