An Adaptive Kalman Filtering Approach to Sensing and Predicting Air Quality Index Values
- Submitting institution
-
Edinburgh Napier University
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 2501508
- Type
- D - Journal article
- DOI
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10.1109/access.2019.2963416
- Title of journal
- IEEE Access
- Article number
- -
- First page
- 4265
- Volume
- 8
- Issue
- -
- ISSN
- 2169-3536
- Open access status
- Compliant
- Month of publication
- January
- Year of publication
- 2020
- 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
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5
- Research group(s)
-
-
- Citation count
- 1
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- The adaptive Kalman filtering based auto-regression prediction model provided a new prediction method for the creation of the context-proactive mechanism for meteorological robots resulting in an international project co-sponsored by the Royal Society of Edinburgh and the China Natural Sciences Foundation Council: Context-driven proactively Auto-recognising and Learning Met-bot for Meteorological Disaster Surveillance and Precautious Warning [Grant No. Ref 62967_Liu_2018_2 ]. There was also significant public engagement: the EU sponsored Explorathon - Cafe Science, demo, poster and public talk, at the Royal Botanic Garden, Edinburgh, resulting in a prize in China (Jinan) Overseas High-level Talents Innovation and Entrepreneurship Competition (2018).
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -