Results 191 to 200 of about 165,608 (287)

A brain-inspired ISMO-PNN framework for neurally-grounded bearing fault diagnosis. [PDF]

Front Neurorobot
Zhang F, Zhang B.
europepmc   +1 more source

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

Advanced 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, Chanhong Hwang, Hyunseok Kang, Yechan Park, Hangwoo Jin, Tuan Anh Nguyen, Eunmi Choi, Dugki Min   +7 more
wiley   +1 more source

Calibration‐Free Electromyography Motor Intent Decoding Using Large‐Scale Supervised Pretraining

Advanced Intelligent Systems, EarlyView.
Calibration‐free electromyography motor intent decoding is enabled through large‐scale supervised pretraining across heterogeneous datasets. A Spatially Aware Feature‐learning Transformer processes variable channel counts and electrode geometries, allowing transfer across users and recording setups. On a held‐out benchmark, fine‐tuned cross‐user models
Alexander E. Olsson, Nebojša Maleševic, Anders Björkman, Christian Antfolk   +3 more
wiley   +1 more source

Multi-Scale Adaptive Light Stripe Center Extraction for Line-Structured Light Vision Based Online Wheelset Measurement. [PDF]

Sensors (Basel)
Liu S, He Q, Fu W, Du B, Feng Q.
europepmc   +1 more source

Deep Learning Approaches for Classifying Crack States With Overload and Predicting Fatigue Parameters in a Titanium Alloy

Advanced Intelligent Systems, EarlyView.
This study proposes a deep learning approach to evaluate the fatigue crack behavior in metals under overload conditions. Using digital image correlation to capture the strain near crack tips, convolutional neural networks classify crack states as normal, overload, or recovery, and accurately predict fatigue parameters.
Seon Du Choi, Suman Timilsina, Youn Woo Shin, Yong Nam Kwon, Kee‐Sun Sohn, Ji Sik Kim   +5 more
wiley   +1 more source

Nonlinear kernel-based fMRI activation detection. [PDF]

Front Neuroimaging
Han C, Yang Z, Zhuang X, Cordes D.
europepmc   +1 more source

Bayesian Optimisation for the Experimental Sciences: A Practical Guide to Data‐Efficient Optimisation of Laboratory Workflows

Advanced Intelligent Systems, EarlyView.
This study provides an introduction to Bayesian optimisation targeted for experimentalists. It explains core concepts, surrogate modelling, and acquisition strategies, and addresses common real‐world challenges such as noise, constraints, mixed variables, scalability, and automation.
Chuan He, Martin Singull, T. Jesper Jacobsson   +2 more
wiley   +1 more source

Advances in deep reinforcement learning enable better predictions of human behavior in time-continuous tasks. [PDF]

PLoS One
Haberland S, Ruge H, Frimmel H.
europepmc   +1 more source

KDLM: Lightweight Brain Tumor Segmentation via Knowledge Distillation

Advanced Intelligent Systems, EarlyView.
A lightweight student network is designed, which is based on multiscale and multilevel feature fusion and combined with the residual channel attention mechanism to achieve efficient feature extraction and fusion with very few parameters. A dual‐teacher collaborative knowledge distillation framework is proposed.
Baotian Li, Haoyu Tang, Jun Zhu, Fang Fang Gou, Jia Wu   +4 more
wiley   +1 more source

Research on precise estimation of line loss rate probability density based on bilateral total variation filtering algorithm. [PDF]

Sci Rep
Zhang J, Liu S, Feng C.
europepmc   +1 more source