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IEEE Networking Letters 2024年第4期6卷 247-251页

作者： Barkatsa, Sofia Diamanti, Maria Charatsaris, Panagiotis Tsiropoulou, Eirini Eleni Papavassiliou, Symeon Institute of Communication and Computer Systems School of Electrical and Computer Engineering National Technical University of Athens Zografou15780 Greece University of New Mexico Department of Electrical and Computer Engineering AlbuquerqueNM87131 United States

Federated Learning (FL), an emerging distributed Artificial Intelligence (AI) technique, is susceptible to jamming attacks during the wireless transmission of trained models. In this letter, we introduce a jamming attack mitigation mechanism for the uplink of wireless FL networks using the power-domain Non-Orthogonal Multiple Access (NOMA) technique. The problem of transmission power allocation for all clients (legitimate and malicious) is formulated and solved distributively as a Bayesian game with incomplete information. The clients aim to successfully transmit their model parameters, minimizing transmission time and consumed power, while having probabilistic knowledge about the malicious behavior of the other clients in the game. © 2019 IEEE.

关键词： Federated learning

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A Data-driven Variable Reduction Approach for Transmission-constrained Unit Commitment of Large-scale systems

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Journal of Modern Power systems and Clean Energy 2023年第1期11卷 254-266页

作者： Yuzhou Zhou Qiaozhu Zhai Lei Wu Moammad Shahidehpour Systems Engineering Institute MOEKLINNS LabXi’an Jiaotong UniversityXi’an 710049China Electrical and Computer Engineering Department Stevens Institute of TechnologyHobokenU.S. Electrical and Computer Engineering Department Illinois Institute of TechnologyChicagoU.S. IEEE

This paper presents a data-driven variable reduction approach to accelerate the computation of large-scale transmission-constrained unit commitment(TCUC).Lagrangian relaxation(LR)and mixed-integer linear programming(MILP)are popular approaches to solving ***,with many binary unit commitment variables,LR suffers from slow convergence and MILP presents heavy computation *** proposed data-driven variable reduction approach consists of offline and online calculations to accelerate computational performance of the MILP-based large-scale TCUC problems.A database including multiple nodal net load intervals and the corresponding TCUC solutions is first built offline via the data-driven and all-scenario-feasible(ASF)approaches,which is then leveraged to efficiently solve new TCUC instances ***/off statuses of considerable units can be fixed in the online calculation according to the database,which would reduce the computation burden while guaranteeing good solution quality for new TCUC instances.A feasibility proposition is proposed to promptly check the feasibility of the new TCUC instances with fixed binary variables,which can be used to dynamically tune parameters of binary variable fixing strategies and guarantee the existence of feasible UC solutions even when system structure *** tests illustrate the efficiency of the proposed approach.

关键词： Unit commitment accelerated algorithm data driven variable reduction

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Adaptive algorithm for controlling the power management system on offshore jack-up drilling rigs

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Energy Sources, Part A: Recovery, Utilization and Environmental Effects 2024年第1期46卷 16717-16737页

作者： Cemeljic, Hrvoje Havelka, Juraj Jeremic, Aleksandar Kuzle, Igor Department of Energy and Power Systems Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia Department of Electrical & Computer Engineering McMaster School of Biomedical Engineering McMaster University HamiltonON Canada Faculty of Electrical Engineering and Computing University of Zagreb Zagreb Croatia

The power systems of offshore jack-up drilling rigs consist of diesel generators running in parallel load-sharing mode, controlled by an automatic Power Management System (PMS). In this paper, the operational performance of the diesel generators (DG) and the PMS on a jack-up drilling rig is investigated, focusing on two critical offshore drilling operations: "pipe tripping" and "pulling/pumping out of the hole" (POOH). During these operations, large power swings occur in the system, subjecting the DGs to sudden load surges. These severe imbalances in the system cause the PMS to intermittently start and stop the engines throughout the operation, leading to various problematic conditions in the power plant. The paper provides a novel solution for intermittent starting and stopping of the DGs in the form of development and implementation of an adaptive PMS algorithm, a mathematical model of the system and a machine learning approach to event prediction of PMS operation in offshore drilling. Research demonstrates a noteworthy decrease in intermittent DG starting and stopping when the adaptive algorithm is implemented, with a 96% reduction during pipe tripping and an 87% reduction during POOH. This improvement comes at the expense of an 11% increase in engine running hours and a 1.5% rise in fuel consumption. Furthermore, by configuring the adaptive algorithm to economy mode, savings of up to 1700 liters of fuel per month are achievable. © 2024 Taylor & Francis Group, LLC.

关键词： Adaptive algorithms

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Operational Current Amplifier-Based Quadrature Oscillators Family

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IEEE Access 2025年 13卷 87281-87291页

作者： Alzaher, Hussain A. Alkhalifa, Ibrahim M. Department of Electrical Engineering Dhahran31261 Saudi Arabia Irc for Communication Systems and Sensing Dhahran31261 Saudi Arabia Department of Electrical and Computer Engineering Thuwal23955 Saudi Arabia

This paper investigates the application of the operational current amplifier (OCA) in the design of quadrature sinusoidal oscillators. There are numerous quadrature sinusoidal oscillators based on the three fundamental amplifiers namely the operational amplifier (OpAmp), the operational transconductance amplifier (OTA), and the operational transresistance amplifier (OTRA);however, their counterparts based on the OCA have not been reported. In this paper, several possible quadrature oscillator structures based on the OCA are systematically developed from their voltage mode active-RC counterparts using adjoint network theorem. Consequently, it is shown that the resulted oscillators do not necessarily preserve the attractive features of the voltage mode quadrature oscillators;namely providing explicit quadrature current output signals with equal amplitudes. When attempting to retain these features, it is found that the minimum OCAs needed is three. Analyzing this issue has paved the way for developing a novel OCA based integrator that promotes the invention of a new quadrature oscillator using only two OCAs. Simulation results obtained from a standard 150 nm CMOS process are provided. It is demonstrated that the proposed oscillator consumes approximately 350μ W while working in the 3 MHz range. Measurement results obtained from a prototype implemented using CMOS OCA ICs also confirms the proposed theory. © 2025 IEEE.

关键词： Circuit oscillations

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Robust Resource Sharing in Network Slicing via Hypothesis Testing

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IEEE Transactions on Network and Service Management 2025年第3期22卷 2731-2746页

作者： Nikolaidis, Panagiotis Baras, John Institute for Systems Research University of Maryland at College Park Department of Electrical and Computer Engineering College ParkMD20742 United States

In network slicing, the network operator needs to satisfy the service level agreements of multiple slices at the same time and on the same physical infrastructure. To do so with reduced provisioned resources, the operator may consider resource sharing mechanisms. However, each slice then becomes susceptible to traffic surges in other slices which degrades performance isolation. To maintain both high efficiency and high isolation, we propose the introduction of hypothesis testing in resource sharing. Our approach comprises two phases. In the trial phase, the operator obtains a stochastic model for each slice that describes its normal behavior, provisions resources and then signs the service level agreements. In the regular phase, whenever there is resource contention, hypothesis testing is conducted to check which slices follow their normal behavior. Slices that fail the test are excluded from resource sharing to protect the well-behaved ones. We test our approach on a mobile traffic dataset. Results show that our approach fortifies the service level agreements against unexpected traffic patterns and achieves high efficiency via resource sharing. Overall, our approach provides an appealing tradeoff between efficiency and isolation. © 2004-2012 IEEE.

关键词： Resource management Quality of service Testing Service level agreements Network slicing Bandwidth Traffic control Costs Delays Anomaly detection

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Multi-Model Fusion Framework Using Deep Learning for Visual-Textual Sentiment Classification

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computers, Materials & Continua 2023年第8期76卷 2145-2177页

作者： Israa K.Salman Al-Tameemi Mohammad-Reza Feizi-Derakhshi Saeed Pashazadeh Mohammad Asadpour Computerized Intelligence Systems Laboratory Department of Computer EngineeringFaculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran Department of Computer Engineering Faculty of Electrical and Computer EngineeringUniversity of TabrizTabriz51368Iran State Company for Engineering Rehabilitation and Testing Iraqi Ministry of Industry and MineralsBaghdad10011Iraq

Multimodal Sentiment Analysis(SA)is gaining popularity due to its broad application *** existing studies have focused on the SA of single modalities,such as texts or photos,posing challenges in effectively handling social media data with multiple ***,most multimodal research has concentrated on merely combining the two modalities rather than exploring their complex correlations,leading to unsatisfactory sentiment classification *** by this,we propose a new visualtextual sentiment classification model named Multi-Model Fusion(MMF),which uses a mixed fusion framework for SA to effectively capture the essential information and the intrinsic relationship between the visual and textual *** proposed model comprises three deep neural *** different neural networks are proposed to extract the most emotionally relevant aspects of image and text ***,more discriminative features are gathered for accurate sentiment ***,a multichannel joint fusion modelwith a self-attention technique is proposed to exploit the intrinsic correlation between visual and textual characteristics and obtain emotionally rich information for joint sentiment ***,the results of the three classifiers are integrated using a decision fusion scheme to improve the robustness and generalizability of the proposed *** interpretable visual-textual sentiment classification model is further developed using the Local Interpretable Model-agnostic Explanation model(LIME)to ensure the model’s explainability and *** proposed MMF model has been tested on four real-world sentiment datasets,achieving(99.78%)accuracy on Binary_Getty(BG),(99.12%)on Binary_iStock(BIS),(95.70%)on Twitter,and(79.06%)on the Multi-View Sentiment Analysis(MVSA)*** results demonstrate the superior performance of our MMF model compared to single-model approaches and current state-of-the-art techniques based on model evaluation cr

关键词： Sentiment analysis multimodal classification deep learning joint fusion decision fusion interpretability

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A New Approach to Determine Feasible Operating Region of Unbalanced Distribution Networks With Distributed Photovoltaics

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IEEE Transactions on Power Delivery 2025年第3期40卷 1493-1504页

作者： Prasad, Manorath Rather, Zakir Hussain Razzaghi, Reza Doolla, Suryanarayana Indian Institute of Technology Bombay Department of Energy Science and Engineering Mumbai400076 India Monash University Department of Electrical and Computer Systems Engineering MelbourneVIC3800 Australia

Amidst rising distributed generation and its potential role in grid management, this article presents a new realistic approach to determine the operational space and flexibility potential of an unbalanced active distribution network. The feasible operating region of an active distribution network is constituted by its range of active-reactive power exchanges with the bulk power system without breaching network constraints. This study explores the potential of highly penetrated single- and three-phase distributed photovoltaics and voltage-sensitive loads to determine the feasible operating region at different operating points. The cumulative response of Volt-Var controlled distributed photovoltaics and voltage-sensitive loads determines the power flow at the bulk power system-active distribution network interconnection, ensuring feasibility with respect to voltage magnitude and unbalance constraints. Further, the correspondence of feasible operating region with the tap-settings of substation transformer enables a novel feature to traverse throughout the available operating region and facilitate bulk power system-active distribution network power coordination. Additionally, a nodal sensitivity-based adjustable Volt-Var control scheme is proposed for distributed photovoltaics, improving the feasible operating region. The proposed strategy is validated on bulk power system-active distribution network integrated test system. Bulk power system: IEEE-9 bus system – Active distribution network: (highly unbalanced IEEE-13 bus distribution system (medium voltage) + modified CIGRE low voltage system). © 1986-2012 IEEE.

关键词： Power distribution networks

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Data-driven output regulation control for constrained linear systems

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Science China(Information Sciences) 2025年第3期68卷 338-353页

作者： Chaoyu XIA Yi DONG Chaoli WANG Shengyuan XU Shanghai Research Institute for Intelligent Autonomous Systems Tongji University College of Electronic and Information Engineering Shanghai Research Institute for Intelligent Autonomous SystemsTongji University Department of Control Science and Engineering School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and Technology Department of Automation Nanjing University of Science and Technology

This study introduces a data-driven approach for state and output feedback control addressing the constrained output regulation problem in unknown linear discrete-time systems. Our method ensures effective tracking performance while satisfying the state and input constraints, even when system matrices are not available. We first establish a sufficient condition necessary for the existence of a solution pair to the regulator equation and propose a data-based approach to obtain the feedforward and feedback control gains for state feedback control using linear programming. Furthermore, we design a refined Luenberger observer to accurately estimate the system state, while keeping the estimation error within a predefined set. By combining output regulation theory, we develop an output feedback control strategy. The stability of the closed-loop system is rigorously proved to be asymptotically stable by further leveraging the concept of λ-contractive sets.

关键词： output regulation constrained system data-driven.

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Multimodal Price Prediction

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Annals of Data Science 2023年第3期10卷 619-635页

作者： Zehtab-Salmasi, Aidin Feizi-Derakhshi, Ali-Reza Nikzad-Khasmakhi, Narjes Asgari-Chenaghlu, Meysam Nabipour, Saeideh Computerized Intelligence Systems Laboratory Department of Computer Engineering University of Tabriz Tabriz Iran Department Computer and Electrical Engineering University of Mohaghegh Ardabili Ardabil Iran

Price prediction is one of the examples related to forecasting tasks and is a project based on data science. Price prediction analyzes data and predicts the cost of new products. The goal of this research is to achieve an arrangement to predict the price of a cellphone based on its specifications. So, five deep learning models are proposed to predict the price range of a cellphone, one unimodal and four multimodal approaches. The multimodal methods predict the prices based on the graphical and non-graphical features of cellphones that have an important effect on their valorizations. Also, to evaluate the efficiency of the proposed methods, a cellphone dataset has been gathered from GSMArena. The experimental results show 88.3% F1-score, which confirms that multimodal learning leads to more accurate predictions than state-of-the-art techniques. © 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

关键词： Price prediction Multimodal learning Convolutional neural network Inception Classification Prediction Multimodal Price Deep learning Data science Mobile phone

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Current and Voltage Scaling for Maximum Transferable Active Power Improvement of Weak-Grid-Tied Grid-Following Inverters 10

Current and Voltage Scaling for Maximum Transferable Active ...

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10th IEEE International Power Electronics and Motion Control Conference, IPEMC 2024 ECCE Asia

作者： Zhou, Weihua Bahrani, Behrooz Monash University Department of Electrical and Computer Systems Engineering Melbourne Australia

ISBN: (纸本)9798350351330

The grid-following inverter's dq admittance model manifests a negative resistance in the low-frequency range due to the phase-locked loop, potentially leading to low-frequency instabilities and limiting the maximum transferable active power (MTAP), especially in weak grid scenarios. This paper introduces a simple and efficient current and voltage scaling scheme to counteract the phase-locked loop's adverse effects, enabling the grid-following inverter to inject per-unit active power even under extremely weak grid conditions. Complemented by a detailed design procedure, this proposed method stands out for its simplicity and effectiveness. Time-domain simulations validate the proposed scheme's efficacy in MTAP enhancement. © 2024 IEEE.

关键词： Voltage scaling

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