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ADs: Active Data-sharing for Data Quality Assurance in Advanced Manufacturing Systems
Machine learning (ML) methods are widely used in industrial applications, which usually require a large amount of training data.
Advancing Additive Manufacturing through Deep Learning: A Comprehensive Review of Current Progress and Future Challenges
Additive manufacturing (AM) has already proved itself to be the potential alternative to widely-used subtractive manufacturing due to its extraordinary capacity of manufacturing highly customized products with minimum material wastage.
Pseudo Replay-based Class Continual Learning for Online New Category Anomaly Detection in Additive Manufacturing
The incorporation of advanced sensors and machine learning techniques has enabled modern manufacturing enterprises to perform data-driven in-situ quality monitoring based on the sensor data collected in manufacturing processes.
Attention-stacked Generative Adversarial Network (AS-GAN)-empowered Sensor Data Augmentation for Online Monitoring of Manufacturing System
To generate the high-quality minority samples, it is vital to learn the underlying distribution of the abnormal states data.
Knowledge Distillation-based Information Sharing for Online Process Monitoring in Decentralized Manufacturing System
In a decentralized manufacturing system, the involved units may fabricate same or similar products and deploy their own machine learning model for online process monitoring.
Model-Informed Generative Adversarial Network (MI-GAN) for Learning Optimal Power Flow
Although traditional optimization techniques, such as stochastic and robust optimization approaches, could be leveraged to address the OPF problem, in the face of renewable energy uncertainty, i. e., the dynamic coefficients in the optimization model, their effectiveness in dealing with large-scale problems remains limited.
A Generative Adversarial Network-based Selective Ensemble Characteristic-to-Expression Synthesis (SE-CTES) Approach and Its Applications in Healthcare
Investigating the causal relationships between characteristics and expressions plays a critical role in healthcare analytics.
