JACoW is a publisher in Geneva, Switzerland that publishes the proceedings of accelerator conferences held around the world by an international collaboration of editors.
@inproceedings{kim:linac2022-thpori02,
author = {D.-H. Kim and J.J. Dang and H.S. Kim and H.-J. Kwon and S. Lee and S.P. Yun},
title = {{Machine Learning for Beam Orbit Correction at KOMAC Accelerator}},
booktitle = {Proc. LINAC'22},
% booktitle = {Proc. 31st International Linear Accelerator Conference (LINAC'22)},
pages = {848--850},
eid = {THPORI02},
language = {english},
keywords = {network, proton, controls, linac, diagnostics},
venue = {Liverpool, UK},
series = {International Linear Accelerator Conference},
number = {31},
publisher = {JACoW Publishing, Geneva, Switzerland},
month = {09},
year = {2022},
issn = {2226-0366},
isbn = {978-3-95450-215-8},
doi = {10.18429/JACoW-LINAC2022-THPORI02},
url = {https://jacow.org/linac2022/papers/thpori02.pdf},
abstract = {{There are approaches to apply machine learning (ML) techniques to efficiently operate and optimize particle accelerators. Deep neural networks-based model is applied to experiments, correcting beam orbit through the low energy beam transport at the proton injector test stand. For more complex applications, time-series analysis model is studied to predict beam orbit in the 100-MeV beamline at KOMAC. This paper describes experimental data to train neural networks model, and presents the performance of the machine learning models.}},
}