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.

Some fourth-order nonlinear solvers with closed formulae for multiple roots

Abstract: In this paper, we present six new fourth-order methods with closed formulae for finding multiple roots of nonlinear equations. The first four of them require one-function and three-derivative evaluation per iteration. The last two require one-function and two-derivative evaluation per iteration. Several numerical examples are given to show the performance of the presented methods compared with some known methods.

Deep Collective Classification in Heterogeneous Information Networks

Abstract: Collective classification has attracted considerable attention in the last decade, where the labels within a group of instances are correlated and should be inferred collectively, instead of independently. Conventional approaches on collective classification mainly focus on exploiting simple relational features (such ascount andexists aggregators on neighboring nodes). However, many real-world applications involve complex dependencies among the instances, which are obscure/hidden in the networks. To capture these dependencies in collective classification, we need to go beyond simple relational features and extract deep dependencies between the instances. In this paper, we study the problem of deep collective classification inHeterogeneous Information Networks (HINs), which involves different types of autocorrelations, from simple to complex relations, among the instances. Different from conventional autocorrelations, which are given explicitly by the links in the network, complex autocorrelations are obscure/hidden in HINs, and should be inferred from existing links in a hierarchical order. This problem is highly challenging due to the multiple types of dependencies among the nodes and the complexity of the relational features. In this study, we proposed a deep convolutional collective classification method, called GraphInception to learn the deep relational features in HINs. The proposed method can automatically generate a hierarchy of relational features with different complexities. Extensive experiments on four real-world networks demonstrate that our approach can improve the collective classification performance by considering deep relational features in HINs.

Fast Entropy Minimization Based Autofocusing Technique for ISAR Imaging

Abstract: Autofocusing technology is an essential step of Inverse Synthetic Aperture Radar (ISAR) imaging, whose performance has great influence on the quality of the radar image As far as the existed autofocusing methods are concerned, the methods based on minimum entropy criterion are robust and have been widely applied in both Synthetic Aperture Radar (SAR) and ISAR imaging However, the Minimum Entropy based Autofocusing (MEA) methods usually suffer from heavy computation burden because of the complex formula of image entropy and the optimal search of the phase error In this paper, a novel fast MEA method based on the Newton method is proposed Moreover, the Simplified Newton (SN) method and the Modified Newton (MN) method are also introduced to the implement of MEA, and provide high computation efficiency Mathematical analysis and experimental results based on real measured data of two airplanes have validated the high computation efficiency of the proposed methods Compared to the widely applied MEA method for ISAR imaging, the proposed methods can improve the computation efficiency by 10 to 20 times

Low-frequency sound absorption of hybrid absorber based on micro-perforated panel and coiled-up channels

Abstract: We propose a hybrid acoustic metamaterial as a super absorber for a relatively broadband low-frequency sound based on a simple construction with deep-subwavelength thickness (5 cm). The hybrid metamaterial absorber is carefully designed and constructed based on a microperforated panel (MPP) and coiled-up Fabry–Perot channels. It is demonstrated analytically, numerically, and experimentally that over 99% of acoustic absorption could be achieved at a resonance frequency (<500 Hz) with the working wavelength about 30 times larger than its total thickness. It is revealed that the superior absorption is mainly caused by the friction losses of acoustic wave energy in the MPP. The frequency of the absorption peak could be tuned by adjusting the geometry parameters of the MPP and the channel folding numbers. The relative absorption bandwidth could also be tuned flexibly (up to 82%) with a fixed deep-subwavelength thickness (5 cm). The absorber has wide potential applications in noise control engineering due to its deep-subwavelength thickness, relatively broad bandwidth, and easy fabrication.

Characterization of nonlinear properties of black phosphorus nanoplatelets with femtosecond pulsed Z-scan measurements.

Abstract: The nonlinear properties of black phosphorus (BP) nanoplatelets (NPs) have been characterized with Z-scan measurements under 800-nm femtosecond pulsed laser excitation. A transition from saturable absorption (SA) to reverse saturable absorption (RSA) with the increase of laser intensity was observed in the open-aperture (OA) measurements. Simultaneously, closed-aperture (CA) measurements were carried out to investigate the nonlinear refractive index of BP NPs together, and a value of n(2) ≃(6.8±0.2)×10(-13) m2/W was obtained. The nonlinear absorption properties were analyzed according to the band structure of BP. A theoretical analysis based on SA and two-photon absorption (TPA) was used to determine the nonlinear absorption coefficients from the experimental results, and the TPA coefficient at 800 nm was estimated about (4.5±0.2)×10(-10) m/W.