Machine learning and structural health monitoring overview with emerging technology and high-dimensional data source highlights
Conventional damage detection techniques are gradually being replaced by state-of-the-art
smart monitoring and decision-making solutions. Near real-time and online damage …
smart monitoring and decision-making solutions. Near real-time and online damage …
Artificial intelligence (AI) in augmented reality (AR)-assisted manufacturing applications: a review
Augmented reality (AR) has proven to be an invaluable interactive medium to reduce
cognitive load by bridging the gap between the task-at-hand and relevant information by …
cognitive load by bridging the gap between the task-at-hand and relevant information by …
Machine learning in manufacturing and industry 4.0 applications
The machine learning (ML) field has deeply impacted the manufacturing industry in the
context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of …
context of the Industry 4.0 paradigm. The industry 4.0 paradigm encourages the usage of …
Hybridized artificial intelligence models with nature-inspired algorithms for river flow modeling: A comprehensive review, assessment, and possible future research …
River flow (Q flow) is a hydrological process that considerably impacts the management and
sustainability of water resources. The literature has shown great potential for nature-inspired …
sustainability of water resources. The literature has shown great potential for nature-inspired …
[HTML][HTML] Spatial and temporal deep learning methods for deriving land-use following deforestation: A pan-tropical case study using Landsat time series
Assessing land-use following deforestation is vital for reducing emissions from deforestation
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
and forest degradation. In this paper, for the first time, we assess the potential of spatial …
Unsupervised domain adaptation of bearing fault diagnosis based on join sliced Wasserstein distance
P Chen, R Zhao, T He, K Wei, Q Yang - ISA transactions, 2022 - Elsevier
Deep neural networks have been successfully utilized in the mechanical fault diagnosis,
however, a large number of them have been based on the same assumption that training …
however, a large number of them have been based on the same assumption that training …
Deep learning with data preprocessing methods for water quality prediction in ultrafiltration
Ultrafiltration (UF) has been widely used to remove colloidal substances and suspended
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
solids in feed water. However, UF membrane breakage can lead to downstream impurities …
[HTML][HTML] Residual LSTM layered CNN for classification of gastrointestinal tract diseases
Abstract nowadays, considering the number of patients per specialist doctor, the size of the
need for automatic medical image analysis methods can be understood. These systems …
need for automatic medical image analysis methods can be understood. These systems …
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 …
An accurate and distraction-free vision-based structural displacement measurement method integrating Siamese network based tracker and correlation-based …
Y Xu, J Zhang, J Brownjohn - Measurement, 2021 - Elsevier
Vision-based displacement measurement receives increasing attention on non-contact
bridge monitoring while it faces challenges in long-time field applications due to the …
bridge monitoring while it faces challenges in long-time field applications due to the …