Results 111 to 120 of about 6,963,782 (253)
Anomaly Detection and Localization via Graph Learning
EnergiesPhasor measurement units (PMUs) are being installed at an unprecedented rate on power systems, offering unique situation awareness capability. This paper presents a graph learning-based method for detecting and locating anomalies using PMU data.
Olabode Amusan, Di Wu
doaj +1 more source
Advanced Engineering Materials, EarlyView.Digitalizing electroplating requires both domain knowledge and interoperability. This work introduces PlatOn, a domain ontology for trivalent chromium plating and coating characterization, and a hybrid pipeline that aligns it to a mid‐level reference ontology by combining eight similarity metrics with language model reasoning. Expert‐validated mappings
Janik Harter +10 more
wiley +1 more source
IEEE Journal of Selected Topics in Applied Earth Observations and Remote SensingRemote sensing (RS) scene classification plays an important role in the intelligent interpretation of RS data. Recently, convolutional neural network (CNN)-based and attention-based methods have become the mainstream of RS scene classification with ...
Chongyang Zhang, Bin Wang
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Advanced Engineering Materials, EarlyView.The OntOMat ontology establishes a structured framework for polymer matrix fiber reinforced composite materials, integrating manufacturing processes, characterization methods, and multiscale design through the VDI/VDE 3682 formalized process description standard.
Nicolas Christ +19 more
wiley +1 more source
Adaptive Foam 3D Printing of Ultralight and Multifunctional Materials
Advanced Engineering Materials, EarlyView.Adaptive foam 3D printing, enabled by expandable microspheres, imparts cellular structures to thermoplastic and thermosetting polymers, manufactured through a variety of processes including fused filament fabrication, direct ink writing, digital light processing, and inkjet printing.
Nariman Rajabifar, Amir Ameli
wiley +1 more source
From Euclidean to Graph-Structured Data : A Survey of Collaborative Learning
The conventional approach to machine learning, that is, collecting data, training models, and performing inference in a single location, faces fundamental limitations, including scalability and privacy, that restrict its applicability.
Fodor, Viktória +2 more
core +1 more source
Advanced Engineering Materials, EarlyView.This article explores the transformative potential of symbolic artificial intelligence (AI) in the field of materials science, particularly in leveraging experimental data. The article presents several symbolic AI models and discusses their applications in materials science.
Ahmed Amrani +7 more
wiley +1 more source
Data-efficient graph learning: Problems, progress, and prospects
Graph-structured data, ranging from social networks to financial transaction networks, from citation networks to gene regulatory networks, have been widely used for modeling a myriad of real-world systems.
Liu, Y, Wang, J, Zhang, C, Ding, K
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Investigating the Low‐Temperature Phase Stability of the Binary Ta–W System
Advanced Engineering Materials, EarlyView.Atomistic simulations show that the binary Ta–W system forms ordered intermetallic phases, B2‐TaW and D03‐TaW3, as 0 K ground states. Configurational entropy, however, lowers the free energy of the disordered bcc solid solution, which becomes the stable phase above about 400 K.
Klemens Lechner +7 more
wiley +1 more source
Supporting AI Readiness Through Digital Workflows in Materials Science
Advanced Engineering Materials, EarlyView.Digitalization drives innovation in materials science by connecting data silos and turning heterogeneous processes into reusable research pipelines. Across 13 MaterialDigital projects, digital workflows reveal complementary pathways toward AI‐ready materials research, founded on structured data, persistent artifacts, executable orchestration, and ...
Marian Bruns +67 more
wiley +1 more source