Results 51 to 60 of about 690,102 (265)

Tensor dictionary learning with sparse TUCKER decomposition

2013 18th International Conference on Digital Signal Processing (DSP), 2013
Dictionary learning algorithms are typically derived for dealing with one or two dimensional signals using vector-matrix operations. Little attention has been paid to the problem of dictionary learning over high dimensional tensor data. We propose a new algorithm for dictionary learning based on tensor factorization using a TUCKER model.
Syed Zubair, Wenwu Wang 0001
openaire   +3 more sources

Compliant Pneumatic Feet with Real‐Time Stiffness Adaptation for Humanoid Locomotion

Advanced Robotics Research, EarlyView.
A compliant pneumatic foot with real‐time variable stiffness enables humanoid robots to adapt to changing terrains. Using onboard vision and pressure control, the foot modulates stiffness within each gait cycle, reducing impact forces and improving balance. The design, cast in soft silicone with embedded air chambers and Kevlar wrapping, offers durable,
Irene Frizza, Hiroshi Kaminaga, Philippe Fraisse, Gentiane Venture   +3 more
wiley   +1 more source

Hyperspectral Image Denoising via Low-Rank Tucker Decomposition with Subspace Implicit Neural Representation

Remote Sensing
Hyperspectral image (HSI) denoising is an important preprocessing step for downstream applications. Fully characterizing the spatial-spectral priors of HSI is crucial for denoising tasks.
Cheng Cheng, Dezhi Sun, Yaoyuan Yang, Zhoucheng Guo, Jiangjun Peng   +4 more
doaj   +1 more source

Tucker Decomposition Network: Expressive Power and Comparison

CoRR, 2019
Deep neural networks have achieved a great success in solving many machine learning and computer vision problems. The main contribution of this paper is to develop a deep network based on Tucker tensor decomposition, and analyze its expressive power. It is shown that the expressiveness of Tucker network is more powerful than that of shallow network. In
Ye Liu 0014, JunJun Pan, Michael K. Ng 0001   +2 more
openaire   +3 more sources

Point Cloud Denoising Based on Tensor Tucker Decomposition [PDF]

2019 IEEE International Conference on Image Processing (ICIP), 2019
5 pages, 1 ...
Jianze Li, Xiao-Ping (Steven) Zhang, Tuan Tran   +2 more
openaire   +4 more sources

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

Supercapacitor Safety: Overcharge‐Induced Failure Mechanisms in Electrochemical Double‐Layer Capacitors

Advanced Science, EarlyView.
Time‐resolved synchrotron radiography reveals the internal evolution of electrochemical double‐layer capacitors under abusive overcharge. EDLCs are shown to follow a thermally driven failure pathway involving electrolyte decomposition, gas generation, and structural collapse, distinct from chemically driven thermal runaway effects of Lithium‐ion ...
Thomas Dore, Inez Kesuma, Abdus S. Ali, Jonas Pfaff, Yuxiao Jin, Rosalie Hamill, Mark Buckwell, Hamish Reid, Thomas Finco, Zeyu Sun, Yuanze Li, Sebastian Schopferer, Siegfried Nau, Nils Böttcher, Henning Markötter, Alexander J. E. Rettie, James Robinson, Paul R. Shearing, Alexander Rack, Thomas S. Miller   +19 more
wiley   +1 more source

A tensor compression algorithm using Tucker decomposition and dictionary dimensionality reduction

International Journal of Distributed Sensor Networks, 2020
Tensor compression algorithms play an important role in the processing of multidimensional signals. In previous work, tensor data structures are usually destroyed by vectorization operations, resulting in information loss and new noise. To this end, this
Chenquan Gan, Junwei Mao, Zufan Zhang, Qingyi Zhu   +3 more
doaj   +1 more source

Faster Quantum State Decomposition with Tucker Tensor Approximation

Quantum Machine Intelligence, 2022
Abstract Researchers have put a lot of effort into reducing the gap between current quantum processing units (QPU) capabilities and their potential supremacy.One approach is to keep supplementary computations in the CPU, and use QPU only for the core of the problem.
Protasov Stanislav, Lisnichenko Marina
openaire   +1 more source

Redefining the Minimal Editable Unit of Supramolecular Synthons: A 1→2 Lattice‐Editing Strategy in Ternary Multicomponent Crystals

Advanced Science, EarlyView.
Inspired by skeletal editing, we propose a lattice‐editing strategy that extends the minimal editable unit in supramolecular crystals from individual components to local supramolecular structural units, enabling non‐stoichiometric one‐to‐many substitution while preserving the parent lattice architecture.
Zheng Lin, Honglei Xia, Min Li, Ruihui Wang, Ying Wang, Xiaoyu Feng, Mingjing Tang, Yuan Zhang, Qinghua Zhang   +8 more
wiley   +1 more source