Zobrazeno 1 - 10
of 142
pro vyhledávání: '"Yongliang Lin"'
Autor:
Jiantao Yao, Yongliang Lin
Publikováno v:
Applied Sciences, Vol 13, Iss 16, p 9210 (2023)
To study the influence law of the seismic response of underground station structures at liquifiable interlayer sites, a two-dimensional numerical model of the interaction between the soil and station structure was established based on the finite diff
Externí odkaz:
https://doaj.org/article/ba10421ece2542f9b47900b070263af2
Autor:
Qi Zhang, Yongliang Lin
Publikováno v:
Applied Sciences, Vol 13, Iss 16, p 9270 (2023)
The mode of the reinforced embankment overlying voids was generated based on discrete element software. By changing the vertical distance H and the horizontal distance L, the influence of the void position on the bearing capacity characteristics, dis
Externí odkaz:
https://doaj.org/article/d5ed76017ff3418593e1ae38aae822a3
Publikováno v:
In The Journal of China Universities of Posts and Telecommunications April 2017 24(2):1
Publikováno v:
Neurocomputing. 455:390-400
Object detection based on deep learning typically relies on a large number of training data, which may be very labor-consuming to prepare. In this paper, we attempt to tackle the problem by addressing the One-Shot Object Detection (OSOD) task. Given
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7c341aadd654a783a4f1032201b6a8ea
https://doi.org/10.1016/j.neucom.2021.04.116
https://doi.org/10.1016/j.neucom.2021.04.116
Reordering Features with Weights Fusion in Multiclass and Multiple-Kernel Speech Emotion Recognition
Publikováno v:
Journal of Electrical and Computer Engineering, Vol 2017 (2017)
The selection of feature subset is a crucial aspect in speech emotion recognition problem. In this paper, a Reordering Features with Weights Fusion (RFWF) algorithm is proposed for selecting more effective and compact feature subset. The RFWF algorit
Externí odkaz:
https://doaj.org/article/56dd8b4d2ca54dc6b38bf2f045c2cd58
Publikováno v:
Complexity, Vol 2017 (2017)
As an important processing step for rough set theory, attribute reduction aims at eliminating data redundancy and drawing useful information. Covering rough set, as a generalization of classical rough set theory, has attracted wide attention on both
Externí odkaz:
https://doaj.org/article/897407b9bf164f36a88d55470fcde9ed
Autor:
Yongliang Lin, Biao Huang, Huihui Gan, David Z. Zhu, Zheyun Zhang, Zhiji Yu, Shuai Peng, Yonglong Hou, Ming Xiao, Huanyan Wang, Wei Zhang
Publikováno v:
Environmental Science and Pollution Research. 28:28571-28584
Permeable pavement is an effective means for stormwater runoff control and pollutant removal. However, relatively few studies have examined the characteristics of permeable brick and corresponding permeable pavement system (PPS). In this work, the pe
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::fdc26ff322309d465f2896061b1620f7
https://doi.org/10.1007/s11356-021-12525-5
https://doi.org/10.1007/s11356-021-12525-5
Publikováno v:
Proceedings of 2021 Chinese Intelligent Automation Conference ISBN: 9789811663710
In this work we present the modeling and control of a fully-actuated hexarotor. A tilt angle for each rotor allows the unmanned aircraft system to exert forces and moments in all the directions. Meanwhile, the hexarotor is capable of bidirectional th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::a4ee76005fe15a07151bf70789716d31
https://doi.org/10.1007/978-981-16-6372-7_59
https://doi.org/10.1007/978-981-16-6372-7_59
Autor:
Bin Xu, Zhongchun Su, Yan Tang, Yongliang Lin, Xing Wei, Zhengzheng Wu, Peng Zhang, Hongdou Fang
Publikováno v:
Journal of Materials Science: Materials in Medicine. 32
There is no effective therapy for the treatment of deep and large area skin wounds. Decellularized scaffolds can be prepared from animal tissues and represent a promising biomaterial for investigation in tissue regeneration studies. In this study, MT
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::eabff1be6031aca24378eb2aef6ee968
https://doi.org/10.1007/s10856-021-06531-9
https://doi.org/10.1007/s10856-021-06531-9
Publikováno v:
ISPRS International Journal of Geo-Information, Vol 5, Iss 10, p 191 (2016)
In this paper, we propose a multiple kernel relevance vector machine (RVM) method based on the adaptive cloud particle swarm optimization (PSO) algorithm to map landslide susceptibility in the low hill area of Sichuan Province, China. In the multi-ke
Externí odkaz:
https://doaj.org/article/012e358c031d43479f688ee4bde535e9