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RIS citation export for TUP030: Reinforcement Learning Based RF Control System for Accelerator Mass Spectrometry

AU  - Kim, H.S.
AU  - Chai, J.-S.
AU  - Gad, Kh.M.
AU  - Ghergherehchi, M.
AU  - Ha, D.H.
AU  - Lee, J.C.
AU  - Namgoong, H.
ED  - Conradie, Lowry
ED  - De Villiers, John Garrett
ED  - Schaa, Volker RW
TI  - Reinforcement Learning Based RF Control System for Accelerator Mass Spectrometry
J2  - Proc. of Cyclotrons2019, Cape Town, South Africa, 23-27 September 2019
CY  - Cape Town, South Africa
T2  - International Conference on Cyclotrons and their Applications
T3  - 22
LA  - english
AB  - Accelerator Mass Spectrometry (AMS) is a powerful method for separating rare isotopes and electrostatic type tandem accelerators have been widely used. At SungKyunKwan University, we are developing a AMS that can be used in a small space with higher resolution based on cyclotron. In contrast to the cyclotron used in conventional PET or proton therapy, the cyclotron-based AMS is characterized by high turn number and low dee voltage for high resolution. It is designed to accelerate not only 14C but also 13C or 12C. The AMS cyclotron RF control model has nonlinear characteristics due to the variable beam loading effect due to the acceleration of various particles and injected sample amounts. In this work, we proposed an AMS control system based on reinforcement learning. The proposed reinforcement learning finds the target control value in response to the environment through the learning process. We have designed a reinforcement learning based controller with RF system as an environment and verified the reinforcement learning based controller designed through the modeled cavity.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 227
EP  - 229
KW  - controls
KW  - cyclotron
KW  - resonance
KW  - network
KW  - cavity
DA  - 2020/06
PY  - 2020
SN  - 978-3-95450-205-9
DO  - doi:10.18429/JACoW-Cyclotrons2019-TUP030
UR  - http://jacow.org/cyclotrons2019/papers/tup030.pdf
ER  -