Results 81 to 90 of about 6,366,089 (278)
Optimization of the Production of Rubber Compounds Using Mathematical Models
Advanced Engineering Materials, EarlyView.Rubber compounds were mixed in a batch internal mixer, and symbolic regression was used to derive mathematical models linking recipe and process parameters to ram path, torque, and mixing quality (incorporation, dispersion, distribution). Subsequent optimization with evolutionary algorithms identified operating conditions that reduce specific energy ...
Anke Bardehle +7 more
wiley +1 more source
Local non‐linear alignment for non‐linear dimensionality reduction
IET Computer Vision, 2017 In manifold learning, alignment is performed with the objective of deriving the global low‐dimensional coordinates of input data from their local coordinates.
Guo Niu, Zhengming Ma
doaj +1 more source
Learning a Robust Local Manifold Representation for Hyperspectral Dimensionality Reduction
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, 2017 Local manifold learning has been successfully applied to hyperspectral dimensionality reduction in order to embed nonlinear and nonconvex manifolds in the data.
Danfeng Hong +2 more
doaj +1 more source
Manifold Constrained Low-Rank and Joint Sparse Learning for Dynamic Cardiac MRI
IEEE Access, 2020 Reconstruction from highly accelerated dynamic magnetic resonance imaging (MRI) is of great significance for medical diagnosis. The application of low-rank and sparse matrix decomposition to MRI can improve imaging speed and efficiency.
Qingmin Meng, Xianchao Xiu, Yan Li
doaj +1 more source
Manifold learning in metric spaces
Applied and Computational Harmonic AnalysisLaplacian-based methods are popular for the dimensionality reduction of data lying in $\mathbb{R}^N$. Several theoretical results for these algorithms depend on the fact that the Euclidean distance locally approximates the geodesic distance on the underlying submanifold which the data are assumed to lie on. However, for some applications, other metrics,
Liane Xu, Amit Singer
openaire +4 more sources
Generalized -Einstein 3-dimensional trans-Sasakian manifold
, 2020 . In the present note we have introduced a new concept called generalized -Einstein manifold in a 3-dimensional trans-Sasakian manifold and have given some preliminary ideas about the same.
Sasakian Manifold +2 more
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A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy
Advanced Engineering Materials, EarlyView.A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle +5 more
wiley +1 more source
Advanced Functional Materials, EarlyView.An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone +11 more
wiley +1 more source
Türk Kardiyoloji Derneği Arşivi, 2019 Objective: Carotid ultrasonography is a reliable and non-invasive method to evaluate atherosclerosis disease and its complications. B-mode cineloops are widely used to assess the severity of atherosclerosis and its progression; ho- wever, tracking rapid ...
Fereshteh Yousefi Rizi +2 more
doaj +1 more source
Manifold mapping learning by regression tree boosting
, 2015 Manifold learning has shown powerful information processing capability for high-dimensional data. In this paper, we proposed a manifold mapping learning algorithm to alleviate the shortage of traditional methods and broaden the applications of manifold ...
Han Z(韩志) +2 more
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