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2009 International Joint Conference on Neural Networks, IJCNN 2009

作者： Leite, Daniel F. Costa Jr., Pyramo Gomide, Fernando Faculty of Electrical and Computer Engineering University of Campinas Brazil Graduate Program in Electrical Engineering Catholic University of Minas Gerais Brazil

ISBN: (纸本)9781424435531

The objective of this study is to introduce the concept of evolving granular neural networks (eGNN) and to develop a framework of information granulation and its role in the online design of neural networks. The suggested eGNN are neural models supported by granule-based learning algorithms whose aim is to tackle classification problems in continuously changing environments. eGNN are constructed from streams of data using fast incremental learning algorithms. eGNN models require a relatively small amount of memory to perform classification tasks. Basically, they try to find information occurring in the incoming data using the concept of granules and T-S neurons as basic processing elements. The main characteristics of eGNN models are continuous learning, self-organization, and adaptation to unknown environments. Association rules and parameters can be easily extracted from its structure at any step during the evolving process. The rule base gives a granular description of the behavior of the system in the input space together with the associated classes. To illustrate the effectiveness of the approach, the paper considers the Iris and Wine benchmark problems. © 2009 IEEE.

关键词： Learning algorithms

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Multiband Patch Antenna Design Using Nature-Inspired Optimization Method

IEEE Open Journal of Antennas and Propagation

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IEEE Open Journal of Antennas and Propagation 2021年 2卷 151-162页

作者： Boursianis, Achilles D. Papadopoulou, Maria S. Pierezan, Juliano Mariani, Viviana C. Coelho, Leandro S. Sarigiannidis, Panagiotis Koulouridis, Stavros Goudos, Sotirios K. ELEDIA@AUTH Research Group School of Physics Aristotle University of Thessaloniki Thessaloniki54124 Greece Department of Electrical Engineering Federal University of Parana Curitiba80060-000 Brazil Industrial and Systems Engineering Graduate Program and Mechanical Engineering Graduate Program Pontifical Catholic University of Parana Curitiba80215-901 Brazil Department of Electrical and Computer Engineering University of Western Macedonia Kozani501 00 Greece Electrical and Computer Engineering Department University of Patras Patras26504 Greece

Radio frequency energy harvesting has attracted considerable interest as a technique of enabling self-powered wireless networks. This technique faces several challenges, such as the receiving and the rectifying modules of a rectenna system. Multiband antennas provide several comparative advantages against the goal of maximizing the amount of energy harvesting. In this work, we present a multiband microstrip patch antenna with three slits operating in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. A feasible solution of the antenna is obtained by the application of a recently introduced nature-inspired optimization technique, namely the Coyote Optimization Algorithm. The proposed antenna operates satisfactorily in the LoRaWAN (Long Range Wide Area Network) and the cellular (GSM-1800 and UMTS) communication frequency bands. Measured results of the proposed antenna exhibit an acceptable performance (multiband frequency operation, maximum gain of 3.94 dBi, broadside operation) and demonstrate features of operation, which make it a strong candidate for various RF energy harvesting applications. © 2020 IEEE.

关键词： Microstrip antennas

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Compact Model Build Upon Dynamic Weighting Artificial Neural Network Approach for Complementary Field Effect Transistors

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IEEE Transactions on Electron Devices 2024年第1期71卷 246-253页

作者： Butola, Rajat Li, Yiming Kola, Sekhar Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan Institute of Pioneer Semiconductor Innovation The Institute of Artificial Intelligence Innovation National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program The Institute of Communications Engineering the Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan

In this work, a dynamic weighting-artificial neural network (DW-ANN) methodology is presented for quick and automated compact model (CM) generation. It takes advantage of both TCAD simulations for high accuracy and SPICE simulations for cost-effectiveness. This methodology is developed for gate-all-around (GAA) silicon (Si) nanosheet (NS) complementary field effect transistor (CFET), a potential candidate for future CMOS technology due to its innate properties. The critical process variation (PV) sources that severely degrade the CFET performance are estimated using DW-ANN. It reduces the computation cost and predicts the effects of PV sources with less than 1% error. Furthermore, a compact DW-ANN-based Verilog-A model has been developed that captures the dc as well as transient behavior accurately for circuit-level analysis. CFET-based circuits such as inverter, 6T-static random access memory (SRAM), and ring oscillator (RO) are characterized and implemented using DW-ANN-based CM. The overall average error of the model is reported as less than 2%. Therefore, the proposed device and circuit modeling approach provides a feasible solution for the rapid compact modeling of new emerging devices with good convergence and high accuracy. © 1963-2012 IEEE.

关键词： Neural networks

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Bidirectional battery charger with planar transformer

Bidirectional battery charger with planar transformer

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2013 12th Brazilian Power Electronics Conference, COBEP 2013

作者： Dalla Vecchia Rocha, Fabio Carati, Emerson Giovani Cardoso, Rafael De Oliveira Stein, Carlos Marcelo UTFPR - Federal University of Technology - Paraná PPGEE - Graduate Program in Electrical Engineering Via do Conhecimento Km 1 Pato Branco - Paraná Brazil

ISBN: (纸本)9781479902729

This paper presents a proposal for Isolated Bidirectional battery charger. The charger in study has capacity to supply power 1.34KVA in a line of 400V DC and battery charging capability up to 5A at 192Vnom. For isolation of the batteries and improve energy efficiency in the conversion was used Planar transformer coupled to the switching transistors IGBTs for low losses. To control the energy flow in both directions was used Full-Bridge Converter suitable for the power level required at 400V bus. The charging of the batteries can be integrated into a network-smart grid, composed of renewable energy sources like photovoltaic and / or wind, or be used in UPS systems 'double conversion', whose load is less fault tolerant power. © 2013 IEEE.

关键词： Charging (batteries)

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High impedance fault classification in microgrids using a transformer-based model with time series harmonic synchrophasors under data quality issues

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Neural Computing and Applications 2024年第23期36卷 14017-14034页

作者： Cieslak, Dionatan A. G. Moreto, Miguel Lazzaretti, André E. Macedo-Júnior, José R. PPGEEL - Graduate Program in Electrical Engineering Federal University of Santa Catarina UFSC R. Eng. Agronômico Andrei Cristian Ferreira s/n Santa Catarina Florianópolis88040-900 Brazil CPGEI - Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná UTFPR Av. Sete de Setembro 3165 Paraná Curitiba80230-901 Brazil PPGEELT - Graduate Program in Eletrical Engineering Federal University of Uberlândia UFU Av. João Naves de Ávila 2121 Minas Gerais Uberlândia38408-100 Brazil

Recent advances in distribution networks, driven by the integration of renewable energy sources, have spurred the emergence of microgrids, elevating concerns regarded reliability and stability. In this context, precise monitoring of events, particularly those elusive to detection like high-impedance faults (HIFs), becomes imperative. The development of phasor measurement units (PMUs) with their harmonic synchronized measurements has enhanced the monitoring task and fostering the application of synchrophasors even on microgrids. This work introduces a novel method for event classification in microgrids, utilizing combined low-rate PMU data and harmonic synchrophasor time series. Central to our approach is the usage of a state-of-the-art transformer neural network, based on the attention mechanism, to effectively discern HIFs from other faulty and non-fault events. Notably, this methodology accounts for prevalent PMU data quality issues, including noise, missing data, and synchronism errors. Results from real-world HIF data demonstrate a robust performance, with an accuracy rate of approximately 98% in event classification. This harmonic synchrophasor-based strategy showcases promise as an original approach for handling commercial PMU data, offering sufficient robustness for deployment in real-world applications. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Phasor measurement units

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Fast saliency detection using sparse random color samples and joint upsampling 29

Fast saliency detection using sparse random color samples an...

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29th SIBGRAPI Conference on Graphics, Patterns and Images, SIBGRAPI 2016

作者： Lie, Maiko Min Ian Borba, Gustavo Benvenutti Neto, Hugo Vieira Gamba, Humberto Remigio Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781509035687

The human visual system employs a mechanism of visual attention, which selects only part of the incoming information for further processing. Through this mechanism, the brain avoids overloading its limited cognitive capacities. In computer vision, this task is usually accomplished through saliency detection, which outputs the regions of an image that are distinctive with respect to its surroundings. This ability is desirable in many technological applications, such as image compression, video quality assessment and content-based image retrieval. In this paper, a saliency detection method based on color distance with sparse random samples and joint upsampling is presented. This approach computes full-resolution saliency maps with short runtime by leveraging both edge-preserving smoothing and joint upsampling capabilities of the Fast Global Smoother. The proposed method is assessed through precision-recall curves, F-measure and average runtime on the MSRA1K dataset. Results show that the method is competitive with state-of-the-art algorithms in both saliency detection accuracy and runtime. © 2016 IEEE.

关键词： Image compression

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Granular approach for evolving system modeling

Granular approach for evolving system modeling

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13th International Conference on Information Processing and Management of Uncertainty, IPMU 2010

作者： Leite, Daniel Costa Jr., Pyramo Gomide, Fernando School of Electrical and Computer Engineering University of Campinas Brazil Graduate Program in Electrical Engineering Pontifical Catholic University of Minas Gerais Brazil

ISBN: (纸本)3642140483

In this paper we introduce a class of granular evolving system modeling approach within the framework of interval analysis. Our aim is to present an interval-based learning algorithm which develops both, granular and singular approximations of nonlinear nonstationary functions using singular data. The algorithm is capable of incrementally creating/adapting both model parameters and structure. These are key features in nonlinear systems modeling. In addition, interval analysis provides rigorous bounds on approximation errors, rounding errors, and on uncertainties in data propagated during computations. The learning algorithm is simple and particularly suited to process stream of data in real time. In this paper we focus on the foundations of the approach and on the details of the learning algorithm. An application concerning economic time series forecasting illustrates the usefulness and efficiency of the approach. © 2010 Springer-Verlag Berlin Heidelberg.

关键词： Learning algorithms

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Evolving granular neural network for semi-supervised data stream classification

Evolving granular neural network for semi-supervised data st...

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2010 6th IEEE World Congress on Computational Intelligence, WCCI 2010 - 2010 International Joint Conference on Neural Networks, IJCNN 2010

作者： Leite, Daniel Costa, Pyramo Gomide, Fernando Department of Computer Engineering and Automation Faculty of Electrical and Computer Engineering University of Campinas Sao Paulo Brazil Graduate Program in Electrical Engineering Catholic University of Minas Gerais Brazil

ISBN: (纸本)9781424469178

In this paper we introduce an adaptive fuzzy neural network framework for classification of data stream using a partially supervised learning algorithm. The framework consists of an evolving granular neural network capable of processing nonstationary data streams using a one-pass incremental algorithm. The granular neural network evolves fuzzy hyperboxes and uses nullnorm based neurons to classify data. The learning algorithm performs structural and parametric adaptation whenever environment changes are reflected in input data. It needs no prior statistical knowledge about data and classes. Computational experiments show that the fuzzy granular neural network is robust against different types of concept drift, and is able to handle unlabeled examples efficiently. © 2010 IEEE.

关键词： Learning algorithms

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Automatic image thumbnailing based on fast visual saliency detection 16

Automatic image thumbnailing based on fast visual saliency d...

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22nd Brazilian Symposium on Multimedia and the Web, WebMedia 2016

作者： Lie, Maiko M. I. Neto, Hugo Vieira Borba, Gustavo B. Gamba, Humberto R. Graduate Program in Electrical and Computer Engineering Federal University of Technology - Paraná Curitiba Brazil Graduate Program in Biomedical Engineering Federal University of Technology - Paraná Curitiba Brazil

ISBN: (纸本)9781450345125

Image retargeting has seen many applications in areas such as content adaptation for small displays and thumbnailing for image database browsing. Most retargeting methods, however, are too expensive computationally to achieve fast performance on common desktop systems. This work addresses the problem of fast automatic thumbnailing for image browsing. A simple approach of automatic thresholding saliency maps and cropping using bounding box extraction is presented. Eight of the fastest saliency detectors in the literature and three automatic thresholding methods are assessed using precision, recall, F-score and execution time on the MSRA1K dataset. The results show that the approach is computationally efficient and adequate for fast automatic image thumbnailing. In particular, saliency detection with difference to random color samples (RS) thresholded by Rosin's method achieved the best trade-off between execution time and F-score. © 2016 ACM.

关键词： Economic and social effects

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Distributed temperature sensing for special induction motor applied in harsh environment 11

Distributed temperature sensing for special induction motor ...

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Photonic Instrumentation engineering XI 2024

作者： Mazzo, Paulo H.R. Torelli, Gabriel A. Dreyer, Uilian J. Pelegrin, Jessé Stel, Henrique Da Silva, Luiz C.T. Romero, Gabriel A. Morales, Rigoberto E.M. da Silva, Jean C.C. Graduate Program in Electrical and Computer Engineering Federal University of Technology UTFPR Paraná PR Curitiba Brazil Graduate Program in Energy Systems Federal University of Technology UTFPR Paraná PR Curitiba Brazil Multiphase Flow Research Center - NUEM Federal University of Technology UTFPR Paraná PR Curitiba Brazil CENPES/PDIEP EE Petrobras Brazil Federal Institute Catarinense IFC Santa Catarina Brazil

ISBN: (纸本)9781510670464

This paper presents a study and development on the thermal mapping of a Three-phase Induction Motor for harsh environments application. These machines’ exceptional performance and precise speed control make them essential equipment in multiple industrial processes, such as Artificial Lift, which integrates with a multistage centrifugal pump to elevate fluids from the reservoirs to the surface. Increased motor speeds for higher production requirements can result in temperature rise, damaging the stator winding. Employing the Raman Distributed Temperature Sensing System (RDTS), we generate a comprehensive heat distribution map of the motor. ANSYS Motor-CAD was applied for computational simulations to perform steady-state thermal analysis, giving an overview of heat transfer among internal components and thermal limits for the instrumented motor. This preliminary study presents a series of experimental tests to evaluate the advantages of fiber optic temperature sensing in harsh environments, emphasizing its significance in improving electrical machines’ performance and reliability. © 2024 SPIE.

关键词： Centrifugal pumps

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