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AI-big data analytics for building automation and management systems: a survey, actual challenges and future perspectives
(
Springer Nature
, 2022 , Article)
In theory, building automation and management systems (BAMSs) can provide all the components and functionalities required for analyzing and operating buildings. However, in reality, these systems can only ensure the control ...
The Emergence of Hybrid Edge-Cloud Computing for Energy Efficiency in Buildings
(
Springer Science and Business Media Deutschland GmbH
, 2022 , Conference)
Edge computing is attracting an increasing attention presently even though most of the building energy efficiency solutions are still using cloud computing for gathering, pre-processing and analyzing energy data. However, ...
Deep and transfer learning for building occupancy detection: A review and comparative analysis
(
Elsevier
, 2022 , Article)
The building internet of things (BIoT) is quite a promising concept for curtailing energy consumption, reducing costs, and promoting building transformation. Besides, integrating artificial intelligence (AI) into the BIoT ...
Flow-based intrusion detection algorithm for supervisory control and data acquisition systems: A real-time approach
(
John Wiley and Sons Inc
, 2021 , Article)
Intrusion detection in supervisory control and data acquisition (SCADA) systems is integral because of the critical roles of these systems in industries. However, available approaches in the literature lack representative ...
Machine learning-based management of electric vehicles charging: Towards highly-dispersed fast chargers
(
MDPI AG
, 2020 , Article)
Coordinated charging of electric vehicles (EVs) improves the overall efficiency of the power grid as it avoids distribution system overloads, increases power quality, and decreases voltage fluctuations. Moreover, the ...
Deep Learning for RF-Based Drone Detection and Identification: A Multi-Channel 1-D Convolutional Neural Networks Approach
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Conference)
Commercial unmanned aerial vehicles, or drones, are getting increasingly popular in the last few years. The fact that these drones are highly accessible to public may bring a range of security and technical issues to ...
A Deep Learning Model for LoRa Signals Classification Using Cyclostationay Features
(
IEEE Computer Society
, 2021 , Conference)
With the witnessed exponential growth of Internet of Things (IoT) nodes deployment following the emerging applications, multiple variants of technologies have been proposed to handle the IoT requirements. Among the proposed ...
Secrecy Outage Performance of Ground-to-Air Communications with Multiple Aerial Eavesdroppers and Its Deep Learning Evaluation
(
Institute of Electrical and Electronics Engineers Inc.
, 2020 , Article)
In this letter, we study the secure information transmission from a ground base station (GBS) to a legitimate unmanned aerial vehicle (UAV) user, in the presence of multiple UAV eavesdroppers. To enhance the secrecy ...
A Greedy Layer-Wise Learning Algorithm for Open-Circuit Fault Diagnosis of Grid-Connected Inverters
(
Institute of Electrical and Electronics Engineers Inc.
, 2021 , Conference)
This paper introduces a greedy layer-wise learning algorithm to diagnose open-circuit faults of grid-connected inverters. Inverters play important roles in energy conversion, especially when converting direct current to ...
Robust biometric system using session invariant multimodal EEG and keystroke dynamics by the ensemble of self-ONNs
(
Elsevier Ltd
, 2022 , Article)
Harnessing the inherent anti-spoofing quality from electroencephalogram (EEG) signals has become a potential field of research in recent years. Although several studies have been conducted, still there are some vital ...