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3rd International Young Engineers Forum, YEF-ECE 2019

作者： Kuc, Barbara Kondratiuk, Miroslaw Ambroziak, Leszek Dzierzek, Kazimierz Department of Automatic Control and Robotics Faculty of Mechanical Engineering Bialystok University of Technology Bialystok Poland

ISBN: (纸本)9781538692820

The paper presents the structure, design and operational functions of the integrated controller of an multicoil electromagnetic launcher (EML) for micro class unmanned aerial vehicle (UAVs). UAVs are used for military and civilian purposes for patrolling forest areas, transporting loads of small sizes and weights, mapping, counting animal populations or observing mass events, etc. Some constructions of unmanned aerial vehicles are not equipped with a chassis allowing independent start using special runways, so there is a need for manual ejection by a man hand or using one of many types of dedicated launcher. In example, there are in usage rubber launchers (bungee), pneumatic, hydraulic, steam, rocket and also electromagnetic one. Especially the last type can be used for micro class UAVs. EMLs enable operator to precisely control thrust parameters, i.e. giving the appropriate initial speed, avoiding acceleration pulses, repeating start procedure in short periods of time. For instance, more advanced task involved EMLs can be creation of UAVs formation flight structure, which is very complex process sometimes even impossible to do with manual controlled thrust. The first part of the work presents the concept and construction of a multi-coil launcher with a moving ferromagnetic core. Project was created at the Faculty of Mechanical Engineering of the Bialystok University of technology. The next part of the work presents the structure, design and construction of the dedicated for EML, programmable controller. Moreover, in the paper the method of UAV ejection control and the selection of the appropriate, crucial parameters were described. In the last part, summary the obtained results was presented and conclusions were drawn. © 2019 IEEE.

关键词： controllers

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Data-driven control of T-Product-based Dynamical systems

arXiv

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arXiv 2025年

作者： He, Ziqin Mei, Yidan Mei, Shenghan Mao, Xin Dong, Anqi Wang, Ren Chen, Can Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States School of Data Science and Society University of North Carolina at Chapel Hill Chapel HillNC27599 United States Division of Decision and Control Systems Department of Mathematics KTH Royal Institute of Technology StockholmSE-100 44 Sweden Department of Electrical and Computer Engineering Illinois Institute of Technology ChicagoIL60616 United States School of Data Science and Society the Department of Mathematics University of North Carolina at Chapel Hill Chapel HillNC27599 United States

Data-driven control is a powerful tool that enables the design and implementation of control strategies directly from data without explicitly identifying the underlying system dynamics. While various data-driven control techniques, such as stabilization, linear quadratic regulation, and model predictive control, have been extensively developed, these methods are not inherently suited for multi-linear dynamical systems, where the states are represented as higher-order tensors. In this article, we propose a novel framework for data-driven control of T-product-based dynamical systems (TPDSs), where the system evolution is governed by the T-product between a third-order dynamic tensor and a third-order state tensor. In particular, we offer necessary and sufficient conditions to determine the data informativity for system identification, stabilization by state feedback, and T-product quadratic regulation of TPDSs with detailed complexity analyses. Finally, we validate our framework through numerical *** Codes 37N35, 93-08, 93A15 © 2025, CC BY.

关键词： Tensors

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HEART: Achieving Timely Multi-Model Training for Vehicle-Edge-Cloud-Integrated Hierarchical Federated Learning

arXiv

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arXiv 2025年

作者： Yang, Xiaohong Liwang, Minghui Wang, Xianbin Cheng, Zhipeng Hosseinalipour, Seyyedali Dai, Huaiyu Jiao, Zhenzhen School of Informatics Xiamen University Fujian China Department of Control Science and Engineering The National Key Laboratory of Autonomous Intelligent Unmanned Systems Japan Frontiers Science Center for Intelligent Autonomous Systems Ministry of Education Tongji University Shanghai China Department of Electrical and Computer Engineering Western University ON Canada School of Future Science and Engineering Soochow University Suzhou China Department of Electrical Engineering University at Buffalo-SUNY BuffaloNY United States Department of Electrical and Computer Engineering NC State University RaleighNC United States iF-Labs Beijing Teleinfo Technology Company Ltd. CAICT Beijing China

The rapid growth of AI-enabled Internet of Vehicles (IoV) calls for efficient machine learning (ML) solutions that can handle high vehicular mobility and decentralized data. This has motivated the emergence of Hierarchical Federated Learning over vehicle-edge-cloud architectures (VEC-HFL). Nevertheless, one aspect which is underexplored in the literature on VEC-HFL is that vehicles often need to execute multiple ML tasks simultaneously, where this multi-model training environment introduces crucial challenges. First, improper aggregation rules can lead to model obsolescence and prolonged training times. Second, vehicular mobility may result in inefficient data utilization by preventing the vehicles from returning their models to the network edge. Third, achieving a balanced resource allocation across diverse tasks becomes of paramount importance as it majorly affects the effectiveness of collaborative training. We take one of the first steps towards addressing these challenges via proposing a framework for multi-model training in dynamic VEC-HFL with the goal of minimizing global training latency while ensuring balanced training across various tasks—a problem that turns out to be NP-hard. To facilitate timely model training, we introduce a hybrid synchronous-asynchronous aggregation rule. Building on this, we present a novel method called Hybrid Evolutionary And gReedy allocaTion (HEART). The framework operates in two stages: first, it achieves balanced task scheduling through a hybrid heuristic approach that combines improved Particle Swarm Optimization (PSO) and Genetic Algorithms (GA);second, it employs a low-complexity greedy algorithm to determine the training priority of assigned tasks on vehicles. Experiments on real-world datasets demonstrate the superiority of HEART over existing methods. © 2025, CC BY-NC-ND.

关键词： Federated learning

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The OWA operator in multiple linear regression

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Applied Soft Computing 2022年 124卷

作者： Flores-Sosa, Martha Avilés-Ochoa, Ezequiel Merigó, José M. Kacprzyk, Janusz University Autonomous of Occidente Av. Lola Beltran Culiacán80020 Mexico School of Information Systems & Modelling Faculty of Engineering and Information Technology University of Technology Sydney 81 Broadway Ultimo2007 NSW Australia Department of Management Control and Information Systems School of Economics and Business University of Chile Av. Diagonal 257 Santiago8330015 Chile Systems Research Institute Polish Academy of Sciences Newelska 6 Warsaw01-447 Poland

Multiple linear regression (MLR) is one of the most widely used statistical procedures for scholarly and research. The main limitation of MLR is that when being estimated with linear methodologies as ordinary least squares (OLS) becomes not functional with complex data. The ordered weighted average (OWA) is an aggregation operator that provides means that collect complex information. This work presents a new application that uses MLR and OWA operators in the same formulation. We developed two applications called MLR-OWA operator and MLR-GOWA operator. The main advantage of the MLR with OWA operators is that we can consider the degree of optimism and pessimism of the environment. We study some of its main properties and particular cases. Finally, an application is tested for a volatility exchange rate estimation problem. © 2022 Elsevier B.V.

关键词： Multiple linear regression

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Exact Differentiator with Lipschitz Continuous Output and Optimal Worst-Case Accuracy Under Bounded Noise

Exact Differentiator with Lipschitz Continuous Output and Op...

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IEEE Conference on Decision and control

作者： Rodrigo Aldana-López Richard Seeber Hernan Haimovich David Gómez-Gutiérrez Department of Computer Science and Systems Engineering University of Zaragoza Zaragoza Spain Christian Doppler Laboratory for Model Based Control of Complex Test Bed Systems Institute of Automation and Control Graz University of Technology Graz Austria Centro Internacional Franco-Argentino de Ciencias de la Información y de Sistemas (CIFASIS) CONICET-UNR Rosario Argentina Intel Tecnología de México Intel Labs Intelligent Systems Research Lab Jalisco Mexico Tecnológico Nacional de México Instituto Tecnológico José Mario Molina Pasquel y Henríquez Unidad Académica Zapopan Jalisco Mexico

The online differentiation of a signal contaminated with bounded noise is addressed. A differentiator is developed that generates a Lipschitz continuous output, is exact in the absence of noise, and provides the optimal worst-case accuracy among all possible exact differentiators when noise is present. This combination of features is not shared by any previously existing differentiator. Tuning of the developed differentiator is very simple, requiring only the knowledge of a bound for the second-order derivative of the signal. The approach consists in regularizing the possibly highly noisy output of a recently introduced linear adaptive robust exact differentiator and feeding it to a first-order sliding-mode filter designed to maintain optimal accuracy. The proposed regularization and filtering of this output allows trading the speed with which exactness is obtained for the feature of a Lipschitz continuous, hence less noisy, output. An illustrative example is provided to highlight the features of the developed differentiator.

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Distributed online convex optimization with compressed communication 22

Distributed online convex optimization with compressed commu...

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Proceedings of the 36th International Conference on Neural Information Processing systems

作者： Zhipeng Tu Xi Wang Yiguang Hong Lei Wang Deming Yuan Guodong Shi Academy of Mathematics and Systems Science Chinese Academy of Sciences China and Australian Center for Field Robotics School of AMME The University of Sydney Australia Department of Control Science and Engineering Tongji University China College of Control Science and Engineering Zhejiang University China School of Automation Nanjing University of Science and Technology China Australian Center for Field Robotics School of AMME The University of Sydney Australia

ISBN: (纸本)9781713871088

We consider a distributed online convex optimization problem when streaming data are distributed among computing agents over a connected communication network. Since the data are high-dimensional or the network is large-scale, communication load can be a bottleneck for the efficiency of distributed algorithms. To tackle this bottleneck, we apply the state-of-art data compression scheme to the fundamental GD-based distributed online algorithms. Three algorithms with difference-compressed communication are proposed for full information feedback (DC-DOGD), one-point bandit feedback (DC-DOBD), and two-point bandit feedback (DC-DO2BD), respectively. We obtain regret bounds explicitly in terms of time horizon, compression ratio, decision dimension, agent number, and network parameters. Our algorithms are proved to be no-regret and match the same regret bounds, w.r.t. time horizon, with their uncompressed versions for both convex and strongly convex losses. Numerical experiments are given to validate the theoretical findings and illustrate that the proposed algorithms can effectively reduce the total transmitted bits for distributed online training compared with the uncompressed baseline.

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Multimodal Emotion Recognition Based on Multi-Scale Facial Features and Cross-Modal Attention 26

Multimodal Emotion Recognition Based on Multi-Scale Facial F...

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26th International Conference on Industrial technology, ICIT 2025

作者： Bao, Chengao Chen, Luefeng Li, Min Wu, Min Pedrycz, Witold Hirota, Kaoru School of Automation China University of Geosciences Wuhan430074 China School of Automation Engineering Research Center of Intelligent Technology for Geo-Exploration Ministry of Education China University of Geosciences Hubei Key Laboratory of Advanced Control and Intelligent Automation for Complex Systems Wuhan430074 China University of Alberta Department of Electrical and Computer Engineering EdmontonABT6R 2G7 Canada Tokyo Institute of Technology Yokohama226-8502 Japan Tokyo Institute of Technology Tokyo226-8502 Japan

ISBN: (纸本)9798331521950

A multi-modal emotion recognition method based on facial multi-scale features and cross-modal attention (MS-FCA) network is proposed. The MSFCA model improves the traditional single-branch ViT network into a two-branch ViT architecture by using classification tokens in each branch to interact with picture embeddings in the other branch, which facilitates effective interactions between different scales of information. Subsequently, audio features are extracted using ResNet18 network. The cross-modal attention mechanism is used to obtain the weight matrices between different modal features, making full use of inter-modal correlation and effectively fusing visual and audio features for more accurate emotion recognition. Two datasets are used for the experiments: eNTERFACE'05 and REDVESS dataset. The experimental results show that the accuracy of the proposed method on the eNTERFACE'05 and REDVESS datasets is 85.42% and 83.84% respectively, which proves the effectiveness of the proposed method. © 2025 IEEE.

关键词： Modal analysis

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Global topological synchronization of weighted simplicial complexes

arXiv

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arXiv 2024年

作者： Wang, Runyue Muolo, Riccardo Carletti, Timoteo Bianconi, Ginestra School of Mathematical Sciences Queen Mary University of London LondonE1 4NS United Kingdom Department of Systems and Control Engineering Tokyo Institute of Technology 2 Chome-12-1 Ookayama Meguro-ku Tokyo152-8552 Japan Department of Mathematics & naXys Namur Institute for Complex Systems University of Namur Rue Grafé 2 NamurB5000 Belgium The Alan Turing Institute The British Library LondonNW1 2DB United Kingdom

Higher-order networks are able to capture the many-body interactions present in complex systems and to unveil new fundamental phenomena revealing the rich interplay between topology, geometry, and dynamics. Simplicial complexes are higher-order networks that encode higher-order topology and dynamics of complex systems. Specifically, simplicial complexes can sustain topological signals, i.e., dynamical variables not only defined on nodes of the network but also on their edges, triangles, and so on. Topological signals can undergo collective phenomena such as synchronization, however, only some higher-order network topologies can sustain global synchronization of topological signals. Here we consider global topological synchronization of topological signals on weighted simplicial complexes. We demonstrate that topological signals can globally synchronize on weighted simplicial complexes, even if they are odd-dimensional, e.g., edge signals, overcoming thus a limitation of the unweighted case. These results thus demonstrate that weighted simplicial complexes are more advantageous for observing these collective phenomena than their unweighted counterpart. In particular, we present two weighted simplicial complexes the Weighted Triangulated Torus and the Weighted Waffle. We completely characterize their higher-order spectral properties and we demonstrate that, under suitable conditions on their weights, they can sustain global synchronization of edge signals. Our results are interpreted geometrically by showing, among the other results, that in some cases edge weights can be associated with the lengths of the sides of curved simplices. Copyright © 2024, The Authors. All rights reserved.

关键词： Synchronization

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Power Quality Management of Interconnected Microgrids using Model Predictive control ⁎

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IFAC-PapersOnLine 2020年第2期53卷 12918-12923页

作者： F. Garcia-Torres S. Vazquez C. Bordons I. Moreno-Garcia A. Gil P. Roncero-Sanchez Centro Nacional del Hidrogeno Puertollano (Ciudad Real) Spain Electronic Engineering Department Universidad de Sevilla Sevilla Spain Systems Engineering and Automatic Control Department Universidad de Sevilla Sevilla Spain Department of Electronic and Computer Engineering Universidad de Cordoba Cordoba Spain Systems Engineering and Automatic Control Department University of Castilla-La Mancha Ciudad Real Spain

In this paper, the power quality of interconnected microgrids is managed using a Model Predictive control (MPC) methodology which manipulates the power converters of the microgrids in order to achieve the requirements. The control algorithm is developed for the microgrids working modes: grid-connected, islanded and interconnected. The results and simulations are also applied to the transition between the different working modes. In order to show the potential of the control algorithm a comparison study is carried out with classical Proportional-Integral Pulse Width Modulation (PI-PWM) based controllers. The proposed control algorithm not only improves the transient response in comparison with classical methods but also shows an optimal behavior in all the working modes, minimizing the harmonics content in current and voltage even with the presence of non-balanced and non-harmonic-free three-phase voltage and current systems.

关键词： Interconnected systems Harmonics Predictive control Power System control

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Smart floor for a more comfortable and safer life 3

Smart floor for a more comfortable and safer life

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3rd International Young Engineers Forum, YEF-ECE 2019

作者： Haraburda, Karol Czygier, Jakub Recko, MacIej Department of Automatic Control and Robotics Faculty of Mechanical Engineering Bialystok University of Technology Bialystok Poland

ISBN: (纸本)9781538692820

The article presents the design of a SmartFloor to monitor pedestrians' movement patterns and collect data for Neural Network Analysis. The floor can be used in smart homes and environments to provide easy interaction with space's occupants. It is also used to determine the gender of the walkers useful during the calculation of attendance monitoring at events. Conducted research proved that the concept does indeed work and can be used for the use mentioned above cases. © 2019 IEEE.

关键词： Machine learning

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