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Centralized Operation of Multi-Energy Microgrids

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Centralized Operation of Multi-Energy Microgrids

International Conference on Environment and electrical engineering (EEEIC)

作者： Ali Esmaeel Nezhad Mohammad Sadegh Javadi Pedro H.J. Nardelli Subham Sahoo Department of Electrical Engineering School of Energy Systems LUT University Lappeenranta Finland Institute for Systems and Computer Engineering Technology and Science (INESC TEC) Porto Portugal Department of Energy Aalborg University Aalborg Denmark

This paper presents a centralized model for operating multi-energy microgrids. The proposed model is based on a linearized optimal power flow (OPF) model for handling the network constraints in the distribution networks. It is assumed that each local microgrid is self-sustaining and can be operated independently from the other microgrids. However, the network access provides more flexibility to the multi-energy microgrid operators to supply their loads. The network-based electrical energy transactions are accepted in this study, while energy transformation from electricity to the other carriers is an asset to minimize the overall operating cost of the centralized multi-energy microgrid operation. The proposed model is tested and verified on the modified IEEE 33-bus test system.

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Multi-Model Federated Learning Optimization Based on Multi-Agent Reinforcement Learning

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Multi-Model Federated Learning Optimization Based on Multi-A...

IEEE International Workshop on Computational Advances in Multi-Sensor Adaptive Processing (CAMSAP)

作者： S. Kaveh Atapour S. Jamal Seyedmohammadi S. Mohammad Sheikholeslami Jamshid Abouei Arash Mohammadi Konstantinos N. Plataniotis Department of Computer & Electrical Engineering Tarbiat Modares University Tehran Iran Concordia Institute of Information Systems Engineering (CIISE) Montreal Canada The Edward S. Rogers Sr. Dept. of Electrical & Computer Engineering University of Toronto Canada Department of Electrical Engineering Yazd University Yazd Iran

This paper addresses the problem of Multi-Model Federated Learning (MMFL) in a typical wireless network, where a cellular Base Station (BS) cooperates with multiple clients to simultaneously train several Machine Learning (ML) models. Accordingly, the objective of this paper is to make an efficient joint decision for client association and communication-computation resource allocation to optimize the performance of the MMFL algorithm. In this regard, an optimization problem is formulated to minimize the average global loss of ML models under clients' energy and delay constraints. It is shown that the problem is a mixed-integer optimization whose objective is implicit in terms of the decision variables. To solve the optimization problem, we propose a Multi-Agent Multi-Model Federated Learning (MAMMFL) scheme based on a cooperative multi-agent configuration to intelligently assign models and resources to clients. Specifically, the problem is first converted to a Markov Decision Process (MDP) problem, then it is divided into four sub-MDP problems, where each problem relates to a phase in MMFL. The reinforcement learning algorithm solves each sub-problem, and a team-Q algorithm is adopted to coordinate agents in a cooperative multi-agent setting. Simulation results show that the proposed method can outperform other baselines in terms of average global loss and resource consumption.

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A Multi-layer Non-Terrestrial Networks Architecture for 6G and Beyond under Realistic Conditions and with Practical Limitations

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arXiv 2025年

作者： Khennoufa, Faical Abdellatif, Khelil Yanikomeroglu, Halim Ozturk, Metin Elganimi, Taissir Kara, Ferdi Rabie, Khaled LGEERE Laboratory Department of Electrical Engineering Echahid Hamma Lakhdar University El-Oued Algeria Laboratory Department of Systems and Computer Engineering Carleton University OttawaONK1S 5B6 Canada Electrical and Electronics Engineering Ankara Yıldırım Beyazıt University Ankara Turkey The Department of Electrical and Electronic Engineering University of Tripoli Libya The Department of Computer Engineering Zonguldak Bulent Ecevit University Zonguldak Turkey Division of Communications Systems KTH Royal Institute of Technology Sweden The Department of Computer Engineering King Fahd University of Petroleum and Minerals Saudi Arabia

In order to bolster the next generation of wireless networks, there has been a great deal of interest in non-terrestrial networks (NTN), including satellites, high altitude platform stations (HAPS), and uncrewed aerial vehicles (UAV). To unlock their full potential, these platforms can integrate advanced technologies such as reconfigurable intelligent surfaces (RIS) and next-generation multiple access (NGMA). However, in practical applications, transceivers often suffer from radio frequency (RF) impairments, which limit system performance. In this regard, this paper explores the potential of multi-layer NTN architecture to mitigate path propagation loss and improve network performance under hardware impairment limitations. First, we present current research activities in the NTN framework, including RIS, multiple access technologies, and hardware impairments. Next, we introduce a multi-layer NTN architecture with hardware limitations. This architecture includes HAPS super-macro base stations (HAPS-SMBS), UAVs–equipped with passive or active transmissive RIS–, and NGMA techniques, like non-orthogonal multiple access (NOMA), as the multiple access techniques to serve terrestrial devices. Additionally, we present and discuss potential use cases of the proposed multi-layer architecture considering hardware impairments. The multi-layer NTN architecture combined with advanced technologies, such as RIS and NGMA, demonstrates promising results;however, the performance degradation is attributed to RF impairments. Finally, we identify future research directions, including RF impairment mitigation, UAV power management, and antenna designs. © 2025, CC0.

关键词： Unmanned aerial vehicles (UAV)

IoT Cloud Computing Middleware for crowd monitoring and evacuation

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International Journal of Circuits, systems and Signal Processing 2021年 15卷 1790-1802页

作者： Gazis, Alexandros Katsiri, Eleftheria Democritus University of Thrace Department of Electrical and Computer Engineering Xanthi67100 Greece Institute for the Management of Information Systems Athena Research & Innovation Center in Information Communication & Knowledge Technologies Marousi15125 Greece

Map-Reduce is a programming model and an associated implementation for processing and generating large data sets. This model has a single point of failure: the master, who coordinates the work in a cluster. On the contrary, wireless sensor networks (WSNs) are distributed systems that scale and feature large numbers of small, computationally limited, low-power, unreliable nodes. In this article, we provide a top-down approach explaining the architecture, implementation and rationale of a distributed fault-tolerant IoT middleware. Specifically, this middleware consists of multiple mini-computing devices (Raspberry Pi) connected in a WSN which implement the Map-Reduce algorithm. First, we explain the tools used to develop this system. Second, we focus on the Map-Reduce algorithm implemented to overcome common network connectivity issues, as well as to enhance operation availability and reliability. Lastly, we provide benchmarks for our middleware as a crowd tracking application for a preserved building in Greece (i.e., M. Hatzidakis’ residence). The results of this study show that IoT middleware with low-power and low-cost components are viable solutions for medium-sized cloud computing distributed and parallel computing centres. Potential uses of this middleware apply for monitoring buildings and indoor structures, in addition to crowd tracking to prevent the spread of COVID-19. © 2021, North Atlantic University Union NAUN. All rights reserved.

关键词： Middleware

Reference-Free Passive RF Imaging Using Near-Field Spatial Processing 1

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Reference-Free Passive RF Imaging Using Near-Field Spatial P...

1st International Conference on Computing, Internet of Things and Microwave systems, ICCIMS 2024

作者： Brennan, Paul Chetty, Kevin Li, Wenda Vishwakarma, Shelly Colone, Fabiola Amiri, Amin University College London Dept of Electronic and Electrical Engineering London United Kingdom University College London Dept of Security and Crime Science London United Kingdom Institute of Sensors Signals and Systems Heriot Watt University Edinburgh United Kingdom University of Southampton Dept of Electronics and Computer Science Southampton United Kingdom Sapienza University of Rome Department of Information Engineering Electronics and Telecommunications Rome Italy

ISBN: (纸本)9798350351736

Passive radar imaging has been extensively studied and is based on an illuminator-of-opportunity, such as a wireless access point, providing a signal that scatters from targets and is incident on a receiving node(s) in order to infer information on the range and velocity of the target scene. Such systems universally require reference signal regeneration, from a dedicated channel, that is cross-correlated with the received signal in order to extract the range/Doppler information of the target. This paper presents one of several techniques developed by the authors to achieve passive radar (or RF) imaging without any requirement for extraction of the reference signal. The concept is analogous to optical systems in which various source(s) provide general illumination of a target scene, the scattered signals of which are gathered and processed at an optical sensor in order to reconstruct the image, but without any requirement to extract the illumination (reference) source itself. Such an approach offers numerous practical advantages in achieving imaging from a single passive sensor without the additional requirement of precise, or indeed any, regeneration of the illuminating source: a reference-free approach. This paper presents one such technique based on sampling of the complex signal distribution over an antenna aperture and near-field processing to form a two/three-dimensional reconstruction of the target scene. The fundamental principles of the technique are described along with analysis of basic performance limits. Modelling is presented to illustrate the capability of the technique, which is validated by experimental measurement with a WiFi access point-based prototype system. © 2024 IEEE.

关键词： Radar imaging

UniSaT: Unified-Objective Belief Model and Planner to Search for and Track Multiple Objects

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arXiv 2024年

作者： Santos, Leonardo Moon, Brady Scherer, Sebastian Van Nguyen, Hoa Federal University of Minas Gerais MG Belo Horizonte Brazil Carnegie Mellon University PittsburghPA United States Curtin University BentleyWA6102 Australia School of Engineering Robotics Institute School of Computer Science United States Department of Electrical and Computer Engineering

Path planning for autonomous search and tracking of multiple objects is a critical problem in applications such as reconnaissance, surveillance, and data gathering. Due to the inherent competing objectives of searching for new objects while maintaining tracks for found objects, most current approaches rely on multi-objective planning methods, leaving it up to the user to tune parameters to balance between the two objectives, usually based on heuristics or trial and error. In this paper, we introduce UniSaT (Unified Search and Track), a novel unified-objective formulation for the search and track problem based on Random Finite Sets (RFS). Our approach models unknown and known objects using a combined generalized labeled multi-Bernoulli (GLMB) filter. For unseen objects, UniSaT leverages both cardinality and spatial prior distributions, allowing it to operate without prior knowledge of the exact number of objects in the search space. The planner maximizes the mutual information of this unified belief model, creating balanced search and tracking behaviors. We demonstrate our work in a simulated environment, presenting both qualitative results and quantitative improvements over a multi-objective method. Copyright © 2024, The Authors. All rights reserved.

关键词： Motion planning

On the Detection of Non-Cooperative RISs: Scan B-Testing via Deep Support Vector Data Description

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arXiv 2024年

作者： Stamatelis, George Gavriilidis, Panagiotis Fakhreddine, Aymen Alexandropoulos, George C. Department of Informatics and Telecommunications National and Kapodistrian University of Athens Greece Institute of Networked and Embedded Systems University of Klagenfurt Austria Department of Electrical and Computer Engineering University of Illinois Chicago United States

In this paper, we study the problem of promptly detecting the presence of non-cooperative activity from one or more Reconfigurable Intelligent Surfaces (RISs) with unknown characteristics lying in the vicinity of a Multiple-Input Multiple-Output (MIMO) communication system using Orthogonal Frequency-Division Multiplexing (OFDM) transmissions. We first present a novel wideband channel model incorporating RISs as well as non-reconfigurable stationary surfaces, which captures both the effect of the RIS actuation time on the channel in the frequency domain as well as the difference between changing phase configurations during or among transmissions. Considering that RISs may operate under the coordination of a third-party system, and thus, may negatively impact the communication of the intended MIMO OFDM system, we present a novel RIS activity detection framework that is unaware of the distribution of the phase configuration of any of the non-cooperative RISs. In particular, capitalizing on the knowledge of the data distribution at the multi-antenna receiver, we design a novel online change point detection statistic that combines a deep support vector data description model with the scan B-test. The presented numerical investigations demonstrate the improved detection accuracy as well as decreased computational complexity of the proposed RIS detection approach over existing change point detection schemes. © 2024, CC BY.

关键词： Orthogonal frequency division multiplexing

Anisotropy of two-dimensional ReS_(2)and advances in its device application

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Rare Metals 2021年第12期40卷 3357-3374页

作者： Ya-Di Cao Ying-Hui Sun Su-Fei Shi Rong-Ming Wang Beijing Advanced Innovation Center for Materials Genome Engineering Beijing Key Laboratory for Magneto-Photoelectrical Composite and Interface ScienceSchool of Mathematics and PhysicsUniversity of Science and Technology BeijingBeijing100083China Department of Chemical and Biological Engineering Rensselaer Polytechnic InstituteTroyNY 12180USA Department of Electrical Computer and Systems EngineeringRensselaer Polytechnic InstituteTroyNY 12180USA

Two-dimensional(2D)transition metal dichalcogenides(TMDCs)have attracted growing interest regarding their potential applications in next-generation electronic and optoelectronic *** to their atomic thickness and tunable bandgap,they exhibit unique mechanical,electrical,and optical *** a specific member of the TMDC family,rhenium disulfide(ReS_(2))has stimulated intensive interest due to its anisotropic crystal structure,weak inter-layer coupling,and anisotropic electrical and optical *** this review,we summarize the distinct crystal structure and intrinsic anisotropic properties of ReS_(2),followed by an introduction to its synthesis *** current applications of ReS_(2)and its heterojunctions are presented based on its anisotropic *** review not only provides a timely summary of the current applications of ReS_(2)and its heterojunctions,but also inspires new approaches to develop other innovative devices based on 2D materials with a low lattice symmetry.

关键词： Transition metal dichalcogenides(TMDCs) Rhenium disulfide Anisotropic property Heterojunction Functional device

Strength in Diversity: Multi-Branch Representation Learning for Vehicle Re-Identification

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arXiv 2023年

作者： Almeida, Eurico Silva, Bruno Batista, Jorge The Institute of Systems and Robotics University of Coimbra Coimbra3030-219 Portugal The Department of Electrical and Computers Engineering Faculty of Science and Technology University of Coimbra Coimbra3030-290 Portugal

This paper presents an efficient and lightweight multi-branch deep architecture to improve vehicle re-identification (V-ReID). While most V-ReID work uses a combination of complex multi-branch architectures to extract robust and diversified embeddings towards re-identification, we advocate that simple and lightweight architectures can be designed to fulfill the Re-ID task without compromising performance. We propose a combination of Grouped-convolution and Loss-Branch-Split strategies to design a multi-branch architecture that improve feature diversity and feature discriminability. We combine a ResNet50 global branch architecture with a BotNet self-attention branch architecture, both designed within a Loss-Branch-Split (LBS) strategy. We argue that specialized loss-branch-splitting helps to improve re-identification tasks by generating specialized re-identification features. A lightweight solution using grouped convolution is also proposed to mimic the learning of loss-splitting into multiple embeddings while significantly reducing the model size. In addition, we designed an improved solution to leverage additional metadata, such as camera ID and pose information, that uses 97% less parameters, further improving re-identification performance. In comparison to state-of-the-art (SoTA) methods, our approach outperforms competing solutions in Veri-776 by achieving 85.6% mAP and 97.7% CMC1 and obtains competitive results in Veri-Wild with 88.1% mAP and 96.3% CMC1. Overall, our work provides important insights into improving vehicle re-identification and presents a strong basis for other retrieval tasks. Our code is available at the link. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles