Missing measurement data recovery methods in structural health monitoring: The state, challenges and case study
In the field of structural health monitoring (SHM), the sensor measurement signals collected
from the structure are the foundation and key of the SHM system. However, the loss of …
from the structure are the foundation and key of the SHM system. However, the loss of …
A combined forecasting system based on statistical method, artificial neural networks, and deep learning methods for short-term wind speed forecasting
Wind speed forecasting is gaining importance as the share of wind energy in electricity
systems increases. Numerous forecasting approaches have been used to predict wind …
systems increases. Numerous forecasting approaches have been used to predict wind …
Bayesian dynamic regression for reconstructing missing data in structural health monitoring
Massive data that provide valuable information regarding the structural behavior are
continuously collected by the structural health monitoring (SHM) system. The quality of …
continuously collected by the structural health monitoring (SHM) system. The quality of …
Machine learning techniques in structural wind engineering: A State-of-the-Art Review
Machine learning (ML) techniques, which are a subset of artificial intelligence (AI), have
played a crucial role across a wide spectrum of disciplines, including engineering, over the …
played a crucial role across a wide spectrum of disciplines, including engineering, over the …
An improved resistance-based thermal model for a pouch lithium-ion battery considering heat generation of posts
An improved three-dimensional thermal model for a pouch battery is established, which
seamlessly integrates two thermal sub-models of the battery body and the current collecting …
seamlessly integrates two thermal sub-models of the battery body and the current collecting …
Reconstruction of structural long-term acceleration response based on BiLSTM networks
Reconstructing lost dynamic responses is significant for structural condition assessment in
structural health monitoring (SHM). Current advanced methods usually employ deep …
structural health monitoring (SHM). Current advanced methods usually employ deep …
Wind speed deterministic forecasting and probabilistic interval forecasting approach based on deep learning, modified tunicate swarm algorithm, and quantile …
J Wang, S Wang, Z Li - Renewable Energy, 2021 - Elsevier
As a renewable, clean and economical energy source, wind energy has rapidly infiltrated
into the modern power grid system. Wind speed forecasting, the crucial technology of wind …
into the modern power grid system. Wind speed forecasting, the crucial technology of wind …
A large-scale sensor missing data imputation framework for dams using deep learning and transfer learning strategy
Structural health monitoring (SHM) is a powerful tool for identifying the underlying dam
structural response anomalies by imitating the self-sensing ability of humans. Unfortunately …
structural response anomalies by imitating the self-sensing ability of humans. Unfortunately …
Risk prediction and factors risk analysis based on IFOA-GRNN and apriori algorithms: Application of artificial intelligence in accident prevention
X Xie, G Fu, Y Xue, Z Zhao, P Chen, B Lu… - Process Safety and …, 2019 - Elsevier
Risk prediction of disasters is one of the most effective ways to prevent accidents. To solve
the problems in multi-factor complex disaster prediction, this paper proposes a new method …
the problems in multi-factor complex disaster prediction, this paper proposes a new method …
Neural network modeling for groundwater-level forecasting in coastal aquifers
Advances in the artificial intelligence-based models can act as robust tools for modeling
hydrological processes. Neural network architectures coupled with learning algorithms are …
hydrological processes. Neural network architectures coupled with learning algorithms are …