University of Science and Technology Beijing

About: University of Science and Technology Beijing is a education organization based out in Beijing, China. It is known for research contribution in the topics: Microstructure & Alloy. The organization has 41558 authors who have published 44473 publications receiving 623229 citations. The organization is also known as: Beijing Steel and Iron Institute.

Control Design for Nonlinear Flexible Wings of a Robotic Aircraft

Abstract: In this brief, the control problem for flexible wings of a robotic aircraft is addressed by using boundary control schemes Inspired by birds and bats, the wing with flexibility and articulation is modeled as a distributed parameter system described by hybrid partial differential equations and ordinary differential equations Boundary control for both wing twist and bending is proposed on the original coupled dynamics, and bounded stability is proved by introducing a proper Lyapunov function The effectiveness of the proposed control is verified by simulations

Multi-shelled CeO2 hollow microspheres as superior photocatalysts for water oxidation

Abstract: A general self-templating method is introduced to construct triple-shelled CeO2 hollow microspheres, which are composed of tiny CeO2 nanoparticles. When the triple-shelled CeO2 hollow microspheres are used as photocatalysts for direct water oxidation with AgNO3 as the electron scavenger, excellent activity and enhanced stability for O2 evolution are achieved, in contrast with commercial CeO2 nanoparticles, single-shelled CeO2 hollow microspheres and double-shelled CeO2 hollow microspheres. Such an outstanding performance is attributed to the unique properties of the triple-shelled CeO2 hollow microspheres including more efficient multiple reflections of the incident light by the inner shells, the larger surface area and more active sites for improving separation of electron–hole pairs, and the more curved surfaces unfavorable for deposition of in situ generated Ag nanoparticles.

Highly stable Zn metal anodes enabled by atomic layer deposited Al 2 O 3 coating for aqueous zinc-ion batteries

Abstract: Rechargeable aqueous zinc-ion batteries (ZIBs) have attracted increasing attention as an energy storage technology for large-scale applications, due to their high capacity (820 mA h g−1 and 5854 A h L−1), inherently high safety, and their low cost. However, the overall performance of ZIBs has been seriously hindered by the poor rechargeability of Zn anodes, because of the dendrite growth, passivation, and hydrogen evolution problems associated with Zn anodes. Herein, Al2O3 coating by an atomic layer deposition (ALD) technique was developed to address the aforementioned problems and improve the rechargeability of Zn anodes for ZIBs. By coating the Zn plate with an ultrathin Al2O3 layer, the wettability of Zn was improved and corrosion was inhibited. As a result, the formation of Zn dendrites was effectively suppressed, with a significantly improved lifetime in the Zn–Zn symmetric cells. With the optimized coating thickness of 100 cycles, 100Al2O3@Zn symmetric cells showed a reduced overpotential (36.5 mV) and a prolonged life span (over 500 h) at 1 mA cm−2. In addition, the 100Al2O3@Zn has been verified in Zn–MnO2 batteries using layered δ-MnO2 as the cathode and consequently exhibits superior electrochemical performance with a high capacity retention of 89.4% after over 1000 cycles at a current density of 1 mA cm−2 (3.33C for MnO2) was demonstrated. It is expected that the novel design of Al2O3 modified Zn anodes may pave the way towards high-performance aqueous ZIBs and shed light on the development of other metal anode-based battery systems.

Understanding and Enhancement of Internal Clustering Validation Measures

Abstract: Clustering validation has long been recognized as one of the vital issues essential to the success of clustering applications. In general, clustering validation can be categorized into two classes, external clustering validation and internal clustering validation. In this paper, we focus on internal clustering validation and present a study of 11 widely used internal clustering validation measures for crisp clustering. The results of this study indicate that these existing measures have certain limitations in different application scenarios. As an alternative choice, we propose a new internal clustering validation measure, named clustering validation index based on nearest neighbors (CVNN), which is based on the notion of nearest neighbors. This measure can dynamically select multiple objects as representatives for different clusters in different situations. Experimental results show that CVNN outperforms the existing measures on both synthetic data and real-world data in different application scenarios.