NFTHz加速器束流横向截面尺寸测量系统研制

朱文超; 魏征宇; 谢春杰; 周泽然; 王琳; 梁钰

doi:10.11884/HPLPB202436.230361

NFTHz加速器束流横向截面尺寸测量系统研制

doi: 10.11884/HPLPB202436.230361

1.
中国科学技术大学国家同步辐射实验室，合肥 230029
2.
中国科学技术大学核科学技术学院，合肥 230029

基金项目: 国家自然科学基金项目( 51627901 )

详细信息

作者简介:
朱文超，zhuwench@mail.ustc.edu.cn

通讯作者:
梁　钰，ly4ever@mail.ustc.edu.cn。

中图分类号: TL503.6
计量
- 文章访问数: 67
- HTML全文浏览量: 25
- PDF下载量: 23
- 被引次数: 0
出版历程
- 收稿日期: 2023-10-19
- 修回日期: 2023-12-21
- 录用日期: 2023-12-11
- 网络出版日期: 2024-02-28
- 刊出日期: 2024-03-15

Development of the NFTHz accelerator beam profile measurement system

1.
National Synchrotron Radiation Laboratory, University of Science and Technology of China, Hefei 230029, China
2.
School of Nuclear Sciences and Technology, University of Science and Technology of China, Hefei 230029, China

摘要

摘要: 针对太赫兹直线加速器，开发了基于EPICS分布式系统的横向截面尺寸测量系统。该系统采用束斑检测器完成束斑到光斑的转换，并通过远心镜头将光斑成像到CCD相机，完成对光斑图像的采集，之后基于ADAravis将相机采集的图像数据汇入到EPICS数据库。由于暗电流以及环境辐射的影响，在采集到的图像中会存在椒盐噪声，因此使用卷积神经网络（CNN）对图像中的椒盐噪声进行抑制，最后对图像进行高斯拟合计算出束流截面尺寸。实验结果表明，CNN可以有效地消除椒盐噪声，并且系统的分辨率达到15.8 μm，满足系统设计要求。
- EPICS /
- ADAravis /
- 束流截面测量 /
- 机器学习 /
- 卷积神经网络
Abstract: The “Composite Light Source” project of the National Synchrotron Radiation Laboratory, Terahertz Near-Field High-Flux Material Property Testing System, consists of an approximately 3-meter electron linear accelerator. To characterize the performance of the accelerator and monitor the status of the beam, it is necessary to measure the beam size. Specifically designed for the terahertz linear accelerator, a beam size measurement system based on the EPICS distributed system has been developed. A beam spot detector is taken for the conversion of the beam spot into an optical spot and a remote mirror is taken to image the optical spot onto a CCD camera for image acquisition. Subsequently, the camera-captured image data is integrated into the EPICS database using ADAravis. Due to the dark current and radiation environment, salt-and-pepper noise is present in the acquired images. Therefore, a Convolutional Neural Network (CNN) is employed to suppress the salt-and-pepper noise in the images. Finally, Gaussian fitting is applied to calculate the beam cross-sectional dimensions from the images. The experimental results indicate that the CNN can effectively eliminate salt-and-pepper noise, and the resolution of this system is 15.8 μm, which satisfies the design requirement.
- EPICS /
- ADAravis /
- beam profile measurement /
- machine learning /
- convolutional neural network

HTML全文

图 1 束流横向截面尺寸测量系统

Figure 1. Beam profile measurement system

下载: 全尺寸图片幻灯片

图 2 海康数字相机MV-CA-016-10GM实物图

Figure 2. Picture of Hikvision digital camera MV-CA-016-10GM

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图 3 CNN网络结构

Figure 3. CNN network structure

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图 4 图像降噪过程

Figure 4. Image denoising processing

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图 5 CNN模型构建工作流程图

Figure 5. The FCN model construction workflow diagram

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图 6 控制电机IOC的软件架构

Figure 6. Software architecture for controlling the Motor IOC

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图 7 AreaDetector软件架构

Figure 7. Software architecture of AreaDetector

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图 8 束流截面测量系统

Figure 8. Beam profile measurement system

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图 9 分辨率标定

Figure 9. Resolution calibration

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图 10 中值滤波与CNN降噪性能对比

Figure 10. Denoising performance comparison between median filtering and CNN

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图 11 不同降噪算法性能对比

Figure 11. Performance comparison of different denoising algorithms

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表 1 GE680与MV-CH089-10GM参数对比

Table 1. Comparison of parameters between GE680 and MV-CH089-10GM

model	resolution	minimum exposure time/μs	onboard RAM/MB
GE680	640×480	25	32
MV-CA-016-10GM	1440×1080	1	128

下载: 导出CSV

表 2 降噪性能对比

Table 2. Denoising performance comparison

noise addition rate	noise image PSNR	medianBlur PSNR	FCN PSNR
0.1	17.79	40.22	52.14
0.2	14.59	28.36	51.95
0.3	12.77	22.39	50.99
0.4	11.22	18.20	49.52
0.5	10.14	15.26	39.59
0.6	9.18	13.41	47.31
0.7	8.36	11.81	41.75
0.8	7.06	9.38	41.83

下载: 导出CSV

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