Results 131 to 140 of about 55,999 (288)
Transfer Learning Approaches in Bioprocess Engineering: Opportunities and Challenges
ABSTRACT Transfer learning (TL) has recently emerged as a promising approach to overcoming one of the key limitations of bioprocess engineering: data scarcity. By leveraging knowledge from one bioprocess to another, TL allows existing models and data sets to be reused efficiently, accelerating process development, improving prediction accuracy, and ...
Daniel Barón Díaz +3 more
wiley +1 more source
EventFlow: Real‐time neuromorphic event‐driven classification of two‐phase boiling flow regimes
We present a real‐time flow regime classification framework that integrates neuromorphic event‐driven sensing with deep recurrent neural networks. Unlike traditional frame‐based approaches, our system captures sparse event streams from an event‐based camera, representing only the dynamic brightness changes at the individual pixel level.
Sanghyeon Chang +9 more
wiley +1 more source
The fused data extracted from the distributed monitoring system as the data basis, combined with dynamic geological data, are imported into a deep learning model. As the geological conditions of mining and excavation change, the risk of water inrush at the working face is retrieved in real time.
Yongjie Li +4 more
wiley +1 more source
Accelerated rescaling of single Monte Carlo simulation runs with the Graphics Processing Unit (GPU). [PDF]
Yang O, Choi B.
europepmc +1 more source
Abstract Background To understand cellular morphology, biologists have relied on traditional optical microscopy of tissues combined with tissue clearing protocols to image structures deep within tissues. Unfortunately, these protocols often struggle to retain cell boundary markers, especially at high enough resolutions necessary for precise cell ...
Sam C. P. Norris +2 more
wiley +1 more source
AI‐based localization of the epileptogenic zone using intracranial EEG
Abstract Artificial intelligence (AI) is rapidly transforming our lives. Machine learning (ML) enables computers to learn from data and make decisions without explicit instructions. Deep learning (DL), a subset of ML, uses multiple layers of neural networks to recognize complex patterns in large datasets through end‐to‐end learning.
Atsuro Daida +5 more
wiley +1 more source
Environmental Control for Edible Fungi Cultivation Based on Temporal Information and Deep Learning
ABSTRACT Currently, there are still prevalent issues in greenhouse environmental regulation, such as response lag, low control accuracy, and difficulty in coping with sudden environmental disturbances. To achieve high‐precision and dynamic control of the edible fungi cultivation environment, this study proposes an edible fungi environmental control ...
Xiangyan Wang +3 more
wiley +1 more source
The immersed boundary method (IBM) was coupled with the moment representation lattice Boltzmann method (MR‐LBM), reducing bandwidth requirements compared to population‐based LBM formulations. A systematic assessment of IBM parameters was conducted to quantify their effect on computational performance.
Marco A. Ferrari +2 more
wiley +1 more source
Review of millimeter‐wave and terahertz near‐field synthetic aperture radar imaging technology
Abstract This paper comprehensively reviews the development of millimeter‐wave (MMW) and terahertz (THz) near‐field imaging technologies, with an emphasis on the state of synthetic aperture radar (SAR)‐based imaging technologies. Near‐field imaging technologies are categorized into passive and active imaging modes, among which active imaging is favored
Qi Yang +3 more
wiley +1 more source
ABSTRACT Background Hyperpolarized 129Xe MRI faces technical challenges including low signal‐to‐noise ratio and breath‐hold constraints. Current literature focuses on proprietary deep learning methods or image‐domain enhancements. Purpose To present a comprehensive evaluation of transformer and hybrid CNN‐transformer architectures integrating dual ...
Ramtin Babaeipour +3 more
wiley +1 more source

