A PID algorithm using a fixed set of parameters in synchrotron radiation beam control systems does not meet the need to control various environmental vibrations. This study proposes a vibration identification method based on convolutional neural network(CNN) to improve the PID algorithm. Firstly, the genetic algorithm is used to rectify the optimal control parameters for various types of vibration in advance, and then the CNN recognizes the vibration types to assist the PID algorithm in selecting the corresponding optimal control parameters online, so as to realize the optimal control under various vibrations. A set of mixed vibration signal inputs are applied to the beam control system to detect the final beam position deviation displacement. The experiment shows that the beam deviation displacement under the improved PID method is smaller and has better results than the traditional PID control method.
Neutron and X-ray CT are powerful nondestructive testing techniques, they can penetrate sample, interact with the material, and comes out internal structure information in three dimensions. However, as the neutrons and X-rays interact with elements by different mechanisms, imaging by them can be used to provide a better understanding of the samples. The Energy-Resolved Neutron Imaging instrument (ERNI) in Chinese Spallation Neutron Source (CSNS) is equipped with in situ X-ray micro-CT which is used to combine neutron CT and provide more structure and composition information of sample. The designed best resolution of the X-ray imaging system is 2 micrometers when using the minimum operating power, also a favorable compensation for neutron imaging whose resolution is about 40 micrometers.
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