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Zhaoyu Liu, Shi Liu, Minxin Chen, Yaofang Zhang and Pengbo Yao
Constrained by cost, measuring conditions and excessive calculation, it is difficult to reconstruct a 3D real-time temperature field. For the purpose of solving these problems, a three-dimensional temperature distribution reconstruction algorithm based o...
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Qianqian Tong, Guannan Liang, Jiahao Ding, Tan Zhu, Miao Pan and Jinbo Bi
Regularized sparse learning with the l0
l
0
-norm is important in many areas, including statistical learning and signal processing. Iterative hard thresholding (IHT) methods are the state-of-the-art for nonconvex-constrained sparse learning due to their ...
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Andrea Magrini, Ernesto Benini, Rita Ponza, Chen Wang, Hamed Haddad Khodaparast, Michael I. Friswell, Volker Landersheim, Dominik Laveuve and Conchin Contell Asins
In the context of ambitious targets for reducing environmental impact in the aviation sector, dictated by international institutions, morphing aircraft are expected to have potential for achieving the required efficiency increases. However, there are sti...
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Florin Stoican and Paul Irofti
The l1
l
1
relaxations of the sparse and cosparse representation problems which appear in the dictionary learning procedure are usually solved repeatedly (varying only the parameter vector), thus making them well-suited to a multi-parametric interpretat...
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Xinqiang Du, Xiangqin Lu, Jiawei Hou and Xueyan Ye
In data-sparse areas, due to the lack of hydrogeological data, numerical groundwater models have some uncertainties. In this paper, a nested model and a multi-index calibration method are used to improve the reliability of a numerical groundwater model i...
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