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International Symposium for Design and Technology of Electronics Packages (SIITME)

作者： Marius-Alexandru Dobrea Stefan Bichiu Ioana Opris Mihaela Vasluianu Faculty of Automatic Control and Computer Science UPB Bucharest Romania Faculty of Power Engineering UPB Bucharest Romania Collective of Automation and Applied Informatics Faculty of Hidrotehnics UTCB Bucharest Romania

ISBN: (数字)9781728175065

ISBN: (纸本)9781728175072

This article aims to calculate the net income achieved by a prosumer for an existing On-Grid system of photovoltaic panels consisting of a set of 12 poly crystalline panels with a nominal power of 250 Wand 60 cells and an On-Grid inverter (StecaGrid model) with a maximum power of 3000 W. The prosumer is located in southern Romania, Ilfov County. Modeling of the photovoltaic panel in Matlab / SIMULINK was performed using a mathematical model, and it was compared with a real data set. Next, an analysis was made of the energy produced by the photovoltaic system, and the energy consumed by the prosumer, the excess energy being introduced in the national energy system. We have achieved a net income saving for March, so the analyzed photovoltaic system has energy efficiency that can significantly reduce electricity costs over time. Thus, using the Matlab / Simulink model, photovoltaic systems can be designed to satisfy the needs of prosumers.

关键词： Photovoltaic systems Mathematical model Solar power generation Software packages Inverters Temperature Temperature measurement

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EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control Framework for Exciting Efficient Oscillations in Mechanical Systems

EigenMPC: An Eigenmanifold-Inspired Model-Predictive Control...

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IEEE Conference on Decision and Control

作者： Andre Coelho Alin Albu-Schaeffer Arne Sachtler Hrishik Mishra Davide Bicego Christian Ott Antonio Franchi Institute of Robotics and Mechatronics of the German Aerospace Center (DLR) Oberpfaffenhofen Germany Dextrous Robotics Inc Memphis TN USA Robotics and Mechatronics Lab Faculty of Electrical Engineering Mathematics & Computer Science University of Twente Netherlands Department of Informatics Technical University of Munich Germany Automation and Control Institute (ACIN) Technical University of Vienna Austria LAAS-CNRS University of Toulouse France

ISBN: (数字)9781665467612

ISBN: (纸本)9781665467629

This paper proposes a Nonlinear Model-Predictive Control (NMPC) method capable of finding and converging to energy-efficient regular oscillations, which require no control action to be sustained. The approach builds up on the recently developed Eigenmanifold theory, which defines the sets of line-shaped oscillations of a robot as an invariant two-dimensional submanifold of its state space. By defining the control problem as a nonlinear program (NLP), the controller is able to deal with constraints in the state and control variables and be energy-efficient not only in its final trajectory but also during the convergence phase. An initial implementation of this approach is proposed, analyzed, and tested in simulation.

关键词： Analytical models Aerospace electronics Energy efficiency Trajectory Mechanical systems Oscillators Robots

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Psychoeducative Social Robots for an Healthier Lifestyle using Artificial Intelligence: a Case-Study

Psychoeducative Social Robots for an Healthier Lifestyle usi...

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2021 International Conference of Yearly Reports on Informatics, Mathematics and engineering, ICYRIME 2021

作者： Ponzi, Valerio Russo, Samuele Bianco, Valerio Napoli, Christian Wajda, Agata Department of Computer Automation and Management Engineering Sapienza University of Rome via Ariosto 25 Roma00185 Italy Department of Psychology Sapienza University of Rome via dei Marsi 78 Roma00185 Italy Department of Medical Surgical Sciences and Translational Medicine Sapienza University of Rome Via di Grottarossa 1035 Roma00189 Italy Department of Mathematics Applications and Methods for Artificial Intelligence Faculty of Applied Mathematics Silesian University of Technology Gliwice44-100 Poland

Smoking is the greatest preventable cause of mortality worldwide. In this paper, we present a social experiment where mobile robot equipped with a cigarette detector alert smokers, in particular those smoking close to children. In our research, we compare different methods. In the first case we trained the cigarette detection model using a homemade dataset based on the pre-trained SSD MobileNet detection model. In the second case we analyzed how smokingNet performs applied to our task. Next to distinguish between children and adults, we take advantage of the Cascade classifier and a neural network. Both networks are built to leverage TensorFlow Lite, a mobile-friendly format that enables inference on-device. When a smoking scene is identified, the mobile robot draws near the smoker and issues a warning based on the circumstances. © 2021 Copyright for this paper by its authors

关键词： Mobile robots

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Research on Person Recognition Model of Domain Adaptive Learning

Research on Person Recognition Model of Domain Adaptive Lear...

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第32届中国控制与决策会议

作者： Ming-hai Jiao Wei-ming Duan Jue Wang Ben-dong Luo Chi Zhang Xiang-yu Sun School of Computer Science and Engineering Northeastern University Faculty of Robot Science and Engineering Northeastern University College of Science Northeastern University School of Mechanical Engineering and Automation Northeastern University

The person recognition problem in different domain is a hard challenge on lower accuracy of object detection with massive image data, that is the gap between person target and background is small and hard to distinguish different posture of person target. The novel domain adaptive person detection model based on transfer data training is proposed for detecting data on new target domain. And domain adaptive loss function is designed to control the difference between the two models with self training, so as to avoid catastrophic forgetting in the source domain. And then the experiment results show that the improved deep self training domain adaptive learning model and loss function algorithm are effective and efficient for person recognition problem.

关键词： Person recognition Deep self training Domain adaptive learning Loss function

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Event-Triggered Sliding Mode Control Strategies for Positioning Systems: An Experimental Assessment

Event-Triggered Sliding Mode Control Strategies for Position...

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International Workshop on Advanced Motion Control (AMC)

作者： Andrej Sarjaš Martin Steinberger Dušan Gleich Martin Horn Faculty of Electrical Engineering and Computer Science University of Maribor Maribor Slovenia Graz University of Technology Institute of Automation and Control Graz Austria

ISBN: (数字)9781728131894

ISBN: (纸本)9781728131900

The paper presents experimental assessments of different event-triggered sliding mode control strategies. The main purpose of the event-triggered control approach is to reduce the system utilization and relax the scheduling of the tasks on real-time systems. Event triggering is a real-time implementation technique, where the execution of the controller is not fixed to a preselected sampling time. The stability and performance of the controlled system need to be preserved with regard to the sporadic nature of the controller update. Sliding mode controllers are employed in the context of event-triggering to achieve robustness with respect to matched perturbations. Three different sliding mode event-triggered strategies are evaluated on the real-time positioning system. All the obtained experimental results are compared to a time-triggered version. The results confirm that event-triggering sliding mode control is well suited and reduce the system use drastically.

关键词： Tracking Computational modeling Real-time systems Stability analysis Robustness Task analysis Sliding mode control

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Comparative evaluation of training schemes for the locally recurrent probabilistic neural network 28

Comparative evaluation of training schemes for the locally r...

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28th International Scientific Conference Electronics, ET 2019 - Proceedings

作者： Dukov, Nikolay Tinkov Ganchev, Todor Dimitrov Department of Computer Science and Engineering Faculty of Computing and Automation Technical University of Varna 1 Studentska str. Varna9000 Bulgaria

ISBN: (纸本)9781728125749

In the present study we evaluate the performance of various training schemes for the locally recurrent probabilistic neural network and seek for advantageous tradeoffs between required training time and classification accuracy. Specifically, we consider training schemes which make use of a simple incremental procedure for adjusting sigma, as well as methods based on particle swarm optimization or differential evolution in different configurations. The experimental evaluation was carried out in common experimental protocol based on the Parkinson speech dataset. The experimental results show that with a proper training configuration a high accuracy can be achieved even with limited training data. © 2019 IEEE.

关键词： Particle swarm optimization (PSO)

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Structural reduction of CRNs with linear sub-CRNs

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IFAC-PapersOnLine 2021年第14期54卷 149-154页

作者： Katalin M. Hangos György Lipták Gábor Szederkényi Systems and Control Laboratory Computer and Automation Research Institute P.O. Box 63 H-1518 Budapest Hungary Department of Electrical Engineering and Information Systems University of Pannonia Veszprém Hungary Faculty of Information Technology and Bionics Péter Pázmány Catholic University Budapest Hungary

A novel systems theory-based structural reduction method is proposed in this paper that can be applied to chemical reaction networks (CRNs) including subsystems consisting of linear reactions, i.e. a linear sub-CRN. The reduced model is a delayed CRN with distributed delays having less complexes (monomials) and reactions than the original model. The reduction is based on the fact that the input/output response of a linear sub-CNR can be seen as a time-delayed input to output relationship, therefore linear sub-CNRs can be interpreted as reactions with distributed delay from their input to their output complexes. The practically important example of a kinetic model describing the spread of the COVID-19 epidemic is used as a case study to illustrate the basic concepts and the computation method.

关键词： Delay Model reduction Chemical Reaction Networks COVID

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Stability of partially unstable discrete-time switched positive nonlinear systems with delays

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Applied Mathematics and Computation 2025年 507卷

作者： Jinyuan Ni Yin Sheng Qiang Xiao Zhigang Zeng Tingwen Huang School of Artificial Intelligence and Automation Huazhong University of Science and Technology Wuhan China the Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Wuhan China Faculty of Computer Science and Control Engineering Shenzhen University of Advanced Technology Shenzhen China

This paper addresses the stability issue of discrete-time switched positive nonlinear systems (SPNSs) characterized by partial unstable subsystems and constant delays. Firstly, we use a parameter to constrain the ratio of active durations of the stable and unstable modes, and derive the exponential stability conditions for SPNSs based on a key function. Then we analyze the relationship between convergence speed and the constant delay. Additionally, we obtain a stability criterion of SPNSs under a particular sequence of switching signals. Finally, we validate the results through a numerical example.

关键词： Exponential stability Switched positive nonlinear systems Time delay Unstable subsystems

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A Secure Healthcare 5.0 System Based on Blockchain Technology Entangled with Federated Learning Technique

arXiv

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arXiv 2022年

作者： Rehman, Abdur Abbas, Sagheer Khan, M.A. Ghazal, Taher M. Adnan, Khan Muhammad Mosavi, Amir School of Computer Science National College of Business Administration and Economics Lahore54000 Pakistan Riphah School of Computing and Innovation Faculty of Computing Riphah International university Pakistan School of Information Technology Skyline University College University City Sharjah Sharjah1797 United Arab Emirates Selangor Bangi43600 Malaysia Department of Software Gachon University Seongnam13120 Korea Republic of Institute of Information Engineering Automation and Mathematics Slovak University of Technology in Bratislava Bratislava81107 Slovakia John von Neumann Faculty of Informatics Obuda University Budapest1034 Hungary Faculty of Civil Engineering TU-Dresden Dresden01062 Germany

In recent years, the global Internet of Medical Things (IoMT) industry has evolved at a tremendous speed. Security and privacy are key concerns on the IoMT, owing to the huge scale and deployment of IoMT networks. Machine learning (ML) and blockchain (BC) technologies have significantly enhanced the capabilities and facilities of healthcare 5.0, spawning a new area known as "Smart Healthcare." By identifying concerns early, a smart healthcare system can help avoid long-term damage. This will enhance the quality of life for patients while reducing their stress and healthcare costs. The IoMT enables a range of functionalities in the field of information technology, one of which is smart and interactive health care. However, combining medical data into a single storage location to train a powerful machine learning model raises concerns about privacy, ownership, and compliance with greater concentration. Federated learning (FL) overcomes the preceding difficulties by utilizing a centralized aggregate server to disseminate a global learning model. Simultaneously, the local participant keeps control of patient information, assuring data confidentiality and security. This article conducts a comprehensive analysis of the findings on blockchain technology entangled with federated learning in healthcare. 5.0. The purpose of this study is to construct a secure health monitoring system in healthcare 5.0 by utilizing a blockchain technology and Intrusion Detection System (IDS) to detect any malicious activity in a healthcare network and enables physicians to monitor patients through medical sensors and take necessary measures periodically by predicting diseases. The proposed system demonstrates that the approach is optimized effectively for healthcare monitoring. In contrast, the proposed healthcare 5.0 system entangled with FL Approach achieves 93.22% accuracy for disease prediction, and the proposed RTS-DELM-based secure healthcare 5.0 system achieves 96.18% accuracy for the e

关键词： Blockchain

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Robust Iterative Learning Control for Spatially Interconnected Systems using 2D Control Theory

Robust Iterative Learning Control for Spatially Interconnect...

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European Control Conference (ECC)

作者： Bartłomiej Sulikowski Krzysztof Gałkowski Daniel Trzciński Eric Rogers Anton Kummert Institute of Control and Computation Engineering University of Zielona Góra Zielona Góra Poland Institute of Automation Electronic and Electrical Engineering University of Zielona Góra Zielona Góra Poland Department of Electronics and Computer Science University of Southampton Southampton UK Faculty of Electrical Information and Media Engineering University of Wuppertal Wuppertal Germany

This paper develops a class of iterative learning control (ILC) laws for a subclass of uncertain spatially interconnected systems. Model uncertainties appear both in the state and the output equation of the dynamics modeled in the 2D systems setting to which existing results are not applicable. The first stage is to write the dynamics in 2D model form. Then the stability theory for a distinct class of 2D systems known as repetitive processes is used to develop the ILC law in the case of differential dynamics. This analysis results in a design algorithm that can be applied using linear matrix inequalities.

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