Results 171 to 180 of about 11,734,216 (296)

Physics‐Based Encoder‐Only Transformer for Structural Hysteresis Prediction and Dynamic Analysis

Earthquake Engineering &Structural Dynamics, EarlyView.
ABSTRACT Accurate modeling of structural hysteresis is a critical task for ensuring the safety of structures through reliable response predictions. However, conventional hysteresis models often struggle to represent diverse hysteresis characteristics observed in real‐world structures, as the manual selection of a model form may not assure validity and ...
Jongha Joo, Jaehwan Jeon, Junho Song
wiley   +1 more source

A Genetic Algorithm‐Optimized LSTM Framework for Mechanics‐Conditioned Reconstruction of Stress–Strain Curves in High‐Performance Cementitious Materials

Energy Science &Engineering, EarlyView.
GA‐optimized LSTM for full stress–strain curve prediction of high‐performance cementitious materials. Compression‐test data are preprocessed (cleaning, normalization, interpolation/standardization, and Savitzky–Golay smoothing). A Genetic Algorithm tunes LSTM hyperparameters, while a physics‐informed loss enforces stiffness, peak response ( σ max ...
Mahmoud AlJamal, Qasim Aljamal, Mohammad Q. Al‐Jamal, Bashar S. Khassawneh, Ayoub Alsarhan, Dimah Al‐Fraihat, Abdulmohsen Algarni, Osama Harfoushi   +7 more
wiley   +1 more source

Open-Circuit Fault Detection and Classification of Modular Multilevel Converters in High Voltage Direct Current Systems (MMC-HVDC) with Long Short-Term Memory (LSTM) Method. [PDF]

Sensors (Basel), 2021
Wang Q, Yu Y, Ahmed HOA, Darwish M, Nandi AK.   +4 more
europepmc   +1 more source

High Precision Prediction Model of Wind Speed for Underground Mine Tunnel by Using ASTF Net

Energy Science &Engineering, EarlyView.
This paper proposes an Adaptive Spectral‐Temporal Fusion Net (ASTF‐Net), which features a novel decomposition‐ensemble framework with adaptive feature fusion. This design deliberately balances spectral and temporal features and effectively boosts prediction accuracy in complex ventilation environments. ABSTRACT An Adaptive Spectral‐Temporal Fusion Net (
Ruo‐Qi Li, Yong‐Qiang Chai, Lian Shi, Fu‐Gang Wang, Mi Liu   +4 more
wiley   +1 more source

Forecasting of COVID-19 using deep layer Recurrent Neural Networks (RNNs) with Gated Recurrent Units (GRUs) and Long Short-Term Memory (LSTM) cells. [PDF]

Chaos Solitons Fractals, 2021
ArunKumar KE, Kalaga DV, Kumar CMS, Kawaji M, Brenza TM.   +4 more
europepmc   +1 more source

Explainable and Uncertainty‐Aware Neural Modeling of Industrial CO2 Emissions With Economic, Energy, and Institutional Interaction Dynamics

Energy Science &Engineering, EarlyView.
A global panel of 5138 country–year observations from 165 countries (1990–2024) integrates industrial CO2 emissions, economic structure, renewable‐energy use, and institutional indicators. A dual‐input neural network combines engineered numeric features with learned country embeddings to forecast industrial CO2 emissions under rolling‐origin temporal ...
Syed Azeem Inam, Saddam Umer, Hashir Shaikh, Amal K. Alkhalifa   +3 more
wiley   +1 more source

Long short-term memory (LSTM) networks for precision prediction of Schottky barrier photodiode behavior at different ıllumination levels. [PDF]

Sci Rep
Tulum G, Nematzadeh S, Taşçıoğlu İ, Altındal Ş, Yakuphanoğlu F.   +4 more
europepmc   +1 more source

LSTM（Long Short-Term Memory）

Journal of Japan Society for Fuzzy Theory and Intelligent Informatics, 2018
openaire   +2 more sources

Environmental Control for Edible Fungi Cultivation Based on Temporal Information and Deep Learning

Food Bioengineering, EarlyView.
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, Shuangcheng Huang, Qingbo Lu, Xinyi Guo   +3 more
wiley   +1 more source

Prediction of COVID-19 cases by multifactor driven long short-term memory (LSTM) model. [PDF]

Sci Rep
Shao Y, Wan TK, Chan KHK.
europepmc   +1 more source