Results 81 to 90 of about 5,834,158 (292)

Rapidly Solidified High‐Strength Invar 36 Prepared by Planar‐Flow Melt Spinning

Advanced Engineering Materials, EarlyView.
The Invar 36 alloy was rapidly solidified using the planar‐flow melt‐spinning technique. Ribbon samples with thicknesses ranging from 20 to 160 mm were produced. As the grain size of the ribbon decreased to sub‐micron levels, the hardness increased by more than 2 times.
Bekir Akgül, Mehmet Kul
wiley   +1 more source

A simple iterative algorithm for parsimonious binary kernel Fisher discrimination [PDF]

, 2010
By applying recent results in optimisation theory variously known as optimisation transfer or majorise/minimise algorithms, an algorithm for binary, kernel, Fisher discriminant analysis is introduced that makes use of a non-smooth penalty on the ...
Pasupa, K., Harrison, Robert F., Pasupa, Kitsuchart, Harrison, R.F.   +3 more
core   +1 more source

An orthogonal forward regression technique for sparse kernel density estimation [PDF]

, 2008
Using the classical Parzen window (PW) estimate as the desired response, the kernel density estimation is formulated as a regression problem and the orthogonal forward regression technique is adopted to construct sparse kernel density (SKD) estimates ...
Chen, Sheng, Hong, X., Harris, Chris J.
core   +2 more sources

High‐Entropy Alloy Interlayers Toward Advanced Joining for High‐Performance Structural Applications: Current Progress and Emerging Challenges

Advanced Engineering Materials, EarlyView.
HEA interlayers offer a versatile route for joining high‐performance structural materials. Their compositional and structural design regulates interfacial reactions, suppresses brittle IMCs, and improves metallurgical bonding. Sandwich interlayers further integrate defect healing with precipitation strengthening, enabling improved strength–ductility ...
Lin Yuan, Jiafen Song, Jinglong Li, Jiangtao Xiong, Tao Yang   +4 more
wiley   +1 more source

A sparse kernel density estimation algorithm using forward constrained regression

, 2007
Using the classical Parzen window (PW) estimate as the target function, the sparse kernel density estimator is constructed in a forward constrained regression manner. The leave-one-out (LOO) test score is used for kernel selection.
Hong, X., Harris, C., Harris, C.J., Chen, S.   +3 more
core  

New bandwidth selection for kernel quantile estimators [PDF]

, 2012
Copyright © 2012 Ali Al-Kenani and Keming Yu. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly ...
Al-Kenani, A, Yu, K, Keming Yu, Ali Al-Kenani   +3 more
core   +1 more source

Toward Full Interoperability in Materials Science: Integrating Workflows With Knowledge Graphs

Advanced Engineering Materials, EarlyView.
The connection of conceptual workflow design, portable execution, and ontology‐based semantics leading to provenance‐rich knowledge graphs are main contributors to interoperability in materials science and a prerequisite to AI‐assisted orchestration and for interoperable Materials Acceleration Platforms.
Jan Janssen, Osamu Waseda, Bernd Bayerlein, Simon Stier, Steffen Brinckmann, Abril Azócar Guzmán, Sarath Menon, Matthias A. Popp, Jesper R. Pedersen, Ivano E. Castelli, Jörg Waitelonis, Wolfgang Wenzel, Jörg Neugebauer, Jörg Schaarschmidt, Tilmann Hickel   +14 more
wiley   +1 more source

sparr: Analyzing Spatial Relative Risk Using Fixed and Adaptive Kernel Density Estimation in R [PDF]

The estimation of kernel-smoothed relative risk functions is a useful approach to examining the spatial variation of disease risk. Though there exist several options for performing kernel density estimation in statistical software packages, there have ...
Tilman M. Davies, Jonathan. C Marshall, Martin L. Hazelton   +2 more
core  

A Multi‐Scale Machine Learning Framework for the Inverse Design of High Entropy Alloys

Advanced Engineering Materials, EarlyView.
High‐entropy alloys offer vast potential for various applications, including electrocatalysis; however, their compositional complexity challenges conventional screening. We introduce an inverse‐design framework combining two neural networks to determine optimal compositions and reconstruct nanoparticle geometry from targeted properties and conventional
Mikael Takoutsin, Marta Campolucci, Nicolas de Andrade Ishiki, Nathan Mirgot, Nadir Zhamantay, Frédéric Kanoufi, Jennifer Peron, Jean Charlety, Emiliano Fonda, Valérie Briois, Marco Faustini, Maria‐Letizia De Marco, Christine Goyhenex, Ovidiu Ersen, Hervé Bulou   +14 more
wiley   +1 more source

Novel kernel function for computing the similarity of text

Tongxin xuebao, 2012
To enhance the performance of detecting similar documents,a novel kernel function named S_Wang kernel was constructed.Based on the actual situation of computing text similarity,the S_Wang kernel was newly bu lt with consideration of the Euclidean ...
Xiu-hong WANG, Shi-guang JU
doaj   +2 more sources