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Jan 10, 2024 · Transfer learning for aluminium extrusion electricity consumption anomaly detection via deep neural networks. Int. J. Comput. Integr. Manuf.,. 31(4-5):396 ...
Apr 3, 2024 · An ML model based on a convolutional neural network for real-time defect detection to prevent production losses and reduce human involvement in quality ...
Missing: extrusion electricity
Jan 23, 2024 · (2022) applied the transfer learning paradigm for anomaly detection based on the operating behavior of a wind turbine through a physics-constrained artificial ...
Missing: aluminium extrusion
Jul 6, 2023 · This study proposes a multisource transfer learning method that generates anomaly detection models for balling defect detection, thus ensuring quality ...
Missing: consumption | Show results with:consumption
Jun 13, 2024 · SYSTEMATIC REVIEW OF MODELS USEDTO HANDLE CLASS IMBALANCE IN ANOMALY DETECTION FOR ENERGY CONSUMPTION - Download as a PDF or view online for free.
Missing: aluminium extrusion via
Feb 5, 2024 · We review conventional and modern machine learning (ML)-assisted works employing closed loop control (CLC) strategies in metal additive manufacturing (AM).
Missing: electricity consumption
Apr 25, 2024 · Emotion Detection Through Electrocardiogram Signal Classification in an IOT Environment with Deep Neural Networks. Aloy Anuja Mary G, Aanandha Saravanan K ...
Missing: extrusion consumption
Mar 8, 2024 · In recent years, there has been widespread adoption of machine learning (ML) technologies to unravel intricate relationships among diverse parameters in ...
Missing: aluminium consumption
Jun 8, 2024 · In this study, finite element simulations with material formability diagrams are used to predict the critical zones in which failure modes may initiate. The ...
Jun 12, 2024 · A review on modern defect detection models using dcnns–deep convolutional neural networks. Journal of Advanced Research, 35:33–48, 2022. [31] ↑ Zhonghe Ren ...
Missing: extrusion | Show results with:extrusion