Applying machine learning methods for characterization of hexagonal prisms from their 2D scattering patterns – an investigation using modelled scattering data
- Submitting institution
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University of Hertfordshire
- Unit of assessment
- 11 - Computer Science and Informatics
- Output identifier
- 13604577
- Type
- D - Journal article
- DOI
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10.1016/j.jqsrt.2017.07.001
- Title of journal
- Journal of Quantitative Spectroscopy and Radiative Transfer
- Article number
- -
- First page
- 115
- Volume
- 201
- Issue
- -
- ISSN
- 0022-4073
- Open access status
- Compliant
- Month of publication
- July
- Year of publication
- 2017
- URL
-
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- Supplementary information
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- 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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4
- Research group(s)
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-
- Citation count
- 3
- Proposed double-weighted
- No
- Reserve for an output with double weighting
- No
- Additional information
- This work investigated the applicability of advanced machine learning methods to help with the inverse problem for hexagonal prisms. This is a useful test case, since virtually all the ice on Earth’s surface and in its atmosphere is of a hexagonal crystalline structure. This paper has inspired a PhD project, entitled "Developing Computational models for characterizing small particles based on their 2-dimensional light scattering patterns”, which started in 2018.
- Author contribution statement
- -
- Non-English
- No
- English abstract
- -