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清华大学 [6]
华南理工大学 [6]
兰州理工大学 [5]
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会议论文 [28]
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Tumor-targeted multifunctional Core@Shell magnetic nanoprobes for near-infrared photodynamic therapy/chemotherapy of gastric cancer stem - like cells
会议论文
上海, 2018
作者:
Aiqing Ma
;
Ting Yin
;
Lintao Cai
;
Minbin Zheng
;
Ruijing Liang
收藏
  |  
浏览/下载:46/0
  |  
提交时间:2019/01/31
Trace al-ti-c-ce on solidification structure transformation and fracture properties of al-cu-mn alloy
会议论文
Yinchuan City, Ningxia, China, July 6, 2018 - July 12, 2018
作者:
Guo, Ting Biao
;
Wang, Chen
;
Li, Qi
;
Zhang, Feng
;
Ding, Wan Wu
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  |  
浏览/下载:0/0
  |  
提交时间:2020/11/15
Aluminum alloys
Aluminum metallography
Binary alloys
Brittleness
Cerium alloys
Copper alloys
Copper metallography
Ductile fracture
Heat treatment
Manganese metallography
Microstructure
Ternary alloys
Testing
Textures
Titanium alloys
Titanium carbide
Titanium metallography
Al-ti-c
As cast microstructure
Cu-Mn alloys
Fracture property
Intergranular precipitates
Microstructure and properties
Solidification structure
T6 heat treatment
Synthesis and Electrochemical Characterization of nanosized Li2MnO3 Cathode Material for Lithium Ion Batteries
会议论文
作者:
Li, Shiyou
;
Lei, Dan
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  |  
浏览/下载:3/0
  |  
提交时间:2019/11/15
Li2MnO3
One-step solid state reaction
Lithium-ion batteries
Microstructure and properties of al-Cu-Mn alloy with Y, Zr and (Y+Zr)
会议论文
Qingdao, China, October 20, 2016 - October 24, 2016
作者:
Guo, Ting Biao
;
Zhang, Feng
;
Li, Qi
;
Wang, Chen
;
Ding, Wan Wu
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  |  
浏览/下载:0/0
  |  
提交时间:2020/11/15
Aluminum alloys
Copper alloys
Ductile fracture
Hardness
Heat treatment
Mechanical properties
Microalloying
Microstructure
Tensile strength
Ternary alloys
Zircaloy
Addition method
Comprehensive properties
Cu-Mn alloys
Microstructure and properties
Microstructures and properties
Refined grain
Strengthening methods
T6 heat treatment
Synthesis of the spinel LiNi0.5Mn1.5O4 as 5V cathode material by carbonate co-precipitation method
会议论文
作者:
Li, Shiyou
;
Geng, Shan
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  |  
浏览/下载:4/0
  |  
提交时间:2019/11/15
lithium-ion batteries
spinel
LiNi0.5Mn1.5O4
carbonate co-precipitation method
High temperature 'Hard' piezoelectric ceramics of BiScO3-PbTiO3-Pb(Nb, Mn)O3with Fe2O3addition
会议论文
Joint IEEE International Symposium on the Applications of Ferroelectric, International Symposium on Integrated Functionalities and Piezoelectric Force Microscopy Workshop, ISAF/ISIF/PFM 2015, 2015-05-24
作者:
Chen, Jianguo[1]
;
Li, Qiang[2]
;
Jin, Guoxi[3]
;
Cheng, Jinrong[4]
收藏
  |  
浏览/下载:3/0
  |  
提交时间:2019/04/26
Wetting Behavior of Polymer Liquid in Insulation Process for Through Silicon Via
会议论文
2013 14th International Conference on Electronic Packaging Technology, ICEPT 2013, Dalian, China
作者:
Zhao Songfang
;
Zhang Guoping
;
Sun Rong
;
Lee S. W. Ricky
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  |  
浏览/下载:13/0
  |  
提交时间:2015/08/27
Airfoil roll control by bang-bang optimal control method with plasma actuators
会议论文
Wei, Qingkai
;
Niu, Zhongguo
;
Chen, Bao
;
Huang, Xun
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  |  
浏览/下载:1/0
  |  
提交时间:2015/11/17
INFORMATION EXTRACTION OF COASTAL LANDSCAPE AND ITS RESPONSE TO DAM DISTURBANCE IN YELLOW RIVER DELTA BASED ON REMOTE SENSING IMAGES
会议论文
2011 3rd International Conference on Computer Technology and Development, 2012
Fu X.
;
Liu G. H.
;
Asme
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  |  
浏览/下载:22/0
  |  
提交时间:2012/12/01
Efficient human action recognition using accumulated motion image and support vector machines (EI CONFERENCE)
会议论文
International Workshop on Advanced Computational Intelligence and Intelligent Informatics, IWACIII 2011, November 19, 2011 - November 23, 2011, Suzhou, China
Cao W.
;
Zhang X.
;
Cao S.
;
Zhang J.
;
Wang M.
;
Han G.
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  |  
浏览/下载:66/0
  |  
提交时间:2013/03/25
Vision-based human action recognition provides an advanced interface
and research in this field of human action recognition has been actively carried out. This paper describes a scheme for recognizing human actions from a video sequences. The proposed method is an extension of the Motion History Image(MHI) method based on the ordinal measure of accumulated motion
which is robust to variations of appearances. We define the accumulated motion image(AMI) using image differences firstly. Then the AMI of the video sequencesis resized to a MN regulation following the standard of training phases. Finally
we employ Support Vector Machine(SVM) as a classifier to distinguish the current activity in target video sequences. In a word
our proposed algorithm not only outperforms the state of art on public available KTH data set and Weizmann data set
but also proves practical to some real world applications
in addition
this method is computationally simple and able to achieve a satisfactory accuracy.
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