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IEEE/ASME International Conference on Mechtronic and Embedded Systems and Applications, MESA

作者： Cristian Felipe Blanco-Diaz Cristian David Guerrero-Mendez Guilherme Rangel Furtado Teodiano Bastos-Filho Rafhael Milanezi de Andrade Dept. of Excellence and AI The BioRobotics Institute Scuola Superiore Sant’Anna Pisa Italy Dept. Electronics and Biomedical Engineering Universidade Estadual de Campinas Campinas SP Brazil Dept. of Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Posgraduate Program in Electrical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil Graduate Program in Mechanical Engineering Universidade Federal do Espírito Santo Vitória ES Brazil

ISBN: (数字)9798331516239

ISBN: (纸本)9798331516246

Embedded Brain-Computer Interfaces (BCI) have emerged as an alternative for rehabilitation/assistance of people with neuromotor impairments. Data-driven methods are usually implemented to design controllers to (de)activate these systems using electroencephalography (EEG), which may limit usability and degrees of freedom. Thus, the development of more adaptive classifiers remains a challenge. This study focuses on the implementation of a Deep Learning (DL) method to classify EEG signals during different phases of gait and pedaling. To address this, public and in-house databases were used, which contain information on EEG and kinematic data during walking on a treadmill and pedaling on a minibike. Kinematic labels of the phases were extracted using k-means and classified by a Convolutional Neural Network (CNN), which demonstrated accuracy above 60%. Furthermore, a negative correlation was found between velocities and classifier performance metrics, suggesting that faster movements can hinder EEG decoding. Our proposal is a starting point to identify current states of lower-limb movement, which can be used for designing state machine-based controllers of robotic BCIs with rehabilitation or assistance purposes, such as motorized mini exercise bikes or leg exoskeletons.

关键词： Legged locomotion Deep learning Mechatronics Kinematics Electroencephalography Brain-computer interfaces Convolutional neural networks Proposals Usability Robots

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Latest wind and solar curtailment information: statistics and future estimations in various countries/areas

Latest wind and solar curtailment information: statistics an...

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22nd Wind and Solar Integration Workshop (WIW 2023)

作者： Y. Yasuda D. Flynn E. Gómez-Lázaro H. Holttinen S. M. Martinez Graduate School of Economics Kyoto University Kyoto Japan School of Electrical and Electronic Engineering University College Dublin Dublin Ireland Department of Electrical Engineering Electronics Automation and Communications Universidad de Castilla-La Mancha Albacete Spain Ricognis Vantaa Finland

An international research collaboration under IEA (the International Energy Agency) Wind TCP (Technical Collaboration programme) Task 25 (Design and Operation of Energy Systems with large amount of Variable Generation) has previously performed an international comparison analysis on the curtailment of wind and solar power in various countries/areas in the world in 2022. This paper gives a comparison overview of the curtailment rates, presented as C-E maps (curtailment as a share of VRE and power system demand). As previous statistical data was as until 2020, some data has been updated. The latest information and the future estimations of curtailment in several countries/areas are summarised, including Ireland, California and Texas in U.S., and Japan. We also discuss the possible impacts of battery storage and demand response, which may contribute to reduced wind & solar curtailment, despite very high VRE (variable renewable energy) shares.

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NOMAD: nonlinear manifold decoders for operator learning 22

NOMAD: nonlinear manifold decoders for operator learning

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Proceedings of the 36th International Conference on Neural Information Processing Systems

作者： Jacob H. Seidman Georgios Kissas Paris Perdikaris George J. Pappas Graduate Program in Applied Mathematics and Computational Science University of Pennsylvania Department of Mechanical Engineering and Applied Mechanics University of Pennsylvania Department of Electrical and Systems Engineering University of Pennsylvania

ISBN: (纸本)9781713871088

Supervised learning in function spaces is an emerging area of machine learning research with applications to the prediction of complex physical systems such as fluid flows, solid mechanics, and climate modeling. By directly learning maps (operators) between infinite dimensional function spaces, these models are able to learn discretization invariant representations of target functions. A common approach is to represent such target functions as linear combinations of basis elements learned from data. However, there are simple scenarios where, even though the target functions form a low dimensional submanifold, a very large number of basis elements is needed for an accurate linear representation. Here we present NOMAD, a novel operator learning framework with a nonlinear decoder map capable of learning finite dimensional representations of nonlinear submanifolds in function spaces. We show this method is able to accurately learn low dimensional representations of solution manifolds to partial differential equations while outperforming linear models of larger size. Additionally, we compare to state-of-the-art operator learning methods on a complex fluid dynamics benchmark and achieve competitive performance with a significantly smaller model size and training cost.

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State-Feedback Control for Discrete-Time Uncertain Positive Linear Systems Under Denial of Service Attacks

State-Feedback Control for Discrete-Time Uncertain Positive ...

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UKACC International Conference on Control (CONTROL)

作者： Paulo S.P. Pessim Felipe A. Silva Márcia L.C. Peixoto Reinaldo M. Palhares Márcio J. Lacerda Graduate Program in Electrical Engineering Federal University of Minas Gerais Brazil Department of Electrical Engineering Federal University of São João del-Rei - UFSJ Brazil LAMIH laboratory UMR CNRS 8201 Université Polytechnique Hauts-de-France Valenciennes France INSA Hauts-de-France Valenciennes France Department of Electronics Engineering Federal University of Minas Gerais Minas Gerais Brazil Centre for Communications Technology School of Computing and Digital Media London Metropolitan University UK

ISBN: (数字)9798350374261

ISBN: (纸本)9798350374278

This paper presents conditions in the form of Linear Matrix Inequalities to design state-feedback controllers for discrete-time uncertain positive linear systems in the presence of denial of service (DoS) attacks. The system under attack is modeled after a switched model that allows us to derive the synthesis conditions. Two strategies for control are considered, a hold strategy and a packet-based approach. Numerical experiments illustrate the efficacy of the proposed method of keeping the positiveness and stability of the closed-loop system under the presence of DoS attacks.

关键词： Linear systems Switched systems Design methodology Switches Stability analysis Numerical models Safety

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Robust triboelectric information-mat enhanced by multi-modality deep learning for smart home

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InfoMat 2023年第1期5卷 139-160页

作者： Yanqin Yang Qiongfeng Shi Zixuan Zhang Xuechuan Shan Budiman Salam Chengkuo Lee Department of Electrical and Computer Engineering National University of SingaporeSingaporeSingapore Center for Intelligent Sensors and MEMS(CISM) National University of SingaporeSingaporeSingapore National University of Singapore Suzhou Research Institute(NUSRI) SuzhouPeople's Republic of China Singapore Institute of Manufacturing Technology and National University of Singapore(SIMTech-NUS)Joint Lab on Large-area Flexible Hybrid Electronics National University of SingaporeSingaporeSingapore Printed Intelligent Device Group Singapore Institute of Manufacturing TechnologyAgency for ScienceTechnology and Research(A*STAR)SingaporeSingapore NUS Graduate School-Integrative Sciences and Engineering Programme(ISEP) National University of SingaporeSingaporeSingapore

In metaverse,a digital-twin smart home is a vital platform for immersive communication between the physical and virtual *** nanogenerators(TENGs)sensors contribute substantially to providing smart-home ***,TENG deployment is hindered by its unstable out-put under environment ***,we develop a digital-twin smart home using a robust all-TENG based information mat(InfoMat),which consists of an in-home mat array and an entry *** interdigital electrodes design allows environment-insensitive ratiometric readout from the mat array to can-cel the commonly experienced environmental *** position sensing is also achieved because of the interval arrangement of the mat ***,the two-channel entry mat generates multi-modality informa-tion to aid the 10-user identification accuracy to increase from 93% to 99% compared to the one-channel ***,a digital-twin smart home is visualized by real-time projecting the information in smart home to virtual reality,including access authorization,position,walking trajectory,dynamic activities/sports,and so on.

关键词： digital twin environment-insensitive multi-modality deep learning scalability smart home triboelectric information-mat

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Drought propagation in an Andean basin in South America: a spatio-temporal analysis approach with monitoring indices

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Arabian Journal of Geosciences 2025年第3期18卷 1-21页

作者： Ocampo-Marulanda, Camilo Mera-Franco, Julián David Lizcano, Víctor Augusto Carvajal-Escobar, Yesid Canchala, Teresita Zorrilla-Millan, Daniel Felipe Caicedo-Bravo, Eduardo Water Resources Engineering and Soil Research Group (IREHISA) School of Natural Resources and Environmental Engineering Universidad del Valle Cali Colombia Graduate Program in Biometrics and Applied Statistics Department of Statistics and Informatics Federal Rural University of Pernambuco Recife Brazil School of Exact Sciences and Engineering Universidad Sergio Arboleda Bogotá Colombia Programa de Ingeniería Ambiental Facultad de Ingeniería Universidad Mariana Pasto Colombia School of Electrical and Electronic Engineering Universidad del Valle Cali Colombia

Drought is a climatic phenomenon that has increased in severity over time. This study analyzed drought spread in the Alto Patía basin (13,047 km2) in South America, applying the Standardized Precipitation Index (SPI), Vegetation Health Index (VHI), and Streamflow Drought Index (SDI) to assess meteorological, agricultural, and hydrological drought, respectively. Synchronous and asynchronous correlations were estimated, and Wavelet analysis was conducted to verify relationships among drought types. Results show that the lag between drought types varies based on the intensity, temporal continuity, and spatial extent of meteorological drought. A lag of zero months (1–2 months) was observed between meteorological and hydrological drought when moderate (severe) meteorological drought partially (fully) covers the basin. Agricultural drought, however, does not always correspond to meteorological drought during isolated rainfall events. As the first study in Colombia to systematically connect these drought types, this research addresses a gap in understanding how drought impacts progress across systems in regions with complex rainfall patterns. These findings offer critical insights into the interdependencies among drought types, supporting enhanced drought monitoring and early warning systems in similar climatic regions. By identifying specific lags between meteorological and hydrological droughts, this study provides practical guidance for land management, water conservation, and crop planning, offering a foundation for future research on drought resilience strategies.

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Bidding Strategy of a Microgrid in Joint Energy and Reserve Markets: An IGDT-Based Approach

Bidding Strategy of a Microgrid in Joint Energy and Reserve ...

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International Universities Power engineering Conference (UPEC)

作者： Mojtaba Mohseni Ali Sedaghatkerdar Mahdi Mohseni Ehsan Heydarian-Forushani Sitki Guner Aydogan Ozdemir Graduate School of Management and Economics Sharif University of Technology Tehran Iran Department of Electrical and Electronics Engineering Shiraz University of Technology Shiraz Iran Department of Electrical and Computer Engineering Qom University of Technology Qom Iran Department of Electrical and Electronics Engineering Eskişehir Technical University Eskişehir Turkey Department of Electrical Engineering Istanbul Technical University Istanbul Turkey

ISBN: (数字)9781665455053

ISBN: (纸本)9781665455060

The increasing penetration rate of distributed energy resources (DERs) influences the operation process of power systems and also results in lots of challenges. One of the major challenges is to scheduling of such sources, and one of the most applicable approaches is to operate them in the form of micro-grids (MGs). Thus, the decision-making framework of the power systems’ operation problems, in the emergence of MGs, has encountered a decentralized form instead of centralized one. Based on this change, the operation problem as well as the bidding strategy of the MGs participating in joint energy and reserve markets need new optimization approaches. Moreover, the control of uncertain parameters in the decision-making of MG operators (MGOs) should be molded through new mathematical Techniques. In this paper, the model of a grid-connected MG’s operation problem is proposed so that the MGO implements the demand response programs (DRPs), and deals with the uncertainties of the reserve deployment probability and the renewable energy sources (RESs). To achieve this aim, an information gap decision theory (IGDT)-based approach is applied with the purpose of controlling the risk-based decision-making of the MGO. Lastly, the validation of the model is investigated by employing it on a test modified 15-bus MG and the results reveal the difference between the decisions of the risk-averse and risk-taker MGO.

关键词： Renewable energy sources Power engineering Uncertainty Decision making Microgrids Mathematical models Power systems

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Real-time temperature correction for magnetoresistive biosensors integrated with temperature modulator

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Biosensors and Bioelectronics: X 2023年 14卷 100356页

作者： Kim, Songeun Wang, Shan X. Lee, Jung-Rok Division of Mechanical and Biomedical Engineering Ewha Womans University Seoul03760 Korea Republic of Graduate Program in Smart Factory Ewha Womans University Seoul03760 Korea Republic of Department of Materials Science and Engineering Stanford University StanfordCA93405 United States Department of Electrical Engineering Stanford University StanfordCA93405 United States

Magnetoresistance-based biosensors utilize changes in electrical resistance upon varying magnetic fields to measure biological molecules or events involved with magnetic tags. However, electrical resistance fluctuates with temperature. To decouple unwanted temperature-dependent signals from the signal of interest, various methods have been proposed to correct signals from magnetoresistance-based biosensors. Yet, there is still a need for a temperature correction method capable of instantaneously correcting signals from all sensors in an array, as multiple biomarkers need to be detected simultaneously with a group of sensors in a central laboratory or point-of-care setting. Here we report a giant magnetoresistive biosensor system that enables real-time temperature correction for individual sensors using temperature correction coefficients obtained through a temperature sweep generated by an integrated temperature modulator. The algorithm with individual temperature correction coefficients obviously outperformed that using the average temperature correction coefficient. Further, temperature regulation did not eliminate temperature-dependent signals completely. To demonstrate that the method can be used in biomedical applications where large temperature variations are involved, binding kinetics experiments and melting curve analysis were conducted with the temperature correction method. The method successfully removed all temperature-dependent artifacts and thus produced more precise kinetic parameters and melting temperatures of DNA hybrids. © 2023 The Authors

关键词： Kinetics

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Field Implementation of a Plc-Controlled Single-Axis Sun Tracker in Commercial Pv Systems

SSRN

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SSRN 2024年

作者： de Almeida, Eduardo Santos Cupertino, Allan Fagner Mendes, Victor Flores Pereira, Heverton Augusto Souza, Silas Brito, Erick Matheus da Silveira Gusman, Lucas Soares Department of Electrical Energy UFJF MG Juiz de Fora Brazil Department of Electrical Engineering UFMG MG Belo Horizonte Brazil Institute of Exact and Technological Sciences UFV MG Viçosa Florestal Brazil Graduate Program in Electrical Engineering Universidade Federal de Minas Gerais Av. Antônio Carlos 6627 MG Belo Horizonte31270-901 Brazil

Photovoltaic generation emerges as a promising solution in the transition towards more sustainable energy sources. Various configurations of solar power plants, ranging from fixed, single-axis to dual-axis orientations of photovoltaic modules, offer different efficiencies in solar capture. This study aims to introduce a single-axis solar tracker controlled by a programmable Logic Controller (PLC). By applying well-established equations from the literature, the algorithm estimates the daily solar position, adjusting to the specified latitude through a human-machine interface (HMI). The system is further enhanced with sensors to protect its physical structure, including limit switches and wind speed monitors. Beyond the hardware solution, this work also delves into the development of Python algorithms to assess the theoretical ideal performance of photovoltaic plants under various configurations (fixed, single, and dual-axis) and latitudes, and to compare the effectiveness of different solar tracker adjustment methodologies throughout the day. The results suggest that, depending on the axis configuration and latitude, the adoption of tracking systems can increase energy production by 20\% to 35\%. The practical implementation of the proposed tracker, tested over a year in Divinópolis, MG, Brazil, demonstrated an average increase of 25.23\% in energy production, compared to the fixed system, for plants in the range of 100 k$W_p$ capacity. Thus, this research validates the effectiveness of the PLC-controlled single-axis solar tracker in optimizing photovoltaic energy generation and contributes by providing a theoretical comparison between different solar tracking technologies. © 2024, The Authors. All rights reserved.

关键词： programmable logic controllers

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Curriculum-Based Reinforcement Learning for an Effective Multi-Agent Path Planning Algorithm in Warehouse Scenarios

Curriculum-Based Reinforcement Learning for an Effective Mul...

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IEEE/IAS International Conference on Industry Applications (INDUSCON)

作者： Iure Rosa L. De Oliveira Kevin B. De Carvalho Alexandre S. Brandão Graduate Program in Computer Science Universidade Federal de Viçosa Viçosa Brazil Electrical Engineering Department Universidade Federal de Vicosa Viçosa Brazil

Multi-robot systems can provide substantial increase in efficiency and/or flexibility in different scenarios. Applications in various settings have been studied in the literature, such as disaster management, surveillance, object transportation as well as search-and-rescue. A particular case that can highly benefit from the employment of multiple agents is the logistics in a warehouse scenario. This work proposes an multi-agent Q-learning based algorithm with curriculum learning and transfer learning to perform the path planning process. With progressively more complex stages of training as well as knowledge transfer from one stage to another, the algorithm is capable of achieve high success rates. In order to validate the proposed method, simulations were done to compare it to other combinations of the used techniques, as well as using Q-learning only. Scalability tests were also performed. The proposed method achieved up to 94% success rate after training.

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