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19th IEEE International Conference on Networking, Sensing and control, ICNSC 2022

作者： Bao, HanQiu Shi, XuDong An, Jing Li, HaoJun Kang, Qi Tongji University Department of Control Science and Engineering China Shanghai Institute of Intelligent Science and Technology Tongji University Shanghai China School of Electrical and Electronic Engineering Shanghai Institute of Technology Shanghai China College of Surveying and Geo-informatics Tongji University Shanghai China

ISBN: (纸本)9781665472432

This paper introduces a generic multi-agent cooperative control framework using finite-time distributed model predictive control, which applies to multiple types and heterogeneous multi-agent systems with directed topology. We design a detect and avoid collisions strategy using the prediction trajectory of agents, and the agents can safely move toward their goals. The formation time upper bound of multi-agents achieves consensus are derived with given connected communication topology. Numerical simulations validated the feasibility and effectiveness of the proposed approach. © 2022 IEEE.

关键词： Multi agent systems

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Multi-population particle swarm optimization concerning the worst information 3

Multi-population particle swarm optimization concerning the ...

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3rd International Conference on Computer Vision and Pattern Analysis, ICCPA 2023

作者： Li, Haiyan Chen, Yanbo Sun, Jiulin Li, Mi Department of Automation Faculty of Information Technology Beijing University of Technology Beijing100124 China Beijing International Collaboration Base on Brain Informatics and Wisdom Services Beijing100124 China Engineering Research Center of Intelligent Perception and Autonomous Control Ministry of Education Beijing100124 China Engineering Research Center of Digital Community Ministry of Education Beijing100124 China

ISBN: (纸本)9781510667563

Currently, the majority of multi-population algorithms ignore the worst particles and instead learn from the gbest or lbest. Hence, it is simple for these strategies to result in inadequate population variety, which causes early convergence. A multi-population particle swarm optimization considering the worst particles into account (CWMPSO) is suggested to demonstrate the importance of the worst particle information. In addition to considering the best particle, the CWMPSO algorithm concentrates on the worst particle globally. The algorithm divides the population into three subgroups, which learns from the gbest, gworst, and gbest + gworst, respectively. The development of the global worst particle information can be achieved by group learning, allowing for a more thorough exploration of potential optimum solutions and an avoidance of local functional ones. 30 complex CEC2017 benchmark functions were used in this work to test a range of algorithms including CWMPSO with various dimensions. The outcomes demonstrate that, in every dimension, the CWMPSO algorithm is better than other advanced PSOs. © 2023 SPIE.

关键词： Particle swarm optimization (PSO)

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Reducing the energy consumption required for the disassembly process implemented on an assembly and disassembly line 24

Reducing the energy consumption required for the disassembly...

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24th International Conference on control Systems and Computer Science, CSCS 2023

作者： Marius-Adrian, Paun Elena, Dragomir Otilia Florin, Dragomir Stelian, Iliescu Sergiu Eugenia, Minca Octavian Gabriel, Duca Valentin, Gurgu Ion University 'Politehnica' of Bucharest Department of Automation and Industrial Informatics Bucharest Romania Valahia University of Targoviste Faculty of Electrical Engineering Electronics and Information Technology Targoviste Romania University 'Politehnica ' of Bucharest Faculty of Automatic Control and Computers Bucharest Romania University 'Dunarea de Jos ' of Galati Department of Automation and Electrical Engineering Galati Romania Institute of Multidisciplinary Research for Science and Technology Valahia University of Targoviste Targoviste Romania

ISBN: (纸本)9798350313390

This paper proposes a method to reduce the energy consumption of an industrial disassembly process implemented on a production line by using an energy-efficient secondary disassembly process. In the case of defective products, the flow directs the simple product with priority to be disassembled in workstation (WS) 7. Therefore, all defective products are redirected to WS7. The disassembly of all simple products in WS7 is problematic for two reasons: the power consumption of WS7 is high compared to the other workstations, additionally there is an increased possibility of bottlenecks in the production line due to the high disassembly time. The proposed solution to reduce the possibility of deadlock and reduce power consumption is to redirect the defective products to the flexible cell (FC). Handling robots located in neighboring stations will participate collaboratively in the disassembly process. The control structure ensures that the two robots 'meet' in the boundary plane of WS3 with WS4 and disassemble each component through collaborative tasks. Collaborative control of disassembly is thought of as a solution to the global 'fluidization' of production, removing the bottleneck, from the overall flow, but also as an ingenious solution to reduce energy consumption. © 2023 IEEE.

关键词： Assembly

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Vehicle Classification in Intelligent Transportation Systems Using Deep Learning and Seismic Data 20

Vehicle Classification in Intelligent Transportation Systems...

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20th IEEE International Conference on Intelligence and Security informatics, ISI 2023

作者： Hashima, Sherief Saad, Mohamed H. Hatano, Kohei Rizk, Hamada Computational Learning Theory Team RIKEN-AIP Fukuoka819-0395 Japan Egyptian Atomic Energy Authority Engineering Dept Cairo13759 Egypt Egyptian Atomic Energy Authority Ncrrt Radiation Engineering Dept Cairo Egypt Kyushu University Department of Informatics Fukuoka819-0395 Japan Graduate School of Information Science and Technology Osaka University Suita565-0871 Japan Tanta University Computer and Control Engineering Department Tanta31733 Egypt

ISBN: (纸本)9798350337730

Intelligent transportation systems have become increasingly important for efficient traffic management and road safety. Vehicle classification is a fundamental task in these systems, enabling various applications such as traffic monitoring, congestion management, and accident prevention. Traditional methods for vehicle classification heavily rely on visual or sensor-based data, such as images or radar signals. However, these methods may encounter limitations in adverse weather conditions, poor lighting, or occlusion scenarios. To address these limitations, this paper introduces a novel approach for vehicle classification using seismic data, which captures the vibrations generated by vehicles and is less susceptible to environmental factors. The proposed approach leverages the fractional wavelet domain to extract both time and frequency signatures of vehicles from the seismic data effectively. These signatures are then used to train a recurrent neural network that captures the temporal features of the input, thereby facilitating vehicle classification. Additionally, the proposed approach incorporates various modules to improve the generalization and robustness of the deep model against noise. The implementation of the proposed approach on realistic data demonstrated its ability to classify vehicles with an accuracy of 98%, improving upon the state-of-the-art techniques. © 2023 IEEE.

关键词： Vehicles

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Effect of Dy2O3 on the phase formation and electrical properties of Tl-1223 HTS

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Fizika Nizkikh Temperatur 2025年第1期51卷 59-63页

作者： Metskhvarishvili, I.R. Menelaou, Melita Lobzhanidze, T.E. Dgebuadze, G.N. Surmanidze, D.L. Bendeliani, B.G. Gabunia, V.M. Metskhvarishvili, M.R. Jishiashvili, D.A. Ilia Vekua Sukhumi Institute of Physics and Technology Laboratory of Cryogenic Technique and Technologies Georgia Tbilisi0186 Georgia Georgian Technical University Faculty of Informatics and Control Systems Department of Microprocessor and Measurement Systems Tbilisi0175 Georgia Department of Mechanical Engineering and Materials Science and Engineering Cyprus University of Technology Limassol3036 Cyprus Ivane Javakhishvili Tbilisi State University Faculty of Exact and Natural Sciences Department of Chemistry Tbilisi0179 Georgia Petre Melikishvili Institute of Physical and Organic Chemistry the Iv. Javakhishvili Tbilisi State University Tbilisi0186 Georgia "Talga" Institute of Georgian Technical University Tbilisi0186 Georgia V. Chavchanidze Institute of Cybernetics the Georgian Technical University Tbilisi0186 Georgia

TlBa2Ca2Cu3DyxO8+δ superconductor samples (x = 0–0.075) were prepared by a two-step solid-state reaction technique to investigate the impact of Dy2O3 (dysprosium sesquioxide) addition on the formation and the transport properties of the Tl-1223 phase. The samples were analyzed using X-ray powder diffraction, ac susceptibility, and high-harmonics temperature dependence to study the effects of Dy2O3 on phase formation, superconducting transition temperature Tc, and the intergrain critical current density Jc. XRD results revealed that the prepared samples are nearly single-phases Tl-1223 with a tetragonal structure. Tc is not suppressed as Dy content increased, while Jc increased up to x = 0.025, then decreased with a further increase in x. Our findings show that the presence of dysprosium sesquioxide promotes the high-Tc phase and enhances the transport critical current densities. © I. R. Metskhvarishvili, Melita Menelaou, T. E. Lobzhanidze, G. N. Dgebuadze, D. L. Surmanidze, B. G. Bendeliani, V. M. Gabunia, M. R. Metskhvarishvili, and D. A. Jishiashvili, 2025.

关键词： X ray powder diffraction

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In-Silico Validation of Insulin Sensitivity Prediction by Neural Network-based Quantile Regression

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IFAC-PapersOnLine 2024年第24期58卷 368-373页

作者： Omer S. Alkhafaf Ameer B. Alsultani Alaa N. Roel Bálint Szabó Petra Pintár Ákos Szlávecz Béla Paláncz Katalin Kovács J. Geoffrey Chase Balázs Benyó Budapest Univ. of Tech. and Eco. Fac. of Electrical Eng. and Information Tech. Dept. of Control Eng. and Information Tech. Budapest Hungary Department of Mechanical Engineering University of Canterbury Christchurch New Zealand. Szechenyi István University Faculty of Informatics and Electrical Engineering Department of Informatics Gyor Hungary Faculty of Dentistry University of Mosul Mosul Iraq

High blood glucose levels and stress-induced hyperglycemia are common issues in intensive care units (ICU). controlling blood glucose levels is crucial but challenging due to patient-specific variability. This challenge was addressed by developing model-based control protocols, which rely on identifying and predicting the patient-specific metabolic state. This study presents the in-silico simulation-based evaluation of a new artificial neural network-based insulin sensitivity (SI) prediction method. The models were trained on a dataset collected during clinical treatment using the stochastic-targeted (STAR) protocol and evaluated by simulating the clinical interventions on virtual patients created from retrospective clinical data. The results show the new models could be safely applied for SI prediction. Furthermore, the adopted method had very similar accuracy in the overall comparison of cohorts, with only minor differences noted in hypoglycemia events.

关键词： Quantile Regression Insulin Sensitivity Glycaemic control Intensive Care Unit In-silico patients

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Computing Challenges of UAV Networks: A Comprehensive Survey

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Computers, Materials & Continua 2024年第11期81卷 1999-2051页

作者： Altaf Hussain Shuaiyong Li Tariq Hussain Xianxuan Lin Farman Ali Ahmad Ali AlZubi School of Computer Science&Technology Chongqing University of Posts and TelecommunicationsChongqing400065China Key Laboratory of Industrial Internet of Things and Networked Control Ministry of EducationChongqing University of Posts and TelecommunicationsChongqing400065China School of Computer Science and Technology Zhejiang Gongshang UniversityHangzhou310018China School of Artificial Intelligence Nanjing University of Information Science&TechnologyNanjing210044China Department of Computer Science and Engineering School of ConvergenceCollege of Computing and InformaticsSungkyunkwan UniversitySeoul03063Republic of Korea Department of Computer Science Community CollegeKing Saud UniversityRiyadh11495Saudi Arabia

Devices and networks constantly upgrade,leading to rapid technological ***-dimensional(3D)point cloud transmission plays a crucial role in aerial computing terminology,facilitating information *** network types,including sensor networks and 5G mobile networks,support this ***,Flying Ad hoc Networks(FANETs)utilize Unmanned Aerial Vehicles(UAVs)as nodes,operating in a 3D environment with Six Degrees of Freedom(6DoF).This study comprehensively surveys UAV networks,focusing on models for Light Detection and Ranging(LiDAR)3D point cloud compression/*** topics covered include autonomous navigation,challenges in video streaming infrastructure,motivations for Quality of Experience(QoE)enhancement,and avenues for future ***,the paper conducts an extensive review of UAVs,encompassing current wireless technologies,applications across various sectors,routing protocols,design considerations,security measures,blockchain applications in UAVs,contributions to healthcare systems,and integration with the Internet of Things(IoT),Artificial Intelligence(AI),Machine Learning(ML),and Deep Learning(DL).Furthermore,the paper thoroughly discusses the core contributions of LiDAR 3D point clouds in UAV systems and their future prediction along with mobility *** also explores the prospects of UAV systems and presents state-of-the-art solutions.

关键词： Autonomous vehicles UAV systems UAV transmission UAV in healthcare future of UAV system security measures

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Application of mixed graph traversal optimization for the vehicle routing problem

Application of mixed graph traversal optimization for the ve...

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European control Conference (ECC)

作者： Lá szló Kocsá ny Emese Gincsainé Szá deczky-Kardoss Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (数字)9783907144077

ISBN: (纸本)9781665497336

The Vehicle routing problem (VRP) is widely discussed in the literature. The goal of the VRP is to provide an optimized traversal of a graph. This paper presents a mixed graph traversal optimization for the VRP. The mixed graph is a graph that contains both directed and undirected edges. The problem to be solved is a parking space search problem in a mixed graph where, from an initial location a vehicle first navigates to a zone, where the selected edges are located, and plans the traversal driving through all selected edges, and returning to the start of the exploration so that the path is repeatable. The paper shows, that the parking space search problem in a mixed graph can be reduced to a travelling salesman problem (TSP), for which an ant colony optimization (ACO) is implemented, to provide a solution, that is minimizing the total cost of the mixed graph traversal.

关键词： Space vehicles Costs Navigation Roads Vehicle routing Europe Directed graphs

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Prediction of the Navigation Angles Using Random Forest Algorithm and Real Flight Data of UAVs

Prediction of the Navigation Angles Using Random Forest Algo...

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International Symposium on Intelligent Systems and informatics (SISY)

作者： Huda Naji Al-sudany Béla Lantos Department of Control Engineering and Information Technology Faculty of Electrical Engineering and Informatics Budapest University of Technology and Economics Budapest Hungary

ISBN: (纸本)9781665489898

Unmanned aerial vehicles (UAVs) are becoming more common in aviation. Predicting and estimating of UAV attitude are important aspects of the communication strategy. Autonomous UAVs' attitude estimate is a crucial component for controlling in flight mission. The attitude can take many forms such as Euler angles and Quaternion. This paper explains attitude estimation of UAV using Random Forest (RF) algorithm based on real flight data of Accelerations, Angular velocity, Magnetic Field, and GPS measurements for prediction current attitude angles (roll, pitch and yaw). The developed algorithm uses features that are derives from sensors measurements. The developed RF model results showed accurate prediction of UAVs attitude angles with small error values.

关键词： Radio frequency Magnetic field measurement Current measurement Sensor phenomena and characterization Autonomous aerial vehicles Prediction algorithms Angular velocity

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Fractional Order vs Integer Order Transfer Function Models of a Pendulum

Fractional Order vs Integer Order Transfer Function Models o...

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International Conference on Automation, AUTOMATION 2021

作者： Oprzędkiewicz, Krzysztof Rosól, Maciej Żegleń-Wlodarczyk, Jakub Department of Automatic Control and Robotics Faculty of Electrotechnics Automatic Control Informatics and Biomedical Engineering AGH University of Science and Technology al. A Mickiewicza 30 Krakow30-059 Poland

ISBN: (纸本)9783030748920

The paper presents the comparison of fractional order to integer order transfer function models of physical pendulum. The considered transfer functions describe the behaviour of the system around the lower, stable equilibrium point. Fractional order models are built using ORA approximation. Models were verified using real experimental data. Results of tests show that the fractional order transfer function can correctly describe the physical pendulum. Integer order models are a little bit more accurate, but they require more parameters to identify. © 2021, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Linear systems

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