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RIS citation export for WEPGW049: Deep Learning Applied for Multi-Slit Imaging Based Beam Size Monitor

AU  - Gao, B.
AU  - Leng, Y.B.
AU  - Xu, X.Y.
ED  - Boland, Mark
ED  - Tanaka, Hitoshi
ED  - Button, David
ED  - Dowd, Rohan
ED  - Schaa, Volker RW
ED  - Tan, Eugene
TI  - Deep Learning Applied for Multi-Slit Imaging Based Beam Size Monitor
J2  - Proc. of IPAC2019, Melbourne, Australia, 19-24 May 2019
CY  - Melbourne, Australia
T2  - International Particle Accelerator Conference
T3  - 10
LA  - english
AB  - In order to satisfy the requirement of high speed measurement and improve the accuracy of BSM (beam size monitor), multi-slit imaging based BSM has been proposed by SSRF at 2017. However, it is very difficult to deconvolve the image and figure out the beam size, which requires dedicated algorithms to solve this issue. Deep learning is one of the most popular algorithms, which can learn to mimic any distribution of data. In the region of Beam instrumentation, they can be taught to deal with many difficult problem. In this paper, multi-layer neural network is used to process the images from the multi-slit imaging system. Training processes, struct of the neural networks and the result of the experiments will be presented.
PB  - JACoW Publishing
CP  - Geneva, Switzerland
SP  - 2587
EP  - 2590
KW  - network
KW  - simulation
KW  - synchrotron
KW  - synchrotron-radiation
DA  - 2019/06
PY  - 2019
SN  - 978-3-95450-208-0
DO  - DOI: 10.18429/JACoW-IPAC2019-WEPGW049
UR  - http://jacow.org/ipac2019/papers/wepgw049.pdf
ER  -