Institution
Harbin Institute of Technology
Education•Harbin, China•
About: Harbin Institute of Technology is a education organization based out in Harbin, China. It is known for research contribution in the topics: Microstructure & Control theory. The organization has 88259 authors who have published 109297 publications receiving 1603393 citations. The organization is also known as: HIT.
Topics: Microstructure, Control theory, Computer science, Alloy, Laser
Papers published on a yearly basis
Papers
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TL;DR: This note proposes an alternative approach to low gain feedback design based on the solution of a parametric Lyapunov equation, which possesses the advantages of both the eigenstructure assignment approach and the ARE-based approach.
Abstract: Low gain feedback has found several applications in constrained control systems, robust control and nonlinear control. Low gain feedback refers to a family of stabilizing state feedback gains that are parameterized in a scalar and go to zero as the scalar decreases to zero. Such feedback gains can be constructed either by an eigenstructure assignment algorithm or through the solution of a parametric algebraic Riccati equation (ARE). The eigenstructure assignment approach leads to feedback gains in the form of a matrix polynomial in the parameter, while the ARE approach requires the solution of an ARE for each value of the parameter. This note proposes an alternative approach to low gain feedback design based on the solution of a parametric Lyapunov equation. Such an approach possesses the advantages of both the eigenstructure assignment approach and the ARE-based approach. It also avoids the possible numerical stiffness in solving a parametric ARE and the structural decomposition of the open loop system that is required by the eigenstructure assignment approach.
220 citations
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TL;DR: In this paper, the oxidation mechanism of ZrB 2 -SiC composites was investigated based on a combination of theory and experiments and a good correspondence was obtained between theoretical predictions and experimental results.
220 citations
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TL;DR: The results suggested that PAEs removal resulted from both biotransformation and adsorption, of which the former was particularly significant in the CAST process, while the latter was more important in the A/A/O process.
220 citations
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TL;DR: The proposed LSDT methods outperform other state-of-the-art representation-based domain adaptation methods and generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space.
Abstract: We propose a novel reconstruction-based transfer learning method called latent sparse domain transfer (LSDT) for domain adaptation and visual categorization of heterogeneous data. For handling cross-domain distribution mismatch, we advocate reconstructing the target domain data with the combined source and target domain data points based on $\ell _{1}$ -norm sparse coding. Furthermore, we propose a joint learning model for simultaneous optimization of the sparse coding and the optimal subspace representation. In addition, we generalize the proposed LSDT model into a kernel-based linear/nonlinear basis transformation learning framework for tackling nonlinear subspace shifts in reproduced kernel Hilbert space. The proposed methods have three advantages: 1) the latent space and the reconstruction are jointly learned for pursuit of an optimal subspace transfer; 2) with the theory of sparse subspace clustering, a few valuable source and target data points are formulated to reconstruct the target data with noise (outliers) from source domain removed during domain adaptation, such that the robustness is guaranteed; and 3) a nonlinear projection of some latent space with kernel is easily generalized for dealing with highly nonlinear domain shift (e.g., face poses). Extensive experiments on several benchmark vision data sets demonstrate that the proposed approaches outperform other state-of-the-art representation-based domain adaptation methods.
220 citations
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TL;DR: T tremendous efforts are required to overcome these challenges to revolutionize microalgae into a novel and green factory of different bioactive compounds for industrial necessities to satisfy and fulfil global demands.
220 citations
Authors
Showing all 89023 results
Name | H-index | Papers | Citations |
---|---|---|---|
Jiaguo Yu | 178 | 730 | 113300 |
Lei Jiang | 170 | 2244 | 135205 |
Gang Chen | 167 | 3372 | 149819 |
Xiang Zhang | 154 | 1733 | 117576 |
Hui-Ming Cheng | 147 | 880 | 111921 |
Yi Yang | 143 | 2456 | 92268 |
Bruce E. Logan | 140 | 591 | 77351 |
Bin Liu | 138 | 2181 | 87085 |
Peng Shi | 137 | 1371 | 65195 |
Hui Li | 135 | 2982 | 105903 |
Lei Zhang | 135 | 2240 | 99365 |
Jie Liu | 131 | 1531 | 68891 |
Lei Zhang | 130 | 2312 | 86950 |
Zhen Li | 127 | 1712 | 71351 |
Kurunthachalam Kannan | 126 | 820 | 59886 |