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Recent Developments in Event-Triggered Control of Nonlinear systems:An Overview

无人系统（英文） 2023年第1期11卷 27-56页

作者： Pengpeng Zhang Tengfei Liu Jie Chen Zhong-Ping Jiang Department of Control Science & Engineering Frontiers Science Center for Intelligent Autonomous SystemsTongji UniversityShanghai 201804P.R.China State Key Laboratory of Synthetical Automation for Process Industries Northeastern UniversityShenyang 110819P.R.China Department of Electrical and Computer Engineering New York University370 Jay StreetBrooklynNY 11201USA

Event-triggered control is a most popular paradigm for transferring feedback information in an economical"as needed"*** study of event-triggered control can be traced back to the *** significant advances on the topic of control over networks and the topic of nonlinear control systems over the last two decades,event-triggered control has quickly emerged as a major theoretical subject in control *** of event-triggered control are wide-spread ranging from embedded control systems and industrial control processes to unmanned systems and cyber-physical transportation *** this paper,we first review developments in the synthesis of event-triggered sampling *** event triggering mechanisms,such as static event trigger,dynamic event trigger,time-regularized event trigger,and event trigger with positive threshold offsets,are systematically ***,we study how to design a stabilizing controller that is robust with respect to the sampling ***,we review some recent results in the directions of self-triggered control,event-triggered tracking control and cooperative control,and event-triggered control of stochastic systems and partial differential equation *** applications of event-triggered control are also discussed.

关键词： Event-triggered control event-triggered sampling mechanism event-triggered controller

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Multi-Level Deep Generative Adversarial Networks for Brain Tumor Classification on Magnetic Resonance Images

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Intelligent Automation & Soft Computing 2023年第4期36卷 127-143页

作者： Abdullah A.Asiri Ahmad Shaf Tariq Ali Muhammad Aamir Ali Usman Muhammad Irfan Hassan A.Alshamrani Khlood M.Mehdar Osama M.Alshehri Samar M.Alqhtani Radiological Sciences Department College of Applied Medical SciencesNajran UniversityNajran61441Saudi Arabia Department of Computer Science COMSATS University IslamabadSahiwal CampusSahiwal57000Pakistan Electrical Engineering Department College of EngineeringNajran UniversityNajran61441Saudi Arabia Anatomy Department Medicine CollegeNajran UniversityNajranSaudi Arabia Department of Clinical Laboratory Sciences Collage of Applied Medical ScienceNajran UniversityNajranSaudi Arabia Department of Information Systems College of Computer Science and Information SystemsNajran UniversityNajran61441Saudi Arabia

The brain tumor is an abnormal and hysterical growth of brain tissues,and the leading cause of death affected patients *** in this technol-ogy-based arena,brain tumor images with proper labeling and acquisition still have a problem with the accurate and reliable generation of realistic images of brain tumors that are completely different from the original *** artiﬁcially created medical image data would help improve the learning ability of physicians and other computer-aided systems for the generation of augmented *** over-come the highlighted issue,a Generative Adversarial Network(GAN)deep learn-ing technique in which two neural networks compete to become more accurate in creating artiﬁcially realistic data for MRI *** GAN network contains mainly two parts known as generator and ***,a generator is the convolutional neural network,and a discriminator is the deconvolutional neural *** this research,the publicly accessible Contrast-Enhanced Mag-netic Resonance Imaging(CE-MRI)dataset collected from 2005-to 2020 from different hospitals in China consists of four classes has been *** proposed method is simple and achieved an accuracy of 96%.We compare our technique results with the existing results,indicating that our proposed technique outper-forms the best results associated with the existing methods.

关键词： GAN network CE-MRI images convolutional neural network brain tumor classiﬁcation

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Derivation of Aggregated Equivalent Models for the Steady-State Analysis of Active Distribution Networks Operated Under Voltage Control Schemes

SSRN

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SSRN 2024年

作者： Pasiopoulou, Ioanna D. Kontis, Eleftherios O. Papadopoulos, Theofilos A. Papagiannis, Grigoris K. Power Systems Laboratory School of Electrical and Computer Engineering Aristotle University of Thessaloniki GR Thessaloniki54124 Greece Department of Electrical and Computer Engineering University of Thessaly Volos Greece Power Systems Laboratory Dept. of Electrical and Computer Engineering Democritus University of Thrace XanthiGR 67100 Greece

To facilitate the coordination between transmission and distribution system operators, accurate equivalent models of power system components are required. Towards this objective, in this paper, a blackbox model is proposed for developing steady-state reduced-order equivalents of active distribution networks (ADNs) that host converter-interfaced renewable energy sources (CI-RES) and operate under various voltage control schemes. Droop-based and traditional voltage control schemes, such as on-load tap changer (OLTC) control strategies and the use of shunt capacitors, are considered. The performance of the proposed model is evaluated via simulations considering various load types and CI-RES penetration levels. Comparisons against the conventional polynomial and exponential models are also conducted, demonstrating the superior performance of the proposed black-box model. © 2024, The Authors. All rights reserved.

关键词： Electric power distribution

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NS3-Based Communication Network Modeling for Multi-Microgrid systems: Design and Evaluation

NS3-Based Communication Network Modeling for Multi-Microgrid...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ola Ali Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

This paper presents a communication emulation model for enhancing the efficiency of multi-microgrid (MMG) networks, employing Network Simulator 3 (ns3). MMG systems require robust communication frameworks for coordination, particularly under varying traffic conditions. The introduced model accommodates multiple local area networks (LANs) that support efficient communication across multiple autonomous AC microgrids. The developed model facilitates seamless bidirectional information exchange between the microgrids' local controllers and the MMG control center (MMGCC), employing TCP/IP protocol to ensure high data transmission integrity and low latency. A detailed design in C++ is demonstrated, including the configuration of the communication nodes, topology, communication links, and quality of service (QoS) analysis. The network performance is rigorously assessed across low and high traffic conditions, with performance metrics such as throughput, delay, and percentage packet loss at each node. Results confirm the effectiveness of the introduced model in sustaining minimal packet loss, high throughput, and low delays under dynamic operating scenarios. These findings establish the developed model as a viable solution for meeting the real-time communication requirements of MMG energy management systems.

关键词： Emulation Packet loss TCPIP Quality of service Microgrids C++ languages Throughput Real-time systems Delays Load modeling

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Resilient Operation of DC Standalone Microgrids: Experimental Validation of Hybrid RNN-PI Control for Pulse Load Management

Resilient Operation of DC Standalone Microgrids: Experimenta...

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IEEE Green Technologies Conference (IEEE-Green)

作者： Ahmed Aghmadi Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami FL USA

ISBN: (数字)9798331520182

ISBN: (纸本)9798331520199

Effective energy storage system (ESS) management is critical for enhancing the performance of standalone DC microgrids, particularly when integrating renewable energy sources such as photovoltaic (PV) or wind systems. This research describes a new hybrid control technique to improve DC microgrid operations' efficiency and resiliency, especially when handling critical loads. This research presents a hybrid closed-loop control technique that uses Recurrent Neural Networks (RNNs) and Proportional-Integral (PI) controllers to address the complex issues of dynamic load situations and transient disturbances. The RNN evaluates voltage errors to generate accurate reference currents, which the PI controllers then adjust to improve the performance of the bidirectional converter. Unlike standard PI-PI controls, our hybrid approach is particularly effective at regulating pulsed power loads (PPLs) and enhancing system response to transitory situations. The experimental results show that the RNN-PI control scheme significantly improves voltage regulation and transient responsiveness, exceeding conventional performance and resilience techniques. This approach provides a convincing solution for enhancing microgrid systems' stability and efficiency in various real-world applications.

关键词： Renewable energy sources Recurrent neural networks PI control Voltage fluctuations Microgrids Robustness Voltage control Transient analysis Standards Resilience

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Large-Scale Radiomics Analysis for Prostate Cancer Detection Harnessing Machine and Deep Learning Models 22

Large-Scale Radiomics Analysis for Prostate Cancer Detection...

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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Mylona, Eugenia Zaridis, Dimitrios I. Kalantzopoulos, Charalampos N. Pezoulas, Vasileios C. Tachos, Nikolaos S. Matsopoulos, George K. Regge, Daniele Papanikolaou, Nikolaos Tsiknakis, Manolis Marias, Kostas Fotiadis, Dimitrios I. Biomedical Research Institute Forth IoanninaGR 45110 Greece University of Ioannina Unit of Medical Technology Intelligent Information Systems Greece School of Electrical & Computer Engineering National Technical University of Athens Biomedical Engineering Laboratory Athens15780 Greece Candiolo Cancer Institute FPO-IRCCS Department of Radiology Strada Provinciale Candiolo Turin10060 Italy Champalimaud Foundation Computational Clinical Imaging Group Portugal Institute of Computer Science Forth Computational Biomedicine Laboratory HeraklionGR 70013 Greece Hellenic Mediterranean University Department of Electrical and Computer Engineering HeraklionGR 71004 Greece

ISBN: (纸本)9798331520526

Prostate cancer (PCa) is the second most common cancer in men, making early detection crucial for effective treatment. This study explores the efficacy of machine learning (ML) and deep learning (DL) models in detecting PCa lesions using radiomics features derived from multiparametric MRI data. Leveraging a dataset of 7621 retrospective and 2132 prospective cases from 12 clinical centers across Europe, we compared the performance of various ML (LASSO, Random Forest, SVM, XGBoost) and DL models (CNN, MLP, Transformer, CNN&MLP). Radiomic features were extracted and refined through a robust feature selection process. Models' performance was evaluated through nested cross-validation (CV) and externally. The CNN&MLP model achieved the highest ROC AUC (0.86) and PR AUC (0.90) in CV, while LASSO showed consistent generalization in external validation. Our findings indicate that DL models excel in high-dimensional data analysis but face challenges in generalizing to external datasets, highlighting the importance of external validation for clinical applicability. © 2025 IEEE.

关键词： Deep learning

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Optimized Design of Sub-Harmonic Synchronous Machines

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IEEE Transactions on Magnetics 2025年

作者： Sajjad Hossain Rafin, M. Ali, Qasim Hussein, Hossam Mohammed, Osama A. Florida International University Energy Systems Research Laboratory Department of Electrical and Computer Engineering MiamiFL33174 United States Sukkur IBA University Department of Electrical Engineering Sukkur Pakistan

This paper presents an optimized design approach for sub-harmonic synchronous machines (SHSMs) to minimize torque ripple and improve performance for high-power electric vehicle applications. The proposed design incorporates a novel rotor harmonic winding configuration and utilizes advanced skewing techniques to achieve optimal spatial distribution of magnetic forces. Using Ansys Maxwell software, finite element analysis (FEA) was conducted on 8-pole, 48-slot SHSMs with a rated output of 1.3 kW and 900 rpm. Compared to earlier designs, the optimized 2-layer and 3-layer SHSMs demonstrated significant reductions in torque ripple—83.94% and 86.84%, respectively. This improvement is attributed to the uniform distribution of harmonic windings and carefully calibrated rotor skewing, both of which enhance spatial efficiency and magnetic field uniformity across the rotor. These configurations achieve lower torque ripple and maintain high efficiency and competitive average torque output, making them suitable alternatives to conventional wound rotor synchronous machines (WRSMs) and permanent magnet synchronous motors (PMSMs). The findings validate the potential of the optimized SHSMs as high-performance, brushless solutions for electric vehicle propulsion, contributing to advancements in synchronous machine design and sustainable transportation technologies. © 1965-2012 IEEE.

关键词： Permanent magnets

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A Hybrid Adaptive Filter for Head Tracking in Augmented Reality (AR)-based Flight Simulators

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IEEE Transactions on computers 2025年

作者： Nwobodo, Onyeka J. Kuaban, Godlove Suila Nkemeni, Valery Wereszczynski, Kamil Cyran, Krzysztof A. Silesian University of Technology Department of Computer Graphics Vision and Digital Systems Faculty of Automatic Control Electronics and Computer Science Gliwice Poland Polish Academy of Sciences Institute of Theoretical and Applied Informatics Poland University of Buea Laboratory of Electrical Engineering and Computing Faculty of Engineering and Technology Buea Cameroon

This paper presents a novel approach for head tracking in augmented reality (AR) flight simulators using an adaptive fusion of Kalman and particle filters. This fusion dynamically balances the strengths of both algorithms, leveraging Kalman filters for computational efficiency and particle filters for handling non-linearities based on real-time factors such as sensor noise and head movement patterns. Our method demonstrates superior tracking precision and reduced latency, making it particularly effective for immersive pilot training in AR-based flight simulations. While focused on flight simulation, our approach holds high potential for broader applications in other AR and virtual reality (VR) environments where precise, real-time head tracking is crucial. These results provide actionable design guidance for developers optimizing tracking systems in environments that require fast response times and high accuracy. Future extensions of this research could explore the generalization of our approach across diverse AR applications, including human-computer interaction (HCI), medical simulations, and gaming. © 1968-2012 IEEE.

关键词： Flight simulators

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Design of Wave Scattering From Gain and Loss Disordered Materials

Design of Wave Scattering From Gain and Loss Disordered Mate...

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Pacific Rim Conference on Lasers and Electro-Optics (CLEO/Pacific Rim)

作者： Ikbeom Lee Kunwoo Park Sunkyu Yu Department of Electrical and Computer Engineering Intelligent Wave Systems Laboratory Seoul National University Seoul Korea

We propose the design of scattering from gain and loss materials using the structure factor. Increased degrees of freedom in the structure factor allow the suppression across the target momentum space.

ISBN: (数字)9798350372076

ISBN: (纸本)9798350372083

关键词： Scattering Lasers and electrooptics Electro-optical waveguides

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LSTM-Based Digital Twin Modeling for LVDC Microgrid Operation with Pulsed Load

LSTM-Based Digital Twin Modeling for LVDC Microgrid Operatio...

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IEEE Industry Applications Society Annual Meeting (IAS)

作者： Mahmoud S. Abdelrahman Mustafa Esoofally Hossam Hussein Ibtissam Kharchouf Osama A. Mohammed Department of Electrical and Computer Engineering Energy Systems Research Laboratory Florida International University Miami USA

ISBN: (数字)9798350372717

ISBN: (纸本)9798350372724

Proper control and energy management is crucial for the effective, practical, and reliable operation of DC microgrids. In DC microgrids, the high certainties of renewables and the dynamic behavior of particular loads propose different modeling, operation, and control challenges. With recent advancements in information and communication technologies offering energy solution services relying on data analytics, machine learning (ML), and IoT technologies, the concept of digital twins (DT) has emerged as a feasible and active model to enhance the operation and resilience of microgrids. This paper proposes a long short-term memory (LSTM) neural networkbased strategy to construct a data-driven digital twin (DT) model of DC microgrids. The model is tested and validated with a standalone DC MG containing Renewable Energy Sources (RES), Energy Storage Sources (ESSs), and different load types. The accuracy of the created twin model is tested and evaluated under different scenarios of the microgrid operation, such as a sudden decrease in renewable generation and energizing pulsed load to the MG.

关键词： Renewable energy sources Accuracy Simulation Microgrids Real-time systems Digital twins Reliability Long short term memory Load modeling Resilience

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