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Clustering Strategy for Megaconstellation with Synergistic Energy and Size Considerations

IEEE Transactions on Aerospace and Electronic Systems 2025年

作者： Zhang, Chengxi Yin, Tianle Wang, Wenshuo Wu, Jin Wang, Jihe Qi, Yuhua He, Wei Jiangnan University Key Laboratory of Advanced Control for Light Industry Processes Ministry of Education Wuxi214122 China Beijing Institute of Technology Mechanical Engineering China Hong Kong University of Science and Technology Department of Electronic and Computer Engineering Hong Kong SAR Hong Kong University of Science and Technology Beijing School of Intelligent Science and Technology Beijing China School of Aeronautics and Astronautics Shenzhen518107 China Sun Yat-sen University No. 135 Xingang West Road Guangzhou China

This paper proposes an energy- and size-aware clustering (ESC) strategy for megaconstellation systems. The methodology constructs a hierarchical architecture with designated cluster heads and member satellites through role differentiation. Cluster heads serve as relay nodes, reducing the communication load on ground infrastructure by interacting with ground stations and other entities. This hierarchical approach enables efficient constellation control through interactions primarily with cluster heads. The ESC strategy begins with a cluster head election process, incorporating orphan handling management to select cluster heads, ensuring full coverage of the entire constellation. Then, a two-stage multi-objective optimization framework is used to deal with division of members to clusters and intra-cluster multi-hop connectivity configurations. This dual-phase approach optimizes energy consumption while maintaining balanced cluster sizes. Simulations on the OneWeb system validate the ESC strategy as a viable solution in that it achieves 55.9% energy consumption reduction compared to traditional discrete gradient optimization methods while maintaining more balanced cluster sizes. © 2025 IEEE.

关键词： Multiobjective optimization

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Predictive Speed control for Automated Vehicles in Urban Area using Speed Zones 18

Predictive Speed Control for Automated Vehicles in Urban Are...

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18th IEEE International Symposium on Intelligent Systems and Informatics, SISY 2020

作者： Szilassy, Peter Nemeth, Balazs Gaspar, Peter Budapest University of Technology and Economics Department of Control for Transportation and Vehicle Systems Budapest Hungary Institute for Computer Science and Control Systems and Control Laboratory Budapest Hungary

ISBN: (纸本)9781728173528

The paper proposes a predictive control for speed trajectory design and tracking in urban area. In the method the urban area is divided into speed zones, in which the reference speed for the automated vehicle is determined by the type of the road. In the paper a Model Predictive control (MPC) based strategy is provided, which contains various constraints, e.g. on keeping safe distance from the surrounding vehicles or on the achievable acceleration of the vehicle. The result of the optimization is an acceleration profile for the automated vehicle, with which the safe distance from the vehicles in the urban area can be guaranteed. The effectiveness of the design method is illustrated through VISSIM simulation examples. © 2020 IEEE.

关键词： Speed

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A deep learning-based unsupervised learning method for spatially resolved transcriptomic data analysist

A deep learning-based unsupervised learning method for spati...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Lihong Peng Xianzhi He Li Zhang Xinhuai Peng Yuankang Lu Zejun Li Xing Chen School of Computer Science Hunan University of Technology Zhuzhou China School of Information and Control Engineering China University of Mining and Technology Xuzhou China Artificial Intelligence Research Institute Hunan Institute of Technology Hengyang China Artificial Intelligence Research Institute China University of Mining and Technology Xuzhou China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Spatially resolved transcriptomic data provide a large quantity of high-throughput gene expression and spatial structure information of tissues. Spatial clusters obtained by spatial transcriptome helps us to identify co-expressed regions and gene modules corresponding to cell types. In this study, we developed a Deep learning-based spatial clustering algorithm (RkDeep) by combining Ratio-cut and k-means. We first preprocessed spatial transcriptome data using graph neural network, and conducted dimensional reduction on the preprocessed data with denoising autoencoder. Finally, we clustered spatial transcriptome data by combining ratio cut and k-means. We compared our proposed RkDeep method with the other two spatial clustering methods, Seurat and Panoview. The results show that RkDeep computed the smallest Davide-Bouldin index and the largest Caliniski Harabaz index, adjusted rand index and normalized mutual information. Moreover, RkDeep was applied to analyze spatial transcriptome data of adult mouse brain, adult mouse kidney, and breast cancer. The results show that RkDeep can more accurately identify cell types from spatial transcriptome data.

关键词： Clustering methods Noise reduction Clustering algorithms Spatial databases Mice Graph neural networks Breast cancer

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Determining Soil Parameters

Determining Soil Parameters

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2020 Ivannikov Memorial Workshop, IVMEM 2020

作者： Zasukhin, Sergey Zasukhina, Elena Moscow Institute of Physics and Technology Dolgoprudny Russia Frc 'Computer Science and Control' of Ras Moscow Russia

ISBN: (纸本)9781728190884

The problem of determining soil parameters is considered. Their exact knowledge is of great importance for planning and managing water systems, assessing the possible size of catastrophic floods, etc. These parameters are proposed to be found by solving some optimal control problem, where the controlled process is described by the Richards equation. The objective function is mean-square deviation of the observed soil moisture values from its simulated values, which are obtained from the solution of the Richards equation with the selected parameters values. Numerical optimization is performed using Newton method. Derivatives of the objective function are calculated using fast automatic differentiation techniques. © 2020 IEEE.

关键词： Soil moisture

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Stabilization of Quasilinear Systems with Multiparameter Singular Perturbations 13

Stabilization of Quasilinear Systems with Multiparameter Sin...

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13th International Conference Management of Large-Scale System Development, MLSD 2020

作者： Dmitriev, Mikhail Makarov, Dmitry Moscow Institute of Physics and Technology Federal Research Center "computer Science and Control" of Ras Moscow Russia

ISBN: (纸本)9781728110943

We consider a construction of a stabilizing controller in a weakly nonlinear singularly perturbed system, where the matrix coefficients depend on state variables. The system contains groups of «slow», «fast» and «ultrafast» variables. The control is constructed by means of the SDRE approach and is based on non-linear corrections of linear composite one. © 2020 IEEE.

关键词： SDRE singularly perturbed systems stabilization

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Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs

arXiv

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arXiv 2022年

作者： Sasahara, Hampei Dán, György Amin, Saurabh Sandberg, Henrik The Department of Systems and Control Engineering School of Engineering Tokyo Institute of Technology Tokyo152-8552 Japan The Division of Network and Systems Engineering School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44 Sweden The Laboratory for Information and Decision Systems Massachusetts Institute of Technology CambridgeMA02139 United States The Division of Decision and Control Systems School of Electrical Engineering and Computer Science Kth Royal Institute of Technology StockholmSE-100 44 Sweden

This paper introduces a "green" routing game between multiple logistic operators (players), each owning a mixed fleet of internal combustion engine vehicle (ICEV) and electric vehicle (EV) trucks. Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs. This cost structure models: 1) limited battery capacity of EVs and their charging requirement;2) shared nature of charging facilities;3) pollution cost levied by regulatory agency on the use of ICEVs. We characterize Nash equilibria of this game and derive a condition for its uniqueness. We also use the gradient projection method to compute this equilibrium in a distributed manner. Our equilibrium analysis is useful to analyze the trade-off faced by players in incurring higher delay due to congestion at charging locations when the share of EVs increases versus a higher pollution cost when the share of ICEVs increases. A numerical example suggests that to increase marginal pollution cost can dramatically reduce inefficiency of equilibria. © 2022, CC BY-NC-ND.

关键词： Economic and social effects

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Switching Multiplicative Watermark Design Against Covert Attacks

arXiv

引用

arXiv 2025年

作者： Gallo, Alexander J. Anand, Sribalaji C. Teixeira, Andre M.H. Ferrari, Riccardo M.G. Department of Electronics Information and Bioengineering Politecnico di Milano Milano Italy School of Electrical Engineering and Computer Science and Digital Futures KTH Royal Institute of Technology Sweden Department of Information Technology Uppsala University PO Box 337 UppsalaSE-75105 Sweden Delft Center for Systems and Control Mechanical Engineering TU Delft Delft Netherlands

Active techniques have been introduced to give better detectability performance for cyber-attack diagnosis in cyber-physical systems (CPS). In this paper, switching multiplicative watermarking is considered, whereby we propose an optimal design strategy to define switching filter parameters. Optimality is evaluated exploiting the so-called output-to-output gain of the closed loop system, including some supposed attack dynamics. A worst-case scenario of a matched covert attack is assumed, presuming that an attacker with full knowledge of the closed-loop system injects a stealthy attack of bounded energy. Our algorithm, given watermark filter parameters at some time instant, provides optimal next-step parameters. Analysis of the algorithm is given, demonstrating its features, and demonstrating that through initialization of certain parameters outside of the algorithm, the parameters of the multiplicative watermarking can be randomized. Simulation shows how, by adopting our method for parameter design, the attacker’s impact on performance diminishes. © 2025, CC BY-NC-ND.

关键词： Networked control systems

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Combined LPV and ultra-local model-based control design approach for autonomous vehicles

Combined LPV and ultra-local model-based control design appr...

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IEEE Conference on Decision and control

作者： Dániel Fényes Tamás Hegedűs Balázs Németh Zoltán Szabó Péter Gáspár Eötvös Loránd Research Network (ELKH) Institute for Computer Science and Control (SZTAKI) Budapest Hungary Department for Control of Transportation and Vehicle Systems Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

The paper presents a novel approach for controlling highly nonlinear systems using the combination of LPV framework and ultra-local model-based solution. Firstly, a new formulation of the ultra-local model is presented, by which the implementation-related issues can be overcome. Secondly, an extended state-space representation is introduced, which includes the nominal model of the considered nonlinear system and the effect of the ultra-local model. This extended state-space representation serves as the basis of LPV-based control design. In this way, the stability of the closed-loop system can be guaranteed, while the variation of the tuning parameter (α) of the ultra-local model can also be handled. The effectiveness and the operation of the proposed control strategy are demonstrated through a vehicle-oriented control problem.

关键词： Uncertainty control design Life estimation Stability analysis Software Closed loop systems Software measurement

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Risk-Averse Certification of Bayesian Neural Networks

arXiv

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arXiv 2024年

作者： Zhang, Xiyue Wang, Zifan Gao, Yulong Romao, Licio Abate, Alessandro Kwiatkowska, Marta Department of Computer Science University of Oxford United Kingdom Division of Decision and Control Systems KTH Royal Institute of Technology Sweden Department of Electrical and Electronic Engineering Imperial College London United Kingdom Department of Wind and Energy Systems Technical University of Denmark Denmark

In light of the inherently complex and dynamic nature of real-world environments, incorporating risk measures is crucial for the robustness evaluation of deep learning models. In this work, we propose a Risk-Averse Certification framework for Bayesian neural networks called RAC-BNN. Our method leverages sampling and optimisation to compute a sound approximation of the output set of a BNN, represented using a set of template polytopes. To enhance robustness evaluation, we integrate a coherent distortion risk measure—Conditional Value at Risk (CVaR)—into the certification framework, providing probabilistic guarantees based on empirical distributions obtained through sampling. We validate RAC-BNN on a range of regression and classification benchmarks and compare its performance with a state-of-the-art method. The results show that RAC-BNN effectively quantifies robustness under worst-performing risky scenarios, and achieves tighter certified bounds and higher efficiency in complex tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： Risk assessment

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A General Black-box Adversarial Attack on Graph-based Fake News Detectors

arXiv

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arXiv 2024年

作者： Zhu, Peican Pan, Zechen Liu, Yang Tian, Jiwei Tang, Keke Wang, Zhen School of Artificial Intelligence Optics and Electronics Northwestern Polytechnical University China School of Computer Science Northwestern Polytechnical University China Air Traffic Control and Navigation College Air Force Engineering University China Cyberspace Institute of Advanced Technology Guangzhou University China

Graph Neural Network (GNN)-based fake news detectors apply various methods to construct graphs, aiming to learn distinctive news embeddings for classification. Since the construction details are unknown for attackers in a black-box scenario, it is unrealistic to conduct the classical adversarial attacks that require a specific adjacency matrix. In this paper, we propose the first general black-box adversarial attack framework, i.e., General Attack via Fake Social Interaction (GAFSI), against detectors based on different graph structures. Specifically, as sharing is an important social interaction for GNN-based fake news detectors to construct the graph, we simulate sharing behaviors to fool the detectors. Firstly, we propose a fraudster selection module to select engaged users leveraging local and global information. In addition, a post injection module guides the selected users to create shared relations by sending posts. The sharing records will be added to the social context, leading to a general attack against different detectors. Experimental results on empirical datasets demonstrate the effectiveness of GAFSI. Copyright © 2024, The Authors. All rights reserved.

关键词： Graphic methods

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