Enabling metrology-oriented specification of geometrical variability : A categorical approach
- Submitting institution
-
The University of Huddersfield
- Unit of assessment
- 12 - Engineering
- Output identifier
- 79
- Type
- D - Journal article
- DOI
-
10.1016/j.aei.2018.11.001
- Title of journal
- Advanced Engineering Informatics
- Article number
- -
- First page
- 347
- Volume
- 39
- Issue
- -
- ISSN
- 1474-0346
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2019
- 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
-
3
- Research group(s)
-
-
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- AI systems require context knowledge in order to achieve their goals. This pioneering paper introduces a standardised format for AI ready semantic documents as knowledge sources for AI systems using a developed high-level category semantic language that can make inferences from the inputted knowledge. This resulted in an EPSRC Innovation Fellowship EP/S001328/1 and research for semantic infrastructures for advanced smart and autonomous manufacturing; with industrial support from Renishaw, NPL and Reliance. and as a keynote speaker at CIRP which led to an active collaboration with Polytechnic University of Milan (Professor Marcello Urgo marcello.urgo@polimi.it) and CNR-STIIMA (Dr Walter Terkaj undefined walter.terkaj@stiima.cnr.it).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -