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About the database ScholarIQanswers from OpenAlex & ORCID
How has Todd Charter's publication output changed over time?
ScholarIQpublication output · 2022–2025
Output grew0% over the shown period — from 2 works in 2022 to 2 in 2025.
2
1
5
2
2022202320242025
What are the most-cited papers on Todd Charter?
ScholarIQmost cited works
Intelligent manufacturing execution systems: A systematic review
Ardeshir Shojaeinasab, Todd Charter, Masoud Jalayer, Maziyar Khadivi, Oluwaseyi Ogunfowora, Nirav Raiyani, Marjan Yaghoubi, Homayoun Najjaran
Journal of Manufacturing Systems. 2022160 Citations
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
Computers & Industrial Engineering. 202541 Citations
Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN
Maryam Ahang, Masoud Jalayer, Ardeshir Shojaeinasab, Oluwaseyi Ogunfowora, Todd Charter, Homayoun Najjaran
Sensors. 202229 CitationsOPEN ACCESS
Deep Reinforcement Learning for Machine Scheduling: Methodology, the State-of-The-Art, and Future Directions
Maziyar Khadivi, Todd Charter, Marjan Yaghoubi, Masoud Jalayer, Maryam Ahang, Ardeshir Shojaeinasab, Homayoun Najjaran
SSRN Electronic Journal. 202315 CitationsOPEN ACCESS
Anomaly detection in automated fibre placement: learning with data limitations
Assef Ghamisi, Todd Charter, Ji Li, Maxime Rivard, G. Lund, Homayoun Najjaran
S4387279249. 202415 CitationsOPEN ACCESS
Related on ScholarIQ
University of Victoria
Institution
Intelligent manufacturing execution systems: A systematic review
Paper
Deep reinforcement learning for machine scheduling: Methodology, the state-of-the-art, and future directions
Paper
Synthesizing Rolling Bearing Fault Samples in New Conditions: A Framework Based on a Modified CGAN
Paper
Deep Reinforcement Learning for Machine Scheduling: Methodology, the State-of-The-Art, and Future Directions
Paper
Anomaly detection in automated fibre placement: learning with data limitations
Paper