Zobrazeno 1 - 10
of 154
pro vyhledávání: '"Zhixun Su"'
Publikováno v:
IEEE Access, Vol 11, Pp 28977-28991 (2023)
The radial basis function (RBF) neural network is a type of universal approximator, and has been widely used in various fields. Improving the training speed and compactness of RBF networks are critical for promoting their applications. In the present
Externí odkaz:
https://doaj.org/article/f4b238be23104001bd4fa2a387fd4a29
Publikováno v:
IEEE Access, Vol 8, Pp 159854-159863 (2020)
During the past two decades, the subspace clustering problem has attracted much attention. Since the data set in real-world problems usually contains a lot of categories, it seems that the large subspace number (LSN) subspace clustering has great sig
Externí odkaz:
https://doaj.org/article/81270bb50cc0451f87ca89bcea17a0fc
Autor:
Yuling Wang, Cheng Peng, Lin Ding, Zhixun Su, Xiaoyun Chen, Xiaofu Wang, Meihao Sun, Junfeng Xu
Publikováno v:
Foods, Vol 12, Iss 3, p 615 (2023)
CRISPR/Cas12a technology is used for nucleic acid detection due to its specific recognition function and non-specific single-stranded DNA cleavage activity. Here, we developed a fluorescence visualisation detection method based on PCR and CRISPR/Cas1
Externí odkaz:
https://doaj.org/article/38d5f426f0774f51b4506a56bf290b54
Publikováno v:
IEEE Access, Vol 7, Pp 84420-84428 (2019)
In this paper, we propose a novel deep learning the based deraining method. The proposed method is motivated by the idea that an effective deraining algorithm should have the ability to remove various remaining rain streaks, which have been processed
Externí odkaz:
https://doaj.org/article/0f60021d277f4ebf901726e2c405b3f2
Publikováno v:
International Journal of Distributed Sensor Networks, Vol 15 (2019)
Wireless multimedia sensor networks have recently emerged as one of the most important technologies to actively perceive physical world and empower a wide spectrum of potential applications in various areas. Due to the advantages of rapid deployment,
Externí odkaz:
https://doaj.org/article/f07a0047d8454cf7963baa28f124d481
Publikováno v:
Applied Sciences, Vol 10, Iss 3, p 939 (2020)
Recent years have witnessed significant advances in single image deblurring due to the increasing popularity of electronic imaging equipment. Most existing blind image deblurring algorithms focus on designing distinctive image priors for blur kernel
Externí odkaz:
https://doaj.org/article/e67bd38a71984a5488ece8ecfa52481e
Publikováno v:
PLoS ONE, Vol 11, Iss 12, p e0168093 (2016)
In this paper, we propose a novel sparse coding and counting method under Bayesian framework for visual tracking. In contrast to existing methods, the proposed method employs the combination of L0 and L1 norm to regularize the linear coefficients of
Externí odkaz:
https://doaj.org/article/0573726e3b1c4c50bc0cce24b3ed91b7
Publikováno v:
Neural Computing and Applications. 35:3203-3219
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c17d844e3f9c926b22a1a83d32077cb
https://doi.org/10.1007/s00521-022-07864-4
https://doi.org/10.1007/s00521-022-07864-4
Publikováno v:
IEEE Transactions on Industrial Electronics. 69:921-929
Point cloud processing is rapidly expanding the applicable scenarios in the industry. The surface normal is a fundamental feature for various point cloud processing tasks. Recently, deep supervised normal estimators outperform traditional normal esti
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::1fd6d487b8309820f98641ada98c8144
https://doi.org/10.1109/tie.2021.3053904
https://doi.org/10.1109/tie.2021.3053904
Publikováno v:
IEEE Transactions on Cybernetics. 51:115-125
Deep convolutional neural networks (CNNs) have contributed to the significant progress of the single-image super-resolution (SISR) field. However, the majority of existing CNN-based models maintain high performance with massive parameters and exceedi
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c24659a7121ccd500bdb3366ff699af6
https://doi.org/10.1109/tcyb.2019.2952710
https://doi.org/10.1109/tcyb.2019.2952710