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14th Mediterranean Conference on Power Generation Transmission, Distribution and Energy Conversion, MEDPOWER 2024

作者： Vontzos, Georgios Laitsos, Vasileios Paraschoudis, Paschalis Bargiotas, Dimitrios Department of Electrical and Computer Engineering University of Thessaly Volos Greece Department of Energy Systems University of Thessaly Larissa Greece

ISBN: (纸本)9781837242689

This study investigates the use of graph convolutional and recurrent neural networks to forecast energy consumption on five Greek islands. The model is trained on historical energy usage data, using graph convolutional networks (GCNs) with graphs constructed from power production and their geographical location which their results are averaged. The evaluation metrics mean absolute error (MAE), mean squared error (MSE), and mean absolute percentage error (MAPE) were utilised to measure the accuracy of the proposed prediction algorithms. The results show that all the suggested EGCRNN models with LSTM, Bi-LSTM, and GRU demonstrated excellent performance, strong model fit, and enhanced predictions of energy consumption. In particular, models which utilise long-short term memory (LSTM) and gated recurrence units (GRU) followed with better prediction in one model each, Thira and Rhodes, respectively. © The Institution of engineering & Technology 2024.

关键词： Prediction models

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Dynamic Evolutionary Game-based Modeling,Analysis and Performance Enhancement of Blockchain Channels

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IEEE/CAA Journal of Automatica Sinica 2023年第1期10卷 188-202页

作者： PeiYun Zhang MengChu Zhou ChenXi Li Abdullah Abusorrah IEEE the School of Computer Science Nanjing University of Information Science&TechnologyNanjing 210044China the Helen and John C.Hartmann Department of Electrical and Computer Engineering New Jersey Institute of TechnologyNewarkNJ 07102 USA the School of Computer and Information Anhui Normal UniversityWuhu 241003China the Department of Electrical and Computer Engineering Faculty of Engineeringand Center of Research Excellence in Renewable Energy and Power SystemsKing Abdulaziz UniversityJeddah 21481Saudi Arabia

The recent development of channel technology has promised to reduce the transaction verification time in blockchain *** transactions are transmitted through the channels created by nodes,the nodes need to cooperate with each *** one party refuses to do so,the channel is unstable.A stable channel is thus *** nodes may show uncooperative behavior,they may have a negative impact on the stability of such *** order to address this issue,this work proposes a dynamic evolutionary game model based on node *** model considers various defense strategies'cost and attack success ratio under *** can dynamically adjust their strategies according to the behavior of attackers to achieve their effective *** equilibrium stability of the proposed model can be *** proposed model can be applied to general channel *** is compared with two state-of-the-art blockchain channels:Lightning network and Spirit *** experimental results show that the proposed model can be used to improve a channel's stability and keep it in a good cooperative stable *** its use enables a blockchain to enjoy higher transaction success ratio and lower transaction transmission delay than the use of its two peers.

关键词： Blockchain channel network evolutionary game malicious behavior secure computing stability analysis

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Correction of Time-varying PMU Phase Angle Deviation with Unknown Transmission Line Parameters

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CSEE Journal of Power and Energy systems 2023年第1期9卷 315-325页

作者： Ancheng Xue He Kong Feiyang Xu Junbo Zhao Naichao Chang Joe H.Chow Haiyan Hong State Key Laboratory of Alternate Electrical Power System with Renewable Energy Sources North China Electric Power UniversityBeijing 102206China Department of Electrical and Computer Engineering University of ConnecticutStorrs06268USA National Electric Power Dispatching and Control Center Beijing 100031China Department of Electrical Computerand Systems EngineeringRensselaer Polytechnic Institute(RPI)TroyNY 12180USA

Phasor measurement units(PMUs)provide useful data for real-time monitoring of the smart ***,there may be time-varying deviation in phase angle differences(PADs)between both ends of the transmission line(TL),which may deteriorate application performance based on *** address that,this paper proposes two robust methods of correcting time-varying PAD deviation with unknown parameters of TL(ParTL).First,the phenomena of time-varying PAD deviation observed from field PMU data are *** general formulations for PAD estimation are then *** simplify the formulations,estimation of PADs is converted into the optimal problem with a single ParTL as the variable,yielding a linear estimation of *** latter is used by second-order Taylor series expansion to estimate PADs *** reduce the impact of possible abnormal amplitude data in field data,the IGG(Institute of Geodesy&Geophysics,Chinese Academy of Sciences)weighting function is *** using both simulated and field data verify the effectiveness and robustness of the proposed methods.

关键词： Correction line parameters parameter identification phasor measurement time-varying phase angle difference deviation

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SEF:A Smart and Energy-Aware Forwarding Strategy for NDN-Based Internet of Healthcare

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computers, Materials & Continua 2024年第12期81卷 4625-4658页

作者： Naeem Ali Askar Adib Habbal Hassen Hamouda Abdullah Mohammad Alnajim Sheroz Khan Department of Computer Engineering Faculty of EngineeringKarabük UniversityKarabük78050Türkiye Department ofManagement Information Systems College of Business AdministrationMajmaah UniversityAl-Majmaah11952Saudi Arabia Department of Information Technology College of ComputerQassim UniversityBuraydah51452Saudi Arabia Department of Electrical Engineering College of Engineering and Information TechnologyOnaizah CollegesQassim51911Saudi Arabia

Named Data Networking(NDN)has emerged as a promising communication paradigm,emphasizing content-centric access rather than location-based *** model offers several advantages for Internet of Healthcare Things(IoHT)environments,including efficient content distribution,built-in security,and natural support for mobility and ***,existing NDN-based IoHT systems face inefficiencies in their forwarding strategy,where identical Interest packets are forwarded across multiple nodes,causing broadcast storms,increased collisions,higher energy consumption,and *** issues negatively impact healthcare system performance,particularly for individuals with disabilities and chronic diseases requiring continuous *** address these challenges,we propose a Smart and Energy-Aware Forwarding(SEF)strategy based on reinforcement learning for NDN-based *** SEF strategy leverages the geographical distance and energy levels of neighboring nodes,enabling devices to make more informed forwarding decisions and optimize next-hop *** approach reduces broadcast storms,optimizes overall energy consumption,and extends network *** system model,which targets smart hospitals and monitoring systems for individuals with disabilities,was examined in relation to the proposed *** SEF strategy was then implemented in the NS-3 simulation environment to assess its performance in healthcare *** demonstrated that SEF significantly enhanced NDN-based IoHT ***,it reduced energy consumption by up to 27.11%,82.23%,and 84.44%,decreased retrieval time by 20.23%,48.12%,and 51.65%,and achieved satisfaction rates that were approximately 0.69 higher than those of other strategies,even in more densely populated *** forwarding strategy is anticipated to substantially improve the quality and efficiency of NDN-based IoHT systems.

关键词： Energy efficient forwarding strategy health information system internet technologies IoHT people with disabilities reinforcement learning Q-learning

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All-photonic artificial-neural-network processor via nonlinear optics

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Physical Review Applied 2024年第1期22卷 014009-014009页

作者： Jasvith Raj Basani Mikkel Heuck Dirk R. Englund Stefan Krastanov Department of Electrical and Electronics Engineering Department of Electrical and Computer Engineering Institute for Research in Electronics and Applied Physics and Department of Electrical Engineering and Computer Science Department of Electrical and Photonics Engineering College of Information and Computer Sciences

Optics and photonics have recently captured interest as a platform to accelerate linear matrix processing, otherwise a bottleneck in traditional digital electronics. In this paper we propose an all-photonic computational accelerator wherein information is encoded in the amplitudes of frequency modes stored in a single ring resonator. Interaction among these modes is enabled by nonlinear optical processes. Both the matrix multiplication and elementwise activation functions on these modes (the artificial neurons) are performed through coherent processes, enabling the direct representation of negative and complex numbers without having to pass through digital electronics, a common limitation in today’s photonic architectures. This design also has a drastically lower hardware footprint compared with today’s electronic and optical accelerators, as the entirety of the matrix multiplication happens in a single multimode resonator on chip. Our architecture is unique in providing a completely unitary, reversible mode of computation, enabling on-chip analog Hamiltonian-echo backpropagation for gradient descent and other self-learning tasks. Moreover, the computational speed increases with the power of the pumps to arbitrarily high rates, as long as the circuitry can sustain the higher optical power. Lastly, the design presented here is a less demanding version of a future room-temperature quantum computational device. Therefore, while this architecture is already viable today, direct reinvestments in it would be enabling its evolution into quantum computational hardware.

关键词： Machine learning Neuromorphic computing Nonlinear optics Photonics Artificial neural networks Deep learning Neural network simulations Optical computing

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Hybrid magnonics with localized spoof surface-plasmon polaritons

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Physical Review Applied 2024年第3期22卷 034009页

作者： Yuzan Xiong Andrew Christy Zixin Yan Amin Pishehvar Muntasir Mahdi Junming Wu James F. Cahoon Binbin Yang Michael C. Hamilton Xufeng Zhang Wei Zhang Department of Physics and Astronomy Department of Chemistry Department of Electrical and Computer Engineering Department of Physics

Hybrid magnonic systems have emerged as a promising direction for information propagation with preserved coherence. Because of the high tunability of magnons, their interactions with microwave photons can be engineered to probe novel phenomena based on strong photon-magnon coupling. Improving the photon-magnon coupling strength can be done by tuning the structure of microwave resonators to better interact with the magnon counterpart. Planar resonators have been explored due to their potential for on-chip integration, but only common modes from stripline-based resonators have been used. Here, we present a microwave spiral resonator supporting spoof localized surface plasmons (LSPs) and implement it in the investigation of photon-magnon coupling for hybrid magnonic applications. We showcase strong magnon-LSP photon coupling using a ferrimagnetic yttrium iron garnet sphere. We discuss the engineering capacity of the photon mode frequency and spatial field distributions of the spiral resonator via both experiment and simulation. As a result of the localized photon mode profiles, the resulting magnetic field concentrates near the surface dielectrics, giving rise to an enhanced magnetic filling factor. The strong coupling and large engineering space render the spoof LSPs an interesting contender in developing novel hybrid magnonic systems and functionalities.

关键词： Magnons Plasmonics Quantum information with hybrid systems Spin optoelectronics Surface plasmon polariton Magnonic crystals Metamaterials Microwave techniques

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Sample-Efficient Multi-Agent DQNs for Scalable Multi-Domain 5G+ Inter-Slice Orchestration

IEEE Transactions on Machine Learning in Communications and ...

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IEEE Transactions on Machine Learning in Communications and Networking 2024年 2卷 956-977页

作者： Doanis, Pavlos Spyropoulos, Thrasyvoulos Communication Systems Department Eurecom Sophia Antipolis06410 France School of Electrical and Computer Engineering Technical University of Crete Chania731 00 Greece

Data-driven network slicing has been recently explored as a major driver for beyond 5G networks. Nevertheless, we are still a long way before such solutions are practically applicable in real problems. Most solutions addressing the problem of dynamically placing virtual network function chains (‘‘slices’’) on top of a physical topology still face one or more of the following hurdles: (i) they focus on simple slicing setups (e.g. single domain, single slice, simple VNF chains and performance metrics);(ii) solutions based on modern reinforcement learning theory have to deal with astronomically high action spaces, when considering multi-VNF, multi-domain, multi-slice problems;(iii) the training of the algorithms is not particularly data-efficient, which can hinder their practical application given the scarce(r) availability of cellular network related data (as opposed to standard machine learning problems). To this end, we attempt to tackle all the above shortcomings in one common framework. For (i), we propose a generic, queuing network based model that captures the inter-slice orchestration setting, supporting complex VNF chain topologies and end-to-end performance metrics. For (ii), we explore multi-agent DQN algorithms that can reduce action space complexity by orders of magnitude compared to standard DQN. For (iii), we investigate two mechanisms to store to and select from the experience replay buffer, in order to speed up the training of DQN agents. The proposed scheme was validated to outperform both vanilla DQN (by orders of magnitude faster convergence) and static heuristics (3× cost improvement). 2024 The Authors. This work is licensed under a Creative Commons Attribution 4.0 License.

关键词： 5G mobile communication systems

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A family of resonance-based tunable frequency oscillators and application to chaos generation

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International Journal of Electronics Letters 2024年第4期12卷 495-505页

作者： Elwakil, A.S. Maundy, B.J. Psychalinos, C. Department of Electrical and Computer Engineering University of Sharjah Sharjah United Arab Emirates Department of Electrical and Software Engineering University of Calgary Alberta Canada Department of Physics Electronics Laboratory University of Patras Rio Patras Greece

We propose a family of second-order resonance-based sinusoidal oscillators with electronically tunable frequencies. Each oscillator is comprised of two amplifiers, surrounded by four impedances which must be a single (Formula presented.), (Formula presented.) or (Formula presented.) and we show that in this configuration eight resonance-based oscillators are possible. We also show the conditions for each oscillator to oscillate along with the frequency of oscillation and then design a non-autonomous chaotic oscillator based on one of these circuits. Circuit simulations using LTspice and experimental results are presented to verify the theory. © 2024 Informa UK Limited, trading as Taylor & Francis Group.

关键词： frequency tuning LC oscillators phase tuning Second-order oscillators

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Effectiveness of Deep Learning Models for Brain Tumor Classification and Segmentation

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computers, Materials & Continua 2023年第7期76卷 711-729页

作者： Muhammad Irfan Ahmad Shaf Tariq Ali Umar Farooq Saifur Rahman Salim Nasar Faraj Mursal Mohammed Jalalah Samar M.Alqhtani Omar AlShorman Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia College of Engineering Najran UniversityNajran61441Saudi Arabia

A brain tumor is a mass or growth of abnormal cells in the *** children and adults,brain tumor is considered one of the leading causes of *** are several types of brain tumors,including benign(non-cancerous)and malignant(cancerous)*** brain tumors as early as possible is essential,as this can improve the chances of successful treatment and *** this problem,we bring forth a hybrid intelligent deep learning technique that uses several pre-trained models(Resnet50,Vgg16,Vgg19,U-Net)and their integration for computer-aided detection and localization systems in brain *** pre-trained and integrated deep learning models have been used on the publicly available dataset from The Cancer Genome *** dataset consists of 120 *** pre-trained models have been used to classify tumor or no tumor images,while integrated models are applied to segment the tumor region *** have evaluated their performance in terms of loss,accuracy,intersection over union,Jaccard distance,dice coefficient,and dice coefficient *** pre-trained models,the U-Net model achieves higher performance than other models by obtaining 95%*** contrast,U-Net with ResNet-50 out-performs all other models from integrated pre-trained models and correctly classified and segmented the tumor region.

关键词： Brain tumor deep learning ensemble detection healthcare

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Strongly interacting phases in twisted bilayer graphene at the magic angle

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Physical Review B 2024年第12期110卷 L121123页

作者： Khagendra Adhikari Kangjun Seo K. S. D. Beach Bruno Uchoa Department of Physics and Astronomy School of Electrical and Computer Engineering Department of Physics and Astronomy The

Twisted bilayer graphene near the magic angle is known to have a cascade of insulating phases at integer filling factors of the low-energy bands. In this Letter we address the nature of these phases through an unrestricted, large-scale Hartree-Fock calculation on the lattice that self-consistently accounts for all electronic bands. Using numerically unbiased methods, we show that Coulomb interactions produce ferromagnetic insulating states at integer fillings ν∈[−3,3] with maximal spin polarization MFM=4−|ν|. We find that the ν=0 state is a pure ferromagnet, whereas all other insulating states are spin-valley polarized. At odd filling factors |ν|=1,3 those states have a quantum anomalous Hall effect with Chern number C=1. Except for the ν=0,−2 states, all other integer fillings have insulating phases with additional sublattice symmetry breaking and antiferromagnetism in the remote bands. We map the metal-insulator transitions of these phases as a function of the effective dielectric constant. Our results establish the importance of large-scale lattice calculations to faithfully determine the ground states of twisted bilayer graphene at integer fillings.

关键词： Quantum many-body systems Topological materials Twisted heterostructures Electron-correlation calculations Many-body techniques Tight-binding model

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