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RIS citation export for TUPDP115: Machine Learning for Compact Industrial Accelerators

TY  - CONF
AU  - Edelen, J.P.
AU  - Diaz Cruz, J.A.
AU  - Edelen, A.L.
AU  - Einstein-Curtis, J.A.
AU  - Henderson, M.J.
AU  - Kilpatrick, M.C.
ED  - Schaa, Volker RW
ED  - Götz, Andy
ED  - Venter, Johan
ED  - White, Karen
ED  - Robichon, Marie
ED  - Rowland, Vivienne
TI  - Machine Learning for Compact Industrial Accelerators
J2  - Proc. of ICALEPCS2023, Cape Town, South Africa, 09-13 October 2023
CY  - Cape Town, South Africa
T2  - International Conference on Accelerator and Large Experimental Physics Control Systems
T3  - 19
LA  - english
AB  - The industrial and medical accelerator industry is an ever-growing field with advancements in accelerator technology enabling its adoption for new applications. As the complexity of industrial accelerators grows so does the need for more sophisticated control systems to regulate their operation. Moreover, the environment for industrial and medical accelerators is often harsh and noisy as opposed to the more controlled environment of a laboratory-based machine. This environment makes control more challenging. Additionally, instrumentation for industrial accelerators is limited making it difficult at times to identify and diagnose problems when they occur. RadiaSoft has partnered with SLAC to develop new machine learning methods for control and anomaly detection for industrial accelerators. Our approach is to develop our methods using simulation models followed by testing on experimental systems. Here we present initial results using simulations of a room temperature s-band system. 
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 846
EP  - 850
KW  - cavity
KW  - controls
KW  - simulation
KW  - industrial-accelerators
KW  - network
DA  - 2024/02
PY  - 2024
SN  - 2226-0358
SN  - 978-3-95450-238-7
DO  - doi:10.18429/JACoW-ICALEPCS2023-TUPDP115
UR  - https://jacow.org/icalepcs2023/papers/tupdp115.pdf
ER  -