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40th Annual ACM Symposium on Applied Computing, SAC 2025

作者： Palma, Alessandro Papadakis, Nikolaos Bouloukakis, Georgios Garcia-Alfaro, Joaquin Sospetti, Mattia Magoutis, Kostas Department of Computer Control and Management Engineering Sapienza University of Rome Rome00185 Italy Samovar Télécom SudParis Institut Polytechnique de Paris Palaiseau91120 France GrottiniLab S.r.L. Porto Recanati Macerata Italy Computer Science Department University of Crete Greece Crete Greece

ISBN: (纸本)9798400706295

Federated data spaces allow organizations to share and control their own data across various domains, but their exposure to cyber attacks has increased due to a surge in newly discovered vulnerabilities. Existing solutions to secure them focus on messaging protocol protection (e.g., using cryptographic means), but this is not sufficient. Attackers may exploit additional vulnerabilities to cause significant issues (e.g., disrupting the availability of services). To this end, we propose SHIELD, a security-by-design approach for federated data spaces, which leverages attack graphs and trust computation to mitigate the risks of cyber attacks. Mitigation is accomplished by proactively assessing the data spaces' weaknesses and implementing security messaging measures to prevent detrimental attacks. A prototype implementation of SHIELD using publish/subscribe as a messaging mechanism is experimentally evaluated over a real architecture in a V2X (Vehicle-to-Everything) scenario. Copyright © 2025 held by the owner/author(s).

关键词： Trusted computing

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Survey of Distributed Algorithms for Resource Allocation over Multi-Agent Systems

arXiv

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arXiv 2024年

作者： Doostmohammadian, Mohammadreza Aghasi, Alireza Pirani, Mohammad Nekouei, Ehsan Zarrabi, Houman Keypour, Reza Rikos, Apostolos I. Johansson, Karl H. Faculty of Mechanical Engineering Semnan University Semnan Iran Department of Electrical Engineering and Computer Science Oregon State University United States Department of Mechanical Engineering University of Ottawa OttawaON Canada City University of Hong Kong China Tehran Iran Faculty of Electrical Engineering Semnan University Semnan Iran Department of Electrical and Computer Engineering Division of Systems Engineering Boston University BostonMA United States Division of Decision and Control Systems KTH Royal Institute of Technology Sweden

Resource allocation and scheduling in multi-agent systems present challenges due to complex interactions and decentralization. This survey paper provides a comprehensive analysis of distributed algorithms for addressing the distributed resource allocation (DRA) problem over multi-agent systems. It covers a significant area of research at the intersection of optimization, multiagent systems, and distributed consensus-based computing. The paper begins by presenting a mathematical formulation of the DRA problem, establishing a solid foundation for further exploration. Real-world applications of DRA in various domains are examined to underscore the importance of efficient resource allocation, and relevant distributed optimization formulations are presented. The survey then delves into existing solutions for DRA, encompassing linear, nonlinear, primal-based, and dual-formulation-based approaches. Furthermore, this paper evaluates the features and properties of DRA algorithms, addressing key aspects such as feasibility, convergence rate, and network reliability. The analysis of mathematical foundations, diverse applications, existing solutions, and algorithmic properties contributes to a broader comprehension of the challenges and potential solutions for this domain. © 2024, CC BY.

关键词： Multi agent systems

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Query-guided Prototype Evolution Network for Few-Shot Segmentation

arXiv

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arXiv 2024年

作者： Cong, Runmin Xiong, Hang Chen, Jinpeng Zhang, Wei Huang, Qingming Zhao, Yao Institute of Information Science Beijing Jiaotong University Beijing100044 China School of Control Science and Engineering Shandong University Jinan250061 China Key Laboratory of Machine Intelligence and System Control Ministry of Education Jinan250061 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China Department of Computer Science City University of Hong Kong Hong Kong School of Computer Science and Technology University of Chinese Academy of Sciences Beijing101408 China

Previous Few-Shot Segmentation (FSS) approaches exclusively utilize support features for prototype generation, neglecting the specific requirements of the query. To address this, we present the Query-guided Prototype Evolution Network (QPENet), a new method that integrates query features into the generation process of foreground and background prototypes, thereby yielding customized prototypes attuned to specific queries. The evolution of the foreground prototype is accomplished through a support-query-support iterative process involving two new modules: Pseudo-prototype Generation (PPG) and Dual Prototype Evolution (DPE). The PPG module employs support features to create an initial prototype for the preliminary segmentation of the query image, resulting in a pseudo-prototype reflecting the unique needs of the current query. Subsequently, the DPE module performs reverse segmentation on support images using this pseudo-prototype, leading to the generation of evolved prototypes, which can be considered as custom solutions. As for the background prototype, the evolution begins with a global background prototype that represents the generalized features of all training images. We also design a Global Background Cleansing (GBC) module to eliminate potential adverse components mirroring the characteristics of the current foreground class. Experimental results on the PASCAL-5i and COCO-20i datasets attest to the substantial enhancements achieved by QPENet over prevailing state-of-the-art techniques, underscoring the validity of our ideas. © 2024, CC BY-NC-SA.

关键词： Semantic Segmentation

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Dynamic Optimization of Electric Vehicle Charging for Sustainable Power Grids using Sine Hunter-Prey Optimization with GRUs

Dynamic Optimization of Electric Vehicle Charging for Sustai...

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International Conference on Electrical Power and Energy Systems (ICEPES)

作者： Mohd Khizar Syed Prakash Chandra Behera P Joel Josephson Shamim Ahmad Khan Timur Kholmukhamedov Ponni Valavan M Department of Physics Kohinoor college Maharashtra India Department of Science St. Claret College Bangalore India Department of ECE Malla Reddy Engineering College Medchal Malkajgiri Telangana India Glocal School of Science & Technology Glocal University Uttar Pradesh India Automatic Control and Computer Engineering Department Turin Polytechnic University Tashkent Uzbekistan Department of AI & DS K.Ramakrishnan College of Engineering Trichy Tamil Nadu India

ISBN: (数字)9798350390728

ISBN: (纸本)9798350390735

The integration of electric vehicles (EVs) into power grids presents a significant challenge for sustainability due to their dynamic charging demands and the need to accommodate renewable energy sources. Existing optimization methods often fail to adequately address the complexity of this problem. In this study, propose a novel approach that combines Sine Hunter-Prey Optimization (SHPO) with Gated Recurrent Units (GRUs) to dynamically optimize EV charging for sustainable power grid operation. Traditional optimization methods struggle to capture the nonlinear and time-varying nature of EV charging demands and renewable energy availability. To overcome this limitation, SHPO's ability to efficiently explore complex solution spaces and GRUs' capability to model temporal dependencies in sequential data. This proposed method optimizes EV charging schedules by considering grid stability, renewable energy utilization, and cost minimization. Validating the approach using historical data on EV charging patterns, renewable energy generation, and grid conditions. Performance metrics include grid stability indices, renewable energy integration rates, and cost savings compared to baseline strategies. Experimental results demonstrate that the method outperforms traditional optimization techniques, leading to more sustainable and resilient power grid operation with 92% stability. Overall, this study contributes to advancing the modern methods in dynamic optimization of EV charging, paving the way for more sustainable transportation and energy systems.

关键词： Renewable energy sources Costs Stability criteria Power system dynamics Optimization methods Power system stability Electric vehicle charging

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Integrated fault-tolerant control of assembly and automated guided vehicle-based transportation layers

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International Journal of computer Integrated Manufacturing 2022年第4-5期35卷 409-426页

作者： Pawel Majdzik Marcin Witczak Bogdan Lipiec Zbigniew Banaszak a Department of Electronics and Computer Science Koszalin University of Technology Koszalin Polandb Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland

ABSTRACTABSTRACTThe paper concerns the digital twin design and fault-tolerant control of a production system. The real laboratory system under consideration consists of two subsystems: an assembly part and an automated guided vehicle transportation one. The proposed approach focuses, simultaneously, on both concurrency and synchronization problems as they are inevitable in such a kind of modern systems. The research contribution deals with the development of an analytical description of both an assembly system and a multiple automated guided vehicles transportation layer along with algorithms that are able to determine an optimum sequence of starting individual operations. Subsequently, diagnostics and fault-tolerant control schemes are elaborated and applied to the two-layer system being investigated. The core advantage of the proposed approach is that it can compensate or eliminate the impact of some faults, e.g. delays occurring in the assembly unit or reduced velocities of transportation means. This makes it possible to keep the desired schedule as far as possible. The paper concludes with a series of illustrative use cases and comparisons which exhibit the performance of the novel approach in both cases, i.e. faulty and fault-free.

关键词： Fault-tolerant control assembly system automated guided vehicles system digital twin concurrency synchronization predictive control

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Autonomous Vehicle Drift With a Soft Actor-critic Reinforcement Learning Agent 20

Autonomous Vehicle Drift With a Soft Actor-critic Reinforcem...

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20th IEEE Jubilee World Symposium on Applied Machine Intelligence and Informatics, SAMI 2022

作者： Hunor Toth, Szilard Janos Viharos, Zsolt Bardos, Adam Institute of Computer Science and Control Research Laboratory on Engineering Management Intelligence Intelligent Processes Research Group Budapest Hungary John von Neumann University Faculty of Economics Kecskemét Hungary Budapest University of Technology and Economics Department of Automotive Technologies Budapest Hungary

ISBN: (纸本)9781665497046

Self-driving vehicles have become a more and more important field in recent years. Supported by the techniques of Artificial Intelligence (AI), the current tendency of positive results in applications is making it a promising area to focus further research. Additionally, Reinforcement Learning (RL) is already proved to be an efficient approach for complex problems, e.g. robots, industrial systems and also games (like chess, Go), *** is a driving technique at handling limits where the driver intentionally oversteers, with loss of traction, while driving the vehicle through the entirety of a corner. It is a very challenging control task and often results in an accident when it occurs on public roads, consequently, the efficient control of this motion is especially important in the safety of autonomous *** paper reports novel research results whose main goal is to develop a self-driving agent for drift motion control based on vehicle simulation by Matlab/Simulink. Longitudinal and lateral velocity together with the yaw rate formed the state representation of the vehicle. The agent action space consists of two continuous actuator values: pedal ratio and roadwheel angle. The goal of the agent is twofold: first, it has to jump into a drifting state, second, it has to keep the vehicle in *** simulation results show that the proposed Soft Actor-Critic (SAC) RL agent is capable of learning to approach a pre-determined drift equilibrium from cornering and staying in this drift situation as well. For the training, the solution excluded using any kind of prior data, it only works with information gained from the simulation model, which is a remarkable difference from the actual state-of-the-art RL-based solutions. © 2022 IEEE.

关键词： Training Roads Simulation Reinforcement learning Mathematical models Data models Artificial intelligence

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A Multi-Layer Long Short-Term Memory Neural Network on Battery State-of-Health Estimation with Transfer Learning

A Multi-Layer Long Short-Term Memory Neural Network on Batte...

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IEEE International Conference on Industrial Electronics for Sustainable Energy Systems (IESES)

作者： Jilun Tian Jiusi Zhang Yuchen Jiang Hao Luo Congsheng Huang Mo-yuen Chow Department of Control Science and Engineering Harbin Institute of Technology Harbin China Department of Electrical Engineering National Taipei University of Technology Taipei Taiwan R.O.C. Department of Electrical and Computer Engineering North Carolina State University Raleigh NC U.S.A.

Due to the important part of batteries in industrial systems, its safety analysis has causes widespread attention from researchers, and its effective maintenance decision-making is needed. Data-driven state-of-health (SOH) estimation can provide useful information by monitoring historical data during the aging process, but it can be failed in the cross-domain scenarios due to the different data distributions. To tackle this issue, we propose a long short-term memory (LSTM) neural network with an additional fully connected dense as a basic predictor, and apply the pretrain and fine-tuning training algorithm to realize the high-performance prediction. By validated in two real-world datasets, we find that blindly expanding the training set may have a negative impact on model accuracy, and the proposed TL-LSTM can achieve a great performance under the cross-domain tasks.

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Deep Learning for Content-Based Personalized Viewport Prediction of 360-Degree VR Videos

IEEE Networking Letters

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IEEE Networking Letters 2020年第2期2卷 81-84页

作者： Chen, Xinwei Kasgari, Ali Taleb Zadeh Saad, Walid Department of Control Science and Engineering Zhejiang University Hangzhou310058 China Electrical and Computer Engineering Department Wireless at VT Virginia Tech BlacksburgVA United States

In this letter, the problem of head movement prediction for virtual reality videos is studied. In the considered model, a deep learning network is introduced to leverage position data as well as video frame content to predict future head movement. For optimizing data input into this neural network, data sample rate, reduced data, and long-period prediction length are also explored for this model. Simulation results show that the proposed approach yields 16.1% improvement in terms of prediction accuracy compared to a baseline approach that relies only on the position data. © 2019 IEEE.

关键词： Virtual reality

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LACK OF DIFFERENTIABILITY OF SEMIGROUPS ASSOCIATED TO DELAYED ABSTRACT THERMOELASTIC SYSTEMS

arXiv

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arXiv 2024年

作者： Ammari, Kaïs Salhi, Makrem Shel, Farhat LR Analysis and Control of PDEs LR 22ES03 Department of Mathematics Faculty of Sciences of Monastir University of Monastir Monastir5019 Tunisia Department of Mathematics and Computer Science Preparatory Institute for Engineering Studies of Sfax University of Sfax Tunisia

In this paper, we consider the associated semigroups to some abstract thermoelastic systems (in particular the α-β system), with a partial delay on the coupled system. We will prove that the corresponding semigroups... 详细信息

关键词： Thermoelasticity

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Model predictive control for tracking using artificial references: Fundamentals, recent results and practical implementation

Model predictive control for tracking using artificial refer...

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IEEE Conference on Decision and control

作者： P. Krupa J. Köhler A. Ferramosca I. Alvarado M.N. Zeilinger T. Alamo D. Limon Computer Science Group Gran Sasso Science Institute (GSSI) L’Aquila Italy ETH Zürich Institute for Dynamic Systems and Control Zürich Switzerland Department of Management Information and Production Engineering University of Bergamo Via Marconi 5 Dalmine (BG) Italy Department of Systems Engineering and Automation Universidad de Sevilla Seville Spain

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper provides a comprehensive tutorial on a family of Model Predictive control (MPC) formulations, known as MPC for tracking, which are characterized by including an artificial reference as part of the decision variables in the optimization problem. These formulations have several benefits with respect to the classical MPC formulations, including guaranteed recursive feasibility under online reference changes, as well as asymptotic stability and an increased domain of attraction. This tutorial paper introduces the concept of using an artificial reference in MPC, presenting the benefits and theoretical guarantees obtained by its use. We then provide a survey of the main advances and extensions of the original linear MPC for tracking, including its non-linear extension. Additionally, we discuss its application to learning-based MPC, and discuss optimization aspects related to its implementation.

关键词： Surveys Asymptotic stability Costs Tutorials Optimization Predictive control

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