Results 71 to 80 of about 26,804 (294)

High‐Temperature Nanoindentation of Metals: Assessing Thermal Drift, Frame Compliance, and Chemical Composition Effects on the Reported Mechanical Properties

Advanced Engineering Materials, EarlyView.
Do not let thermal drift and instrument artifacts deceive high‐temperature nanoindentation results. We compare classical Oliver–Pharr and automatic image recognition analyses across steels and a Ni alloy to quantify these effects. Accounting for artifacts reveals systematic softening with temperature, while Cr and Ni additions boost resistance ...
Velislava Yonkova, Steffen Brinckmann, Ruth Schwaiger   +2 more
wiley   +1 more source

A Study on Dimensionality Reduction and Parameters for Hyperspectral Imagery Based on Manifold Learning

Sensors
With the rapid advancement of remote-sensing technology, the spectral information obtained from hyperspectral remote-sensing imagery has become increasingly rich, facilitating detailed spectral analysis of Earth’s surface objects.
Wenhui Song, Xin Zhang, Guozhu Yang, Yijin Chen, Lianchao Wang, Hanghang Xu   +5 more
doaj   +1 more source

Probabilistic Nonlinear Dimensionality Reduction

, 2022
High-dimensional datasets are present across scientific disciplines. In the analysis of such datasets, dimensionality reduction methods which provide clear interpretations of their model parameters are required. Principal components analysis (PCA) has long been a preferred method for linear dimensionality reduction, but is not recommended for data ...
openaire   +1 more source

Convolutional 2D LDA for Nonlinear Dimensionality Reduction [PDF]

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence, 2017
Representing high-volume and high-order data is an essential problem, especially in machine learning field. Although existing two-dimensional (2D) discriminant analysis achieves promising performance, the single and linear projection features make it difficult to analyze more complex data. In this paper, we propose a novel convolutional two-dimensional
Qi Wang 0009, Zequn Qin, Feiping Nie 0001, Yuan Yuan 0001   +3 more
openaire   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Principal Tensor Embedding for Unsupervised Tensor Learning

IEEE Access, 2020
Tensors and multiway analysis aim to explore the relationships between the variables used to represent the data and find a summarization of the data with models of reduced dimensionality. However, although in this context a great attention was devoted to
Claudio Turchetti, Laura Falaschetti, Lorenzo Manoni   +2 more
doaj   +1 more source

Low‐rank isomap algorithm

IET Signal Processing, 2022
Isomap is a well‐known nonlinear dimensionality reduction method that highly suffers from computational complexity. Its computational complexity mainly arises from two stages; a) embedding a full graph on the data in the ambient space, and b) a complete ...
Eysan Mehrbani, Mohammad Hossein Kahaei
doaj   +1 more source

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

Nonlinear dimensionality reduction for cluster identification in metagenomic samples [PDF]

, 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/, Hammer, Barbara ; https://orcid.org/, Banissi, Ebad, Gisbrecht, Andrej, Mokbel, Bassam   +4 more
core   +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, Andrés Marino Álvarez-Meza, Genaro Daza-Santacoloma, Carlos Daniel Acosta-Medina, César Germán Castellanos-Domínguez   +4 more
openaire   +1 more source