Results 91 to 100 of about 26,484 (294)
Dimensionality Reduction Nonlinear Partial Least Squares Method for Quality-Oriented Fault Detection
MathematicsUnlike traditional fault detection methods, quality-oriented fault detection further classifies the types of faults into quality-related and non-quality-related faults.
Jie Yuan, Hao Ma, Yan Wang
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Nonlinear dimensionality reduction using approximate nearest neighbors
, 2007 Nonlinear dimensionality reduction methods often rely on the nearest-neighbors graph to extract low-dimensional embeddings that reliably capture the underlying structure of high-dimensional data.
Lydia E. Kavraki, Erion Plaku
core
, 2021 Identifying collective variables for chemical reactions is essential to reduce the 3$N$ dimensional energy landscape into lower dimensional basins and barriers of interest.
Ernesto, Martinez-Baez +6 more
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Advanced Engineering Materials, EarlyView.A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi +3 more
wiley +1 more source
Experimental Characterization of Mycelium‐Based Composites Under Multiple Loading Conditions
Advanced Engineering Materials, EarlyView.This study examines the mechanical response of mycelium‐based composites under compression, shear, and tension using mechanical testing and imaging methods. The comparison between unpressed and hot‐pressed specimens shows that hot pressing is associated with higher compression and shear stiffnesses.
Shaghayegh Elahi +5 more
wiley +1 more source
Complexity, 2019 In this paper, a four-dimensional (4-D) memristor-based Colpitts system is reaped by employing an ideal memristor to substitute the exponential nonlinear term of original three-dimensional (3-D) Colpitts oscillator model, from which the initials ...
Yunzhen Zhang +5 more
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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.
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Advanced Engineering Materials, EarlyView.Si‐doped AlCoCrFeNi high‐entropy alloys are synthesized by mechanical alloying to reveal the effect of Si content and milling time on phase evolution, microstructural refinement, and tribological behavior. A transition from FCC to BCC structure, significant grain refinement, and enhanced hardness and wear resistance are achieved, with the 4 at% Si ...
Mustafa Okumuş +2 more
wiley +1 more source
Forward Stepwise Deep Autoencoder-based Monotone Nonlinear Dimensionality Reduction Methods. [PDF]
J Comput Graph Stat, 2021 Fong Y, Xu J.
europepmc +1 more source
Multiple Manifold Learning by Nonlinear Dimensionality Reduction [PDF]
, 2011 Methods for nonlinear dimensionality reduction have been widely used for different purposes, but they are constrained to single manifold datasets. Considering that in real world applications, like video and image analysis, datasets with multiple manifolds are common, we propose a framework to find a low-dimensional embedding for data lying on multiple ...
Juliana Valencia-Aguirre +4 more
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