Tool Chain to Extract and Contextualize Process Data for AI Applications
Research output: Contribution to journal › Research article › Contributed › peer-review
Contributors
Abstract
Summarizing a key use case of a research workstream of the German publicly funded KEEN project, methods and tool chains are demonstrated to extract and to contextualize process data in an automated way based on engineering information. The contextualized process data serves as a high-quality data source for machine learning methods. The article covers the applied basic methodical approaches, design decisions and the results of a successful pilot installation of the developed tool chain.
Details
Original language | English |
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Pages (from-to) | 1070-1076 |
Number of pages | 7 |
Journal | Chemie Ingenieur Technik |
Volume | 95 |
Issue number | 7 |
Publication status | Published - 21 Apr 2023 |
Peer-reviewed | Yes |
External IDs
Scopus | 85152526368 |
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Mendeley | 471ec302-938e-3516-a50f-4c723816a0db |
WOS | 000971665200001 |
ORCID | /0009-0000-3287-0295/work/142237521 |
ORCID | /0000-0001-8719-5741/work/173053658 |
Keywords
Research priority areas of TU Dresden
Subject groups, research areas, subject areas according to Destatis
ASJC Scopus subject areas
Keywords
- artificial intelligence, Data contextualization, Data management, Process industry, Tool chain, Artificial intelligence