A symbiotic human–machine learning approach for production ramp-up
- Submitting institution
-
Loughborough University
- Unit of assessment
- 12 - Engineering
- Output identifier
- 586
- Type
- D - Journal article
- DOI
-
10.1109/THMS.2017.2717885
- Title of journal
- IEEE Transactions on Human-Machine Systems
- Article number
- -
- First page
- 229
- Volume
- 48
- Issue
- 3
- ISSN
- 2168-2291
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
-
- Supplementary information
-
-
- Request cross-referral to
- -
- Output has been delayed by COVID-19
- No
- COVID-19 affected output statement
- -
- Forensic science
- No
- Criminology
- No
- Interdisciplinary
- No
- Number of additional authors
-
2
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This paper impacts the state of the art by exploring the need for symbiotic relations between humans and machines through combined reinforcement. A number of industrial partners understand this work to be significant and have been involved with collaboration projects based partly on this research. These include £16million worth of European projects; Reborn, openMOS, and Selsus. These European collaborations have included companies such as Masmec (medical devices system integrators) Critical Manufacturing (software) IF werner (manufacturing equipment), Afag (equipment) and Elrest (controllers).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -
