AI-based modeling and data-driven evaluation for smart manufacturing processes
Smart manufacturing refers to optimization techniques that are implemented in production
operations by utilizing advanced analytics approaches. With the widespread increase in …
operations by utilizing advanced analytics approaches. With the widespread increase in …
K-Means Clustering-Based Kernel Canonical Correlation Analysis for Multimodal Emotion Recognition in Human–Robot Interaction
L Chen, K Wang, M Li, M Wu… - IEEE Transactions on …, 2022 - ieeexplore.ieee.org
In this article, K-meansclustering-based Kernel canonical correlation analysis algorithm is
proposed for multimodal emotion recognition in human–robot interaction (HRI). The …
proposed for multimodal emotion recognition in human–robot interaction (HRI). The …
Deep learning for industrial KPI prediction: When ensemble learning meets semi-supervised data
Soft-sensing techniques are of great significance in industrial processes for monitoring and
prediction of key performance indicators. Due to the effectiveness of nonlinear feature …
prediction of key performance indicators. Due to the effectiveness of nonlinear feature …
MoniNet with concurrent analytics of temporal and spatial information for fault detection in industrial processes
Modern industrial plants generally consist of multiple manufacturing units, and the local
correlation within each unit can be used to effectively alleviate the effect of spurious …
correlation within each unit can be used to effectively alleviate the effect of spurious …
Multisource-refined transfer network for industrial fault diagnosis under domain and category inconsistencies
Unsupervised cross-domain fault diagnosis has been actively researched in recent years. It
learns transferable features that reduce distribution inconsistency between source and …
learns transferable features that reduce distribution inconsistency between source and …
Adaptive multimode process monitoring based on mode-matching and similarity-preserving dictionary learning
In real industrial processes, factors, such as the change in manufacturing strategy and
production technology lead to the creation of multimode industrial processes and the …
production technology lead to the creation of multimode industrial processes and the …
Fault detection of pressurized heavy water nuclear reactors with steady state and dynamic characteristics using data-driven techniques
Nuclear energy is a crucial source to bridge the deficit of energy demand as fossil fuel
reserves are continuously depleting over time. However, nuclear reactors operation is highly …
reserves are continuously depleting over time. However, nuclear reactors operation is highly …
Multirate mixture probability principal component analysis for process monitoring in multimode processes
In the multirate sampling processes, the process data are usually collected from various
operating conditions and display multimodal characteristics. To monitor these multirate …
operating conditions and display multimodal characteristics. To monitor these multirate …
Monitoring multimode processes: A modified PCA algorithm with continual learning ability
For multimode processes, one generally establishes local monitoring models corresponding
to local modes. However, the significant features of previous modes may be catastrophically …
to local modes. However, the significant features of previous modes may be catastrophically …
Intelligent fault diagnosis for chemical processes using deep learning multimodel fusion
Deep learning technology has been widely used in fault diagnosis for chemical processes.
However, most deep learning technologies currently adopted only use a single network …
However, most deep learning technologies currently adopted only use a single network …