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8th Southern Power Electronics Conference and the 17th Brazilian Power Electronics Conference, SPEC / COBEP 2023

作者： De Oliveira, Jonathan Silva Da Silveira Brito, Erick Matheus Pereira, Heverton Augusto Cupertino, Allan Fagner Centro Federal de Educação Tecnológica de Minas Gerais Graduate Program in Electrical Engineering Belo Horizonte Brazil Institute of Exact and Technological Sciences Universidade Federal de Viçosa Florestal Brazil Universidade Federal de Viçosa Department of Electrical Engineering Viçosa Brazil Universidade Federal de Juiz de Fora Department of Electrical Energy Juiz de Fora Brazil

ISBN: (纸本)9798350321128

This work evaluates the state-of-charge (SOC) and state-of-health (SOH) balancing techniques for energy storage systems with distributed power electronics architecture, i.e., batteries with integrated power converters. Two approaches are compared: a SOC balancing technique described in the literature and a cascaded SOH/SOC balancing technique proposed in this paper. In the proposed technique, the SOH balancing computes a SOC deviation, leading to different degradation rates in each battery of the pack. The techniques are compared through long-term simulations using: average models for the power converters, a battery-equivalent electric circuit, and an online degradation model. Comparative graphs of capacity and SOH are presented to compare the techniques. Based on simulation results, it was noticed that SOC balancing has fast dynamics equalization, but it leaves the SOH unbalanced if different capacity initial conditions are considered. On the other hand, the SOH balancing is responsible for imposing imbalance on SOC until the SOH reaches the balance. The results reveal that the proposed technique can balance SOH and leads to higher reliability when compared with the traditional SOC balancing. © 2023 IEEE.

关键词： Balancing Smart Battery State of charge State of health

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Quantitative Analysis of Ankle Spasticity Through Electromyography Signal

Quantitative Analysis of Ankle Spasticity Through Electromyo...

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2024 Medical Technologies Congress, TIPTEKNO 2024

作者： Gurluk, Ayse Bilal, Hafiz Muhammad Hocaoglu, Elif Living Robotics Laboratory Graduate School of Health Sciences Physiotherapy and Rehabilitation Program Istanbul Medipol University Istanbul Turkey Living Robotics Laboratory Graduate School of Engineering and Natural Sciences Electrical-Electronics Engineering and Cyber-Systems Program Istanbul Medipol University Istanbul Turkey Living Robotics Laboratory Department of Electrical and Electronics Engineering Istanbul Medipol University Istanbul Turkey

ISBN: (纸本)9798331529819

This study presents a more accurate evaluation method for assessing ankle spasticity compared to conventional goniometric techniques by utilizing measurements obtained from Inertial Measurement Units (IMU) and surface electromyography (sEMG) devices. Integrating IMU and EMG sensors provides substantial benefits for accurately determining the range of motion (ROM) associated with spasticity during targeted movements. In this study, the proposed method provides precise measurement and an objective evaluation of onset time detection of antagonistic muscle co-contraction, and accordingly the ROM of the actuated joint. Moreover, the implementation of cost-effective wearable sensors enables the monitoring of muscle spasticity in clinical environments and can be particularly valuable in regions with limited access to experienced therapists, providing a more efficient approach to tracking spasticity levels. © 2024 IEEE.

关键词： Electromyography

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Analysis of Brain Excitability After Transcranial Direct Current Stimulation and Brain-Computer Interface Based on Motor Imagery on a Post-stroke Patient 9th

Analysis of Brain Excitability After Transcranial Direct Cu...

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

作者： Silva, Leticia Lima, Jéssica Schreider, Sheila Delisle-Rodriguez, Denis Krishnan, Sridhar Bastos-Filho, Teodiano Postgraduate Program in Electrical Engineering Universidade Federal do Espirito Santo Vitoria Brazil Postgraduate Program in Biotechnology Universidade Federal do Espirito Santo Vitoria Brazil Edmond and Lily Safra International Institute of Neurosciences Instituto Santos Dumont Macaiba Brazil Department of Electrical Computer and Biomedical Engineering Toronto Metropolitan University Toronto Canada

ISBN: (纸本)9783031494062

Stroke is a neurological syndrome that may cause severe cognitive and motor impairments for survival. Alternative rehabilitation techniques have been developed to recover lower-limb movements and gait of post-stroke patients, such as Motor Imagery (MI)-based Brain-Computer Interfaces (BCIs), Virtual Reality (VR), and robotic devices. Transcranial Direct Current Stimulation (tDCS) is a non-invasive technique to stimulate the brain and modulate cortical excitability. This study aims to analyze the short-term effects of tDCS compared to sham stimulation before a lower-limb MI-BCI pedaling training (through a robotic monocycle) task in a patient with subacute stroke. We studied a post-stroke patient with 2 months of ischemic stroke and right hemiparesis who was submitted to the following tasks: active pedaling, sham tDCS and MI-BCI, and tDCS and MI-BCI. We evaluated the relative power around the foot area (Cz electrode), and a significant (p-value © 2024, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Brain computer interface

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A Physical-Based Artificial Neural Networks Compact Modeling Framework for Emerging FETs

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IEEE Transactions on Electron Devices 2024年第1期71卷 223-230页

作者： Yang, Ya-Shu Li, Yiming Kola, Sekhar Reddy Reddy National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory The Institute of Communications Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Institute of Communications Engineering Institute of Biomedical Engineering Department of Electronics and Electrical Engineering Hsinchu300093 Taiwan National Yang Ming Chiao Tung University Parallel and Scientific Computing Laboratory Electrical Engineering and Computer Science International Graduate Program Hsinchu300093 Taiwan

We report a compact modeling framework based on the Grove-Frohman (GF) model and artificial neural networks (ANNs) for emerging gate-all-around (GAA) MOSFETs. The framework consists of two ANNs;the first ANN constructed with the drain current model not only can capture the main trend of device I - V characteristics but also can predict its variation even when the amount of training data for the ANN is insufficient or outside the range of applied biases. The second one is then designed to improve the model accuracy by further minimizing the errors between the target and the model outputs. We implement the proposed framework to accurately model emerging GAA nanosheet (NS) MOSFETs and complementary FETs (CFETs) without suffering from divergent issues in circuit simulation. In addition, nonphysical behaviors, such as nonzero current at zero bias, do not occur in the modeling framework. Compared to recently reported machine-learning (ML) models, our approach can achieve a similar level of model accuracy with merely 20% amount of the training data. © 1963-2012 IEEE.

关键词： Neural networks

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Overview of Versatile Video Coding (H.266/VVC) and Its Coding Performance Analysis

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IEIE Transactions on Smart Processing and Computing 2023年第2期12卷 122-154页

作者： Lee, Minhun Song, HyeonJu Park, Jeeyoon Jeon, Byeungwoo Kang, Jungwon Kim, Jae-Gon Lee, Yung-Lyul Kang, Je-Won Sim, Donggyu Department of Computer Engineering Kwangwoon University Seoul Korea Republic of Department of Computer Engineering Sejong University Seoul Korea Republic of Department of Electrical and Computer Engineering Sungkyunkwan University Kyunggi-do Korea Republic of Communication and Media Research Laboratory Electronics and Telecommunications Research Institute Deajeon Korea Republic of School of Electronics and Information Engineering Korea Aerospace University Kyunggi-do Korea Republic of Department of Electronic and Electrical Engineering Graduate Program in Smart Factory Ewha Womans University Seoul Korea Republic of

Versatile Video Coding (H.266/VVC) is the newest video coding standard jointly developed by the Joint Video Experts Team (JVET), which is organized by the ITU-T Video Coding Experts Group (VCEG) and the ISO/IEC Moving Picture Experts Group (MPEG). H.266/VVC provides about 40% bitrate reduction compared with High Efficiency Video Coding (H.265/HEVC) for the same visual quality. This paper introduces in detail the core structure of H.266/VVC by highlighting its features within block partitioning structure, intra/inter prediction, transform, quantization, and in-loop filtering, compared to its predecessor (H.265/HEVC). H.266/VVC yields significantly improved the coding performance, but it increased the computational complexity, particularly for the encoder side, which remains a problem to be tackled for successful commercialization in the future. This paper examined the statistical performance of H.266/VVC coding tools from the bitstreams encoded by the VVC test model (VTM12.0) encoder through rate-distortion optimization under the JVET common test conditions. In addition, the complexity and performance analyses are conducted on the block partitioning structure and group of picture structure. It is expected that an optimized H.266/VVC encoder can be designed and developed by minimizing the coding loss based on the analysis data. © 2023 Institute of Electronics and Information Engineers. All rights reserved.

关键词： Image compression

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Machine Learning for Unmanned Aerial Vehicles Navigation: An Overview

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SN Computer Science 2024年第2期5卷 256页

作者： Fagundes-Junior, Leonardo A. de Carvalho, Kevin B. Ferreira, Ricardo S. Brandão, Alexandre S. Núcleo de Especialização em Robótica Department of Electrical Engineering Graduate Program in Computer Science Federal University of Viçosa-UFV Av. Petter Henry Rolfs MG Viçosa 36570-900 Brazil

Unmanned aerial vehicles (UAVs) are a valuable source of data for a wide range of real-time applications, due to their functionality, availability, adaptability, and maneuverability. Working as mobile sensors, they can provide a cost-effective solution for extremely complex tasks, such as inspection, air-to-ground communications, search and rescue, surveillance, among others. Nevertheless, the robots needs to navigate in quite distinct environments and in different dynamism levels, usually facing unpredicted situations, very often using limited sensing and computing capabilities. A large number of solutions to this problem has been featured by the scientific community in the last years, some of them based on machine-learning (ML) methods. Due to its great capability to deal with big data and complexity, as well as its speedy and high-accuracy processing, the ML framework has been used to improve existing technologies and control techniques. In this context, its adoption in several UAV navigation strategies is expected to provide solutions for various problems where UAVs are used in real-time applications. Thus, in order to contextualize the most recent advances, this work provides a detailed survey of relevant researches in which ML techniques have been used in UAV navigation to improve some functional aspects, such as energy-efficiency, communication, execution time, resource management, obstacle avoidance, and path planning. © The Author(s), under exclusive licence to Springer Nature Singapore Pte Ltd 2024.

关键词： Autonomous navigation Path planning UAV-based solutions Visual-based navigation

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Fault Analysis of a Ladle Furnace Transformer caused by Switching Transient and Ferroresonance 8

Fault Analysis of a Ladle Furnace Transformer caused by Swit...

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8th International Conference on engineering and Emerging Technologies, ICEET 2022

作者： Onofri, Virna C. Maia, Thales A. C. Universidade Federal de Minas Gerais Graduate Program in Electrical Engineering MG Belo Horizonte Brazil

ISBN: (纸本)9781665491068

Electric Arc Furnace and Ladle Furnace transformers are very important in steel production industry. The overvoltages caused by vacuum circuit breaker during operation are very repetitive and can lead to failures and, consequently, large financial losses. The use of protection devices, as RC filters and surge arresters, can attenuate theses transient overvoltages. However, the clamping of one of the circuit breaker phases, which is a common event to occur in a system like this, can cause failure to one of the phases of the transformer protection system, due to ferroresonance effect. Operation of this transformer without one of the phases of the RC filter and surge arrest will likely lead it to failure after repetitive overvoltages. This paper presents the study and analysis of the cause of insulation failure in a no-load Ladle Furnace (LF) transformer caused by overvoltages during vacuum circuit breaker switching transients and by ferroresonance effects. The system of a real steel mill is modelled and will be used to perform simulations in Simulink/Matlab. © 2022 IEEE.

关键词： Electric arcs

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Methodology for minimizing electric field at ground level from transmission lines using sensitivity analysis

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Journal of Microwaves, Optoelectronics and Electromagnetic Applications 2021年第4期20卷 702-713页

作者： Paganotti, A.L. Saldanha, R.R. Lisboa, A.C. Afonso, M.M. Melo, M.Q. Schroeder, M.A.O. Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Belo Horizonte Brazil Electrical Engineering Postgraduate Program UFSJ-CEFET-MG Belo Horizonte Brazil

In this paper, a new methodology is applied to optimize the geometry of conductors of overhead transmission lines (TL) with high precision and low computational cost. This methodology is based on the sensitivity analysis of the electrical charge of the transmission lines using the adjoint method. This information is used with the gradient method and the golden section algorithm to minimize the electric field at the ground level of three-phase TL with two cables per phase. The approximation using central finite differences to obtain sensitivity is adopted for validation and comparisons. The TL’s with high surge impedance loading is obtained after the optimization process. © 2021 SBMO/SBMag.

关键词： Electric lines

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Multi-agent Architecture for Passive Rootkit Detection with Data Enrichment

Multi-agent Architecture for Passive Rootkit Detection with...

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International Conference on Computer Science, Electronics, and Industrial engineering, CSEI 2022

作者： Trinks, Maickel Gondim, João Albuquerque, Robson Professional Post-Graduate Program in Electrical Engineering - PPEE - Department of Electrical Engineering University of Brasília DF Brasília Brazil Department of Computer Science Universidade de Brasília DF Brasília Brazil

ISBN: (纸本)9783031305917

The added value of the information transmitted in a cybernetic environment has resulted in a sophisticated malicious actions scenario aimed at data exfiltration. In situations with advanced actors, like APTs, such actions use obfuscation techniques of harmful activities as persistence assurance on strategic targets. The MADEX and NERD architectures proposed flow analysis solutions to detect rootkits that hide network traffic;however, it presents some operational cost, either in traffic volume or due to lack of aggregated information. In that regard, this work changes and improves user flow analysis techniques to eliminate impacts on network traffic, with data enrichment on local and remote bases, detection of domains consulted by rootkits and aggregation of information to generate threat intelligence, while maintaining high performance. The results show the possibility of aggregating information to data flows used by rootkits in order to have effective cyber defense actions against cybernetic threats without major impacts on the existing network infrastructure. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Multi agent systems

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Spectral Learning of Shared Dynamics Between Generalized-Linear Processes 38

Spectral Learning of Shared Dynamics Between Generalized-Lin...

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38th Conference on Neural Information Processing Systems, NeurIPS 2024

作者： Oganesian, Lucine L. Sani, Omid G. Shanechi, Maryam M. Ming Hsieh Department of Electrical and Computer Engineering University of Southern California Los AngelesCA United States Ming Hsieh Department of Electrical and Computer Engineering Thomas Lord Department of Computer Science Alfred E. Mann Department of Biomedical Engineering Neuroscience Graduate Program University of Southern California Los AngelesCA United States

Generalized-linear dynamical models (GLDMs) remain a widely-used framework within neuroscience for modeling time-series data, such as neural spiking activity or categorical decision outcomes. Whereas the standard usage of GLDMs is to model a single data source, certain applications require jointly modeling two generalized-linear time-series sources while also dissociating their shared and private dynamics. Most existing GLDM variants and their associated learning algorithms do not support this capability. Here we address this challenge by developing a multi-step analytical subspace identification algorithm for learning a GLDM that explicitly models shared vs. private dynamics within two generalized-linear time-series. In simulations, we demonstrate our algorithm's ability to dissociate and model the dynamics within two time-series sources while being agnostic to their respective observation distributions. In neural data, we consider two specific applications of our algorithm for modeling discrete population spiking activity with respect to a secondary time-series. In both synthetic and real data, GLDMs learned with our algorithm more accurately decoded one time-series from the other using lower-dimensional latent states, as compared to models identified using existing GLDM learning algorithms. © 2024 Neural information processing systems foundation. All rights reserved.

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