Zobrazeno 1 - 10
of 26
pro vyhledávání: '"Yuanqiang Liu"'
Publikováno v:
Applied Sciences, Vol 14, Iss 21, p 10044 (2024)
This study experimentally investigated the effects of selected factors on the bending moment capacity (BMC) of case furniture joints. The main aim was to explore mixed applications of wood-based materials and fasteners in manufacturing case furniture
Externí odkaz:
https://doaj.org/article/493222b2c3c5459fadd03a7da0d7db6a
Publikováno v:
Aerospace, Vol 10, Iss 10, p 903 (2023)
Surface suction provides an efficient way to delay boundary layer transitions. In order to verify the suction effects and determine the mechanism of suction control in transonic swept wing boundary layers, wind tunnel transition measurements in a hyb
Externí odkaz:
https://doaj.org/article/6825d2c0b0e8401eb729bbf6b133c509
Publikováno v:
IEEE Access, Vol 8, Pp 184426-184436 (2020)
The connected vehicle environment is significant for the future road network. For constructing the connected vehicle environment, real-time data acquirement is always the prerequisite. Recently, using Light Detection and Ranging (LiDAR)-based roadsid
Externí odkaz:
https://doaj.org/article/e62fec85108b481d98527b9af6df4955
Autor:
Qingliang ZENG, Guangyu ZHOU, Jifei SHI, Yongqing LI, Yuanqiang LIU, Tianming CHEN, Liang WANG
Publikováno v:
Mechanics. 28:308-316
Workspace analysis is an indispensable part of robot research. The workspace volume is an important factor to measure the working ability of parallel robot. In order to maximize the workspace of parallel robot, this paper solves the workspace of robo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::3cab4d2e1317b57c7fe39d4d7ac0d244
https://doi.org/10.5755/j02.mech.32035
https://doi.org/10.5755/j02.mech.32035
Publikováno v:
Proceedings of the ACM SIGCOMM 2022 Conference.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::9abd7305efcc3683f6af4d88eb50cc68
https://doi.org/10.1145/3544216.3544224
https://doi.org/10.1145/3544216.3544224
Publikováno v:
IEEE Access, Vol 8, Pp 184426-184436 (2020)
The connected vehicle environment is significant for the future road network. For constructing the connected vehicle environment, real-time data acquirement is always the prerequisite. Recently, using Light Detection and Ranging (LiDAR)-based roadsid
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::579a240f5bfb568bcdb63592b8f7b64a
https://ieeexplore.ieee.org/document/9216093/
https://ieeexplore.ieee.org/document/9216093/
Autor:
Yuanqiang Liu, Huaiguo Jing
Publikováno v:
Mobile Information Systems.
These days, many researchers are interested in sports video behavior recognition (SVBR), which has emerged as a core research domain for visual understanding and analysis of athlete performance. Despite significant results in simple scenarios, SVBR r
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::c0870d65c98cd97ef06b47052231b275
Autor:
Xin Li, Yiwen Qi, Tienan Zhao, Yuanqiang Liu, Lei Zhang, Hai Xu, Wenke Yu, Xiujuan Zhao, Chi Zhang
Publikováno v:
2021 China Automation Congress (CAC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73fd1cc75b245dbaed34c3a21908fa4b
https://doi.org/10.1109/cac53003.2021.9728191
https://doi.org/10.1109/cac53003.2021.9728191
Publikováno v:
EMDL@MobiSys
We are witnessing the emergence of ubiquitous learning, where each device (smartphones, wearables, IoTs, etc) can learn from their environments either alone or collaboratively. Such a new paradigm is enabled by deep learning techniques, or more speci
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::ec1c77b916436179d4f8e91b4f20a076
https://doi.org/10.1145/3469116.3470009
https://doi.org/10.1145/3469116.3470009
Publikováno v:
ICSE
Deep Learning (DL) is finding its way into a growing number of mobile software applications. These software applications, named as DL based mobile applications (abbreviated as mobile DL apps) integrate DL models trained using large-scale data with DL
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f77f82116acb2d7c97ee8fbe22b9efe4
http://arxiv.org/abs/2101.04930
http://arxiv.org/abs/2101.04930
