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TY - CONF AU - Xu, X.Y. AU - Leng, Y.B. AU - Zhou, Y.M. ED - Schaa, Volker RW ED - Jansson, Andreas ED - Shea, Thomas ED - Olander, Johan TI - Machine Learning Image Processing Technology Application in Bunch Longitudinal Phase Data Information Extraction J2 - Proc. of IBIC2019, Malmö, Sweden, 08-12 September 2019 CY - Malmö, Sweden T2 - International Beam Instrumentation Conferenc T3 - 8 LA - english AB - To achieve the bunch-by-bunch longitudinal phase measurement, Shanghai Synchrotron Radiation Facility (SSRF) has developed a high resolution measurement system. We used this measurement system to study the injection transient process, and obtained the longitudinal phase of the refilled bunch and the longitudinal phase of the original stored bunch. A large number of parameters of the synchronous damping oscillation are included in this large amount of longitudinal phase data, which are important for the evaluation of machine state and bunch stability. The multi-turn phase data of a multi-bunch is a large two-dimensional array that can be converted into an image. The convolutional neural network (CNN) is a machine learning model with strong capabilities in image processing. We hope to use the convolutional neural network to process the longitudinal phase two-dimensional array data, and extract important parameters such as the oscillation amplitude and the synchrotron damping time. PB - JACoW Publishing CP - Geneva, Switzerland SP - 568 EP - 571 KW - network KW - damping KW - injection KW - synchrotron KW - SRF DA - 2019/11 PY - 2019 SN - 2673-5350 SN - 978-3-95450-204-2 DO - doi:10.18429/JACoW-IBIC2019-WEPP021 UR - http://jacow.org/ibic2019/papers/wepp021.pdf ER -