Results 171 to 180 of about 20,281,849 (282)

dynoGP: Deep Gaussian Processes for Dynamic System Identification

International Journal of Adaptive Control and Signal Processing, EarlyView.
This work introduces a novel class of deep models for system identification, dynamical deep Gaussian processes, which combine the strengths of data‐driven methods, such as those based on neural network architectures, with the ability to output a probability distribution for uncertainty representation.
Alessio Benavoli, Marco Forgione, Dario Piga, Marco Zaffalon   +3 more
wiley   +1 more source

Object Detection and Scene Perception for Connected and Autonomous Vehicles Using LM-JEPA. [PDF]

Sensors (Basel)
Gupta A, Sultana A.
europepmc   +1 more source

Characterization of Defect Distribution in an Additively Manufactured AlSi10Mg as a Function of Processing Parameters and Correlations with Extreme Value Statistics

Advanced Engineering Materials, EarlyView.
Predicting extreme defects in additive manufacturing remains a key challenge limiting its structural reliability. This study proposes a statistical framework that integrates Extreme Value Theory with advanced process indicators to explore defect–process relationships and improve the estimation of critical defect sizes. The approach provides a basis for
Muhammad Muteeb Butt, Varma Kvvssn, Hossein Laieghi, Zia Uddin, Peyman Ansari, E. Kıvanç Sadak, Can Barış Toprak, Hüseyin Kızıl, Enrico Salvati   +8 more
wiley   +1 more source

Hyperbolic adaptive spatial-aware multivariate time series anomaly detection. [PDF]

Nat Commun
Han J, Huo X, Xiao Y, Ye Z, Zheng Y, Zhao H, Sun M, Liu Z.   +7 more
europepmc   +1 more source

What Do Large Language Models Know About Materials?

Advanced Engineering Materials, EarlyView.
If large language models (LLMs) are to be used inside the material discovery and engineering process, they must be benchmarked for the accurateness of intrinsic material knowledge. The current work introduces 1) a reasoning process through the processing–structure–property–performance chain and 2) a tool for benchmarking knowledge of LLMs concerning ...
Adrian Ehrenhofer, Thomas Wallmersperger, Gianaurelio Cuniberti   +2 more
wiley   +1 more source

Integrating neural decoding, memristive materials, and adaptive control frameworks for next-generation hippocampal memory prosthetics. [PDF]

iScience
Mo F, Zhao X, Xu Y, Tang C, Zhang D, Li S, Chen D, Li W, Song Z, He S.   +9 more
europepmc   +1 more source

Air‐Pressure–Actuated Vibroacoustic Metamaterial With Tunable Bandgap: Design, Modeling, and Characterization

Advanced Engineering Materials, EarlyView.
This article presents the design, modeling, and characterization of air‐pressure–actuated programmable vibroacoustic metamaterials (PVAMM). The study focuses on leveraging air pressure to dynamically tune resonance frequencies for effective noise attenuation.
William Kaal, Michael M. Becker, Sarah C. L. Fischer   +2 more
wiley   +1 more source

Intelligent chemical sensors: learning-enabled platforms for adaptive chemical detection. [PDF]

RSC Adv
Batool R, Sonil NI, Nazar MF, Ullah Z.
europepmc   +1 more source

Field Report from Collaborative Research Center 1625: Heterogeneous Research Data Management Using Ontology Representations

Advanced Engineering Materials, EarlyView.
A unified research data management framework for heterogeneous materials data is presented. The system integrates multimodal datasets using ontologies and knowledge graphs, enabling interoperability and FAIR (findable, accessible, interoperable, reusable) data principles. By linking data across scales and workflows, it supports reproducible, Artifitial
Doaa Mohamed, Samuel García Vázquez, Behnam Mardani, Victor Dudarev, Alfred Ludwig, Maribel Acosta, Markus Stricker   +6 more
wiley   +1 more source

Urban environmental exposure as a factor in adaptive neuromodulation: a conceptual framework. [PDF]

Front Hum Neurosci
Qamili A, Cenameri A.
europepmc   +1 more source