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DEEP LEARNING IN ROBOTICS AND BIG DATA FOR APPLICATIONS IN WIND-TURBINE INSPECTIONS
Machkour, Zakariae
(PI)
Ortiz Arroyo, Daniel
(Supervisor)
Durdevic, Petar
(Supervisor)
Esbjerg Energy Section
The Faculty of Engineering and Science
AAU Energy
Intelligent Energy Systems and Flexible Markets
AI for the People
Overview
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Explore the research topics touched on by this project. These labels are generated based on the underlying awards/grants. Together they form a unique fingerprint.
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Keyphrases
Wind Turbine Inspection
100%
Deep Learning in Robotics
100%
Big Data
100%
UAV
50%
Self-financing
50%
Autonomous Robots
50%
Deep Learning Methods
50%
Offshore Wind Turbine
50%
Computer Vision
50%
Degree of Autonomy
50%
Big Data Challenges
50%
Deep Learning
50%
New Architecture
50%
Onshore Wind
50%
Engineering
Compressed Air Motors
100%
Deep Learning Method
100%
Big Data
100%
Unmanned Aerial Vehicle
33%
Learning Technique
33%
Offshore Wind Turbines
33%
Computervision
33%
Computer Science
Big Data
100%
Deep Learning Method
100%
Big Data Problem
50%
Deep Learning Technique
50%
Unmanned Aerial Vehicle
50%
Computer Vision
50%