Results 71 to 80 of about 28,509 (255)

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, Andreas Limper, Ulrich Giese, Carlos Lucke, Mario Beiner, Christoph Gögelein, Lisa Leuchtenberger‐Engel, Christian Hopmann   +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, Naoto Yokoya, Xiao Xiang Zhu   +2 more
doaj   +1 more source

Unsupervised Nonlinear Manifold Learning [PDF]

2007 IEEE International Conference on Image Processing, 2007
This communication deals with data reduction and regression. A set of high dimensional data (e.g., images) usually has only a few degrees of freedom with corresponding variables that are used to parameterize the original data set. Data understanding, visualization and classification are the usual goals.
Matthieu Brucher, Christian Heinrich, Fabrice Heitz, Jean-Paul Armspach   +3 more
openaire   +1 more source

Modeling Dislocation Cutting of γ′ Precipitates in Ni‐Base Superalloys: Linking Atomistic and Dislocation Dynamics Simulations

Advanced Engineering Materials, EarlyView.
Dislocation cutting of γ′ precipitates in Ni‐based superalloys is investigated by linking atomistic simulations with discrete dislocation dynamics. The critical cutting stress is shown to be governed by the antiphase boundary energy, while line tension effects promote edge‐preferred cutting.
Frédéric Houllé, Siwen Gao, Marc Fivel, Anxin Ma, Daria Smirnova, Sriram Anand, Arun Prakash, Julien Guénolé, Alexander Hartmaier, Erik Bitzek   +9 more
wiley   +1 more source

Adaptive Feature Selection and Image Classification Using Manifold Learning Techniques

IEEE Access
Manifold learning techniques aim to the non-linear dimension reduction of data. Dimension reduction is the field of interest and demand of many data analysts and is widely used in computer vision, image processing, pattern recognition, neural networks ...
Amna Ashraf, Nazri Mohd Nawi, Muhammad Aamir   +2 more
doaj   +1 more source

Motion-compensated frame rate up-conversion in carotid ultrasound images using optical flow and manifold learning

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, Sima Navabian, Zahra Alizadeh Sani   +2 more
doaj   +1 more source

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, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source

Manifold Learning in Wasserstein Space

SIAM Journal on Mathematical Analysis
This paper aims at building the theoretical foundations for manifold learning algorithms in the space of absolutely continuous probability measures $\mathcal{P}_{\mathrm{a.c.}}(Ω)$ with $Ω$ a compact and convex subset of $\mathbb{R}^d$, metrized with the Wasserstein-2 distance $\mathbb{W}$. We begin by introducing a construction of submanifolds $Λ$ in $
Keaton Hamm, Caroline Moosmüller, Bernhard Schmitzer, Matthew Thorpe   +3 more
openaire   +5 more sources

Learning on dynamic statistical manifolds

Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 2020
Hyperbolic balance laws with uncertain (random) parameters and inputs are ubiquitous in science and engineering. Quantification of uncertainty in predictions derived from such laws, and reduction of predictive uncertainty via data assimilation, remain an open challenge.
F. Boso, D. M. Tartakovsky
openaire   +5 more sources