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Institution

University of Macau

EducationMacao, Macau, China
About: University of Macau is a education organization based out in Macao, Macau, China. It is known for research contribution in the topics: Population & Control theory. The organization has 6636 authors who have published 18324 publications receiving 327384 citations. The organization is also known as: UM & UMAC.


Papers
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Journal ArticleDOI
TL;DR: This note proposes an adaptive method to relax such a requirement to allow non-identical control directions, under the condition that some control directions are known.
Abstract: Existing Nussbaum function based results on consensus of multi-agent systems require that the unknown control directions of all the agents should be the same. This note proposes an adaptive method to relax such a requirement to allow non-identical control directions, under the condition that some control directions are known. Technically, a novel idea is proposed to construct a new Nussbaum function, from which a conditional inequality is developed to handle time-varying input gains. Then, the inequality is integrated with adaptive control technique such that the proposed Nussbaum function for each agent is adaptively updated. Moreover, in addition to parametric uncertainties, each agent has non-parametric bounded modelling errors which may include external disturbances and approximation errors of static input nonlinearities. Even in the presence of such uncertainties, the proposed control scheme is still able to ensure the states of all the agents asymptotically reach perfect consensus. Finally, simulation study is performed to show the effectiveness of the proposed approach.

179 citations

Journal ArticleDOI
TL;DR: A new feature input space is proposed and an LBP-like descriptor that operates in the local line-geometry space is defined, thus proposing a new image descriptor, local line directional patterns (LLDP).

178 citations

Journal ArticleDOI
TL;DR: This paper develops a novel medical image fusion, denoising, and enhancement method based on low-rank sparse component decomposition and dictionary learning that consistently outperforms existing state-of-the-art methods in terms of both visual and quantitative evaluations.

178 citations

Journal ArticleDOI
TL;DR: Investigation of self-reported levels of PSU, depression, anxiety, and daily depressive mood relate to objectively measured smartphone use over one week found depression and anxiety severity were not related to screen time minutes, but negatively correlated with frequency of phone screen unlocking.

178 citations

Journal ArticleDOI
TL;DR: A sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm and prediction accuracy of the PDBM model outperforms existing methods by more than 10%.
Abstract: It is important to forecast the wind speed for managing operations in wind power plants. However, wind speed prediction is extremely complex and difficult due to the volatility and deviation of the wind. As existing forecasting methods directly model the raw wind speed data, it is difficult for them to provide higher inference accuracy. Differently, this paper presents a sophisticated deep-learning technique for short-term and long-term wind speed forecast, i.e., the predictive deep Boltzmann machine (PDBM) and corresponding learning algorithm. The proposed deep model forecasts wind speed by analyzing the higher level features abstracted from lower level features of the wind speed data. These automatically learnt features are very informative and appropriate for the prediction. The proposed PDBM is a deep stochastic model that can represent the wind speed very well, and is inspired by two aspects. 1)The stochastic model is suitable to capture the probabilistic characteristics of wind speed. 2)Recent developments in neural networks with deep architectures show that deep generative models have competitive capability to approximate nonlinear and nonsmooth functions. The evaluation of the proposed PDBM model is depicted by both hour-ahead and day-ahead prediction experiments based on real wind speed datasets. The prediction accuracy of the PDBM model outperforms existing methods by more than 10%.

178 citations


Authors

Showing all 6766 results

NameH-indexPapersCitations
Henry T. Lynch13392586270
Chu-Xia Deng12544457000
H. Vincent Poor109211667723
Peng Chen10391843415
George F. Gao10279382219
MengChu Zhou96112436969
Gang Li9348668181
Rob Law8171431002
Zongjin Li8063022103
Han-Ming Shen8023727410
Heng Li7974523385
Lionel M. Ni7546628770
C. L. Philip Chen7448220223
Chun-Su Yuan7239721089
Joao P. Hespanha7241839004
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Performance
Metrics
No. of papers from the Institution in previous years
YearPapers
202345
2022307
20212,579
20202,357
20192,075
20181,714