scholarly journals Online monitoring method for insulator self-explosion based on edge computing and deep learning

2021 ◽  
Keyword(s):  
Deep Learning ◽  
Online Monitoring ◽  
Edge Computing ◽  
Monitoring Method

Backfill Quality Monitoring Method of Complex Geological Substation Based on Deep Learning and Edge Computing

2021 ◽  
Author(s):  
Jiyuan Zhang ◽  
Yuan Huang ◽  
Xinyu Fan ◽  
Jingyao Wang
Keyword(s):  
Deep Learning ◽  
Edge Computing ◽  
Quality Monitoring ◽  
Monitoring Method

A Bed-Exit and Bedside Fall Warning System Based on Deep Learning Edge Computing Techniques

2021 ◽  
Author(s):  
Wan-Jung Chang ◽  
Liang-Bi Chen ◽  
Yang-Kun Ou ◽  
Aaron Raymond See ◽  
Tzu-Chin Yang
Keyword(s):  
Deep Learning ◽  
Warning System ◽  
Edge Computing

On unifying deep learning and edge computing for human motion analysis in exergames development

2021 ◽  
Author(s):  
Antonis Pardos ◽  
Andreas Menychtas ◽  
Ilias Maglogiannis
Keyword(s):  
Deep Learning ◽  
Motion Analysis ◽  
Human Motion ◽  
Edge Computing ◽  
Human Motion Analysis

scholarly journals A Citizen Science Unmanned Aerial System Data Acquisition Protocol and Deep Learning Techniques for the Automatic Detection and Mapping of Marine Litter Concentrations in the Coastal Zone

Drones ◽  
2021 ◽  
Vol 5 (1) ◽  
pp. 6
Author(s):  
Apostolos Papakonstantinou ◽  
Marios Batsaris ◽  
Spyros Spondylidis ◽  
Konstantinos Topouzelis
Keyword(s):  
Deep Learning ◽  
Data Acquisition ◽  
Citizen Science ◽  
Coastal Zone ◽  
Automatic Detection ◽  
Monitoring Methods ◽  
Monitoring Method ◽  
Marine Litter ◽  
Density Maps ◽  
Learning Techniques

Marine litter (ML) accumulation in the coastal zone has been recognized as a major problem in our time, as it can dramatically affect the environment, marine ecosystems, and coastal communities. Existing monitoring methods fail to respond to the spatiotemporal changes and dynamics of ML concentrations. Recent works showed that unmanned aerial systems (UAS), along with computer vision methods, provide a feasible alternative for ML monitoring. In this context, we proposed a citizen science UAS data acquisition and annotation protocol combined with deep learning techniques for the automatic detection and mapping of ML concentrations in the coastal zone. Five convolutional neural networks (CNNs) were trained to classify UAS image tiles into two classes: (a) litter and (b) no litter. Testing the CCNs’ generalization ability to an unseen dataset, we found that the VVG19 CNN returned an overall accuracy of 77.6% and an f-score of 77.42%. ML density maps were created using the automated classification results. They were compared with those produced by a manual screening classification proving our approach’s geographical transferability to new and unknown beaches. Although ML recognition is still a challenging task, this study provides evidence about the feasibility of using a citizen science UAS-based monitoring method in combination with deep learning techniques for the quantification of the ML load in the coastal zone using density maps.

Algorithmic implementation of deep learning layer assignment in edge computing based smart city environment

2021 ◽  
Vol 89 ◽  
pp. 106909
Author(s):  
Kyuchang Lee ◽  
Bhagya Nathali Silva ◽  
Kijun Han
Keyword(s):  
Deep Learning ◽  
Smart City ◽  
Edge Computing ◽  
City Environment ◽  
Layer Assignment

An autonomous computation offloading strategy in Mobile Edge Computing: A deep learning-based hybrid approach

2021 ◽  
Vol 178 ◽  
pp. 102974
Author(s):  
Ali Shakarami ◽  
Ali Shahidinejad ◽  
Mostafa Ghobaei-Arani
Keyword(s):  
Deep Learning ◽  
Hybrid Approach ◽  
Edge Computing ◽  
Computation Offloading ◽  
Mobile Edge Computing
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