no code implementations • 4 Oct 2023 • Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
This paper serves as a valuable resource for researchers to assess the current state of DRL-based machine scheduling and identify research gaps.
no code implementations • 24 Jun 2022 • Maryam Ahang, Masoud Jalayer, Ardeshir Shojaeinasab, Oluwaseyi Ogunfowora, Todd Charter, Homayoun Najjaran
The proposed method is validated on a real-world bearing dataset, and fault data are generated for different conditions.
no code implementations • 9 Feb 2022 • Masoud Jalayer, Amin Kaboli, Carlotta Orsenigo, Carlo Vercellis
In many real applications of fault detection and diagnosis, data tend to be imbalanced, meaning that the number of samples for some fault classes is much less than the normal data samples.
no code implementations • 2 May 2021 • Masoud Jalayer, Reza Jalayer, Amin Kaboli, Carlotta Orsenigo, Carlo Vercellis
The proposed augmentation algorithm extracts objects from the real samples and blends them randomly, to generate new samples and enhance the performance of the image processor.