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4th International Conference on Simulation and Modeling Methodologies, Technologies and Applications, SIMULTECH 2014

作者： Filho, Flávio Torres de Fátima Queiroz Vieira, Maria Post-Graduate Program in Electrical Engineering - PPgEE - COPELE Campina Grande Brazil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brazil

ISBN: (纸本)9789897580383

This paper presents a framework to support the development of three-dimensional virtual scenarios for an operator training simulator for electrical systems. Given the need to represent a variety of scenarios of interest, this can be an effort and time consuming task. The proposed framework promotes a systematic approach when building scenarios and is supported by tools from the ontology-based domain. An editing tool is also under construction. As it will be discussed in the paper, this approach resulted in the simplification of the scenario building process which is achieved by interchanging descriptions at different levels of abstraction. The descriptions concern the situation to be represented;plant objects' representation, both visual and behavioural, the latter represented as Coloured Petri Nets (CPN) models. © 2014 SciTePress. All rights reserved.

关键词： Ontology

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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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Single-Phase Six-Switch Universal Active Power Filter 10

Single-Phase Six-Switch Universal Active Power Filter

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10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018

作者： Rodrigues, Phelipe L.S. Jacobina, Cursino B. Post-Graduate Program in Electrical Engineering - PPgEE - COPELE Brazil Campina Grande Paraíba58429-900 Brazil

ISBN: (纸本)9781479973118

In this paper, three structures were developed and analyzed, these configurations are based on three-leg ac/dc/ac converters operating as a universal active power filter (UAPF) also known as unified power quality conditioner (UPQC). The proposed configurations are appropriate for a voltage sag/swell compensation, reactive power compensation, reducing losses and switches, as well as for applications where the input voltage is twice the output voltage, for example, applications in systems in Brazil where the voltages vary between 220 V (rms) and 110 V (rms) from one state to another. The proposed six-switch single-phase UAPF are compared with the conventional three-leg and four-leg single-phase UAPF solutions. The pulsewidth modulation (PWM) and control techniques are developed and presented. For validation purposes, the simulation and experimental results for proposed configurations are shown. © 2018 IEEE.

关键词： Pulse width modulation

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Three-Leg Single-Phase Universal Active Power Filter 10

Three-Leg Single-Phase Universal Active Power Filter

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10th Annual IEEE Energy Conversion Congress and Exposition, ECCE 2018

作者： Rodrigues, Phelipe L. S. Jacobina, Cursino B. PPgEE COPELE Post-Graduate Program in Electrical Engineering Brazil Campina Grande Paraíba58429-900 Brazil

ISBN: (纸本)9781479973118

This paper presents a topology based on three-leg ac-dc-ac converters for active power filter (APF) applications. The proposed configuration is appropriate for voltage sag/swell compensation, reactive power compensation, and for reducing losses and the number of switches. The proposed three-leg single-phase universal active power filter (UAPF) is compared with conventional four-leg and three-leg single-phase UAPF solutions. Pulsewidth modulation (PWM) and control techniques are developed and presented. For validation purposes, simulation and experimental results of proposed configurations are shown. © 2018 IEEE.

关键词： Pulse width modulation

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Real-time wavelet transform algorithms for the processing of continuous streams of data

Real-time wavelet transform algorithms for the processing of...

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2005 IEEE International Workshop on Intelligent Signal Processing

作者： Mota, Hilton De O. Vasconcelos, Fláio H. Da Silva, Rogério M. Post-graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Electrical Engineering Department UFMG Belo Horizonte Brazil

ISBN: (纸本)078039030X

Two new algorithms to compute the direct and the inverse discrete wavelet transform of continuous streams of data are discussed in this paper. The algorithms are optimized to be used in uniprocessor systems, presenting as one of their main features the ability to compute over the borders of the data segments without relying on any techniques that are traditionally used for this task, like zero padding. A modified version of the Recursive Pyramid Algorithm was used to compute the direct transform, keeping just the features to minimize data storage but eliminating its dependency on extension techniques. The inverse transform is computed by an algorithm inspired in the RPA, the quadrature mirror filter bank and the overlap-save method for filter convolutions. To evaluate the performance the algorithms were implemented on a DSP coupled to a digitizer through its external memory bus, what allowed deterministic behavior. After a stage of optimization an analysis of data storage and computation load were made. These results and the potential applications are discussed at the end of the paper. ©2005 IEEE.

关键词： Data processing

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Real-time optimization using gradient adaptive selection and classification from infrared sensors measurement for esterification oleic acid with glycerol

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International Journal of Intelligent Computing and Cybernetics 2017年第2期10卷 130-144页

作者： Iwan Aang Soenandi Taufik Djatna Ani Suryani Irzaman Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia and Faculty of Engineering and Computer ScienceKrida Wacana Christian UniversityJakartaIndonesia Post Graduate Program of Agro-industrial Technology Bogor Agricultural UniversityBogorIndonesia Department of Physics Faculty of Mathematics and Natural SciencesBogor Agricultural UniversityBogorIndonesia

Purpose-The production of glycerol derivatives by the esterification process is subject to many constraints related to the yield of the production target and the lack of process *** accurate monitoring and controlling of the process can improve production yield and *** purpose of this paper is to propose a real-time optimization(RTO)using gradient adaptive selection and classification from infrared sensor measurement to cover various disturbances and uncertainties in the ***/methodology/approach-The integration of the esterification process optimization using self-optimization(SO)was developed with classification process was combined with necessary condition optimum(NCO)as gradient adaptive selection,supported with laboratory scaled medium wavelength infrared(mid-IR)sensors,and measured the proposed optimization system indicator in the batch *** Process Modeling and Notation(BPMN 2.0)was built to describe the tasks of SO workflow in collaboration with NCO as an abstraction for the conceptual ***,Stateflow modeling was deployed to simulate the three states of gradient-based adaptive control combined with support vector machine(SVM)classification and Arduino microcontroller for ***-This new method shows that the real-time optimization responsiveness of control increased product yield up to 13 percent,lower error measurement with percentage error 1.11 percent,reduced the process duration up to 22 minutes,with an effective range of stirrer rotation set between 300 and 400 rpm and final temperature between 200 and 210℃ which was more efficient,as it consumed less *** limitations/implications-In this research the authors just have an experiment for the esterification process using glycerol,but as a development concept of RTO,it would be possible to apply for another chemical reaction or *** implications-This research introduces new development of an RTO approach to optimal con

关键词： Gradient technique Infrared sensor Real-time optimization Simulation and modelling Support vector machine

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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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