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URLhttps://doi.org/10.18429/JACoW-LINAC2024-THPB040
TitleLLM integration into EPICS
Authors
  • E. Adams, B. Sobhani
    Oak Ridge National Laboratory
AbstractThe utilization of large language models (LLMs) such as ChatGPT has seen a remarkable increase in various fields over the past few years. These models have demonstrated their versatility and capability in understanding and generating human-like text, making them invaluable tools in numerous applications. In this project, we explore the integration of a LLM into the Experimental Physics and Industrial Control System (EPICS). The primary focus of this integration is to employ the LLM for advanced image processing and spatial analysis on images obtained from the beamlines. By leveraging the capabilities of the LLM, we aim to enhance the accuracy and efficiency of image interpretation, enabling more precise data analysis and decision-making within the EPICS framework. This integration not only showcases the potential of LLMs in scientific and industrial applications but also sets the stage for future advancements in automated control systems.
Paperdownload: THPB040.pdf
CiteBibTeX, LaTeX, Text/Word, RIS, EndNote
Conference32nd Linear Accelerator Conference (LINAC2024)
Series
LocationChicago, IL, USA
Date25-30 Aug 2024
PublisherJACoW Publishing, Geneva, Switzerland
Editorial BoardKelly Jaje (ANL), Kip Bishofberger (LANL), Ryan Brody (ANL), Gustavo Bruno (ANL), Jan Chrin (PSI), Cathy Eyberger (ANL), Maggie Montes (SLAC), Denise Skiadopoulos (ANL), Ling Wang (MSU/FRIB), Kent Wootton (ANL)
Online ISBN978-3-95450-219-6
Online ISSN2226-0366
Received09 August 2024
Revised28 August 2024
Accepted29 August 2024
Issued23 October 2024
DOI10.18429/JACoW-LINAC2024-THPB040
Pages716-717