Results 111 to 120 of about 478,588 (342)
Advanced Engineering Materials, EarlyView.The study presents an efficient simulation approach for the polymer laser powder bed fusion process polymers process, validated with polyamide 12, polyamide 6, and polyetherketoneketone. It shows that inter layer time affects part density, with 90s yielding dense parts.
Claas Bierwisch +4 more
wiley +1 more source
Optimal linear and nonlinear feature extraction based on the minimization of the increased risk of misclassification [PDF]
General classes of nonlinear and linear transformations were investigated for the reduction of the dimensionality of the classification (feature) space so that, for a prescribed dimension m of this space, the increase of the misclassification risk is ...
Defigueiredo, R. J. P.
core +1 more source
Smart Rubber Extrusion Line Combining Multiple Sensor Techniques for AI‐Based Process Control
Advanced Engineering Materials, EarlyView.This publication presents a digitalization approach for a laboratory rubber extrusion line, employing innovative measurement methods and artificial intelligence (AI)‐based process control. The results demonstrate that the measurement systems are capable of detecting changes in the process and extrudate quality.
Alexander Aschemann +18 more
wiley +1 more source
A Tangent Distance Preserving Dimensionality Reduction Algorithm [PDF]
arXiv, 2019 This paper considers the problem of nonlinear dimensionality reduction. Unlike existing methods, such as LLE, ISOMAP, which attempt to unfold the true manifold in the low dimensional space, our algorithm tries to preserve the nonlinear structure of the manifold, and shows how the manifold is folded in the high dimensional space.
arxiv
Nonlinear Embedded Map Projection for Dimensionality Reduction [PDF]
, 2009 We describe a dimensionality reduction method used to perform similarity search that is tested on document image retrieval applications. The approach is based on data point projection into a low dimensional space obtained by merging together the layers of a Growing Hierarchical Self Organizing Map (GHSOM) trained to model the distribution of objects to
MARINAI, SIMONE +2 more
openaire +3 more sources
On the convergence of maximum variance unfolding
, 2013 Maximum Variance Unfolding is one of the main methods for (nonlinear) dimensionality reduction. We study its large sample limit, providing specific rates of convergence under standard assumptions.
Arias-Castro, Ery, Pelletier, Bruno
core +2 more sources
Electrospinning Technology, Machine Learning, and Control Approaches: A Review
Advanced Engineering Materials, EarlyView.Electrospinning produces micro‐ and nanoscale fibers, holding great promise in biomedical engineering. Industrial adoption faces challenges in controlling fiber properties, reproducibility, and scalability. This review explores electrospinning techniques, modeling, and machine learning for process optimization.
Arya Shabani +5 more
wiley +1 more source
Improving reduced-order models through nonlinear decoding of projection-dependent outputs
Patterns, 2023 Summary: A fundamental hindrance to building data-driven reduced-order models (ROMs) is the poor topological quality of a low-dimensional data projection.
Kamila Zdybał +2 more
doaj
Graph Embedding and Nonlinear Dimensionality Reduction
, 2011 Traditionally, spectral methods such as principal component analysis (PCA) have been applied to many graph embedding and dimensionality reduction tasks. These methods aim to find low-dimensional representations of data that preserve its inherent structure.
Blake Shaw, Tony Jebara
openaire +3 more sources
A Case‐Based Reasoning Approach to Model Manufacturing Constraints for Impact Extrusion
Advanced Engineering Materials, EarlyView.A hybrid modeling approach is presented that combines constraint‐based process modeling and case‐based reasoning. The model formalizes manufacturing constraints and integrates simulation data to model complex manufacturing processes. The approach supports manufacturability analysis during product design through an adaptive modeling environment.
Kevin Herrmann +5 more
wiley +1 more source