L1‐type smoothness indicators based weighted essentially nonoscillatory scheme for Hamilton‐Jacobi equations
Sign Up to like & getrecommendations! Published in 2020 at "International Journal for Numerical Methods in Fluids"
DOI: 10.1002/fld.4855
Abstract: This article presents an improved fifth‐order finite difference weighted essentially nonoscillatory (WENO) scheme to solve Hamilton‐Jacobi equations. A new type of nonlinear weights is introduced with the construction of local smoothness indicators on each local… read more here.
Keywords: essentially nonoscillatory; jacobi equations; weighted essentially; hamilton jacobi ... See more keywords
On the Efficient Computation of Smoothness Indicators for a Class of WENO Reconstructions
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DOI: 10.1007/s10915-019-00974-7
Abstract: Common smoothness indicators used in Weighted Essentially Non-Oscillatory (WENO) reconstructions (Jiang and Shu in J Comput Phys 126:202–228, 1996) have quadratic cost with respect to the order. A set of novel smoothness indicators with linear cost… read more here.
Keywords: yamaleev carpenter; weno reconstructions; computation; smoothness indicators ... See more keywords
Pathwise smooth splittable congestion games and inefficiency
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DOI: 10.1016/j.jmateco.2019.10.006
Abstract: Abstract Very recently the inefficiency of Nash equilibria has been analyzed in the context of S p l i t t a b l e C o n g e s t i o n G… read more here.
Keywords: pathwise smooth; splittable congestion; smoothness; congestion ... See more keywords
An Objective Fabric Smoothness Assessment Method Based on a Multi-Scale Spatial Masking Model
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DOI: 10.1109/access.2019.2918845
Abstract: Fabric smoothness appearance assessment plays an important role in the textile and apparel industry. It remains a challenging task to evaluate the fabric smoothness objectively. In the existing objective methods based on the image analysis,… read more here.
Keywords: method; fabric smoothness; model; smoothness ... See more keywords
A Novel Method for Function Smoothness in Neural Networks
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DOI: 10.1109/access.2022.3189363
Abstract: Existing methods for function smoothness in neural networks have limitations. These methods can make training sensitive to their hyperparameters, or their smoothness constraints can limit model capacity. These methods can impose too much smoothness, even… read more here.
Keywords: function smoothness; neural networks; function; smoothness neural ... See more keywords
A continuation of solutions to convolution equations with the loss of smoothness
Sign Up to like & getrecommendations! Published in 2017 at "Lobachevskii Journal of Mathematics"
DOI: 10.1134/s1995080217030180
Abstract: In the present paper the smoothness loss of a continuation of solutions to convolution equations is studied. Also examples for some kinds of convolvers are given. read more here.
Keywords: continuation solutions; smoothness; convolution equations; solutions convolution ... See more keywords
Regularized Nyström subsampling in regression and ranking problems under general smoothness assumptions
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DOI: 10.1142/s021953051850029x
Abstract: In the supervised learning, the Nyström type subsampling is considered as a tool for reducing the computational complexity of regularized kernel methods in the big data setting. Up to now, the theoretical analysis of this… read more here.
Keywords: regression; regularized nystr; case; smoothness ... See more keywords
Proximal-distal differences in movement smoothness reflect differences in biomechanics.
Sign Up to like & getrecommendations! Published in 2017 at "Journal of neurophysiology"
DOI: 10.1152/jn.00712.2015
Abstract: Smoothness is a hallmark of healthy movement. Past research indicates that smoothness may be a side product of a control strategy that minimizes error. However, this is not the only reason for smooth movements. Our… read more here.
Keywords: movement; smoothness; biomechanics; impedance ... See more keywords
Local Smoothness of Graph Signals
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DOI: 10.1155/2019/3208569
Abstract: Analysis of vertex-varying spectral content of signals on graphs challenges the assumption of vertex invariance and requires the introduction of vertex-frequency representations as a new tool for graph signal analysis. Local smoothness, an important parameter… read more here.
Keywords: vertex frequency; graph signals; smoothness; local smoothness ... See more keywords
Smoothness: an Unexplored Window into Coordinated Running Proficiency
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DOI: 10.1186/s40798-019-0215-y
Abstract: Over the expanse of evolutionary history, humans, and predecessor Homo species, ran to survive. This legacy is reflected in many deeply and irrevocably embedded neurological and biological design features, features which shape how we run,… read more here.
Keywords: smoothness unexplored; coordinated running; movement; smoothness ... See more keywords
Regularized Inversion of Coseismic Slip Distribution Based on Smoothness-Constrained Model
Sign Up to like & getrecommendations! Published in 2017 at "Advances in Geosciences"
DOI: 10.12677/ag.2017.75066
Abstract: 同震滑动的空间分布估计问题是不适定的，其反演结果存在非唯一性。为了获取稳定的同震滑动分布，本文采用了光滑模型约束的正则化反演方法。算法实施过程中，构建了断层面非均匀离散的滑动量分布光滑约束模型，并设计一种快速稳定的正则化因子求取方法；为了获得合理的滑动分布，采用了非负最小二乘求解方法。理论均匀滑动分布模型的反演试算，验证了反演算法的有效性和稳定性，且非负最小二乘方法能规避不合理的滑动分布。利用光滑约束反演方法反演了2005年Nias地震的同震滑动分布，并与前人的研究结果进行了对比分析。2005年Nias地震反演结果显示：断层面最大滑动量为12.8 m，这与Konca等反演得到的结果一致，且滑动分布所释放的地震矩为9.91 × 1012 Nm，其地震震级为Mw = 8.6，与USGS公布的结果一致。通过理论滑动分布模型和实际震例的反演结果表明，光滑约束正则化反演方法是可行的，并能合理地重建断层面的同震滑动分布。 Estimating the spatial distribution of coseismic slip is an ill-posed inverse problem, and the solution is non-unique. In order to obtain stable solution for coseismic slip inversion, regularization… read more here.
Keywords: slip; inversion; smoothness; coseismic slip ... See more keywords