Institution
Beihang University
Education•Beijing, China•
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.
Topics: Control theory, Microstructure, Nonlinear system, Artificial neural network, Feature extraction
Papers published on a yearly basis
Papers
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TL;DR: A design framework that involves a design process to support PSS customization in early design phase is proposed that is module-based and thus flexible according to the user needs and takes advantage of some existing methods.
179 citations
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TL;DR: The research framework combines an air pollutant emission projection model (GAINS), an air quality model (GEOS-Chem), a health model using the latest exposure-response functions, medical prices and value of statistical life (VSL), and a general equilibrium model (CGE) to compare the PM2.5 and ozone pollution-related health impacts based on an integrated approach.
179 citations
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TL;DR: It is found that the coupled reaction-diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled Reaction-Diffusion Neural networks with spatial diffusion coupling.
Abstract: Two types of coupled neural networks with reaction–diffusion terms are considered in this paper In the first one, the nodes are coupled through their states In the second one, the nodes are coupled through the spatial diffusion terms For the former, utilizing Lyapunov functional method and pinning control technique, we obtain some sufficient conditions to guarantee that network can realize synchronization In addition, considering that the theoretical coupling strength required for synchronization may be much larger than the needed value, we propose an adaptive strategy to adjust the coupling strength for achieving a suitable value For the latter, we establish a criterion for synchronization using the designed pinning controllers It is found that the coupled reaction–diffusion neural networks with state coupling under the given linear feedback pinning controllers can realize synchronization when the coupling strength is very large, which is contrary to the coupled reaction–diffusion neural networks with spatial diffusion coupling Moreover, a general criterion for ensuring network synchronization is derived by pinning a small fraction of nodes with adaptive feedback controllers Finally, two examples with numerical simulations are provided to demonstrate the effectiveness of the theoretical results
178 citations
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TL;DR: This paper reveals a necessary and sufficient condition for utility functions which work for KCC and investigates some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC.
Abstract: The objective of consensus clustering is to find a single partitioning which agrees as much as possible with existing basic partitionings. Consensus clustering emerges as a promising solution to find cluster structures from heterogeneous data. As an efficient approach for consensus clustering, the K-means based method has garnered attention in the literature, however the existing research efforts are still preliminary and fragmented. To that end, in this paper, we provide a systematic study of K-means-based consensus clustering (KCC). Specifically, we first reveal a necessary and sufficient condition for utility functions which work for KCC. This helps to establish a unified framework for KCC on both complete and incomplete data sets. Also, we investigate some important factors, such as the quality and diversity of basic partitionings, which may affect the performances of KCC. Experimental results on various real-world data sets demonstrate that KCC is highly efficient and is comparable to the state-of-the-art methods in terms of clustering quality. In addition, KCC shows high robustness to incomplete basic partitionings with many missing values.
178 citations
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TL;DR: In this article, a few-layer black phosphorus (BP) was synthesized with metal-ion-modification against oxidation and degradation, and then the feasibility of BP-coated microfiber as an optical Kerr switcher and a four-wave-mixing-based wavelength converter was demonstrated.
Abstract: Two-dimensional (2D) black phosphorus (BP) shows thickness dependent direct energy band-gaps in association with strong light-matter interaction and broadband optical response, rendering it with promising optoelectronic advantages particularly at the telecommunication band. However, intrinsic BP suffers from irreversible oxidization, restricting its competences toward real device applications. As one potential of 2D materials, all-optical signal processing sensitively depends on the strength of light–matter interaction. BP can be utilized as a novel optical medium. Herein, few-layer BP is synthesized with metal-ion-modification against oxidation and degradation, and then the feasibility of BP-coated microfiber as an optical Kerr switcher and a four-wave-mixing-based wavelength converter is demonstrated. The wavelength-tuning, long-term stability, wide-band RF frequency, and time-repeating measurements confirm that this optical device can operate as a broadband all-optical processor. It is further anticipa...
178 citations
Authors
Showing all 67500 results
Name | H-index | Papers | Citations |
---|---|---|---|
Yi Chen | 217 | 4342 | 293080 |
H. S. Chen | 179 | 2401 | 178529 |
Alan J. Heeger | 171 | 913 | 147492 |
Lei Jiang | 170 | 2244 | 135205 |
Wei Li | 158 | 1855 | 124748 |
Shu-Hong Yu | 144 | 799 | 70853 |
Jian Zhou | 128 | 3007 | 91402 |
Chao Zhang | 127 | 3119 | 84711 |
Igor Katkov | 125 | 972 | 71845 |
Tao Zhang | 123 | 2772 | 83866 |
Nicholas A. Kotov | 123 | 574 | 55210 |
Shi Xue Dou | 122 | 2028 | 74031 |
Li Yuan | 121 | 948 | 67074 |
Robert O. Ritchie | 120 | 659 | 54692 |
Haiyan Wang | 119 | 1674 | 86091 |