scholarly journals Troubleshooting: a Dynamic Solution for Achieving Reliable Fault Detection by Combining Augmented Reality and Machine Learning

2021 ◽  
Author(s):  
Sara Scheffer ◽  
Nick Limmen ◽  
Roy Damgrave ◽  
Alberto Martinetti ◽  
Bojana Rosic ◽  
...  
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Fault Detection ◽  
Dynamic Solution

A Study on Augmented Reality Assisted Navigation App Using Machine Learning and Computer Vision

2019 ◽  
Vol 7 (2) ◽  
pp. 412-415
Author(s):  
Waheeda Dhokley ◽  
Asif Syed ◽  
Nitika Tomar ◽  
Riya Patil
Keyword(s):  
Machine Learning ◽  
Computer Vision ◽  
Augmented Reality ◽  
Assisted Navigation

scholarly journals Machine Learning for Communications

Entropy ◽  
2021 ◽  
Vol 23 (7) ◽  
pp. 831
Author(s):  
Vaneet Aggarwal
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Video Streaming ◽  
Communication Networks ◽  
Data Analytics ◽  
Data Traffic ◽  
Traffic Demand ◽  
Performance Requirements

Due to the proliferation of applications and services that run over communication networks, ranging from video streaming and data analytics to robotics and augmented reality, tomorrow’s networks will be faced with increasing challenges resulting from the explosive growth of data traffic demand with significantly varying performance requirements [...]

scholarly journals Fault Detection in the Wind Farm Turbine Using Machine Learning Based On SVM Algorithm

2021 ◽  
Vol 1964 (5) ◽  
pp. 052015
Author(s):  
S Muthukrishnan ◽  
Arun Kumar Pallekonda ◽  
R Saravanan ◽  
B Meenakshi
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Wind Farm ◽  
Svm Algorithm

An optimized machine learning technology scheme and its application in fault detection in wireless sensor networks

2021 ◽  
pp. 1-18
Author(s):  
Fang Fan ◽  
Shu-Chuan Chu ◽  
Jeng-Shyang Pan ◽  
Chuang Lin ◽  
Huiqi Zhao
Keyword(s):  
Machine Learning ◽  
Wireless Sensor Networks ◽  
Sensor Networks ◽  
Fault Detection ◽  
Wireless Sensor ◽  
Learning Technology

Application of Machine Learning in Industrial Boilers: Fault Detection, Diagnosis, and Prognosis

2021 ◽  
Author(s):  
Yang Meng ◽  
Xinyun Wu ◽  
Jumoke Oladejo ◽  
Xinyue Dong ◽  
Zhiqian Zhang ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Diagnosis And Prognosis ◽  
Industrial Boilers

scholarly journals Augmented Reality and Machine Learning Incorporation Using YOLOv3 and ARKit

2021 ◽  
Vol 11 (13) ◽  
pp. 6006
Author(s):  
Huy Le ◽  
Minh Nguyen ◽  
Wei Qi Yan ◽  
Hoa Nguyen
Keyword(s):  
Machine Learning ◽  
Augmented Reality ◽  
Object Detection ◽  
Feature Detection ◽  
Detection Methods ◽  
Detection Accuracy ◽  
Data Annotation ◽  
Machine Learning Model ◽  
Potential Benefits ◽  
Feature Detection And Tracking

Augmented reality is one of the fastest growing fields, receiving increased funding for the last few years as people realise the potential benefits of rendering virtual information in the real world. Most of today’s augmented reality marker-based applications use local feature detection and tracking techniques. The disadvantage of applying these techniques is that the markers must be modified to match the unique classified algorithms or they suffer from low detection accuracy. Machine learning is an ideal solution to overcome the current drawbacks of image processing in augmented reality applications. However, traditional data annotation requires extensive time and labour, as it is usually done manually. This study incorporates machine learning to detect and track augmented reality marker targets in an application using deep neural networks. We firstly implement the auto-generated dataset tool, which is used for the machine learning dataset preparation. The final iOS prototype application incorporates object detection, object tracking and augmented reality. The machine learning model is trained to recognise the differences between targets using one of YOLO’s most well-known object detection methods. The final product makes use of a valuable toolkit for developing augmented reality applications called ARKit.

Machine learning-based system for fault detection on anchor rods of cable-stayed power transmission towers

2021 ◽  
Vol 194 ◽  
pp. 107106
Author(s):  
M.S. Coutinho ◽  
L.R.G.S. Lourenço Novo ◽  
M.T. de Melo ◽  
L.H.A. de Medeiros ◽  
D.C.P. Barbosa ◽  
...  
Keyword(s):  
Machine Learning ◽  
Fault Detection ◽  
Power Transmission ◽  
Transmission Towers
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