Results 211 to 220 of about 288,791 (262)

GraphNeuralCloth: A Graph‐Neural‐Network‐Based Framework for Non‐Skinning Cloth Simulation

Advanced Intelligent Systems, EarlyView.
This study presents a cloth motion capture system and a point‐cloud‐to‐mesh processing method to support the prediction of real‐world fabric deformation. GraphNeuralCloth, a graph neural‐network (GNN)‐based framework is also proposed to estimate the cloth morphology change in real time.
Yingqi Li, Xiaomei Wang, Xiaoqi Song, Chenchen Fan, Bohao Zhu, Jianan Peng, Qixin Liang, James Lam, Kam‐Yim Sze, Ka‐Wai Kwok   +9 more
wiley   +1 more source

Artificial Intelligence for Multiscale Modeling in Solid‐State Physics and Chemistry: A Comprehensive Review

Advanced Intelligent Systems, EarlyView.
This review explores the transformative impact of artificial intelligence on multiscale modeling in materials research. It highlights advancements such as machine learning force fields and graph neural networks, which enhance predictive capabilities while reducing computational costs in various applications.
Artem Maevskiy, Vitalii Kapitan, Andrey Ustyuzhanin   +2 more
wiley   +1 more source

P_VggNet: A convolutional neural network (CNN) with pixel-based attention map. [PDF]

PLoS One, 2018
Liu K, Zhong P, Zheng Y, Yang K, Liu M.
europepmc   +1 more source

Deep Learning Methods for Assessing Time‐Variant Nonlinear Signatures in Clutter Echoes

Advanced Intelligent Systems, EarlyView.
Motion classification from biosonar echoes in clutter presents a fundamental challenge: extracting structured information from stochastic interference. Deep learning successfully discriminates object speed and direction from bat‐inspired signals, achieving 97% accuracy with frequency‐modulated calls but only 48% with constant‐frequency tones. This work
Ibrahim Eshera, Gaurav Duggal, Rolf Müller   +2 more
wiley   +1 more source

Methods for Setting Device Specifications for Analog In‐Memory Computing Inference

Advanced Intelligent Systems, EarlyView.
Non‐volatile memories (NVMs) are being developed for analog in‐memory computing for energy‐efficient, high‐speed deep learning inference. As technology is moving to industry adoption, a method to define required NVM specifications is critical for improving performance and reducing manufacturing cost.
Zhenyu Wu, Xin Su, Malte J. Rasch, Ning Li   +3 more
wiley   +1 more source

Artificial Intelligence in Autonomous Mobile Robot Navigation: From Classical Approaches to Intelligent Adaptation

Advanced Intelligent Systems, EarlyView.
Artificial intelligence (AI) is reshaping autonomous mobile robot navigation beyond classical pipelines. This review analyzes how AI techniques are integrated into core navigation tasks, including path planning and control, localization and mapping, perception, and context‐aware decision‐making. Learning‐based, probabilistic, and soft‐computing methods
Giovanna Guaragnella, Angelo Cardellicchio, Cosimo Patruno, Massimiliano Nitti, Nicola Pedrocchi, Vito Renò   +5 more
wiley   +1 more source

Comparative effectiveness of convolutional neural network (CNN) and recurrent neural network (RNN) architectures for radiology text report classification. [PDF]

Artif Intell Med, 2019
Banerjee I, Ling Y, Chen MC, Hasan SA, Langlotz CP, Moradzadeh N, Chapman B, Amrhein T, Mong D, Rubin DL, Farri O, Lungren MP.   +11 more
europepmc   +1 more source

Entropy‐Guided Convolutional Neural Network Classification of Sensor Signals for Real‐Time Surface Quality Monitoring in Direct Laser Interference Patterning

Advanced Intelligent Systems, EarlyView.
Neural‐network pipeline for real‐time DLIP surface‐quality monitoring: spectral entropy of WLI topographies is used to generate interpretable K‐means labels, which are transferred to time‐resolved photodiode traces. A compact dual‐input 1D‐CNN (signal + laser parameters) learns discriminative spatiotemporal features and predicts “OK/NOK” surface ...
Marcelo Daniel Sallese, Ignacio Tabares, Wei Wang, Marcos Soldera, Andrés Fabián Lasagni   +4 more
wiley   +1 more source

RT‐DETR‐DA for Complex Scenes: Distracted Driving Detection With Feature Interaction and Dynamic Perception

Advanced Intelligent Systems, EarlyView.
This work proposes RT‐DETR‐DA, an enhanced real‐time detection framework for identifying distracted driving in complex, real‐world environments. The model introduces a dynamic sparse gating multiscale attention module and an attention‐guided dual‐path fusion module to strengthen multiscale perception and cross‐layer feature interaction.
Yi Liu, QiaoXing Li, XiBin Wang, Ying He, Sen Zhang   +4 more
wiley   +1 more source