Results 171 to 180 of about 36,988 (280)

Field‐Programable Dynamics in a Soft Magnetic Actuator Enabling True Random Number Generation and Reservoir Computing

Advanced Intelligent Systems, EarlyView.
Complex dynamics, often avoided in electromechanical design, can enhance soft robotics. We develop durable magnetic soft actuators operating in tunable dynamic regimes, enabling random number generation, stochastic computing, and time‐series prediction.
Eduardo Sergio Oliveros‐Mata, Oleksandr V. Pylypovskyi, Eleonora Raimondo, Rico Illing, Yevhen Zabila, Lin Guo, Guannan Mu, Mónica Navarro López, Xu Wang, Georgios Tzortzinis, Angelos Filippatos, Gilbert Santiago Cañón Bermúdez, Francesca Garescì, Giovanni Finocchio, Denys Makarov   +14 more
wiley   +1 more source

Dynamic performance enhancement of adjustable blade pitch angle for wind generation system applications based on artificial neural network control techniques. [PDF]

Sci Rep
Ameen AG, Mohamed S, Abdel-Jaber GT, Hamdan I.   +3 more
europepmc   +1 more source

Tandem Neural Network Rapidly Solves Multivalued Inverse Problems: Application to Oxide‐Semiconductor Characterization

Advanced Intelligent Systems, EarlyView.
A tandem neural network directly solves the multivalued inverse problem of extracting semiconductor parameters from transistor measurements. Trained on only 1000 simulations, the network infers six material parameters (e.g., defect states, carrier concentration, mobility) in under 1 ms, demonstrating a broadly applicable framework for semiconductor ...
Masatoshi Kimura, Keisuke Ide, Kuan‐Ju Zhou, Atsushi Shimizu, Takayoshi Katase, Hidenori Hiramatsu, Kei Terayama, Hideo Hosono, Toshio Kamiya   +8 more
wiley   +1 more source

DCSwinLSTM for spatiotemporal meteorological drought forecasting. [PDF]

iScience
Peng H, Wu C, Du Y, Liu H.
europepmc   +1 more source

Enhanced Monocular Depth Estimation Using ResNet Backbone and Multiscale Feature Decoding for Precise Object Distance Measurement

Advanced Intelligent Systems, EarlyView.
A lightweight monocular perception framework generates high‐fidelity depth maps and integrates YOLOv8 detection to estimate object‐wise distances from a single RGB image. Evaluated on KITTI and the proposed D‐Far250 dataset, the system demonstrates accurate long‐range perception up to 250 m while maintaining real‐time performance, enabling scalable ...
Faseeh Muhammad, Misbah Bibi, Murad Ali Khan, Syed Shehryar Ali Naqvi, Adnan Ejaz Muhammad, Do-Hyeun Kim   +5 more
wiley   +1 more source

Machine Learning Application to Predict Bicycle Ergometer Test Results: a Prospective Cohort Study. [PDF]

Sovrem Tekhnologii Med
Berezina EV, Blinova KA, Dmitrieva OA, Mishina IE.   +3 more
europepmc   +1 more source

Artificial Neural Network Driven Computational Modeling of Naegleria fowleri Epidemics Using Variational Optimization

Advanced Physics Research, EarlyView.
ABSTRACT This study presents a mathematical framework to analyze the transmission dynamics of an amoeba‐induced central nervous system infection. The population is divided into compartments including susceptible, exposed, infected, quarantined, hospitalized, recovered, protected, and deceased.
Wakeel Ahmed, Areej Yaseen, Ghulamullah Saeedi, Shakeel Ahmed   +3 more
wiley   +1 more source

Developing a Gudermannian neural network for solving the Painlevé model-II in the context of nonlinear optics. [PDF]

Sci Rep
Faisal S, Sabir Z, Khan SU, Aamir M, Cyran KA.   +4 more
europepmc   +1 more source

Modeling and parameter estimation for fractional large‐scale interconnected Hammerstein systems

Asian Journal of Control, EarlyView.
Abstract This paper addresses the challenge of modeling and identifying large‐scale interconnected systems exhibiting memory effects, hereditary properties, and non‐local interactions. We propose a fractional‐order extension of the Hammerstein architecture that incorporates Grünwald–Letnikov operators to capture complex dynamics through multiple ...
Mourad Elloumi, Omar Naifar, Imed Bouzida   +2 more
wiley   +1 more source

Exploring the spiking neural autoencoder: from hyperexcitability to noise-driven compensation. [PDF]

Front Syst Neurosci
Khodashenas M, Martins DP.
europepmc   +1 more source