Generalized Cohomology Operations and H-Spaces of Low Rank

1969 ◽  
Vol 141 ◽  
pp. 335 ◽  
Author(s):  
J. R. Hubbuck
Keyword(s):  
Low Rank ◽  
Cohomology Operations ◽  
Generalized Cohomology

The Influences of the Rank of Coal on Methane Sorption Capacity in Coals/Wpływ Rzędu Węgla Na Pojemność Sorpcyjną Metanu W Węglach

2014 ◽  
Vol 59 (2) ◽  
pp. 509-516
Author(s):  
Andrzej Olajossy
Keyword(s):  
Sorption Capacity ◽  
Coalbed Methane ◽  
Vitrinite Reflectance ◽  
Sorption Isotherms ◽  
Volatile Matter ◽  
Low Rank ◽  
Hard Coal ◽  
Petrographic Composition ◽  
Methane Sorption ◽  
Indian Coals

Abstract Methane sorption capacity is of significance in the issues of coalbed methane (CBM) and depends on various parameters, including mainly, on rank of coal and the maceral content in coals. However, in some of the World coals basins the influences of those parameters on methane sorption capacity is various and sometimes complicated. Usually the rank of coal is expressed by its vitrinite reflectance Ro. Moreover, in coals for which there is a high correlation between vitrinite reflectance and volatile matter Vdaf the rank of coal may also be represented by Vdaf. The influence of the rank of coal on methane sorption capacity for Polish coals is not well understood, hence the examination in the presented paper was undertaken. For the purpose of analysis there were chosen fourteen samples of hard coal originating from the Upper Silesian Basin and Lower Silesian Basin. The scope of the sorption capacity is: 15-42 cm3/g and the scope of vitrinite reflectance: 0,6-2,2%. Majority of those coals were of low rank, high volatile matter (HV), some were of middle rank, middle volatile matter (MV) and among them there was a small number of high rank, low volatile matter (LV) coals. The analysis was conducted on the basis of available from the literature results of research of petrographic composition and methane sorption isotherms. Some of those samples were in the form (shape) of grains and others - as cut out plates of coal. The high pressure isotherms previously obtained in the cited studies were analyzed here for the purpose of establishing their sorption capacity on the basis of Langmuire equation. As a result of this paper, it turned out that for low rank, HV coals the Langmuire volume VL slightly decreases with the increase of rank, reaching its minimum for the middle rank (MV) coal and then increases with the rise of the rank (LV). From the graphic illustrations presented with respect to this relation follows the similarity to the Indian coals and partially to the Australian coals.

Technology for firing low rank of gaseous fuel based on multistage preheating by recirculated hot flue gas

2014 ◽  
Author(s):  
An Wang ◽  
Donglin Chen ◽  
Shan Cheng ◽  
Xuepeng Jiao ◽  
Wenwei Chen
Keyword(s):  
Flue Gas ◽  
Gaseous Fuel ◽  
Low Rank

A low-rank nonlinear ensemble filter for vortex models of aerodynamic flows

2021 ◽  
Author(s):  
Mathieu Le Provost ◽  
Ricardo Baptista ◽  
Youssef Marzouk ◽  
Jeff Eldredge
Keyword(s):  
Low Rank ◽  
Aerodynamic Flows

Semi-Orthogonal Low-Rank Matrix Factorization for Deep Neural Networks

2018 ◽  
Author(s):  
Daniel Povey ◽  
Gaofeng Cheng ◽  
Yiming Wang ◽  
Ke Li ◽  
Hainan Xu ◽  
...  
Keyword(s):  
Neural Networks ◽  
Matrix Factorization ◽  
Deep Neural Networks ◽  
Low Rank ◽  
Rank Matrix ◽  
Low Rank Matrix

A Novel Reconstruction Approach Combining Global and Local Low-rank Constraints for Undersampled Dynamic MRI

2018 ◽  
Vol 14 (5) ◽  
pp. 732-743
Author(s):  
Changfeng Xi ◽  
Jinxu Tao ◽  
Bensheng Qiu ◽  
Zhongfu Ye ◽  
Jinzhang Xu
Keyword(s):  
Dynamic Mri ◽  
Low Rank ◽  
Rank Constraints ◽  
Global And Local

An Algorithm Combining Analysis-based Blind Compressed Sensing and Nonlocal Low-rank Constraints for MRI Reconstruction

2019 ◽  
Vol 15 (3) ◽  
pp. 281-291 ◽  
Author(s):  
Mei Sun ◽  
Jinxu Tao ◽  
Zhongfu Ye ◽  
Bensheng Qiu ◽  
Jinzhang Xu ◽  
...  
Keyword(s):  
Compressed Sensing ◽  
Signal Reconstruction ◽  
Low Rank ◽  
Mr Images ◽  
Scanning Time ◽  
Previous State ◽  
Rank Approximation ◽  
Mri Reconstruction ◽  
Magnetic Resonance Imaging Mri ◽  
Rank Constraints

Background: In order to overcome the limitation of long scanning time, compressive sensing (CS) technology exploits the sparsity of image in some transform domain to reduce the amount of acquired data. Therefore, CS has been widely used in magnetic resonance imaging (MRI) reconstruction. </P><P> Discussion: Blind compressed sensing enables to recover the image successfully from highly under- sampled measurements, because of the data-driven adaption of the unknown transform basis priori. Moreover, analysis-based blind compressed sensing often leads to more efficient signal reconstruction with less time than synthesis-based blind compressed sensing. Recently, some experiments have shown that nonlocal low-rank property has the ability to preserve the details of the image for MRI reconstruction. Methods: Here, we focus on analysis-based blind compressed sensing, and combine it with additional nonlocal low-rank constraint to achieve better MR images from fewer measurements. Instead of nuclear norm, we exploit non-convex Schatten p-functionals for the rank approximation. </P><P> Results & Conclusion: Simulation results indicate that the proposed approach performs better than the previous state-of-the-art algorithms.

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