National University of Defense Technology

About: National University of Defense Technology is a education organization based out in Changsha, China. It is known for research contribution in the topics: Computer science & Radar. The organization has 39430 authors who have published 40181 publications receiving 358979 citations. The organization is also known as: Guófáng Kēxuéjìshù Dàxué & NUDT.

Simultaneous Reduction and Surface Functionalization of Graphene Oxide for Hydroxyapatite Mineralization

Abstract: In this study, we present the synthesis of reduced graphene oxide/hydroxyapatite (RGO/HA) hybrid materials by an environmental-friendly route. Graphene oxide (GO) was first simultaneously reduced and surface functionalized by one-step oxidative polymerization of dopamine (PDA). The bioinspired surface was further used for biomimetic mineralization of hydroxyapatite. When incubated in a simulated body fluid (SBF), the PDA layer enabled efficient interaction between the RGO surface and the mineral ions to improve the bioactivity, promoted the formation of the HA nanoparticles. A detailed structural and morphological characterization of the mineralized composite was performed. The HA-based hybrid materials exhibited no cytotoxic effect on L929 fibroblast cells, showing potential capacity of being a scaffold material for bone tissue regeneration and implantation. This facile strategy also can be a useful platform for other RGO-based nanocomposites.

Velocity/Position Integration Formula Part I: Application to In-Flight Coarse Alignment

Abstract: The in-flight alignment is a critical stage for airborne inertial navigation system/Global Positioning System (INS/GPS) applications. The alignment task is usually carried out by the Kalman filtering technique that necessitates a good initial attitude to obtain a satisfying performance. Due to the airborne dynamics, the in-flight alignment is much more difficult than the alignment on the ground. An optimization-based coarse alignment approach that uses GPS position/velocity as input, founded on the newly-derived velocity/position integration formulae is proposed. Simulation and flight test results show that, with the GPS lever arm well handled, it is potentially able to yield the initial heading up to 1 deg accuracy in 10 s. It can serve as a nice coarse in-flight alignment without any prior attitude information for the subsequent fine Kalman alignment. The approach can also be applied to other applications that require aligning the INS on the run.

Recent Advance in Long-Pulse HPM Sources With Repetitive Operation in S-, C-, and X-Bands

Abstract: Recent experimental results of three kinds of long-pulse high-power microwave (HPM) sources operating in S-, C-, and X-bands are reported. The difficulties in producing a long-pulse HPM for the O-type Cerenkov HPM source were analyzed theoretically. In S- and C-bands, single-mode relativistic backward-wave oscillators were designed to achieve long-pulse HPM outputs; in X-band, because of its shorter wavelength, an O-type Cerenkov HPM source with overmoded slow-wave systems was designed to increase power capacity. In experiments, driven by a repetitive long-pulse accelerator, both S- and C-band sources generated HPMs with power of about 2 GW and pulse duration of about 100 ns in single-shot mode, and the S-band source operated stably with output power of 1.2 GW in 20-Hz repetition mode. The X-band source generated 2 GW microwaves power with pulse duration of 80 ns in the single-shot mode and 1.2 GW microwave power with pulse duration of about 100 ns in the 20-Hz repetition mode. The experiments show good performances of the O-type Cerenkov HPM source in generating repetitive long-pulse HPMs, especially in S- and C-bands. It was suggested that explosive emissions on surfaces of designed eletrodynamic structures restrained pulse duration and operation stability.

Online Self-Reconfiguration with Performance Guarantee for Energy-Efficient Large-Scale Cloud Computing Data Centers

Abstract: In a typical large-scale data center, a set of applications are hosted over virtual machines (VMs) running on a large number of physical machines (PMs). Such a virtualization technique can be used for conserving power consumption by minimizing the number of PMs that should be turned on according to the application requirements to resource. However, the resource demands for VMs is dynamic in nature since the number of user requests the applications should handle is rapidly changing in practice. It is a great challenge to online reconfigure the VMs (i.e., optimize the number and the locations for the VMs) according to the dynamic resource demands. Especially for the emerging applications of large-scale data centers for cloud computing systems, existing approaches either fails to find a best configuration of VMs or cannot produce a result in an acceptable time. In this paper, we propose an online self-reconfiguration approach for reallocating VMs in large-scale data centers. It first accurately predicts the future workloads of the applications with Brown’s quadratic exponential smoothing. Based on such a prediction, it adopts a genetic algorithm to efficiently find the optimal reconfiguration policy. The resource utilization of large-scale cloud computing data centers can thus be improved and their energy consumption can be greatly conserved. We conduct extensive experiments and the results verify that our approach can effectively switch off more unnecessary running PMs comparing with current approaches without a performance degradation of the whole system.