Results 61 to 70 of about 4,379,261 (288)
Advanced Engineering Materials, EarlyView.This article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal +2 more
wiley +1 more source
Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction [PDF]
, 2018 Training image dataset used in the manuscript "Visualizing histopathologic deep learning classification and anomaly detection using nonlinear feature space dimensionality reduction"
Djuric, Ugljesa +27 more
core +1 more source
Advanced Engineering Materials, EarlyView.A simplified thermoplastic pultrusion model is developed to predict thermal fields in glass fiber/polyethylene terephthalate (GF/PET) composites with reduced computational cost. By combining effective material homogenization, validation against literature data, and Gaussian‐process‐based optimization, the study reveals how heating limits, pulling speed,
Elder Soares +3 more
wiley +1 more source
A Geometric-Nonlinear Optimization Algorithm for Solving Pose Matrices in Aircraft Visual Navigation
Journal of Applied MathematicsWe propose a noniterative geometric dimensionality reduction algorithm for solving the pose estimation problem of an onboard camera in visual navigation.
Congwei Wu +3 more
doaj +1 more source
A New Approach to Improve the Topological Stability in Non-Linear Dimensionality Reduction
IEEE Access, 2020 Dimensionality reduction in the machine learning field mitigates the undesired properties of high-dimensional spaces to facilitate classification, compression, and visualization of high-dimensional data.
Mohammed Elhenawy +3 more
doaj +1 more source
Nonlinear dimensionality reduction of gene expression data [PDF]
, 2006 Using microarray measurements techniques, it is possible to measure the activity of genes simultaneously across the whole genome. Since genes influence each others activity levels through complex regulatory networks, such gene expression measurements are
Nilsson, Jens
core
Nonlinear dimensionality reduction for cluster identification in metagenomic samples
, 2013 Gisbrecht A, Hammer B, Mokbel B, Sczyrba A. Nonlinear dimensionality reduction for cluster identification in metagenomic samples. In: Banissi E, ed. 17th International Conference on Information Visualisation IV 2013.
Sczyrba, Alexander ; https://orcid.org/ +8 more
core +1 more source
Advanced Engineering Materials, EarlyView.This study examines how helicoidal architectures with different porosities respond to bending. Adjusting layer angle and spacing in 3D‐printed polymers reveals clear tradeoffs between stiffness, strength, and energy absorption. Experiments and simulations highlight designs that distribute stress effectively, offering pathways for optimizing lightweight
Praveenkumar Subhash Patil +2 more
wiley +1 more source
Locality constrained dictionary learning for non‐linear dimensionality reduction and classification
IET Computer Vision, 2017 In view of the incremental dimensionality reduction problem of existing non‐linear dimensionality reduction methods, a novel algorithm, based on locality constrained dictionary learning (LCDL), is proposed in this study.
Lina Liu, Shiwei Ma, Ling Rui, Jian Lu
doaj +1 more source
Learning a kernel matrix for nonlinear dimensionality reduction [PDF]
Twenty-first international conference on Machine learning - ICML '04, 2004 We investigate how to learn a kernel matrix for high dimensional data that lies on or near a low dimensional manifold. Noting that the kernel matrix implicitly maps the data into a nonlinear feature space, we show how to discover a mapping that "unfolds" the underlying manifold from which the data was sampled.
Kilian Q. Weinberger +2 more
openaire +2 more sources