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IEEE Conference on Vehicular Technology (VTC)

作者： Fangzhou Zhao Gaurav Bagwe Ezedin Mohammed Lei Feng Lan Zhang Yao Sun James Watt School of Engineering University of Glasgow Glasgow UK Department of Electrical and Computer Engineering Michigan Technological University Michigan USA State Key Laboratory of Networking and Switching Technology BUPT Beijing China

As a new communication paradigm, neural network-driven semantic communication (SemCom) has demonstrated considerable promise in enhancing resource efficiency by transmitting the semantics rather than all bits of source information. Using a large semantic coding model can accurately distil semantics, and significantly save the required bandwidth. However, this consumes a large amount of computing resources, which are also precious in the network. In this paper, we investigate the joint computing resources and bandwidth allocation for SemCom networks. We first introduce the computing latency model in SemCom, and formulate the joint computing resources and bandwidth allocation optimization problem with the objective of maximizing semantic accuracy. Then, we transform this problem into a deep reinforcement learning framework and exploit a multi-agent proximal policy optimization to solve it. Numerical results show that the proposed method significantly improves the average semantic accuracy in the resource-constrained cases, compared with the two baselines.

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Optimal Bidding Strategy for a Wind Power Producer in the Energy and Spinning Reserve Markets

Optimal Bidding Strategy for a Wind Power Producer in the En...

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2021 North American Power Symposium, NAPS 2021

作者： Stratman, Anne Qiao, Wei Qu, Liyan University of Nebraska-Lincoln Power and Energy Systems Laboratory Department of Electrical and Computer Engineering LincolnNE68588-0511 United States

ISBN: (纸本)9781665420815

Wind power is one of the world's fastest-growing renewable energy resources. To achieve high levels of wind power penetration in the power supply, it will be necessary for wind power producers (WPPs) to contribute to system-wide reserves. This paper proposes a stochastic optimization model to determine the optimal offer strategy for a WPP that participates in the day-ahead and real-time energy and spinning reserve markets. The objective function maximizes the WPP's total expected profit while minimizing risk by allowing the WPP to split day-ahead offers between the energy and spinning reserve markets, which have lower penalties than the energy market for failing to deliver the cleared day-ahead offer. The model is solved using the simplex method, and hourly offer curves are generated for several levels of risk-aversion. Case study results show that the proposed offer strategy increases expected profit and significantly decreases risk compared to the case where the WPP only participates in the energy market. © 2021 IEEE.

关键词： Wind power

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Multi-Unit Floor Plan Recognition and Reconstruction Using Improved Semantic Segmentation of Raster-Wise Floor Plans

arXiv

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arXiv 2024年

作者： Kratochvila, Lukas de Jong, Gijs Arkesteijn, Monique Bilík, Šimon Zemčík, Tomáš Horak, Karel Rellermeyer, Jan S. Department of Control and Instrumentation Faculty of Electrical Engineering and Communication Brno University of Technology Brno Czech Republic Department of Software Technology Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands Department of Management in the Built Environment Faculty of Architecture and the Built Environment TU Delft Delft Netherlands Computer Vision and Pattern Recognition Laboratory Department of Computational Engineering Lappeenranta-Lahti University of Technology LUT Lappeenranta Finland Dependable and Scalable Software Systems Institute of Systems Engineering Faculty of Electrical Engineering and Computer Science Leibniz University Hannover Hannover Germany

Digital twins have a major potential to form a significant part of urban management in emergency planning, as they allow more efficient designing of the escape routes, better orientation in exceptional situations, and faster rescue intervention. Nevertheless, creating the twins still remains a largely manual effort, due to a lack of 3D-representations, which are available only in limited amounts for some new buildings. Thus, in this paper we aim to synthesize 3D information from commonly available 2D architectural floor plans. We propose two novel pixel-wise segmentation methods based on the MDA-Unet and MACU-Net architectures with improved skip connections, an attention mechanism, and a training objective together with a reconstruction part of the pipeline, which vectorizes the segmented plans to create a 3D model. The proposed methods are compared with two other state-of-the-art techniques and several benchmark datasets. On the commonly used CubiCasa benchmark dataset, our methods have achieved the mean F1 score of 0.86 over five examined classes, outperforming the other pixel-wise approaches tested. We have also made our code publicly available to support research in the field. © 2024, CC BY.

关键词： Semantic Segmentation

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Innate Cyber-Immunity and Acquired Cyber-Immunity across Multi-Area Power systems

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IEEE Transactions on Power systems 2025年

作者： Hou, Jiazuo Song, Yue Hou, Yunhe Peng, Jimmy Chih-Hsien National University of Singapore Department of Electrical and Computer Engineering Singapore Tongji University Department of Control Science and Engineering National Key Laboratory of Autonomous Intelligent Unmanned Systems Shanghai Institute of Intelligent Science and Technology Shanghai201804 China The University of Hong Kong Department of Electrical and Electronic Engineering Hong Kong University of Hong Kong Shenzhen Institute of Research and Innovation Shenzhen518057 China

Organisms have evolved innate and acquired immune systems to defend against pathogens like coronaviruses. Similarly, power networks, threatened by cyber-attacks, desire cyber-immunity. Inspired by the immunology research, this study develops a cyber-immune strategy-characterizing innate cyber-immunity and establishing acquired cyber-immunity-for any selected portion of a power network (termed as power subnetwork) against stealthy false data injection (FDI) cyberattacks. Therein, without extrinsic protection and information asymmetry, this paper for the first time confirms the existence and provides closed-form conditions for power subnetworks that are innately immune to FDI cyber-attacks despite all measurements being compromised. Single-leader-multi-follower bi-level optimization models are then developed to strategically allocate measurement protection, leading to the acquired cyber-immunity, whose conditions for existence and the least cost are explicitly formulated. More importantly, a bi-directional cyber-immunity propagation mechanism is presented to provide the optimal spatial allocation of defense resources across adjacent subnetworks. It also explicitly determines the temporal allocation sequence, revealing its sequential and cumulative impacts on establishing cyber-immunity. This is achieved by designing recursive traversal algorithms, one of which is modelled as a post-order depth-first-search problem of a multi-fork tree. The proposed subnetwork-specific cyber-immune strategy is verified in a modified Australian National Electricity Market. © 1969-2012 IEEE.

关键词： Phishing

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Intelligent Control Based in Neural-Fuzzy of Adaptive Backstepping for The Double Stator IPMSM Powered by a Five-Level NPC Inverter

Intelligent Control Based in Neural-Fuzzy of Adaptive Backst...

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Electric And Power engineering (EPEi), IEEE International Conference And Exposition On

作者： Rabie Benaissa Smail Mansouri Omar Ouledali Department of Electrical Engineering Laboratory for Sustainable Development and Computer Science Faculty of Sciences and Technology University of Adrar Algeria Department of Hydrocarbons and Renewable Energies Laboratory for Energy Environment and Information Systems Faculty of Sciences and Technology University of Adrar Algeria

ISBN: (数字)9798350356199

ISBN: (纸本)9798350356205

This article supplies a proposed approach Neuro Fuzzy Controller (NFC)-Adaptive Backstepping Controller (ABC)-Space Vector Modulation (SVM) for a five-level NPC inverter-Double Stator Interior Permanent Magnet Synchronous Motor (DSIPMSM). This paper’s primary objective is to enhance DSIPMSM’s performance by the use of strong control using the proposed approach. Given its benefits—including high torque density, high efficiency, high power density, and minimal maintenance—DSIPMSM, have recently been the focus of much research. Using the input-output linearization approach and Lyapunov stability theory, asymptotically stable trajectory-following dynamics are demonstrated using nonlinear adaptive backstepping control. The use of fuzzy logic and intelligence-based controllers are increasingly being used to enhance traditional control’s performance and output. In addition, the fuzzy logic controller has a lot of flaws that are presently being fixed by the extremely effective NFC tool. The proposed method’s worth was demonstrated by the simulation results in terms of durability, effective tracking dynamics, precision, and good disturbance rejection with little ripple.

关键词： Fuzzy logic Adaptation models Backstepping Torque Simulation Modulation Stators Vectors Inverters Lyapunov methods

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Evaluating Large Language Models in Interaction with Open Government Data 24

Evaluating Large Language Models in Interaction with Open Go...

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Proceedings of the 28th Pan-Hellenic Conference on Progress in Computing and Informatics

作者： Ioannis Maslaris Areti Karamanou Evangelos Kalampokis Konstantinos Tarabanis Database & Information Retrieval Laboratory Department of Electrical & Computer Engineering Democritus University of Thrace Xanthi Greece Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece Information Technologies Institute Center for Research & Technology HELLAS (CERTH) Thessaloniki Greece and Information Systems Laboratory Department of Business Administration University of Macedonia Thessaloniki Greece

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Accelerated Saddle Flow Dynamics for Bilinearly Coupled Minimax Problems

Accelerated Saddle Flow Dynamics for Bilinearly Coupled Mini...

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IEEE Conference on Decision and Control

作者： Yingzhu Liu Enrique Mallada Zhongkui Li Pengcheng You College of Engineering Peking University Beijing China State Key Laboratory for Turbulence and Complex Systems Peking University Beijing China Department of Electrical and Computer Engineering Johns Hopkins University Baltimore MD USA

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

Minimax problems have attracted much attention due to various applications in constrained optimization problems and zero-sum games. Identifying saddle points within these problems is crucial, and saddle flow dynamics offer a straightforward yet useful approach. This study focuses on a class of bilinearly coupled minimax problems with strongly convex-linear objective functions. We design an accelerated algorithm based on saddle flow dynamics, achieving a convergence rate beyond the stereotype limit (the strong convexity constant). The algorithm is derived from a sequential two-step transformation of a given objective function. First, a change of variables is applied to render the objective function better-conditioned, introducing strong concavity (from linearity) while preserving strong convexity. Second, proximal regularization, when staggered with the first step, further enhances the strong convexity of the objective function by shifting some of the obtained strong concavity. After these transformations, saddle flow dynamics based on the new objective function can be tuned for accelerated exponential convergence. Besides, such an approach can be extended to weakly convex-weakly concave functions and still guarantees exponential convergence to one stationary point. The theory is verified by a numerical test on an affine equality-constrained convex optimization problem.

关键词： Couplings Heuristic algorithms Linearity Games Linear programming Convex functions Game theory Optimization Convergence

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Meet the Co-Editor

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Micro and Nanosystems 2022年第3期14卷 187-187页

作者： Deen, Jamal Nano-Systems Laboratory Department Electrical Computer Engineering & School of Biomedical Engineering McMaster University HamiltonON Canada

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Adaptive Low-Rank Gradient Descent

Adaptive Low-Rank Gradient Descent

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IEEE Conference on Decision and Control

作者： Ali Jadbabaie Anuran Makur Amirhossein Reisizadeh Laboratory for Information & Decision Systems Massachusetts Institute of Technology Cambridge MA USA Department of Computer Science School of Electrical & Computer Engineering Purdue University West Lafayette IN USA

Low-rank structures have been observed in several recent empirical studies in many machine and deep learning problems, where the loss function demonstrates significant variation only in a lower dimensional subspace. While traditional gradient-based optimization algorithms are computationally costly for high-dimensional parameter spaces, such low-rank structures provide an opportunity to mitigate this cost. In this paper, we aim to leverage low-rank structures to alleviate the computational cost of first-order methods and study Adaptive Low-Rank Gradient Descent (AdaLRGD). The main idea of this method is to begin the optimization procedure in a very small subspace and gradually and adaptively augment it by including more directions. We show that for smooth and strongly convex objectives and any target accuracy $\epsilon$ , AdaLRGD's complexity is $\mathcal{O}(r\ln(r/\epsilon))$ for some rank $r$ no more than dimension $d$ . This significantly improves upon gradient descent's complexity of $\mathcal{O}(d\ln(1/\epsilon))$ when $r\ll d$ . We also propose a practical implementation of AdaLRGD and demonstrate its ability to leverage existing low-rank structures in data.

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Robust Decentralized Learning with Local Updates and Gradient Tracking

arXiv

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arXiv 2024年

作者： Ghiasvand, Sajjad Reisizadeh, Amirhossein Alizadeh, Mahnoosh Pedarsani, Ramtin Electrical and Computer Engineering Department UC Santa Barbara Santa BarbaraCA United States Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA United States

As distributed learning applications such as Federated Learning, the Internet of Things (IoT), and Edge Computing grow, it is critical to address the shortcomings of such technologies from a theoretical perspective. As an abstraction, we consider decentralized learning over a network of communicating clients or nodes and tackle two major challenges: data heterogeneity and adversarial robustness. We propose a decentralized minimax optimization method that employs two important modules: local updates and gradient tracking. Minimax optimization is the key tool to enable adversarial training for ensuring robustness. Having local updates is essential in Federated Learning (FL) applications to mitigate the communication bottleneck, and utilizing gradient tracking is essential to proving convergence in the case of data heterogeneity. We analyze the performance of the proposed algorithm, Dec-FedTrack, in the case of nonconvex-strongly-concave minimax optimization, and prove that it converges a stationary point. We also conduct numerical experiments to support our theoretical findings. © 2024, CC BY.

关键词： Convex optimization

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