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SEPIC DC-DC Converter With Multilevel Flying Capacitor Switching Cell

SEPIC DC-DC Converter With Multilevel Flying Capacitor Switc...

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IEEE Energy Conversion Congress and Exposition, ECCE

作者： Montiê Alves Vitorino Rhavél Batista Morais Armando J. G. Abrantes-Ferreira Maurício B. de R. Corrêa Graduate Program in Electrical Engineering (PPgEE–COPELE) Industrial Electronics and Machine Drive Laboratory (LEIAM) Federal University of Campina Grande (UFCG)

Multilevel power converters have been aim of research in the last years due to their advantages when compared to traditional two-level topologies: better harmonic performance, filters reduction, lower components voltage stress and higher power process capability. Following this trend, the present work proposes two SEPIC DC-DC converters composed of multilevel flying capacitor asymmetric totem-pole switching cell. These structures are achieved by replacing one leg of switch/diode of the traditional two-level asymmetric totem-pole cell by a similar multilevel flying capacitor cell. An operation analysis of the proposed converter is presented, as well as hardware-in-the-loop real-time simulation results to validate the theoretical approach.

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Three-phase Multilevel NPC Rectifier with High-Frequency Pulsating DC-bus, Reduced Number of Switches and Galvanic Isolation with Transformer with Center Tap

Three-phase Multilevel NPC Rectifier with High-Frequency Pul...

引用

IEEE Energy Conversion Congress and Exposition, ECCE

作者： Yuri Cavalcanti Ailton Do Egito Dutra Montiê Alves Vitorino Graduate Program in Electrical Engineering (PPgEE–COPELE) Industrial Electronics and Machine Drive Laboratory (LEIAM) Federal University of Campina Grande (UFCG)

This paper proposes a three-phase three-level AC/DC converter with reduced number of controlled switches and high-frequency DC-bus based on NPC (Neutral Point Clamped) topology, for unidirectional power flow applications. Galvanic isolation is reached by using a center tap transformer, which requires only a full-bridge at it primary side. No bulky electrolytic capacitors are needed on the DC-bus, in contrast to conventional multilevel topologies. A PWM strategy with carriers arranged in alternating phase opposition (PWM-APOD) is used to operate the proposed converter. The applied PWM technique is able to operate the converter stage on the AC side with only two switches commuting per switching period. The switches of the isolated pulsating DC/DC converter are commutated with half the switching frequency of the converter on the AC side. A dual-loop control strategy with PI voltage and PI resonant current controllers is adopted to ensure reference load voltage and almost sinusoidal grid currents. Simulation and experimental results are presented to validate the proposed converter operation.

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Towards a wireless energy autonomous IoT module with a self-adaptable data diet 5

Towards a wireless energy autonomous IoT module with a self-...

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5th International Symposium on Instrumentation Systems, Circuits and Transducers, INSCIT 2021

作者： Vitoriano, Felipe Augusto Gomes, Gabriel Pimentel De Lima Monteiro, Davies William Graduate Program on Electrical Engineering - PPGEE/UFMG Belo Horizonte Brazil School of Engineering Electrical Engineering UFMG Belo Horizonte Brazil UFMG Dept. of Electrical Engineering Belo Horizonte Brazil

ISBN: (纸本)9781728197265

This paper describes a proof of concept of an Internet of Things (IoT) module, with wireless communication, sensing and energy harvesting capabilities. A self-adaptable power approach for balancing energy consumption and generation is also discussed, in view of a modular architecture for the system. Some preliminary results are shown pointing out bottlenecks and possibilities for improvements, considering the constraints in size, energy and communication of IoT technologies. © 2021 IEEE.

关键词： Energy harvesting

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Detection of polymeric insulator defects based on YOLOv8

Detection of polymeric insulator defects based on YOLOv8

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International Conference on High Voltage engineering and Application, ICHVE

作者： Samuel C. Nóbrega George R. S. Lira Pablo B. Vilar Post-Graduate Program in Electrical Engineering Federal University of Campina Grande Campina Grande Brazil Department of Electrical Engineering Federal University of Campina Grande Campina Grande Brazil

ISBN: (数字)9798350374988

ISBN: (纸本)9798350374995

This work proposes using the You Only Look Once (YOLO) architecture for defect detection in suspension polymeric insulators from images. Firstly, an image dataset of 138 kV polymeric suspension insulators was developed. Then, the dataset was augmented by horizontal and vertical flips to increase the training data. Afterward, YOLOv8n and YOLOv8s architectures were trained and used to detect insulator defects from the captured images. The developed models can detect four insulator defects: exposed rod, end fitting corrosion, cracks, and damaged sheds. Finally, the trained models were evaluated based on precision, recall, mean Average Precision (mAP), and confusion matrix. The results showed that YOLOv8s presented satisfactory results and potential for deploying and experimentation on UAVs to monitor polymeric insulators.

关键词： YOLO Training Sensitivity Computational modeling Corrosion Fitting Training data

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Multi-Objective Grey Wolf Optimizer for the Identification Process of a real MIMO System

Multi-Objective Grey Wolf Optimizer for the Identification P...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Allan Christian Krainski Ferrari Gideon Villar Leandro Leandro dos Santos Coelho Eletrical Engineering Department Federal University of Paraná UFPR Curitiba Brazil Electrical Engineering Graduate Program (PPGEE) Federal University of Paraná (UFPR) and Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Paraná (PUCPR)

This article implements the multi-objective grey wolf optimizer (MOGWO) in the identification process of a multivariable system. For a sequence of 100 runs, MOGWO approach had compared and analyzed with the grey wolf optimizer (GWO), the single-objective version. In the computational simulation, the determination coefficient values and mean square error had used to analyze the quality of the models obtained. In the results, MOGWO showed superior to GWO to the differences between the order of magnitude of the system outputs. The implementation shows that MOGWO and other multi-objective metaheuristics can be an alternative method to solve problems of identification processes of multivariable systems.

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Efficient Utilization of Metaheuristic Techniques for Profit Maximization in Combinatorial Production Planning: A Comparative Study

Efficient Utilization of Metaheuristic Techniques for Profit...

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International Conference on Control, Instrumentation, Energy and Communication (CIEC)

作者： Arijit Mukherjee Surojit Mukherjee Deptartment of Electrical Engineering Future Institute of Technology Kolkata India Post-Graduate Program in Management Great Lakes Institute of Management Chennai India

Efficient production planning requires well-structured models compatible with metaheuristic and other optimization techniques. This study compares three established metaheuristic methods - sanitized-teaching-learning-based optimization (s-TLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations - for profit-maximizing production planning. The focus is on a single-level problem involving complex constraints, non-linear costs, budget and raw material limitations. The paper presents two solution strategies: a penalty-based and a correction-based approach. Analyzing 300 unique instances of the problem reveals that s-TLBO implemented with correction-based approach consistently outperforms the other methods in devising optimal production plans for two Saudi Arabian petrochemical industries with 24 products, 54 processes, and three raw materials for plant 1 and two raw materials for plant 2.

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Optimal Integration of Metaheuristic Techniques with Correction-Based Approaches for Profit Maximization in Combinatorial Production Planning: A Comparative Analysis

Optimal Integration of Metaheuristic Techniques with Correct...

引用

International Conference on Control, Instrumentation, Energy and Communication (CIEC)

To enhance production planning efficiency, it’s crucial to have well-structured models that align with metaheuristic and other optimization techniques. This research conducts a comparative analysis of three unique metaheuristic methods: sanitized-teaching-learning-based optimization (sTLBO), Particle Swarm Optimization (PSO), and Differential Evolution (DE) with a termination criterion of equal number of functional evaluations. The primary focus lies on profit-maximizing production planning within a single-level framework, encompassing intricate domain constraints, nonlinear costs, budget limitations and raw material constraints. The paper introduces three correction-based solution approaches. Upon evaluating 300 distinct instances of the problem, the study reveals that s-TLBO integrated with correction-based approach 1 and 3 consistently outperforms the other methods when it comes to devising optimal production plans for a Saudi Arabian petrochemical industry. This industry scenario involves 24 products, 54 processes, and three raw materials.

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Manta Ray Foraging Optimization Approaches on Multivariable PID Controller Tuning

Manta Ray Foraging Optimization Approaches on Multivariable ...

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Congress on Evolutionary Computation

作者： Yan Lieven Souza Luúio Luiza Scapinello Aquino Da Silva Viviana Cocco Mariani Leandro Dos Santos Coelho Electrical Engineering Graduate Program (PPGEE) Federal University of Parana (UFPR) Curitiba Brazil Department of Electrical Engineering Mechanical Engineering Graduate Program (PPGEM) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Parana Brazil Department of Electrical Engineering Industrial and Systems Engineering Graduate Program (PPGEPS) Pontifical Catholic University of Parana (PUCPR) and Federal University of Parana (UFPR) Curitiba Brazil

This work presents a performance comparison between the newly developed Manta Ray Foraging Optimization (MRFO) and two different variants created for tuning a decentralized fractional order proportional-integral-derivative (FOPID) controller for a multiple-input multiple-output (MIMO) application. The application consists of a ball mill pulverizing system to pulverize coal and maximize fuel efficiency. MRFO and its applications have the task of finding the optimal controller variable values to control the system's temperature and pressure. The MRFO is a metaheuristic based on the behavior of manta rays, and although it shows efficient performance it may be improved through the use of techniques which generate new variants of the algorithm. Two techniques were used in the de-velopment of new variants: Quantum mechanics and opposition-based learning. The three different versions of MRFO were used to minimize a custom fitness function which is a combination of the integral time squared error (ITSE) and the overshoot of the system response. Simulations were carried using Simulink and Matlab softwares. For analyzing the performances, statistical measures such as minimum, maximum, best, mean, median, and standard deviation of the fitness function over 50 runs were used. Additionally, the different variants were also compared for minimizing 10 benchmark functions. The results show that the use of the previously mentioned techniques improve the performance of the original MRFO in optimizing the FOPID to control the ball mill pulverizing system.

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ADHD Subtype Diagnosis Through Convolutional Neural Networks Evaluation of the Connectivity Networks in Brain fMRI 9th

ADHD Subtype Diagnosis Through Convolutional Neural Networks...

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9th Latin American Congress on Biomedical engineering, CLAIB 2022 and 28th Brazilian Congress on Biomedical engineering, CBEB 2022

作者： Pedrollo, Guilherme Rodrigues Franco, Alexandre Rosa Balbinot, Alexandre Post Graduate Program in Electrical Engineering Universidade Federal do Rio Grande do Sul RS Porto Alegre90020-000 Brazil Child Mind Institute New YorkNY10022 United States Nathan Kline Institute for Psychiatric Research New YorkNY10962 United States

ISBN: (纸本)9783031494031

Attention-deficit hyperactivity disorder (ADHD) affects at least 5% of the world population. ADHD typically starts in childhood and can affect normal development that can cause serious consequences during adulthood. Moreover, there are three subtypes of ADHD, each with its symptomatic differences that require particular treatment. The development of tools capable of detecting the brain differences between each subtype and a control group can help in directing future neuropsychological studies and guide treatments. Here we investigate the hypothesis of using Convolutional Neural Networks (CNN) as a tool to detect ADHD in connectivity networks of the brain. This research used the Yeo brain parcellation maps from 28 healthy volunteers, 36 combined subtype ADHD, and 26 inattentive type ADHD. The CNN achieved a 64.71% accuracy at detecting the multi-class diagnosis (Control, ADHD combined, or ADHD inattentive) when using the complete connectivity network matrix, surpassing the individual accuracies achieved when using only the within network connectivity measures. However, the separate analysis of each network showed that the Dorsal, Default and Frontoparietal networks have better accuracy of performing the classification compared to using other within brain connectivity measures. The Default Network has shown greater accuracy at identifying ADHD-I than the other within networks or whole brain connectivity measures. These results suggest that the neurological differences of the presence of ADHD and its subtypes may be present and, therefore, affect more than one connectivity network functionality. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2024.

关键词： Convolution

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MOS Capacitor Modeling and Optimization for Fully Integrated DC-DC Converters

MOS Capacitor Modeling and Optimization for Fully Integrated...

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International Symposium on Instrumentation Systems, Circuits and Transducers (INSCIT)

作者： Marcos G. Bernardo Raimundo C. S. Freire Antonio A. L. de Souza Arthur S. Souza Henrique D. Silva Post-Graduation Program in Electrical Engineering PPgEE/UFCG Federal University of Campina Grande UFCG Campina Grande Brazil Federal University of Paraiba UFPB João Pessoa Brazil

In this paper, we propose a model for the rapid characterization of MOS-type capacitors in the accumulation region. Closed-form analytical expressions have been used to estimate the equivalent circuit elements, which are calculated as a function of the design parameters of the layout and of the technological process. To verify the modeling carried out, a set of MOS capacitors with different geometries were simulated in ADS® and the main characteristics of the capacitor were compared. Based on the model, an analysis/optimization was implemented in MATLAB® in order to determine the capacitor/cell layout parameters that optimize its structure, based on a figure of merit. Compared to another work in the literature, the proposed method showed a chip-area reduction of 20.82% and an ESR of 16.38% smaller for the same capacitance value. Capacitor optimization not only contributes to reducing costs but also to improving the performance of the circuit it will make part of.

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