X-ray nanometer focusing at the SSRF based on a multilayer Laue lens

Zhu, Jingtao; Tu, Yuchun; Li, Haochuan; Yue, Shuaipeng; Huang, Qiushi; Li, Aiguo; Wang, Zhanshan

doi:10.1088/1674-1137/39/12/128001

Cited by 4 publications

(1 citation statement)

References 10 publications

(12 reference statements)

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“…Anhui province 2014 Fishes discriminant [18] Henan Province 2015 Feature ions-discriminant [19] Shanxi Province 2015 Support vector machine [20] Anhui compared to the linear algorithm. According to literature research, the ANN [27] and SVM [20] have been implement in this area.…”

Section: Coal Mine Districts Years Algorithm Sourcesmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

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Water inrush is a major threat to the working safety for coal mines in the Northern China coal district. The inrush pattern, threaten level, and also the geochemical characteristics varies according to the different of water sources. Therefore, identifying the water source correctly is an important task to predict and control the water inrush accidents. In this chapter, the algorithms and attempts to identify the water inrush sources, especially in the Northern China coal mine district, are reviewed. The geochemical and machine learning algorithms are two main methods to identify the water inrush sources. Four main steps need to apply, namely data processing, feature selection, model training, and evaluation, in the process of machine learning (ML) modelling. According to a calculation instance, most of the major ions, and some trace elements, such as Ti, Sr, and Zn, were identified to be important in light of geochemical analysis and machine learning modelling. The ML algorithms, such as random forest (RF), support vector machine (SVM), Logistica regression (LR) perform well in the source identification of coal mine water inrush.

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Section: Coal Mine Districts Years Algorithm Sourcesmentioning

confidence: 99%

Sources Identification of Water Inrush in Coal Mines Using Technique of Multiple Non-Linear Machine Learning Modelling

Shan¹

2021

Groundwater Management and Resources

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Multilayer Optics for Synchrotron Applications

Singhapong,

Bowen,

Wang

et al. 2024

Adv Materials Technologies

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X‐ray multilayer optics play a vital role in synchrotron optics due to their ability to generate constructive interference. These devices typically consist of several tens to hundreds of periods of alternating layers coated on a substrate. In contrast to single‐layer mirrors that reflect X‐rays within a specific energy range, multilayer optics can be tailored to achieve a high reflection over a broad energy spectrum. This is a sought‐after property for many beamlines and has led to the development of numerous new X‐ray applications and capabilities. This review highlights advances in multilayer optics, including fabrication techniques, layer structure design, material choice, and strategies to enhance performance. This is placed in the context of recent applications of such multilayers as monochromator and gratings, focusing devices, and polarizers. Current challenges and the future outlook within this field are also proposed. This comprehensive summary of a rapidly advancing field highlights recent achievements and is intended to promote practical applications in terms of the use of multilayer synchrotron optics.

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