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Institution

National University of Defense Technology

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


Papers
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Journal ArticleDOI
TL;DR: This paper proposes an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm, which proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix to address issues of intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance.
Abstract: Incomplete multi-view clustering (IMVC) optimally combines multiple pre-specified incomplete views to improve clustering performance. Among various excellent solutions, the recently proposed multiple kernel $k$ k -means with incomplete kernels (MKKM-IK) forms a benchmark, which redefines IMVC as a joint optimization problem where the clustering and kernel matrix imputation tasks are alternately performed until convergence. Though demonstrating promising performance in various applications, we observe that the manner of kernel matrix imputation in MKKM-IK would incur intensive computational and storage complexities, over-complicated optimization and limitedly improved clustering performance. In this paper, we first propose an Efficient and Effective Incomplete Multi-view Clustering (EE-IMVC) algorithm to address these issues. Instead of completing the incomplete kernel matrices, EE-IMVC proposes to impute each incomplete base matrix generated by incomplete views with a learned consensus clustering matrix. Moreover, we further improve this algorithm by incorporating prior knowledge to regularize the learned consensus clustering matrix. Two three-step iterative algorithms are carefully developed to solve the resultant optimization problems with linear computational complexity, and their convergence is theoretically proven. After that, we theoretically study the generalization bound of the proposed algorithms. Furthermore, we conduct comprehensive experiments to study the proposed algorithms in terms of clustering accuracy, evolution of the learned consensus clustering matrix and the convergence. As indicated, our algorithms deliver their effectiveness by significantly and consistently outperforming some state-of-the-art ones.

90 citations

Journal ArticleDOI
TL;DR: In this article, a general review on the achievements of various kinds of high-power fiber lasers based on the tandem pumping scheme in the past few years is presented, and the underlying challenges for further power scaling, including the nonlinear effect suppression and special fiber design, are briefly discussed.
Abstract: Power scaling of fiber lasers is challenged by several factors, such as the brightness of the pump source, the nonlinear effect, modal instability, and so on. Pumping active fibers with high-brightness fiber lasers instead of laser diodes is a promising solution for the brightness limitation and modal instability. In this paper, for the first time to our knowledge, we present a general review on the achievements of various kinds of high-power fiber lasers based on the tandem pumping scheme in the past few years. The requirements for tandem pumping ytterbium (Yb), erbium (Er), thulium (Tm), and holmium (Ho)-doped fibers are analyzed, and corresponding achievements are summarized. Hundreds of watts of fiber lasers at ∼1020, ∼1500, and 1900 nm and hundred-watt-level fiber lasers at ∼1150 and ∼1180 nm have been successfully achieved. Then, these powerful fiber lasers with high brightness can be employed as pump sources for Yb-, Er-, Tm- and Ho-doped fibers. Moreover, a recent experimental result of a 3.5 kW Yb-doped fiber amplifier in an all-fiber format is reported in addition to previous typical achievements. The underlying challenges for further power scaling, including the nonlinear effect suppression and special fiber design, are briefly discussed. Exploring the tandem pumping scheme in novel application fields is discussed as well.

90 citations

Proceedings ArticleDOI
09 May 2011
TL;DR: A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.
Abstract: This paper presents a distributed algorithm for performing joint localisation of a team of robots. The mobile robots have heterogeneous sensing capabilities, with some having high quality inertial and exteroceptive sensing, while others have only low quality sensing or none at all. By sharing information, a combined estimate of all robot poses is obtained. Inter-robot range-bearing measurements provide the mechanism for transferring pose information from well-localised vehicles to those less capable. In our proposed formulation, high frequency egocentric data (e.g., odometry, IMU, GPS) is fused locally on each platform. This is the distributed part of the algorithm. Inter-robot measurements, and accompanying state estimates, are communicated to a central server, which generates an optimal minimum mean-squared estimate of all robot poses. This server is easily duplicated for full redundant decentralisation. Communication and computation are efficient due to the sparseness properties of the information-form Gaussian representation. A team of three indoor mobile robots equipped with lasers, odometry and inertial sensing provides experimental verification of the algorithms effectiveness in combining location information.

90 citations

Posted Content
TL;DR: More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics as mentioned in this paper.
Abstract: Object detection, one of the most fundamental and challenging problems in computer vision, seeks to locate object instances from a large number of predefined categories in natural images. Deep learning techniques have emerged as a powerful strategy for learning feature representations directly from data and have led to remarkable breakthroughs in the field of generic object detection. Given this period of rapid evolution, the goal of this paper is to provide a comprehensive survey of the recent achievements in this field brought about by deep learning techniques. More than 300 research contributions are included in this survey, covering many aspects of generic object detection: detection frameworks, object feature representation, object proposal generation, context modeling, training strategies, and evaluation metrics. We finish the survey by identifying promising directions for future research.

90 citations

Journal ArticleDOI
TL;DR: In this article, the effects of the geometric parameters, i.e., the upstream depth, the downstream depth, and the swept angle, on the drag force of the cavity flameholder for a heated flow were investigated using the variance analysis method.

90 citations


Authors

Showing all 39659 results

NameH-indexPapersCitations
Rui Zhang1512625107917
Jian Li133286387131
Chi Lin1251313102710
Wei Xu103149249624
Lei Liu98204151163
Xiang Li97147242301
Chang Liu97109939573
Jian Huang97118940362
Tao Wang97272055280
Wei Liu96153842459
Jian Chen96171852917
Wei Wang95354459660
Peng Li95154845198
Jianhong Wu9372636427
Jianhua Zhang9241528085
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
20241
202397
2022468
20212,986
20203,468
20193,695