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Conference Paper/Proceeding/Abstract 302 views 92 downloads

Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data

ALEXANDER MILNE, Xianghua Xie Orcid Logo, Gary Tam Orcid Logo

ACIVS 2025 / LNCS

Swansea University Authors: ALEXANDER MILNE, Xianghua Xie Orcid Logo, Gary Tam Orcid Logo

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Published in: ACIVS 2025 / LNCS
Published:
URI: https://cronfa.swan.ac.uk/Record/cronfa69563
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spelling 2025-12-04T14:45:10.2686246 v2 69563 2025-05-23 Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data c6da9da5c99754b6850e882895b86ca5 ALEXANDER MILNE ALEXANDER MILNE true false b334d40963c7a2f435f06d2c26c74e11 0000-0002-2701-8660 Xianghua Xie Xianghua Xie true false e75a68e11a20e5f1da94ee6e28ff5e76 0000-0001-7387-5180 Gary Tam Gary Tam true false 2025-05-23 Conference Paper/Proceeding/Abstract ACIVS 2025 / LNCS 0 0 0 0001-01-01 COLLEGE NANME COLLEGE CODE Swansea University Not Required This work was funded by EPSRC Industrial Case award (EP/V519601/1). For the purpose of open access the authors have applied a Creative Commons Attribution (CC BY) licence to any Author Accepted Manuscript version arising from this submission. 2025-12-04T14:45:10.2686246 2025-05-23T13:18:00.5793451 Faculty of Science and Engineering School of Mathematics and Computer Science - Computer Science ALEXANDER MILNE 1 Xianghua Xie 0000-0002-2701-8660 2 Gary Tam 0000-0001-7387-5180 3 69563__34344__7ad48e9ce5d34eb680ea5d9a9f3adbbd.pdf case_study_pretraining_tata_data_acivs2025.pdf 2025-05-23T13:20:58.5179403 Output 1384616 application/pdf Accepted Manuscript true Author accepted manuscript document released under the terms of a Creative Commons CC-BY licence using the Swansea University Research Publications Policy (rights retention). true eng https://creativecommons.org/licenses/by/4.0/deed.en
title Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
spellingShingle Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
ALEXANDER MILNE
Xianghua Xie
Gary Tam
title_short Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
title_full Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
title_fullStr Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
title_full_unstemmed Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
title_sort Pretraining Techniques for Ra Prediction with Long Thin Spatial Industrial Data
author_id_str_mv c6da9da5c99754b6850e882895b86ca5
b334d40963c7a2f435f06d2c26c74e11
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author_id_fullname_str_mv c6da9da5c99754b6850e882895b86ca5_***_ALEXANDER MILNE
b334d40963c7a2f435f06d2c26c74e11_***_Xianghua Xie
e75a68e11a20e5f1da94ee6e28ff5e76_***_Gary Tam
author ALEXANDER MILNE
Xianghua Xie
Gary Tam
author2 ALEXANDER MILNE
Xianghua Xie
Gary Tam
format Conference Paper/Proceeding/Abstract
container_title ACIVS 2025 / LNCS
institution Swansea University
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hierarchy_parent_id facultyofscienceandengineering
hierarchy_parent_title Faculty of Science and Engineering
department_str School of Mathematics and Computer Science - Computer Science{{{_:::_}}}Faculty of Science and Engineering{{{_:::_}}}School of Mathematics and Computer Science - Computer Science
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published_date 0001-01-01T17:57:22Z
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