Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Tian Hui Ma"'
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 12, Iss 1, Pp 424-442 (2021)
It is one of the most important problems to judge anchoring force of constant resistant bolt in anchoring support. This involves the properties of rock, binder and bolt material as well as the interaction among them in the process of stress. In this
Externí odkaz:
https://doaj.org/article/5f340d7619ca43c48e5f35967c7f0025
Publikováno v:
Geomatics, Natural Hazards & Risk, Vol 12, Iss 1, Pp 2956-2983 (2021)
Rockburst hazard is one of the most outstanding problems in deep rock mechanics, which has become the bottleneck for deep underground engineering in China. Rockburst occurs with some evident precursors, however, the rockburst mechanism is not clearly
Externí odkaz:
https://doaj.org/article/bda2c065c4464e20b491465c73298c84
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1628-1644 (2021)
In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs an
Externí odkaz:
https://doaj.org/article/b6a087b671f648d28a168582afa94aee
Publikováno v:
Journal of Central South University. 30:289-305
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6eb46eb9d63501b1c0f9481305b70157
https://doi.org/10.1007/s11771-023-5233-8
https://doi.org/10.1007/s11771-023-5233-8
Publikováno v:
IEEE Transactions on Geoscience and Remote Sensing. 60:1-17
Hyperspectral images (HSIs) are often contaminated by several types of noise, which significantly limits the accuracy of subsequent applications. Recently, low-rank modeling based on tensor singular value decomposition (T-SVD) has achieved great succ
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::5c493e355356a2f379aa57a9178bc157
https://doi.org/10.1109/tgrs.2020.3045169
https://doi.org/10.1109/tgrs.2020.3045169
Publikováno v:
Remote Sensing, Vol 12, Iss 8, p 1278 (2020)
Noise removal is a fundamental problem in remote sensing image processing. Most existing methods, however, have not yet attained sufficient robustness in practice, due to more or less neglecting the intrinsic structures of remote sensing images and/o
Externí odkaz:
https://doaj.org/article/d0dd181fe65f44d8a52a065dc4b8d9be
Publikováno v:
Journal of Alloys and Compounds. 945:168902
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::9f2a8914adeb09edbed591b1344f019f
https://doi.org/10.1016/j.jallcom.2023.168902
https://doi.org/10.1016/j.jallcom.2023.168902
Publikováno v:
Remote Sensing, Vol 11, Iss 6, p 608 (2019)
Remote sensing images have been applied to a wide range of fields, but they are often degraded by various types of stripes, which affect the image visual quality and limit the subsequent processing tasks. Most existing destriping methods fail to expl
Externí odkaz:
https://doaj.org/article/262987b4fcc24253aee493f3817614a8
Publikováno v:
IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol 14, Pp 1628-1644 (2021)
In hyperspectral image (HSI) processing, a fundamental issue is to restore HSI data from various degradations such as noise corruption and information missing. However, most existing methods more or less ignore the abundant prior knowledge on HSIs an
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4b9046bc26383b57a062fd219d0b1cca
https://ieeexplore.ieee.org/document/9302700/
https://ieeexplore.ieee.org/document/9302700/
Publikováno v:
Information Sciences. 532:170-189
As low-rank modeling has achieved great success in tensor recovery, many research efforts devote to defining the tensor rank. Among them, the recent popular tensor tubal rank, defined based on the tensor singular value decomposition (t-SVD), obtains
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::8ddffe2db99294a2ac634da6698927cf
https://doi.org/10.1016/j.ins.2020.05.005
https://doi.org/10.1016/j.ins.2020.05.005