Results 91 to 100 of about 14,357 (268)

Gradient Boosted Trees and Denoising Autoencoder to Correct Numerical Wave Forecasts

Journal of Marine Science and Engineering
This paper is dedicated to correcting the WAM/ICON numerical wave model predictions by reducing the residue between the model’s predictions and the actual buoy observations. The two parameters used in this paper are significant wave height and wind speed.
Ivan Yanchin, C. Guedes Soares
doaj   +1 more source

TSTScope Unifies Single‐Cell Multi‐Omics to Identify Functional T Cell States Predictive of Immunotherapy Response

Advanced Science, EarlyView.
TSTScope is an interpretable AI framework that integrates single‐cell transcriptomes with TCR information through curated gene‐program constraints. By linking receptor context to functional T cell states, it reveals response‐associated tumor‐specific T cell programs in lung cancer immunotherapy cohorts and defines an MPR score associated with ...
Shiwei Cao, Jinyu Cheng, Feng‐ao Wang, Chenxin Yi, Jiajun Chen, Keyue Wang, Lulu Liu, Junwei Liu, Yixue Li   +8 more
wiley   +1 more source

A Fault Diagnosis Approach Utilizing Artificial Intelligence for Maritime Power Systems within an Integrated Digital Twin Framework

Applied Sciences
This research focuses on enhancing the preventive maintenance strategies currently employed for induction motors within ship propulsion systems, advocating for a shift towards a predictive maintenance model. It introduces a real-time monitoring framework
Fation Fera, Christos Spandonidis
doaj   +1 more source

Livestock Multi‐Omics Integration: A Systematic Framework From Statistical Association to Causal Interpretation

Advanced Science, EarlyView.
A three‐tier livestock multi‐omics framework resolves four typical analytical pitfalls. Moving from statistical association through machine learning preprocessing to triple‐modal causal inference, it converts omics results into genomic selection and gene editing strategies to achieve One Health, underpinned by multi‐omics data, multimodal sequencing ...
Jiying Wen, Zhongyu Wang, Jieping Huang, Fen Li, Ningbo Chen, Yun Ma   +5 more
wiley   +1 more source

Simulation of Full Wavefield Data with Deep Learning Approach for Delamination Identification

Applied Sciences
In this work, a novel approach of guided wave-based damage identification in composite laminates is proposed. The novelty of this research lies in the implementation of ConvLSTM-based autoencoders for the generation of full wavefield data of propagating ...
Saeed Ullah, Pawel Kudela, Abdalraheem A. Ijjeh, Eleni Chatzi, Wieslaw Ostachowicz   +4 more
doaj   +1 more source

STWave: Fine‐Scale Spatial Structure Discovery in Microscopic‐Resolution Spatial Transcriptomics via Patchwise Wavelet Graphs

Advanced Science, EarlyView.
STWave transforms massive microscopic‐resolution spatial transcriptomics into interpretable fine‐scale tissue maps through patch‐wise inference, wavelet‐based multi‐scale encoding, and dual‐domain reconstruction. It reduces noise while preserving weak spatial signals, enabling efficient analysis of 6 40 000 spots of 2.47 GB GPU memory and revealing ...
Tao Jiang, Songming Zhang, Xiaofeng Chen, Xiongtao Xiao, Wenming Cao, Weikai Li, Tong Zhao, Bing Li, Xinyue Xu, Zhongshan Li   +9 more
wiley   +1 more source

Artificial Intelligence for Fluorite Ferroelectric Materials: From Discovery to Optimization

Advanced Electronic Materials, EarlyView.
Artificial intelligence accelerates the discovery and optimization of HfO2‐based fluorite ferroelectrics by linking synthesis, structure, properties, and device performance. Machine learning, deep‐learning analysis, and AI‐driven atomistic modeling enable predictive design, dopant screening, and closed‐loop optimization toward next‐generation ...
Faizan Ali, David Lehninger, Florencio Sánchez, Ignasi Fina   +3 more
wiley   +1 more source

An Autoencoder‐Based Deep‐Learning Method for Augmenting the Sensing Capability of Piezoelectric Microelectromechanical System Sensors in a Fluid‐Dynamic System

Advanced Intelligent Systems
Herein, an innovative deep‐learning architecture is proposed to enhance the sensing capabilities of a microelectromechanical system (MEMS) used in fluid dynamic applications.
Mohammadrahim Kazemzadeh, Iman Mehdipour, Massimo De Vittorio, Ferruccio Pisanello   +3 more
doaj   +1 more source

Machine Learning Interatomic Potentials for Energy Materials: Architectures, Training Strategies, and Applications

Advanced Energy Materials, EarlyView.
Machine learning interatomic potentials bridge quantum accuracy and computational efficiency for materials discovery. Architectures from Gaussian process regression to equivariant graph neural networks, training strategies including active learning and foundation models, and applications in solid‐state electrolytes, batteries, electrocatalysts ...
In Kee Park, Saeed Pourasad, Jinhong Mun, Arunkumar Chitteth Rajan, Tae Hyeon Park, Anand Rohit, Mohammad Zafari, Tan‐Lien Pham, Eunjae Jung, Bonseung Koo, Miran Ha, Gi Beom Sim, Hyun Gyu Park, Won Woong Choi, Kwanghyeon Jo, Soohaeng Yoo Willow, David ChangMo Yang, Chang Woo Myung, Geunsik Lee, Kwang S. Kim   +19 more
wiley   +1 more source

Scheduled denoising autoencoders

, 2014
We present a representation learning method that learns features at multiple different levels of scale. Working within the unsupervised framework of denoising autoencoders, we observe that when the input is heavily corrupted during training, the network tends to learn coarse-grained features, whereas when the input is only slightly corrupted, the ...
Geras, Krzysztof, Sutton, Charles
openaire   +3 more sources