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Prediction of Irregularities in Condensate Measurements using Deep Learning Network

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Main Author: Khor, Chun Sheng
Format: Final Year Project
Published: 2024
Subjects:
TK Electrical engineering. Electronics Nuclear engineering
Online Access:http://utpedia.utp.edu.my/id/eprint/28653/
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id oai:utpedia.utp.edu.my:28653
record_format eprints
spelling oai:utpedia.utp.edu.my:286532024-08-08T07:30:15Z http://utpedia.utp.edu.my/id/eprint/28653/ Prediction of Irregularities in Condensate Measurements using Deep Learning Network Khor, Chun Sheng TK Electrical engineering. Electronics Nuclear engineering 2024-01 Final Year Project NonPeerReviewed Khor, Chun Sheng (2024) Prediction of Irregularities in Condensate Measurements using Deep Learning Network. [Final Year Project] (Submitted)
institution Universiti Teknologi Petronas
building UTP Resource Centre
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknologi Petronas
content_source UTP Electronic and Digitized Intellectual Asset
url_provider http://utpedia.utp.edu.my/
topic TK Electrical engineering. Electronics Nuclear engineering
spellingShingle TK Electrical engineering. Electronics Nuclear engineering
Khor, Chun Sheng
Prediction of Irregularities in Condensate Measurements using Deep Learning Network
format Final Year Project
author Khor, Chun Sheng
author_facet Khor, Chun Sheng
author_sort Khor, Chun Sheng
title Prediction of Irregularities in Condensate Measurements using Deep Learning Network
title_short Prediction of Irregularities in Condensate Measurements using Deep Learning Network
title_full Prediction of Irregularities in Condensate Measurements using Deep Learning Network
title_fullStr Prediction of Irregularities in Condensate Measurements using Deep Learning Network
title_full_unstemmed Prediction of Irregularities in Condensate Measurements using Deep Learning Network
title_sort prediction of irregularities in condensate measurements using deep learning network
publishDate 2024
url http://utpedia.utp.edu.my/id/eprint/28653/
_version_ 1807051983794733056
score 13.235308

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