scholarly journals Super-resolving material microstructure image via deep learning for microstructure characterization and mechanical behavior analysis

2021 ◽  
Vol 7 (1) ◽  
Author(s):  
Jaimyun Jung ◽  
Juwon Na ◽  
Hyung Keun Park ◽  
Jeong Min Park ◽  
Gyuwon Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Behavior Analysis ◽  
Super Resolution ◽  
Local Stress ◽  
Mechanical Analysis ◽  
Microstructure Characterization ◽  
Material Behavior ◽  
Fe Model ◽  
Materials Research ◽  
Resolution Imaging

AbstractThe digitized format of microstructures, or digital microstructures, plays a crucial role in modern-day materials research. Unfortunately, the acquisition of digital microstructures through experimental means can be unsuccessful in delivering sufficient resolution that is necessary to capture all relevant geometric features of the microstructures. The resolution-sensitive microstructural features overlooked due to insufficient resolution may limit one’s ability to conduct a thorough microstructure characterization and material behavior analysis such as mechanical analysis based on numerical modeling. Here, a highly efficient super-resolution imaging based on deep learning is developed using a deep super-resolution residual network to super-resolved low-resolution (LR) microstructure data for microstructure characterization and finite element (FE) mechanical analysis. Microstructure characterization and FE model based mechanical analysis using the super-resolved microstructure data not only proved to be as accurate as those based on high-resolution (HR) data but also provided insights on local microstructural features such as grain boundary normal and local stress distribution, which can be only partially considered or entirely disregarded in LR data-based analysis.

Deep learning as an alternative to super-resolution imaging in UAV systems

2020 ◽  
pp. 177-213
Author(s):  
Anand Deshpande ◽  
Prashant P. Patavardhan ◽  
Vania V. Estrela ◽  
Navid Razmjooy ◽  
Jude Hemanth
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Resolution Imaging

Deep-learning facilitated whole live cell fast super-resolution imaging

2021 ◽  
Author(s):  
Yun-Qing Tang ◽  
Cai-Wei Zhou ◽  
Hui-Wen Hao ◽  
Yu-Jie Sun
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Live Cell ◽  
Resolution Imaging

Deep learning enabled Raman hyperspectral super-resolution imaging

2021 ◽  
Author(s):  
Conor C. Horgan ◽  
Magnus Jensen ◽  
Anika Nagelkerke ◽  
Jean-Philippe St-Pierre ◽  
Tom Vercauteren ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Resolution Imaging

scholarly journals Deep learning-based point-scanning super-resolution imaging

2021 ◽  
Author(s):  
Linjing Fang ◽  
Fred Monroe ◽  
Sammy Weiser Novak ◽  
Lyndsey Kirk ◽  
Cara R. Schiavon ◽  
...  
Keyword(s):  
Deep Learning ◽  
Super Resolution ◽  
Resolution Imaging

scholarly journals Characterizing chromatin folding coordinate and landscape with deep learning

2019 ◽  
Author(s):  
Wen Jun Xie ◽  
Yifeng Qi ◽  
Bin Zhang
Keyword(s):  
Deep Learning ◽  
Driving Forces ◽  
Super Resolution ◽  
Variational Autoencoder ◽  
Spatial Environment ◽  
Substantial Progress ◽  
Random Structures ◽  
Chromatin Folding ◽  
Resolution Imaging ◽  
Folded Structures

Genome organization is critical for setting up the spatial environment of gene transcription, and substantial progress has been made towards its high-resolution characterization. The underlying molecular mechanism for its establishment is much less understood. We applied a deep-learning approach, variational autoencoder (VAE), to analyze the fluctuation and heterogeneity of chromatin structures revealed by single-cell super-resolution imaging and to identify a reaction coordinate for chromatin folding. This coordinate monitors the progression of topologically associating domain (TAD) formation and connects the seemingly random structures observed in individual cohesin-depleted cells as intermediate states along the folding pathway. Analysis of the folding landscape derived from VAE suggests that well-folded structures similar to those found in wild-type cells remain energetically favorable in cohesin-depleted cells. The interaction energies, however, are not strong enough to overcome the entropic penalty, leading to the formation of only partially folded structures and the disappearance of TADs from contact maps upon averaging. Implications of these results for the molecular driving forces of chromatin folding are discussed.

Numerical Analysis of Bolted Trunnion Systems of Packages for Radioactive Materials

2012 ◽  
Author(s):  
Jens Sterthaus ◽  
Viktor Ballheimer ◽  
Christian Kuschke ◽  
Frank Wille
Keyword(s):  
Local Stress ◽  
Radioactive Material ◽  
Stress And Strain ◽  
Fe Model ◽  
Fe Method ◽  
Contact Conditions ◽  
Federal Institute ◽  
Materials Research ◽  
Calculation Results ◽  
Solid Finite Elements

Packages for the transport of radioactive material are generally equipped with particular components for crane operations and supporting the package during transport. As an example of such components the bolted trunnion system of heavy packages are analysed in this paper. According to the German guideline BAM-GGR 012 created by the BAM Federal Institute for Materials Research and Testing for analysis and assessment of bolted lid and trunnion systems, the finite element (FE) method is to be used preferably in the analysis of such kind of structures to obtain more accurate and detailed information about their stressing. The FE model of a trunnion system should include the trunnion itself, the bolts and an appropriate part of the packaging wall with necessary contact conditions on all interfaces between these components. The use of solid finite elements, which is generally recommended in the BAM-GGR 012 guideline, leads to local stress and strain fields as a result of the calculation. The modelling strategy, e.g. the way of modelling interaction between the components of the trunnion system, has an essential impact on these local values. The effect of different assumptions in a FE model is demonstrated in this paper using the calculation results for a reference trunnion system. The approaches for preparation and interpretation of results in connection with local or nominal assessment criteria are discussed as well.

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