Intelligent Systems: Methods and Implementation

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (31 March 2024) | Viewed by 2189

Special Issue Editors


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Guest Editor
Department of Electrical Engineering, College of Engineering, Jouf University, Sakaka 72388, Saudi Arabia
Interests: embedded system; algorithm-architecture matching; SoC/SoPC architecture; VLSI; high-level synthesis; image and video processing; image denoising; medical image diagnostics; artificial intelligence

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Guest Editor
1. Institute for Intelligent Systems and Robotics (ISIR), Sorbonne University, CNRS, 75005 Paris, France
2. ENOVA Robotics S.A., Novation City, Sousse 4051, Tunisia
Interests: hybrid systems; manipulation planning using PRM; learning dexterous manipulation by imitation; visual servoing for manipulation; dexterous manipulation; co-manipulation systems in medical robotics

Special Issue Information

Dear Colleagues,

An intelligent system is a highly developed computer system with the ability to observe its environment, process that information, and act accordingly. It can cooperate and exchange information with other agents, such as humans and other computers. It has the capacity to acquire knowledge over time and adapt in response to new information.

This Special Issue emphasizes the following lines of investigation related to the use of intelligent processes, techniques, methods, and their implementation in hardware and/or software contexts that enable a wide variety of applications:

  • Human identification (visual surveillance, image and video processing, biometric monitoring, character or speech recognition);
  • Public health (medical diagnostics, violence pattern detection, biomedical engineering);
  • Transportation (traffic control systems, traffic flow analysis and congestion monitoring, autonomous cars, public transportation systems, accident prevention);
  • Aerospace (drones, mission planning, advanced guidance and navigation, air traffic control);
  • Robotics (advanced robotics and automation, autonomous systems, visual servoing).

Dr. Ahmed Ben Atitallah
Dr. Anis Sahbani
Guest Editors

Manuscript Submission Information

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Published Papers (1 paper)

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Research

30 pages, 2013 KiB  
Article
A Reinforcement Learning Approach for Integrating an Intelligent Home Energy Management System with a Vehicle-to-Home Unit
by Ohoud Almughram, Sami Abdullah ben Slama and Bassam A. Zafar
Appl. Sci. 2023, 13(9), 5539; https://doi.org/10.3390/app13095539 - 29 Apr 2023
Cited by 5 | Viewed by 1760
Abstract
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems [...] Read more.
These days, users consume more electricity during peak hours, and electricity prices are typically higher between 3:00 p.m. and 11:00 p.m. If electric vehicle (EV) charging occurs during the same hours, the impact on residential distribution networks increases. Thus, home energy management systems (HEMS) have been introduced to manage the energy demand among households and EVs in residential distribution networks, such as a smart micro-grid (MG). Moreover, HEMS can efficiently manage renewable energy sources, such as solar photovoltaic (PV) panels, wind turbines, and vehicle energy storage. Until now, no HEMS has intelligently coordinated the uncertainty of smart MG elements. This paper investigated the impact of PV solar power, MG storage, and EVs on the maximum solar radiation hours. Several deep learning (DL) algorithms were utilized to account for the uncertainties. A reinforcement learning home centralized photovoltaic (RL-HCPV) scheduling algorithm was developed to manage the energy demand between the smart MG elements. The RL-HCPV system was modelled according to several constraints to meet household electricity demands in sunny and cloudy weather. Additionally, simulations demonstrated how the proposed RL-HCPV system could incorporate uncertainty, and efficiently handle the demand response and how vehicle-to-home (V2H) can help to level the appliance load profile and reduce power consumption costs with sustainable power production. The results demonstrated the advantages of utilizing RL and V2H technology as potential smart building storage technology. Full article
(This article belongs to the Special Issue Intelligent Systems: Methods and Implementation)
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