Zobrazeno 1 - 9
of 9
pro vyhledávání: '"Qiuyu Guan"'
Publikováno v:
AIP Advances, Vol 11, Iss 4, Pp 045028-045028-8 (2021)
In the present study, the machine learning algorithm is utilized for the first time to improve the probe diagnosis. Machine learning methods are utilized to improve the Langmuir probe diagnostic accuracy and the diagnosable plasma parameter range wit
Externí odkaz:
https://doaj.org/article/fc87f795d5c946869b53c19193d6a6e6
Publikováno v:
International Journal of Remote Sensing. 42:9542-9564
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::bcd64242f894e639c55449a3a64c076f
https://doi.org/10.1080/01431161.2021.1995071
https://doi.org/10.1080/01431161.2021.1995071
Publikováno v:
Contributions to Plasma Physics. 62
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::c0737afa3f2300e9ae05c028e55b47fa
https://doi.org/10.1002/ctpp.202100152
https://doi.org/10.1002/ctpp.202100152
Publikováno v:
Digital.CSIC. Repositorio Institucional del CSIC
instname
instname
In recent years, the emergence of convolutional neural networks (CNN) has greatly promoted the development of the object detection field, and many CNN-based detectors have achieved excellent performance on object detection in remote sensing images. T
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2f47761f4d61f5c224d219390f452599
http://hdl.handle.net/10261/261115
http://hdl.handle.net/10261/261115
Publikováno v:
2019 IEEE 5th International Conference on Computer and Communications (ICCC).
The most widely used object detectors to date are based on the two-stage approach popularized by R-CNN, where two locators cooperate on estimating objects’ position information. However, when a network reaches the best performance point, the first
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::499dde5c28d4c5b6c7b0a1f4165d51aa
https://doi.org/10.1109/iccc47050.2019.9064342
https://doi.org/10.1109/iccc47050.2019.9064342
Publikováno v:
AIP Advances, Vol 11, Iss 4, Pp 045028-045028-8 (2021)
In the present study, the machine learning algorithm is utilized for the first time to improve the probe diagnosis. Machine learning methods are utilized to improve the Langmuir probe diagnostic accuracy and the diagnosable plasma parameter range wit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5d97c50d1c27e6a1e0d118ac8187ace5
https://doaj.org/article/fc87f795d5c946869b53c19193d6a6e6
https://doaj.org/article/fc87f795d5c946869b53c19193d6a6e6
Publikováno v:
CSAI/ICIMT
Defect detection is a basic and essential task in automatic parts production, especially for automotive engine precision parts. In this paper, we propose a new idea to construct a deep convolutional network combining related knowledge of feature proc
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c115682435f82672421dcc94c84ebb37
http://arxiv.org/abs/1810.12061
http://arxiv.org/abs/1810.12061
Publikováno v:
Image and Graphics Technologies and Applications ISBN: 9789811317019
IGTA
IGTA
Orientation field extraction is a basic and essential task in an Automated Fingerprint Identification System (AFIS). Previous works failed when dealing with latent images due to the complicate background and strong noise. In this paper, an algorithm
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::788eaa3dc311f5e9434eca138a9e4e50
https://doi.org/10.1007/978-981-13-1702-6_28
https://doi.org/10.1007/978-981-13-1702-6_28
Publikováno v:
Artificial Neural Networks and Machine Learning – ICANN 2018 ISBN: 9783030014230
ICANN (3)
ICANN (3)
Orientation field is an important characteristic of fingerprints. Many biometrics processing steps rely on its accurate estimation. Previous works on this task failed because of blurry fingerprint patterns and severe background noises. In this paper,
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::09cd7efbfecb3dd0e06c8c58cdadec6b
https://doi.org/10.1007/978-3-030-01424-7_43
https://doi.org/10.1007/978-3-030-01424-7_43