Results 71 to 80 of about 8,931 (261)

Geometric multiscale decompositions of dynamic low-rank matrices [PDF]

Computer Aided Geometric Design, 2013
SAM Research Report, 2012 ...
openaire   +2 more sources

3D Printing of Soft Robotic Systems: Advances in Fabrication Strategies and Future Trends

Advanced Robotics Research, EarlyView.
Collectively, this review systematically examines 3D‐printed soft robotics, encompassing material selections, function integration, and manufacturing methodologies. Meanwhile, fabrication strategies are analyzed in order of increasing complexity, highlighting persistent challenges with proposed solutions.
Changjiang Liu, Bin Cui, Xumin Huang, Xiaoyu (Rayne) Zheng, T. Thang Vo‐Doan, Ruirui Qiao   +5 more
wiley   +1 more source

Solid Harmonic Wavelet Bispectrum for Image Analysis

Advanced Science, EarlyView.
The Solid Harmonic Wavelet Bispectrum (SHWB), a rotation‐ and translation‐invariant descriptor that captures higher‐order (phase) correlations in signals, is introduced. Combining wavelet scattering, bispectral analysis, and group theory, SHWB achieves interpretable, data‐efficient representations and demonstrates competitive performance across texture,
Alex Brown, Mathilda Avirett‐Mackenzie, Carolin Villforth, Georgios Exarchakis   +3 more
wiley   +1 more source

Retracted: Wavelet-Based Multiscale Decomposition Algorithm for Trajectory Capture of Tennis Serves. [PDF]

Comput Intell Neurosci, 2023
Intelligence And Neuroscience C.
europepmc   +1 more source

High‐Throughput Screening and Interpretable Machine Learning for Rational Design of Bimetallic Catalysts for Methane Activation

Advanced Science, EarlyView.
ABSTRACT Methane's efficient catalytic removal is vital for sustainable development. Bimetallic catalysts, though promising for methane activation, pose a design challenge due to their complex compositional space. This work introduces an integrated framework that combines high‐throughput density functional theory (DFT) and interpretable machine ...
Mingzhang Pan, Tian Zhang, Jiawei Dong, Yubao Xie, Kaifeng Zhong, Wei Guan, Haiqiao Wei, Changcheng Fu, Yaqiong Su   +8 more
wiley   +1 more source

AB-PINNs: adaptive-basis physics-informed neural networks for residual-driven domain decomposition

Machine Learning: Science and Technology
We introduce adaptive-basis physics-informed neural networks (AB-PINNs), an adaptive domain decomposition framework for PINNs in which learnable subdomains dynamically evolve during training to align with intrinsic features of the unknown solution. Local
Jonah Botvinick-Greenhouse, Wael H Ali, Mouhacine Benosman, Saviz Mowlavi   +3 more
doaj   +1 more source

Harnessing Phase Separation for the Development of High‐Performance Hydrogels

Advanced Science, EarlyView.
ABSTRACT Hydrogels are indispensable for the development of next‐generation bioelectronics, soft robotics, and biomedical devices, where their mechanical properties determine performance and reliability. Among strategies to enhance hydrogel mechanics, phase separation enables controlled heterogeneity resulting in gel networks that are reinforced by ...
Yue Shao, Yiming Ma, Baihao Shao, Molly M. Stevens   +3 more
wiley   +1 more source

Infrared and visible image fusion algorithm based on Retinex⁃enhanced multiscale decomposition

Journal of Measurement Science and Instrumentation
Aiming at the problems of poor contrast of fusion results, blurring of target margins and loss of background detail information under low illumination conditions of traditional infrared and visible image fusion algorithms, an infrared and visible image ...
GUO Yin, DU Lixia
doaj  

Intelligent Fault Diagnosis for Rotating Mechanical Systems: An Improved Multiscale Fuzzy Entropy and Support Vector Machine Algorithm

Algorithms
Rotating mechanical systems (RMSs) are widely applied in various industrial fields. Intelligent fault diagnosis technology plays a significant role in improving the reliability and safety of industrial equipment.
Yuxin Pan, Yinsheng Chen, Xihong Fei, Kang Wang, Tian Fang, Jing Wang   +5 more
doaj   +1 more source

Signal decomposition with multiscale learning algorithms [PDF]

, 1996
In this paper, neural networks based on orthonormal wavelets are constructed to decompose signals into full scale space. Two algorithms, the global multiscale learning (GML) and the pyramid multiscale learning (PML) were proposed and comparatively studied for their convergence speed, accuracy, and multiresolution representation property in functional ...
J. W. Wang, C. H. Chen, J. C. Luo
openaire   +1 more source