Results 131 to 140 of about 295,971 (251)

Explaining the Origin of Negative Poisson's Ratio in Amorphous Networks With Machine Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
This review summarizes how machine learning (ML) breaks the “vicious cycle” in designing auxetic amorphous networks. By transitioning from traditional “black‐box” optimization to an interpretable “AI‐Physics” closed‐loop paradigm, ML is shown to not only discover highly optimized structures—such as all‐convex polygon networks—but also unveil hidden ...
Shengyu Lu, Xiangying Shen
wiley   +1 more source

Overcoming the Nyquist Limit in Molecular Hyperspectral Imaging by Reinforcement Learning

open access: yesAdvanced Intelligent Discovery, EarlyView.
Explorative spectral acquisition guide automatically selects informative spectral bands to optimize downstream tasks, outperforming full‐spectrum acquisition. The selected hyperspectral data are used for tasks such as unmixing and segmentation. BandOptiNet encodes selection states and outputs optimal bands to guide spectral acquisition. Recent advances
Xiaobin Tang   +4 more
wiley   +1 more source

Design, Control, and Clinical Applications of Magnetic Actuation Systems: Challenges and Opportunities

open access: yesAdvanced Intelligent Systems, Volume 7, Issue 3, March 2025.
This review aims to provide a broad understanding for interdisciplinary researchers in engineering and clinical applications. It addresses the development and control of magnetic actuation systems (MASs) in clinical surgeries and their revolutionary effects in multiple clinical applications.
Yingxin Huo   +3 more
wiley   +1 more source

Predicting Performance of Hall Effect Ion Source Using Machine Learning

open access: yesAdvanced 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   +8 more
wiley   +1 more source

VAE+DDPG: An Attention‐Enhanced Variational Autoencoder for Deep Reinforcement Learning‐Based Autonomous Navigation in Low‐Light Environments

open access: yesAdvanced Intelligent Systems, EarlyView.
Variational Autoencoder+Deep Deterministic Policy Gradient addresses low‐light failures of infrared depth sensing for indoor robot navigation. Stage 1 pretrains an attention‐enhanced Variational Autoencoder (Convolutional Block Attention Module+Feature Pyramid Network) to map dark depth frames to a well‐lit reconstruction, yielding a 128‐D latent code ...
Uiseok Lee   +7 more
wiley   +1 more source

Degeneracy Sensing Light Detection and Ranging‐Inertial Simultaneous Localization and Mapping with Dual‐Layer Resistant Odometry and Scan‐Context Loop‐Closure Detection Backend in Diverse Environments

open access: yesAdvanced Intelligent Systems, EarlyView.
This paper presents a degeneracy‐aware light detection and ranging (LiDAR)‐inertial framework that enhances LiDAR simultaneous localization and mapping performance in challenging environments. The proposed system integrates a dual‐layer robust odometry frontend with a Scan‐Context‐based loop‐closure detection backend.
Haoming Yang   +4 more
wiley   +1 more source

Mimicking Life: Autonomous Oscillating Artificial Cilia Driven by Chemical Power

open access: yesAdvanced Intelligent Systems, EarlyView.
The synthesis and motion analysis of chemically actuated, individually autonomous artificial cilia are presented. Driven by an internal chemical reaction, the self‐driven individual cilia require no external stimuli. They undergo periodic oscillatory motion with a 3D beat pattern and exhibit chemotactic shifts, reminiscent of biological systems.
Rajata Suvra Chakrovorty   +2 more
wiley   +1 more source

Rapid Assignment of Chemical Shifts From Crystal Structures in Solid‐State NMR

open access: yesAngewandte Chemie, EarlyView.
Chemical shift assignment in solids is a long and tedious process that relies on complex 1D and 2D NMR experiments. With prior knowledge of the 3D structure, this process can be significantly sped up by a Bayesian probabilistic assignment approach based on predicted chemical shifts.
Ruben Rodriguez‐Madrid   +2 more
wiley   +2 more sources

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