Identification of rock pore structures and permeabilities using electron microscopy experiments and deep learning interpretations

Yu, Qingxi; Xiong, Ziwei; Du, Chao; Dai, Zhenxue; Soltanian, Mohamad Reza; Soltanian, Mojtaba; Yin, Shangxian; Liu, Wei; Liu, Chen; Wang, Chengbin; Song, Zeyu

doi:10.1016/j.fuel.2020.117416

Cited by 42 publications

(12 citation statements)

References 34 publications

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“…Accurate identification of the void structures enables the accurate calculation of macroscopic ILSS parameters of the composite laminates using a theoretically derived equation. The commonly used image segmentation methods for analyzing optical images include thresholding, 13,14 machine learning, 14‐16 and deep learning 17‐22 . Conventionally, thresholding techniques, which include global and local thresholding, involve the selection of threshold based on the image information and the division of the image based on the gray level.…”

Section: Introductionmentioning

confidence: 99%

“…Tremendous efforts have been devoted to address the limitations of image segmentation using deep learning methods 17‐22 . Consequently, the application of machine learning techniques for image segmentation has emerged as an effective method to improve the application of image segmentation analysis.…”

Section: Introductionmentioning

confidence: 99%

“…Consequently, the application of machine learning techniques for image segmentation has emerged as an effective method to improve the application of image segmentation analysis. Machine learning techniques have been successfully applied for image segmentation in medical applications, 17 cow health detection, 18,19 and analysis of porous microstructures 20 . For example, Yu et al 20 analyzed the pore characteristics using a fully convolutional neural network and investigated the relationship between the microscopic pore characteristics and the macroscopic permeability parameters of sandstone.…”

Section: Introductionmentioning

confidence: 99%

“…Machine learning techniques have been successfully applied for image segmentation in medical applications, 17 cow health detection, 18,19 and analysis of porous microstructures 20 . For example, Yu et al 20 analyzed the pore characteristics using a fully convolutional neural network and investigated the relationship between the microscopic pore characteristics and the macroscopic permeability parameters of sandstone. Huang et al 21 applied DeepLabV3+ to map retrogressive thaw slumps automatically from Planet CubeSat images.…”

Section: Introductionmentioning

confidence: 99%

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Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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The internal void defects induced during the manufacturing process of polymer‐matrix composites can significantly degrade the mechanical properties of the composite, particularly the interlaminar shear strength (ILSS). In this study, we developed an innovative integrated methodology based on a deep learning semantic segmentation algorithm, named DeepLabV3+, and a theoretically driven equation to automatically identify voids in optical images and investigate the relationship between the microscopic voids and macroscopic ILSS parameters of the composite laminates. Results suggest that for the best fine‐tuned DeepLabV3+ framework, the corresponding mean pixel accuracy and intersection over union scores on the testing set were 99.84% and 90.82%, respectively, thereby indicating the potential of the generalized trained model. In addition, detailed experiments revealed that the proposed method can successfully obtain the ILSS values of laminates with different void contents. In addition, the ILSS values of the carbon/epoxy laminates decreased by approximately 27% with an increase in the void content from 0.07% to 3.14%.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Luo

Zhang

Lei

et al. 2021

Polymers for Advanced Techs

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“…However, it is not known currently to what extent deep learning can be used to analyse soil structures. A promising path was developed in [ 7 ] applying a fully convolutional networks to learn pore characteristics.…”

Section: Introductionmentioning

confidence: 99%

Use of deep learning for structural analysis of computer tomography images of soil samples

et al. 2021

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Soil samples from several European countries were scanned using medical computer tomography (CT) device and are now available as CT images. The analysis of these samples was carried out using deep learning methods. For this purpose, a VGG16 network was trained with the CT images (X). For the annotation (y) a new method for automated annotation, ‘surrogate’ learning, was introduced. The generated neural networks (NNs) were subjected to a detailed analysis. Among other things, transfer learning was used to check whether the NN can also be trained to other y-values. Visually, the NN was verified using a gradient-based class activation mapping (grad-CAM) algorithm. These analyses showed that the NN was able to generalize, i.e. to capture the spatial structure of the soil sample. Possible applications of the models are discussed.

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Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data

Houhou

Bocklitz

2021

Analytical Science Advances

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Artificial intelligence‐based methods such as chemometrics, machine learning, and deep learning are promising tools that lead to a clearer and better understanding of data. Only with these tools, data can be used to its full extent, and the gained knowledge on processes, interactions, and characteristics of the sample is maximized. Therefore, scientists are developing data science tools mentioned above to automatically and accurately extract information from data and increase the application possibilities of the respective data in various fields. Accordingly, AI‐based techniques were utilized for chemical data since the 1970s and this review paper focuses on the recent trends of chemometrics, machine learning, and deep learning for chemical and spectroscopic data in 2020. In this regard, inverse modeling, preprocessing methods, and data modeling applied to spectra and image data for various measurement techniques are discussed.

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Identification of rock pore structures and permeabilities using electron microscopy experiments and deep learning interpretations

Cited by 42 publications

References 34 publications

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Identification of voids and interlaminar shear strengths of polymer‐matrix composites by optical microscopy experiment and deep learning methodology

Use of deep learning for structural analysis of computer tomography images of soil samples

Trends in artificial intelligence, machine learning, and chemometrics applied to chemical data

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