Results 121 to 130 of about 229,000 (311)
Discriminant Analysis with Graph Learning for Hyperspectral Image Classification
Remote Sensing, 2018 Linear Discriminant Analysis (LDA) is a widely-used technique for dimensionality reduction, and has been applied in many practical applications, such as hyperspectral image classification.
Mulin Chen, Qi Wang, Xuelong Li
doaj +1 more source
Advanced Engineering Materials, EarlyView.The present study investigates recycling of NiTi shape memory alloys via vacuum induction melting. An ingot was synthesized from elemental Ni and Ti and subjected to three subsequent remelting cycles. Remelting increases process durations and impurity levels and adversely affects microstructures and functional properties.
Sakia Sophia Noorzayee +7 more
wiley +1 more source
Advanced Engineering Materials, EarlyView.A novel workflow for investigating hydride vapor phase epitaxy for GaN bulk crystal growth is proposed. It combines Design of experiments (DoE) with physical simulations of mass transport and crystal growth kinetics, serving as an intermediate step between DoE and experiments.
J. Tomkovič +7 more
wiley +1 more source
Federated Subgraph Learning via Global-Knowledge-Guided Node Generation
SensorsFederated graph learning (FGL) is a combination of graph representation learning and federated learning that utilizes graph neural networks (GNNs) to process complex graph-structured data while addressing data silo issues.
Yuxuan Liu +6 more
doaj +1 more source
Learning and reasoning with graph data
Frontiers in Artificial Intelligence, 2023 Reasoning about graphs, and learning from graph data is a field of artificial intelligence that has recently received much attention in the machine learning areas of graph representation learning and graph neural networks. Graphs are also the underlying structures of interest in a wide range of more traditional fields ranging from logic-oriented ...
openaire +3 more sources
Advanced Engineering Materials, EarlyView.Reproduction of stacking fault energy calculations from literature with a semi‐automated large language model‐assisted extraction procedure: extraction of simulation protocol, atomistic structures, computational parameters, and reported results, ontology alignment, knowledge graph construction and, finally, recomputation forvalidation.
Sepideh Baghaee Ravari +5 more
wiley +1 more source
Future InternetEdge computing (EC) plays a critical role in advancing the next-generation Industrial Internet of Things (IIoT) by enhancing production, maintenance, and operational outcomes across heterogeneous network boundaries. This study builds upon EC intelligence
Abdullah Mohammed Alharthi +4 more
doaj +1 more source
Advanced Engineering Materials, EarlyView.Knowledge‐based atomistic workflows are presented for mechanical and thermodynamic properties. By coupling modular simulations with ontology‐aligned metadata and provenance, Fe case studies on elastic behavior, defects, thermal properties, and Hall–Petch strengthening reveal how FAIR, queryable, and reusable simulation data can be generated. Mechanical
Abril Azócar Guzmán +5 more
wiley +1 more source
Learning Graph Structures With Autoregressive Graph Signal Models
IEEE Open Journal of Signal ProcessingThis paper presents a novel approach to graph learning, GL-AR, which leverages estimated autoregressive coefficients to recover undirected graph structures from time-series graph signals with propagation delay.
Kyle Donoghue, Ashkan Ashrafi
doaj +1 more source
spa: Semi-Supervised Semi-Parametric Graph-Based Estimation in R [PDF]
In this paper, we present an R package that combines feature-based (X) data and graph-based (G) data for prediction of the response Y . In this particular case, Y is observed for a subset of the observations (labeled) and missing for the remainder ...
Mark Culp
core +1 more source