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International Conference on Electrical engineering and Informatics, ICEEI

作者： S. Golestan M. Monfared J. M. Guerrero Department of Electrical Engineering Islamic Azad University Abadan Iran Department of Electrical Engineering Ferdowsi University of Mashhad Mashhad Iran Department of Automatic Control Systems Computer Engineering Universitat Politècnica de Catalunya Barcelona Spain

A synchronous reference frame controller with a multi-resonant harmonic compensator for single-phase grid-connected inverters is proposed in this paper. The essence of the proposed control strategy is to use a synchronous reference frame PI controller (SRFPI) to regulate the output current with zero steady state error, together with a multi-resonant harmonic compensator to provide an efficient attenuation of the grid voltage background harmonic distortion, and an average power controller to regulate the active and reactive powers. A detailed design procedure for the proposed control strategy is presented based on a frequency response approach. Extensive simulations confirm the validity of the proposed strategy.

关键词： Harmonic analysis Inverters Voltage control Steady-state Regulators Reactive power Power system stability

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design of switching state-feedback controllers for linear systems subject to asymmetric saturations *

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IFAC-PapersOnLine 2023年第2期56卷 3265-3270页

作者： Adrián Ruiz Damiano Rotondo Bernardo Morcego Research Center for Supervision Safety and Automatic Control Universitat Politècnica de Catalunya (UPC) Rambla Sant Nebridi 22 08222 Terrassa Spain Department of Electrical and Computer Engineering (IDE) University of Stavanger (UiS) Kristine Bonnevies vei 22 4021 Stavanger Norway

This paper considers the problem of controlling a linear system affected by asymmetrical input saturation. The proposed solution is based on using a linear matrix inequality (LMI)-based methodology to find the gains of a switching state-feedback controller. The main difference and contribution when compared to existing approaches is that the switching rule is chosen based on the closed-loop performance that each of the non-saturating controller gains can achieve when used with the current value of the state vector. Although the main focus of the paper is on time-invariant systems, the possible extension to linear parameter-varying (LPV) systems is discussed. An illustrative example is used to show the main features of the proposed approach.

关键词： Linear systems Saturation Linear matrix inequalities (LMIs)

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Observer Attack on Stream Ciphers

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IFAC-PapersOnLine 2022年第30期55卷 13-18页

作者： Ramachandran Anantharaman Virendra Sule Research Center for Supervision Safety and Automatic Control Universitat Politècnica de Catalunya (UPC) Rambla Sant Nebridi 22 08222 Terrassa Spain Department of Electrical and Computer Engineering (IDE) University of Stavanger (UiS) Kristine Bonnevies vei 22 4021 Stavanger Norway

This paper proposes an application of a new observer theory for non-linear systems developed previously to solve the Cryptanalysis problem of a special class of pseudorandom generators which are commonly used in Cryptography. The Cryptanalysis problem addressed here is that of the recovery of internal state of the non-linear dynamic stream generator from the output stream. The proposed methodology is termed as observability attack. It is also shown that for a special class of generators, the computations are of complexity O (D 4 ) in pre-computation and of O(D) for online computation, where D  = σ d i=0 (n i) for this class of stream generators with n states and d the degree of the output function. The attack is technically applicable over general finite fields as well as most dynamic systems arising from models of stream ciphers and appropriate bounds on computation are estimated. From these complexity bounds, it follows that this attack is feasible in realistic cases and gives important estimates of time and memory resources required for Cryptanalysis of a class of stream ciphers.

关键词： Stream ciphers Cryptanalysis Boolean Dynamical Systems Observer design Koopman Operator

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Simple feedback linearizing controller to reduce audiosusceptibility and load disturbance in the full-bridge current doubler synchronous rectifier

Simple feedback linearizing controller to reduce audiosuscep...

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Annual IEEE Conference on Applied Power Electronics Conference and Exposition (APEC)

作者： J.M. Guerrero L.G. de Vicuna N. Berbel J. Sosa M. Castilla Department of Automatic Control Systems and Computer Engineering Universidad de Los Andes Merida Venezuela Department of Automatic Control Systems and Computer Engineering Sustainable Distributed Generation Systems and Renewable Energy Group Tècnica Industrial de Barcelona Barcelona Spain Department of Electronic Engineering Universitat Politècnica de Catalunya Spain Departamento: Laboratorio de Instrumentación Científica. Universidad de Los Andes Mérida Venezuela

The full-bridge converter with current-doublet synchronous rectifier (FB-CDSR) is a promising topology to implement low-voltage high-current power supplies. This paper proposes a novel control for this converter, which provides the following features: fast transient response, fixed switching frequency, and high robustness against large-signal disturbances in the input-voltage and the load-current. Furthermore, this controller avoids the use of any current sensor and can be implemented by means of cheap analog circuits, which highlights its low-cost and simplicity. The parallel operation of several HB-CDSR is also discussed Finally, experimental results are provided in order to show the validity of the proposed control technique

关键词： Linear feedback control systems Rectifiers Topology Zero voltage switching Power supplies Switching converters Bridge circuits Power engineering and energy Transient response Robustness

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IARS SegNet: Interpretable Attention Residual Skip connection SegNet for melanoma segmentation

arXiv

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arXiv 2023年

作者： Narayanan, Shankara Sikha, O.K. Benitez, Raul Department of Computer Science and Engineering Amrita School of Computing Amrita Vishwa Vidyapeetham Coimbatore641112 India Department of Automatic Control Universitat Politècnica de Catalunya Av. d’Eduard Maristany Barcelona08019 Spain Barcelona08034 Spain

Skin lesion segmentation plays a crucial role in the computer-aided diagnosis of melanoma. deep Learning models have shown promise in accurately segmenting skin lesions, but their widespread adoption in real-life clinical settings is hindered by their inherent black-box nature. In domains as critical as healthcare, interpretability is not merely a feature but a fundamental requirement for model adoption. This paper proposes IARS SegNet an advanced segmentation framework built upon the SegNet baseline model. Our approach incorporates three critical components: Skip connections, residual convolutions, and a global attention mechanism onto the baseline Segnet architecture. These elements play a pivotal role in accentuating the significance of clinically relevant regions, particularly the contours of skin lesions. The inclusion of skip connections enhances the model’s capacity to learn intricate contour details, while the use of residual convolutions allows for the construction of a deeper model while preserving essential image features. The global attention mechanism further contributes by extracting refined feature maps from each convolutional and deconvolutional block, thereby elevating the model’s interpretability. This enhancement highlights critical regions, fosters better understanding, and leads to more accurate skin lesion segmentation for melanoma diagnosis. This study primarily focuses on the interpretation of performance improvements in the base model resulting from the integration of each of these three components. To comprehensively assess the performance gain achieved with each addition, we employ two sets of evaluation metrics, quantifying performance based on both regions and contours. The results underscore the superior segmentation capabilities of the proposed architecture compared to the SegNet and U-Net models. Notably, it provides interpretable results, particularly when applied to the PH2 dataset. Copyright © 2023, The Authors. All rights reserved

关键词： Convolution

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Simple feedback linearizing controller to reduce audiosusceptibility and load disturbance in the full-bridge current doubler synchronous rectifier

Conference Proceedings - IEEE Applied Power Electronics Conf...

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Conference Proceedings - IEEE Applied Power Electronics Conference and Exposition - APEC 2005年 2卷 689-694页

作者： Guerrero, Josep M. García de Vicuña, Luis Berbel, Nestor Sosa, Jorge Castilla, Miguel Sustainable Distributed Generation Systems and Renewable Energy Group Escola Universitària d'Enginyeria Tècnica Industrial de Barcelona C/Comte d'Urgell 187. 08036 - Barcelona Spain Department of Automatic Control Systems and Computer Engineering Department of Electronic Engineering Universitat Politècnica de Catalunya Spain Departamento: Laboratorio de Instrumentación Científica Universidad de Los Andes Mérida Venezuela

ISBN: (纸本)0780389751

The full-bridge converter with current-doubter synchronous rectifier (FB-CDSR) is a promising topology to implement low-voltage high-current power supplies. This paper proposes a novel control for this converter, which provides the following features: fast transient response, fixed switching frequency, and high robustness against large-signal disturbances in the input-voltage and the load-current. Furthermore, this controller avoids the use of any current sensor and can be implemented by means of cheap analog circuits, which highlights its low-cost and simplicity. The parallel operation of several HB-CDSR is also discussed Finally, experimental results are provided in order to show the validity of the proposed control technique. ©2005 IEEE.

关键词： Electric rectifiers

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Semi-supervised active transfer learning for fetal ECG arrhythmia detection

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computer Methods and Programs in Biomedicine Update 2023年 3卷

作者： Mohebbian, Mohammad Reza Marateb, Hamid Reza Wahid, Khan A. Department of Electrical and Computer Engineering University of Saskatchewan Saskatoon S7N 5A9 SK Canada Biomedical Engineering Department Engineering Faculty University of Isfahan Isfahan Iran Biomedical Engineering Research Centre (CREB) Automatic Control Department (ESAII) Universitat Politècnica de Catalunya-Barcelona Tech (UPC) Barcelona Spain

deep learning has demonstrated excellent results for ECG anomaly detection, wherein most approaches used supervised learning. The requirement of thousands of manually annotated samples is a concern for state-of-the-art anomaly detection systems, especially for fetal ECG (FECG), and currently, there is not a publicly available FECG dataset annotated for each FECG beat. In this paper, we offer a modified active learning technique based on transfer learning, calibration probability, and autoencoder-based sampling to reduce number of samples requires to annotate. In this regard, we used 25,000 s of recording from 47 patients from the MIT-BIH Arrhythmia Database to train a deep learning model to detect anomalies in non-fetus subjects. Then we used the unlabeled Non-Invasive Fetal ECG Arrhythmia Database (NIFEA DB) of 26 subjects to fine-tune the trained model to fine-tune the trained model based on active learning to detect anomalies in binary form for fetal. A variational autoencoder is trained on all data (adult and fetal ECG), and clustering is applied to latent features extracted from data after dimension reduction. Then, the sampling process of active learning selected samples from different clusters with low confidence to cover all data distribution. Moreover, a probability calibration based on mc-dropout and isotonic regression is used to calibrate confidences, helping to select reliable low-confidence samples. Various ablation studies were performed to show the influence of autoencoder-based sampling, calibration, and transfer learning, which showed that the proposed method could achieve 92% accuracy using 399 training samples. In contrast, other methods required more training samples to reach the same level of accuracy without calibration or an autoencoder and clustering approach or training without active learning. The study also found that transfer learning significantly impacted faster convergence and that the proposed active learning approach was more effect

关键词： Active learning Anomaly Arrhythmia FECG Transfer learning

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Guest Editorial

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IEEE Transactions on Industrial Electronics 2011年第4期58卷 1074-1080页

作者： Marco Liserre Josep Balcells Thomas Basso Jan T. Bialasiewicz Carlo Cecati Sudipta Chakraborty Josep M. Guerrero Mehrdad Kazerani Friederich Kupzog Adel Nasiri Peter Palensky José Rodriguez Pedro Rodríguez Thilo Sauter Remus Teodorescu Polytechnic University of Bari Bari Italy Universitat Politéchnica de Catalunya Barcelona Spain National Renewable Energy Laboratory (NREL) Golden CO USA Electrical Engineering Department University of Colorado Denver CO USA Department of Electrical and Information Engineering University of L'Aquila L'Aquila Italy National Renewwable Energy Laboratory (NREL) Golden CO USA Department of Automatic Control Systems and Computer Engineering Technical University of Catalonia Barcelona Spain Department of Electrical and Computer Engineering University of Waterloo Waterloo ON Canada Institue of Computer Technology Vienna Technical University Vienna Austria Department of Electrical Engineering and Computer Science University of Wisconsin Milwaukee WI USA Sustainable Building Technologies Austrian Institue of Technology (AIT) Vienna Austria Department of Electronics Engineering Universidad Técnica Federico Santa María Valparaiso Chile Research Group on Renewable Electrical Energy Systems Department of Electrical Engineering Universitat Politécnica de Catalunya (UPC) Barcelona Spain Austrian Academy of Sciences Institute of Integraded Sensor Systems Wiener Neustadt Austria

The 20 papers in this second part of this special section are classified by energy sources (wind, photovoltaic, hydro, etc.), grid-related issues (islanding, synchronization, microgrid, and smart grid), and converters... 详细信息

关键词： Special issues and sections Smart grids Renewable energy resources Photovoltaic systems Power conversion Wind energy Hydroelectric power generation Power grids

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26th Annual Computational Neuroscience Meeting (CNS*2017): Part 3 Antwerp, Belgium. 15-20 July 2017 Abstracts

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 95-176页

作者： [Anonymous] Department of Neuroscience Yale University New Haven CT 06520 USA Department Physiology & Pharmacology SUNY Downstate Brooklyn NY 11203 USA NYU School of Engineering 6 MetroTech Center Brooklyn NY 11201 USA Departament de Matemàtica Aplicada Universitat Politècnica de Catalunya Barcelona 08028 Spain Institut de Neurobiologie de la Méditerrannée (INMED) INSERM UMR901 Aix-Marseille Univ Marseille France Center of Neural Science New York University New York NY USA Aix-Marseille Univ INSERM INS Inst Neurosci Syst Marseille France Laboratoire de Physique Théorique et Modélisation CNRS UMR 8089 Université de Cergy-Pontoise 95300 Cergy-Pontoise Cedex France Department of Mathematics and Computer Science ENSAT Abdelmalek Essaadi’s University Tangier Morocco Laboratory of Natural Computation Department of Information and Electrical Engineering and Applied Mathematics University of Salerno 84084 Fisciano SA Italy Department of Medicine University of Salerno 84083 Lancusi SA Italy Dipartimento di Fisica Università degli Studi Aldo Moro Bari and INFN Sezione Di Bari Italy Data Analysis Department Ghent University Ghent Belgium Coma Science Group University of Liège Liège Belgium Cruces Hospital and Ikerbasque Research Center Bilbao Spain BIOtech Department of Industrial Engineering University of Trento and IRCS-PAT FBK 38010 Trento Italy Department of Data Analysis Ghent University Ghent 9000 Belgium The Wellcome Trust Centre for Neuroimaging University College London London WC1N 3BG UK Department of Electronic Engineering NED University of Engineering and Technology Karachi Pakistan Blue Brain Project École Polytechnique Fédérale de Lausanne Lausanne Switzerland Departement of Mathematics Swansea University Swansea Wales UK Laboratory for Topology and Neuroscience at the Brain Mind Institute École polytechnique fédérale de Lausanne Lausanne Switzerland Institute of Mathematics University of Aberdeen Aberdeen Scotland UK Department of Integrativ

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26th Annual Computational Neuroscience Meeting (CNS*2017) of the Organization for Computational Neuroscience Antwerp, Belgium, July 15-20, 2017

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BMC NEUROSCIENCE 2017年第SUPPL 1期18卷 59-59页

作者： [Anonymous] Indiana University Purdue University Indianapolis Indianapolis IN 46032 USA Stark Neurosciences Research Institute Indiana University School of Medicine Indianapolis IN 46032 USA Department of Mathematics East Carolina University Greenville NC 27858 USA Jülich Supercomputing Centre Forschungszentrum Jülich 52425 Jülich Germany Future Systems Swiss National Supercomputing Centre 8092 Zurich Switzerland User Engagement and Support Swiss National Supercomputing Centre 6900 Lugano Switzerland Institut de Neurosciences des Systèmes Aix Marseille Univ 13005 Marseille France Simulation Lab Neuroscience Forschungszentrum Jülich Jülich Germany Department of Experimental Psychology Ghent University 9000 Ghent Belgium Donders Center for Cognitive Neuroimaging Radboud University 6525HR Nijmegen The Netherlands Department of Electrical Computer and Energy Engineering University of Colorado Boulder CO 80309 USA Department of Neurosurgery Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Neurology Johns Hopkins School of Medicine Baltimore MD 21287 USA Department of Otolaryngology Johns Hopkins School of Medicine Baltimore MD 21287 USA INSERM U968 Paris France Sorbonne Universités UPMC University Paris 06 UMR_S 968 Institut de la Vision Paris France CNRS UMR_7210 Paris France Department of Computer Architecture and Technology University of Granada (CITIC) Granada Spain Sorbonne Universités UPMC Univ Paris 06 INSERM CNRS Institut de la Vision Paris France Department of Adaptive Machine Systems Osaka University Osaka Japan Department of Computer Science University of Cergy-Pontoise Cergy-Pontoise France Department of Physics and Astronomy College of Charleston Charleston SC 29424 USA School of Physics Faculty of Science University of Sydney Sydney NSW 2006 Australia Center of Excellence for Integrative Brain Function Australian Research Council Sydney Australia Max Planck Institute for Human Cognitive and Brain Sciences Saxony Lei

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