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Deep learning for analysing synchrotron data streams
Boyu Wang
, Ziqiao Guan
, Shun Yao
, Hong Qin
, Minh Hoai Nguyen
, Kevin Yager
,
Dantong Yu
Research output
:
Chapter in Book/Report/Conference proceeding
›
Conference contribution
10
Scopus citations
Overview
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Dive into the research topics of 'Deep learning for analysing synchrotron data streams'. Together they form a unique fingerprint.
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Keyphrases
Synchrotron Light Source
100%
Deep Learning
100%
Image Stream
100%
Synchrotron Data
100%
X-ray
66%
Physical Properties
33%
Structural Properties
33%
Traditional Data
33%
Chemical Properties
33%
Image Pattern
33%
Neural Network
33%
Granularity
33%
Beamline
33%
Brookhaven National Laboratory
33%
Complex Materials
33%
Characterization Tools
33%
Deep Learning Methods
33%
Material System
33%
Support Vector Machine
33%
Imaging Methods
33%
Diffraction Methods
33%
Convolutional Neural Network
33%
Image Features
33%
K-means
33%
X-ray Beam
33%
Image Labeling
33%
Scattering Image
33%
Big Data Challenges
33%
Deep Convolutional Neural Network (deep CNN)
33%
Data Analysis Practices
33%
Kilohertz Frame Rate
33%
Applied Deep Learning
33%
Megapixel
33%
Google TensorFlow
33%
X-ray Characterization
33%
Computer Science
Data Stream
100%
Deep Learning
100%
Deep Learning
100%
Granularity
50%
Big Data
50%
Complex Material
50%
Support Vector Machine
50%
image feature
50%
Convolutional Neural Network
50%
Deep Convolutional Neural Networks
50%
Engineering
Data Stream
100%
Light Source
100%
Deep Learning
100%
Convolutional Neural Network
66%
Big Data
33%
Length Scale
33%
Material System
33%
Support Vector Machine
33%
Frame Rate
33%
Granularity
33%
Powerful Tool
33%
X-Ray Probe
33%
Physics
Synchrotron
100%
Deep Learning
100%
Light Source
75%
Convolutional Neural Network
50%
Beamline
25%
Big Data
25%
Earth and Planetary Sciences
Synchrotron
100%
Data Transmission
25%
Timescale
25%
Support Vector Machine
25%
Big Data
25%
Material Science
Physical Property
100%
Chemical Property
100%