Cite
A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study.
MLA
Maggipinto, Marco, et al. “A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study.” IEEE Transactions on Automation Science & Engineering, vol. 19, no. 3, July 2022, pp. 1477–90. EBSCOhost, https://doi.org/10.1109/TASE.2022.3141186.
APA
Maggipinto, M., Beghi, A., & Susto, G. A. (2022). A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study. IEEE Transactions on Automation Science & Engineering, 19(3), 1477–1490. https://doi.org/10.1109/TASE.2022.3141186
Chicago
Maggipinto, Marco, Alessandro Beghi, and Gian Antonio Susto. 2022. “A Deep Convolutional Autoencoder-Based Approach for Anomaly Detection With Industrial, Non-Images, 2-Dimensional Data: A Semiconductor Manufacturing Case Study.” IEEE Transactions on Automation Science & Engineering 19 (3): 1477–90. doi:10.1109/TASE.2022.3141186.