Results 71 to 80 of about 728,251 (279)

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

PRISMA: PRoximal Iterative SMoothing Algorithm

, 2012
Motivated by learning problems including max-norm regularized matrix completion and clustering, robust PCA and sparse inverse covariance selection, we propose a novel optimization algorithm for minimizing a convex objective which decomposes into three ...
Argyriou, Andreas, Orabona, Francesco, Srebro, Nathan   +2 more
core   +1 more source

Algorithmic Iteration for Computational Intelligence [PDF]

Minds and Machines, 2017
Machine awareness is a disputed research topic, in some circles considered a crucial step in realising Artificial General Intelligence. Understanding what that is, under which conditions such feature could arise and how it can be controlled is still a matter of speculation.
openaire   +2 more sources

Evaluation of Plasticity and Creep Parameters From Tensile Stress–Strain Data for a Range of Strain Rates

Advanced Engineering Materials, EarlyView.
This plot compares experimental tensile stress–strain curves (with 4 different strain rates) and corresponding modelled curves (obtained using the optimised sets of Voce and Miller–Norton parameter values shown). The inferred M‐N values, characterizing the creep, are very similar to those obtained via conventional creep testing.
S. Ooi, R. P. Thompson, T. W. Clyne
wiley   +1 more source

An Experimental High‐Throughput Approach for the Screening of Hard Magnet Materials

Advanced Engineering Materials, EarlyView.
An entire workflow for the high‐throughput characterization and analysis of compositionally graded magnetic films is presented. Characterization protocols, data management tools and data analysis approaches are illustrated with test case Sm(Fe, V)12 based films.
William Rigaut, Pierre Le Berre, Hamdi Jaballah, Lukas Fink, Victor Poline, Richard Haettel, Stéphane Grenier, Gilbert Chahine, Ewen Bellec, Guillaume Buatier, Wilfried Hortschitz, Santa Pile, Thomas Schrefl, Nils Blanc, Yuan Hong, Thibaut Devillers, Nora M. Dempsey   +16 more
wiley   +1 more source

A Study on Iterative Algorithm for Stochastic Distribution Free Inventory Models

Abstract and Applied Analysis, 2013
We studied the iterative algorithm in Tung et al. (2010) to find out that their assertion is questionable. We derived two new relations between safe factor and order quantity so that we can execute the iterative algorithm proposed by Wu and Ouyang (2001)
Jennifer Lin
doaj   +1 more source

All‐in‐One Analog AI Hardware: On‐Chip Training and Inference with Conductive‐Metal‐Oxide/HfOx ReRAM Devices

Advanced Functional Materials, EarlyView.
An all‐in‐one analog AI accelerator is presented, enabling on‐chip training, weight retention, and long‐term inference acceleration. It leverages a BEOL‐integrated CMO/HfOx ReRAM array with low‐voltage operation (<1.5 V), multi‐bit capability over 32 states, low programming noise (10 nS), and near‐ideal weight transfer.
Donato Francesco Falcone, Victoria Clerico, Wooseok Choi, Tommaso Stecconi, Folkert Horst, Laura Bégon‐Lours, Matteo Galetta, Antonio La Porta, Nikhil Garg, Fabien Alibart, Bert Jan Offrein, Valeria Bragaglia   +11 more
wiley   +1 more source

A simple iterative algorithm for maxcut

, 2019
We propose a simple iterative (SI) algorithm for the maxcut problem through fully using an equivalent continuous formulation. It does not need rounding at all and has advantages that all subproblems have explicit analytic solutions, the cut values are ...
Shao, Sihong, Zhang, Dong, Zhang, Weixi
core  

Unleashing the Power of Machine Learning in Nanomedicine Formulation Development

Advanced Functional Materials, EarlyView.
A random forest machine learning model is able to make predictions on nanoparticle attributes of different nanomedicines (i.e. lipid nanoparticles, liposomes, or PLGA nanoparticles) based on microfluidic formulation parameters. Machine learning models are based on a database of nanoparticle formulations, and models are able to generate unique solutions
Thomas L. Moore, Cristiano Pesce, Antonietta Greco, Claudia Pisante, Greta Avancini, Valentina Di Francesco, Yosi Shamay, Paolo Decuzzi   +7 more
wiley   +1 more source

Analytic time-of-flight positron emission tomography reconstruction: two-dimensional case

Visual Computing for Industry, Biomedicine, and Art, 2019
In a positron emission tomography (PET) scanner, the time-of-flight (TOF) information gives us rough event position along the line-of-response (LOR). Using the TOF information for PET image reconstruction is able to reduce image noise.
Gengsheng L. Zeng, Ya Li, Qiu Huang
doaj   +1 more source