Results 171 to 180 of about 384,787 (322)

Predicting Performance of Hall Effect Ion Source Using Machine Learning

Advanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This study introduces HallNN, a machine learning tool for predicting Hall effect ion source performance using a neural network ensemble trained on data generated from numerical simulations. HallNN provides faster and more accurate predictions than numerical methods and traditional scaling laws, making it valuable for designing and optimizing Hall ...
Jaehong Park, Guentae Doh, Dongho Lee, Youngho Kim, Changmin Shin, Su‐Jin Shin, Young‐Chul Ghim, Sanghoo Park, Wonho Choe   +8 more
wiley   +1 more source

Computational Models of Multisensory Integration with Recurrent Neural Networks: A Critical Review and Future Directions

Advanced Intelligent Systems, EarlyView.
This review outlines how recurrent neural networks model multisensory integration by capturing temporal and probabilistic features of sensory input. Key developments, challenges, and future directions are summarized, providing insights into biologically inspired AI. Multisensory integration (MSI) is a core brain function underlying perception, learning,
Ehsan Bolhasani, Seyed Hamed Aboutalebi, Yaser Merrikhi   +2 more
wiley   +1 more source

Soft Magnetic Sensor Array for Amphibious Measurement of 3D Muscle Deformation Distribution for Human Motion Recognition

Advanced Intelligent Systems, EarlyView.
This article develops a soft magnetic sensor array to extract 3D and distributional muscle deformations, which has highly consistent measurements in amphibious environments, robustness to hydraulic pressure, and about 200 ms faster response than an inertial measurement unit, achieving over 98% classification accuracy and below 3% phase estimation ...
Yuchao Liu, Zihan Chen, Chuxuan Guo, Zijie Liu, Yibin Chen, Xuan Wu, Zhuo Li, Qining Wang, Jiajie Guo   +8 more
wiley   +1 more source

Kolmogorov–Arnold Network for Transistor Compact Modeling

Advanced Intelligent Systems, EarlyView.
This work introduces Kolmogorov–Arnold network (KAN) for the transistor—an architecture that integrates interpretability with high precision in physics‐based function modeling. The results reveal that despite achieving superior prediction accuracy for critical figures of merit, KAN demonstrates unique inherent challenges for transistor modeling ...
Rodion Novkin, Hussam Amrouch
wiley   +1 more source

A quenched functional central limit theorem for random walks in random environments under $(T)_gamma$ [PDF]

, 2014
Élodie Bouchet, Christophe Sabot, Renato Soares dos Santos   +2 more
openalex   +1 more source

Harnessing Nonidealities in Analog In‐Memory Computing Circuits: A Physical Modeling Approach for Neuromorphic Systems

Advanced Intelligent Systems, EarlyView.
This work harnesses nonidealities in analog in‐memory computing (IMC) by training physical neural networks modeled with ordinary differential equations. A differentiable spike‐time discretization accelerates training by 20× and reduces memory usage by 100×, enabling large IMC‐equivalent models to learn the CIFAR‐10 dataset.
Yusuke Sakemi, Yuji Okamoto, Takashi Morie, Sou Nobukawa, Takeo Hosomi, Kazuyuki Aihara   +5 more
wiley   +1 more source

BiT‐HyMLPKANClassifier: A Hybrid Deep Learning Framework for Human Peripheral Blood Cell Classification Using Big Transfer Models and Kolmogorov–Arnold Networks

Advanced Intelligent Systems, EarlyView.
This study presents BiT‐HyMLPKANClassifier, a novel hybrid deep learning framework for automated human peripheral blood cell classification. Model combines Big Transfer models with multilayer perceptron and efficient Kolmogorov–Arnold Network architectures, achieving over 97% accuracy.
Ömer Miraç KÖKÇAM, Ferhat UÇAR
wiley   +1 more source

A Discrete-time Particle Filter and Central Limit Theorem

, 2014
Zi Ye
openalex   +1 more source

A Robotic Urinary Bladder Enabling Volume Monitoring and Assisted Micturition

Advanced Intelligent Systems, EarlyView.
An implantable robotic bladder is presented that can store urine in an origami‐designed enclosure. An inductance sensing principle can monitor and transfer the urine volume in real‐time. It can actively expand based on the amount of urine collected from kidneys and apply on‐demand mechanical compression to assist urination.
Izadyar Tamadon, Michele Ibrahimi, Federica Semproni, Veronica Iacovacci, Arianna Menciassi   +4 more
wiley   +1 more source

Variances and covariances in the Central Limit Theorem for the output of a transducer.

Eur J Comb, 2015
Heuberger C, Kropf S, Wagner S.
europepmc   +1 more source