IBIC2018 - List of Authors (Jiang, R.)

Paper	Title	Page
MOPC02	Identification of Faulty Beam Position Monitor Based Clustering by Fast Search and Find of Density Peaks	114
	R. Jiang, Y.B. Leng SSRF, Shanghai, People’s Republic of China F.Z. Chen, Z.C. Chen, Y.B. Leng SINAP, Shanghai, People’s Republic of China
	The accuracy and stability of beam position moni-tors(BPMs) are important for all kinds of measurement systems and feedback systems in particle accelerator field. A proper method detecting faulty beam position monitor or monitoring their stability could optimize accel-erator operating conditions. With development in ma-chine learning methods, a series of powerful analysis approaches make it possible for detecting beam position monitor’s stability. Here, this paper proposed a clustering analysis approach to detect the defective BPMs. The method is based on the idea that cluster centres are char-acterized by a higher density than their neighbours and by a relatively large distance from points with higher densi-ties. The results showed that clustering by fast search and find of density peaks could classify beam data into dif-ferent clusters on the basis of their similarity. And that, aberrant data points could be detected by decision graph. So the algorithm is appropriate for BPM detecting and it could be a significant supplement for data analysis in accelerator physics.
DOI •	reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-MOPC02
About •	paper received ※ 05 September 2018 paper accepted ※ 11 September 2018 issue date ※ 29 January 2019
Export •	reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)

Paper

Title

Page

Identification of Faulty Beam Position Monitor Based Clustering by Fast Search and Find of Density Peaks

114

R. Jiang, Y.B. Leng
SSRF, Shanghai, People’s Republic of China
F.Z. Chen, Z.C. Chen, Y.B. Leng
SINAP, Shanghai, People’s Republic of China

The accuracy and stability of beam position moni-tors(BPMs) are important for all kinds of measurement systems and feedback systems in particle accelerator field. A proper method detecting faulty beam position monitor or monitoring their stability could optimize accel-erator operating conditions. With development in ma-chine learning methods, a series of powerful analysis approaches make it possible for detecting beam position monitor’s stability. Here, this paper proposed a clustering analysis approach to detect the defective BPMs. The method is based on the idea that cluster centres are char-acterized by a higher density than their neighbours and by a relatively large distance from points with higher densi-ties. The results showed that clustering by fast search and find of density peaks could classify beam data into dif-ferent clusters on the basis of their similarity. And that, aberrant data points could be detected by decision graph. So the algorithm is appropriate for BPM detecting and it could be a significant supplement for data analysis in accelerator physics.

DOI •

reference for this paper ※ https://doi.org/10.18429/JACoW-IBIC2018-MOPC02

About •

paper received ※ 05 September 2018 paper accepted ※ 11 September 2018 issue date ※ 29 January 2019

Export •

reference for this paper using ※ BibTeX, ※ LaTeX, ※ Text/Word, ※ RIS, ※ EndNote (xml)