Results 131 to 140 of about 522,669 (248)

Is an Apple an Orange? A Large Language Model Benchmark for Candidate Term Extraction and Subclass Decisions Against Upper Ontologies in Engineering and Materials Science

Advanced Engineering Materials, EarlyView.
Building machine‐readable vocabularies for materials science is slow, expert‐driven work. This study benchmarks 13 large language models on two of its first steps: finding candidate terms in engineering articles and deciding where they belong in a class hierarchy.
Thomas Bjarsch, Maximilian Holland, Klaus Drechsler, Johannes Schilp   +3 more
wiley   +1 more source

Compression Safeguards: Building Trust into Lossy Data Compression

. The growth in data volumes produced by scientific models is accelerating. Data production of high-resolution weather and climate models is outpacing the methods and budgets for storing, sharing, and analysing this data, posing a threat to scientific ...
Sara Faghih-Naini, Juniper Tyree, Daniel Köhler, Clément Bouvier, Robert Underwood, Peter Dueben, Tim Reichelt, Heikki Järvinen, Milan Klöwer   +8 more
core   +1 more source

ADVANTAGES AND DRAWBACKS OF TWO-STEP APPROACH TO PROVIDING DESIRED PARAMETERS IN LOSSY IMAGE COMPRESSION

Сучасні інформаційні системи
The object of the study is the process of lossy image compression. The subject of the study is the two-step approach to providing desired parameters (quality and compression ratio) for different coders. The goals of the study are to review advantages of
Fangfang Li , Sergey Abramov , Ihor Dohtiev , Vladimir Lukin   +3 more
doaj   +1 more source

Impact of lossy compression of X-ray projections onto reconstructed tomographic slices. [PDF]

J Synchrotron Radiat, 2020
Marone F, Vogel J, Stampanoni M.
europepmc   +1 more source

Current Status and Challenges in Data Collection for Aerospace Coatings Deposited by Plasma Spraying

Advanced Engineering Materials, EarlyView.
An innovative approach has been integrated into the GRENAT project to optimize plasma spraying and coating performance. Raw materials are accelerated and melted in the plasma generated by torches, creating coatings. Monitoring sensors collect process data which are combined with ex situ characterization data.
Lila Randriamananjara, Vincent Rat, Quentin Dubreuil, Patrice Valorge, Simon Goutier, Alain Denoirjean, Olivier Messe, Céline Ruelle, Jean‐Philippe Poli   +8 more
wiley   +1 more source

Less is more: On-board lossy compression of accelerometer data increases biologging capacity. [PDF]

J Anim Ecol, 2020
Nuijten RJM, Gerrits T, Shamoun-Baranes J, Nolet BA.   +3 more
europepmc   +1 more source

[A new lossy compression method for fetal heart rate signals-Convolutional Codec Network]. [PDF]

Nan Fang Yi Ke Da Xue Xue Bao, 2021
Que Y, Chen D, Tong L, Chen C.
europepmc   +1 more source

Microstructure Reconstruction in Battery Electrodes Using Machine Learning Based on Low‐Voltage Focused Ion Beam–Scanning Electron Microscopy Tomography Images

Advanced Engineering Materials, EarlyView.
Low‐voltage FIB‐SEM tomography combined with a image preprocessing pipeline improves phase contrast and enables reliable machine‐learning segmentation of conductive networks in lithium‐ion battery electrodes. Structural descriptors are extracted from segmented images, done semimanually and automated, and compared.
Lisa Beran, Vincent Nebel, Dominik Perius, Kshitij Kumar, Yannis Heim, Laura Bies, Tobias Kraus   +6 more
wiley   +1 more source

Inverse Identification of Energy‐Dependent Laser Absorptivity in NiTi Laser Powder‐Bed Fusion via Calibrated Melt Pool Simulation

Advanced Engineering Materials, EarlyView.
A combined experimental–computational framework identifies energy‐dependent laser absorptivity for NiTi in laser powder‐bed fusion, applicable to conduction and transition modes. Single‐track experiments and thermofluid smoothed particle hydrodynamics simulations are coupled through inverse analysis of melt pool geometry.
Mohamadreza Afrasiabi, Hadi Moutablaleh, Ignatius A. Setiawan, Mehrshad Mehrpouya   +3 more
wiley   +1 more source

A Practical Noise2Noise Denoising Pipeline for High‐Throughput Raman Spectroscopy

Advanced Engineering Materials, EarlyView.
A lightweight and reproducible denoising pipeline for high‐throughput Raman spectroscopy is introduced, based on a 1D convolutional autoencoder trained with a Noise2Noise strategy. Using only repeated short‐exposure acquisitions, the method suppresses stochastic noise without reference spectra, enabling reliable spectral reconstruction while preserving
David Martin‐Calle, Cesar Alvarez‐Llamas, Vincent Motto‐Ros, Christophe Dujardin, Jérémie Margueritat, David Rodney   +5 more
wiley   +1 more source