Advances in AI-Empowered Beamline Automation and Data Science in Advanced Photon Sources

Abstract submission deadline

closed (20 October 2023)

Manuscript submission deadline

closed (20 December 2023)

Viewed by

8953

Topic Information

Dear Colleagues,

Advancements in the instrumentations of advanced photon sources such as synchrotron light sources and X-ray free-electron lasers are unlocking new possibilities to facilitate scientific discoveries. However, they are also imposing a tremendous burden on scientific software in experimental control and data acquisition and analysis, as well as algorithm development for processing big data and the application of AI. The following are examples of advances in AI-empowered beamline automation and data science in advanced photon sources. Image analysis/processing effective AI-empowered image analysis/processing algorithms for better scientific data interpretation, including image denoising and enhancement, image segmentation and correlation and image compression/reduction methods for high-throughput multimodal data. High-throughput acquisition topics relating to large experimental data acquisition capabilities, experimental control and scan mechanism optimization for large scientific datasets, data formats designed for acquired big data and online data reduction methods to ease the burden on transmission, computation and storage ends. Beamline automation AI-assisted techniques to automate pre-experiments as well as experiments, including beam adjustment, sample stage, detector auto-alignment, axis auto-correction, experimental control and data acquisition process automation, such as automatic beam positioning, scanning ROI selection, etc. Scientific software integrating advanced image analysis/processing and data reduction tools with capable data processing and visualization functionalities, preferably forming a unified software framework that covers the whole experimental life cycle. Artificial intelligence and big data AI for the scientific paradigm in beamlines; leading-edge AI computing systems (GPUs) aiming to handle the largest datasets and the most computationally intensive workloads at future beamlines; cloud-based computing nodes that work cooperatively to tackle highly heterogeneous experimental data and high-performance computing infrastructures fully supporting EB-scale big data generation, ingestion, dispatch and storage with regard to future photon sources.

Dr. Yi Zhang
Dr. Xiaogang Yang
Dr. Chunpeng Wang
Dr. Junrong Zhang
Topic Editors

Participating Journals

Journal Name	Impact Factor	CiteScore	Launched Year	First Decision (median)	APC
Applied Sciences applsci	2.5	5.3	2011	17.8 Days	CHF 2400
Big Data and Cognitive Computing BDCC	3.7	7.1	2017	18 Days	CHF 1800
Photonics photonics	2.1	2.6	2014	14.8 Days	CHF 2400
Processes processes	2.8	5.1	2013	14.4 Days	CHF 2400
Remote Sensing remotesensing	4.2	8.3	2009	24.7 Days	CHF 2700
Automation automation	-	2.9	2020	20.6 Days	CHF 1000

MDPI Topics is cooperating with Preprints.org and has built a direct connection between MDPI journals and Preprints.org. Authors are encouraged to enjoy the benefits by posting a preprint at Preprints.org prior to publication:

1. Immediately share your ideas ahead of publication and establish your research priority;
2. Protect your idea from being stolen with this time-stamped preprint article;
3. Enhance the exposure and impact of your research;
4. Receive feedback from your peers in advance;
5. Have it indexed in Web of Science (Preprint Citation Index), Google Scholar, Crossref, SHARE, PrePubMed, Scilit and Europe PMC.

Published Papers (4 papers)

Order results

Content type Publication Date

Result details

Normal Extended Compact

Journals

All Applied Sciences BDCC Photonics Processes Remote Sensing Automation

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

Error

Oops... you haven't selected anything for export.

Ok

clear

13 pages, 6675 KiB  

Open AccessArticle

Linear Optics Calibration in a Storage Ring Based on Machine Learning

by Ruichun Li, Bocheng Jiang, Qinglei Zhang, Zhentang Zhao, Changliang Li and Kun Wang

Appl. Sci. 2023, 13(14), 8034; https://doi.org/10.3390/app13148034 - 10 Jul 2023

Cited by 1 | Viewed by 1205

Abstract

Inevitably, various errors occur in an actual storage ring, such as magnetic field errors, magnet misalignments, and ground settlement deformation, which cause closed orbit distortion and tuning shift. Therefore, linear optics calibration is an essential procedure for storage rings. In this paper, we [...] Read more.

Inevitably, various errors occur in an actual storage ring, such as magnetic field errors, magnet misalignments, and ground settlement deformation, which cause closed orbit distortion and tuning shift. Therefore, linear optics calibration is an essential procedure for storage rings. In this paper, we introduce a new method using machine learning to calibrate linear optics. This method is different from the traditional linear optics from closed orbit (LOCO) method, which is based on singular value decomposition (SVD). The machine learning model does not need to be computed by SVD. Our study shows that the machine-learning-based method can significantly reduce the difference between the model response matrix and the measurement response matrix by adjusting the strength of the quadrupoles. Full article

(This article belongs to the Topic Advances in AI-Empowered Beamline Automation and Data Science in Advanced Photon Sources)

►▼ Show Figures

Figure 1

attachment

Supplementary material:
Supplementary File 1 (ZIP, 590 KiB)

17 pages, 5287 KiB  

Open AccessArticle

Periodic Artifacts Generation and Suppression in X-ray Ptychography

by Shilei Liu, Zijian Xu, Zhenjiang Xing, Xiangzhi Zhang, Ruoru Li, Zeping Qin, Yong Wang and Renzhong Tai

Photonics 2023, 10(5), 532; https://doi.org/10.3390/photonics10050532 - 5 May 2023

Cited by 3 | Viewed by 2198

Abstract

As a unique coherent diffraction imaging method, X-ray ptychography has an ultrahigh resolution of several nanometers for extended samples. However, ptychography is often degraded by various noises that are mixed with diffracted signals on the detector. Some of the noises can transform into [...] Read more.

As a unique coherent diffraction imaging method, X-ray ptychography has an ultrahigh resolution of several nanometers for extended samples. However, ptychography is often degraded by various noises that are mixed with diffracted signals on the detector. Some of the noises can transform into periodic artifacts (PAs) in reconstructed images, which is a basic problem in raster-scan ptychography. Herein, we propose a novel periodic-artifact suppressing algorithm (PASA) and present a new understanding of PAs or raster-grid pathology generation mechanisms, which include static intensity (SI) as an important cause of PAs. The PASA employs a gradient descent scheme to iteratively separate the SI pattern from original datasets and a probe support constraint applied in the object update. Both simulative and experimental data reconstructions demonstrated the effectiveness of the new algorithm in suppressing PAs and improving ptychography resolution and indicated a better performance of the PASA method in PA removal compared to other mainstream algorithms. In the meantime, we provided a complete description of SI conception and its key role in PA generation. The present work enhances the feasibility of raster-scan ptychography and could inspire new thoughts for dealing with various noises in ptychography. Full article

(This article belongs to the Topic Advances in AI-Empowered Beamline Automation and Data Science in Advanced Photon Sources)

►▼ Show Figures

Figure 1

13 pages, 2729 KiB  

Open AccessArticle

Automatic Feedback System for X-ray Flux at BL08U1A Soft X-ray Spectromicroscopy Beamline of Shanghai Synchrotron Radiation Facility

by Chi Zhang, Haigang Liu, Chunpeng Wang, Zhi Guo, Xiangzhi Zhang, Zijian Xu, Xiangjun Zhen, Yong Wang and Renzhong Tai

Appl. Sci. 2023, 13(9), 5456; https://doi.org/10.3390/app13095456 - 27 Apr 2023

Viewed by 1330

Abstract

An online automatic feedback system has been successfully installed and commissioned at the BL08U1A Soft X-ray Spectromicroscopy Beamline of Shanghai Synchrotron Radiation Facility, which can monitor the incident X-ray beam in real time by measuring the blade-edge signals of the exit slit and [...] Read more.

An online automatic feedback system has been successfully installed and commissioned at the BL08U1A Soft X-ray Spectromicroscopy Beamline of Shanghai Synchrotron Radiation Facility, which can monitor the incident X-ray beam in real time by measuring the blade-edge signals of the exit slit and automatically adjust the elliptical cylindrical mirror parameters to achieve beam calibration and maintain the optimal X-ray flux of the sample. This work provides a comprehensive description of the hardware composition, system implementation, feedback logic, function and software design, system optimization and commission, as well as the online experimental results supported by the system. The experimental results demonstrated that the online automatic feedback system is capable of effectively maintaining the optimal X-ray beam flux for X-ray absorption spectroscopy experiments. Its success can provide valuable technique assistance for the design, construction and optimization of similar systems at various beamlines in synchrotron sources in the future. Full article

(This article belongs to the Topic Advances in AI-Empowered Beamline Automation and Data Science in Advanced Photon Sources)

►▼ Show Figures

Figure 1

17 pages, 8440 KiB  

Open AccessArticle

Cloud–Edge Hybrid Computing Architecture for Large-Scale Scientific Facilities Augmented with an Intelligent Scheduling System

by Jing Ye, Chunpeng Wang, Jige Chen, Rongzheng Wan, Xiaoyun Li, Alessandro Sepe and Renzhong Tai

Appl. Sci. 2023, 13(9), 5387; https://doi.org/10.3390/app13095387 - 26 Apr 2023

Cited by 1 | Viewed by 2132

Abstract

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, [...] Read more.

Synchrotron radiation sources are widely used in interdisciplinary research, generating an enormous amount of data while posing serious challenges to the storage, processing, and analysis capabilities of the large-scale scientific facilities worldwide. A flexible and scalable computing architecture, suitable for complex application scenarios, combined with efficient and intelligent scheduling strategies, plays a key role in addressing these issues. In this work, we present a novel cloud–edge hybrid intelligent system (CEHIS), which was architected, developed, and deployed by the Big Data Science Center (BDSC) at the Shanghai Synchrotron Radiation Facility (SSRF) and meets the computational needs of the large-scale scientific facilities. Our methodical simulations demonstrate that the CEHIS is more efficient and performs better than the cloud-based model. Here, we have applied a deep reinforcement learning approach to the task scheduling system, finding that it effectively reduces the total time required for the task completion. Our findings prove that the cloud–edge hybrid intelligent architectures are a viable solution to address the requirements and conditions of the modern synchrotron radiation facilities, further enhancing their data processing and analysis capabilities. Full article

(This article belongs to the Topic Advances in AI-Empowered Beamline Automation and Data Science in Advanced Photon Sources)

►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less

Select all

Export citation of selected articles as:

Plain Text BibTeX BibTeX (without abstracts) Endnote Endnote (without abstracts) Tab-delimited RIS

 

Error

Oops... you haven't selected anything for export.

Ok

clear

Displaying articles 1-4