Results 111 to 120 of about 853,051 (231)

U‐Net‐Based Prediction of Concealed Volume Loss in AZ31 From Optical Corrosion Imprints and As‐Exposed Surface Topography

open access: yesAdvanced Intelligent Discovery, EarlyView.
How much material loss is concealed beneath a layer of corrosion products? This proof‐of‐concept study demonstrates how concealed volume loss is inferred from the optical appearance and as‐exposed surface topography of AZ31 corrosion imprints. By predicting postcleaning depth maps, a multimodal U‐Net model estimates hidden volume loss without chromic ...
Lars Dammann   +8 more
wiley   +1 more source

Artificial Intelligence for Advanced Functional Materials: Progress and Emerging Frontiers

open access: yesAdvanced Intelligent Systems, EarlyView.
Artificial intelligence is transforming the discovery of functional materials by linking synthesis, characterization, simulation, and design in unified workflows. Advances in machine learning, autonomous experimentation, and foundation models are accelerating innovation across energy, electronics, and biomedicine, while revealing new frontiers for ...
Cristiano Malica   +38 more
wiley   +1 more source

Construction of Cultural Tourism Integration Knowledge Graph Method Based on Multimodal Large Models

open access: yesJournal of Applied Science and Engineering
This paper presents a multimodal large-model pipeline for constructing a cultural-tourism integration knowledge graph (CTI-KG). By harmonizing text, imagery, audio and geospatial data, the pipeline automatically extracts and links cultural entities ...
Xuxiang Zhang
doaj   +1 more source

Semantics of Units of Phrasal-Semantic Fields Richness and Poverty in the Russian and Latvian Languages

open access: yes, 2009
Roberts Aleksandroae. Frazeosemantisko lauku "bagātība", "nabadzība" vienību semantika krievu un latviešu valodā: bakalaura darbs. Rīga, 2009.-33 lpp. Darbā tiek pētītas krievu un latviešu valodas sakāmvārdi un parunas ar semantiku „bagtība” – „nabadzība”
Aleksandroae, Roberts
core  

A Generalizable Framework for Building Executable Domain‐Specific Large Language Models Under Data Scarcity: Demonstration on Semiconductor Technology Computer‐Aided Design Simulation

open access: yesAdvanced Intelligent Systems, EarlyView.
A schema‐first alignment framework builds compact, executable domain‐specific language models under data scarcity. Large‐scale synthetic question‐answer generation instills domain knowledge, and a code‐centric IR‐to‐DPO pipeline aligns generation with tool‐executable syntax.
Di Wang   +4 more
wiley   +1 more source

Using semantic richness for metaphor detection using deep learning [PDF]

open access: yes
The Natural Language Processing (NLP) encounters difficulties with metaphors, known for their creative and non-literal usage. Metaphors involve using words or phrases from one context in entirely different contexts, making the meaning less clear and ...
Alnafesah, Ghadi
core   +4 more sources

Semantic Similarity Assessment of Volunteered Geographic Information

open access: yesJournal of Engineering, 2016
The recent development in communication technologies between individuals allows for the establishment of more informal collaborative map data projects which are called volunteered geographic information (VGI).
Maythm M. Albakri, Dr
doaj  

LLM‐Based Multimodal Robotic Endoscope Control Framework for Enhancing Human–Robot Interaction in Minimally Invasive Surgery

open access: yesAdvanced Intelligent Systems, EarlyView.
An large language model‐powered multimodal framework is developed for robotic endoscope control. It integrates speech recognition and real‐time instrument tracking, achieving 89.47% command accuracy with ~1s latency for natural human–robot interaction in minimally invasive surgery.
Yisen Huang   +7 more
wiley   +1 more source

Dual‐View Representation Learning for Deployable Humanoid Whole‐Body Control

open access: yesAdvanced Intelligent Systems, EarlyView.
Humanoid robots must learn control from limited onboard sensing, although simulation provides richer training‐only states. DVP learning pairs these views of each simulated motion and aligns their latent control features inside the policy‐learning loop. This direct, deployment‐aware supervision accelerates learning while the deployed policy relies only ...
Haolin Song   +3 more
wiley   +1 more source

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