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37th IEEE International Symposium on Defect and Fault Tolerance in VLSI and Nanotechnology Systems, DFT 2024

作者： Kotnik, Bojan Selcan, David Erker, Matic Rotovnik, Tomaz Gacnik, Dejan Furano, Gianluca Kramberger, Iztok SkyLabs D.o.o. Maribor Slovenia Esa - Estec Noordwijk Netherlands University of Maribor Faculty of Electrical Engineering and Computer Science Maribor Slovenia

ISBN: (纸本)9798350366884

This paper presents SkyLabs' concept of advanced and safe satellite electronics design utilizing COTS components. The recent ESA mission class classification paradigm allows the use of COTS components in Class 5, 4, and 3 missions under specific conditions. SkyLabs proposes the utilization of hierarchical FDIR topology using autonomous Latch-up Current Limiter (LCL) on the subsystem level and controlled LCLs on the component level, fault tolerant digital design concepts, and thorough execution of MAIT (manufacture, assembly, integration, and test) process, including TID and SEE characterizations. The proposed concepts are demonstrated on SkyLabs high-performance RISC-V based on-board computer named NANOhpm-obc. © 2024 IEEE.

关键词： Fault current limiters

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TCIP: Transformed Colour Intensity Profiles analysis for fault detection in power line insulators

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Multimedia Tools and Applications 2025年第8期84卷 4503-4534页

作者： Tomaszewski, Michal Gasz, Rafal Kasana, Singara Singh Osuchowski, Jakub Singh, Simranjit Zator, Slawomir Department of Computer Science Faculty of Electrical Engineering Automatic Control and Informatics Opole University of Technology Prószkowska 76 Opole45-758 Poland Department of Computer Science and Information Technology Central University of Haryana Haryana Mahendergarh123031 India Department of Information Technology Dr B. R. Ambedkar National Institute of Technology Punjab Jalandhar India Department of Safety Engineering and Technical Systems Faculty of Production Engineering and Logistics Opole University of Technology Prószkowska 76 Opole45-758 Poland

In response to the escalating demand for electricity, the aging process and inherent failures in power lines have become unavoidable challenges in their operational integrity. This research addresses the imperative need for a robust method to detect damage in power insulator disc images, introducing a novel feature extraction technique grounded in color intensity profile transformation and ensemble learning. For the purposes of the research, a specific dataset was developed based on publicly available images of power insulators. The collection consists of 9232 images of insulator discs which included 2184 images of damaged discs. The discernible damages include breakages and burnouts. Two distinct analysis variants were employed. In the first variant (V1), two classification groups were selected: disc with damages and disc without damages. In the second variant (V2), three classification groups were selected: discs without damage, broken discs, and burnout discs. The described research was divided into two successive stages. In the first of them (S1), the transformed color intensity profiles for the prepared data set were built and various machine learning models for their classification were trained and evaluated. In the next stage (S2), previously developed, highest efficient machine learning models, were used to make the detection of various damaged types of overhead line insulator discs. For the first stage of research high-performance results and a high value of the F1 score parameters were obtained (F1 score = 0.999 for the first variant (S1V1) and F1 score = 0.992 for the second variant (S1V2). In the second stage of research and the first variant (S2V1), the highest result of the F1 score was 0.932 and for the second variant (S2V2) F1 score was 0.930. © The Author(s), under exclusive licence to Springer science+Business Media, LLC, part of Springer Nature 2024.

关键词： Feature extraction

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An Overview of Machine Learning Applications in Blockchain for Green Energy Exchanges 33

An Overview of Machine Learning Applications in Blockchain f...

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33rd Southern African Universities Power engineering Conference, SAUPEC 2025

作者： Nemakonde, Pfano Nemangwele, Fhulufhelo Ratshitanga, Mukovhe University of Venda Faculty Science Engineering and Agriculture Dept. of Physics Thohoyandou South Africa Cape Peninsula University of Technology Dept. of Electrical Electronic and Computer Engineering Bellville South Africa

ISBN: (纸本)9798331535162

There is an increasing demand for affordable, decentralised and distributed electricity, which is one of the key motivating factors that has incentivised this review article's writing. Industrial 4.0 has been a conduit through which the conventional grids shift towards a self-autonomous system, and blockchain platforms are emerging as viable solutions to facilitate that paradigm. With the advent of peer-to-peer (P2P) energy trading, community-based energy exchanges have significantly contributed to reduced grid dependency. Machine learning (ML) is being implemented in predictive analytics for energy forecasting (EF), thereby aiding both distribution network operators (DNOs) and aggregators to precisely and predictably monitor, optimise and stabilise grid operations through smart agents. The main aim of this review paper is to provide a detailed analysis of the existing research and implementation approaches on blockchain-based P2P energy trading and a comparative overview of how ML applications aid predictive analytics in community-based energy exchanges. © 2025 IEEE.

关键词： Predictive analytics

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Application of shunt active power filters in active distribution networks

Application of shunt active power filters in active distribu...

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2022 IEEE PES Innovative Smart Grid Technologies Conference Europe, ISGT-Europe 2022

作者： Primorac, Mario Klaic, Zvonimir Znidarec, Matej Job, Josip Computer Science and Information Technology Osijek Faculty of Electrical Engineering Osijek Croatia

ISBN: (数字)9781665480321

ISBN: (纸本)9781665480321

Constant increase of electricity demand and integrated renewable energy sources in the grid results in harmonic distortions which can impair power quality (PQ). This paper proposes a methodology for the selection of shunt active power filters as a possible solution to these problems and suggests a way to determine the reference current of the filter. Presented analysis results of the installation of filter units at individual points of the test network, which represent real life distribution grid and power quality measurements, show positive effects of reducing the total harmonic distortion (THD). © 2022 IEEE.

关键词： Power quality

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Enhanced superconductivity in SrTiO3-based interfaces via amorphous Al2O3 capping

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Physical Review Materials 2024年第8期8卷 084803-084803页

作者： I. Silber A. Azulay A. Basha D. Ketchker M. Baskin A. Yagoda L. Kornblum A. Kohn Y. Dagan School of Physics and Astronomy Department of Materials Science and Engineering The Iby and Aladar Fleischman Faculty of Engineering The Andrew and Erna Viterbi Faculty of Electrical and Computer Engineering

Oxide interfaces feature unique two-dimensional (2D) electronic systems with diverse electronic properties such as tunable spin-orbit interaction and superconductivity. Conductivity emerges in these interfaces when the thickness of an epitaxial polar layer surpasses a critical value, leading to charge transfer to the interface. Here, we show that depositing amorphous Al2O3 on top of the polar oxide can reduce the critical thickness and enhance the superconducting properties for SrTiO3-based interfaces. A detailed transmission electron microscopy analysis reveals that the enhancement of the superconducting properties is linked to the expansion of the SrTiO3 lattice in a direction perpendicular to the interface. We propose that the increase in the superconducting critical temperature, Tc, is a result of epitaxial strain.

关键词： electrical conductivity electrical properties Phase diagrams Strain Superconducting phase transition Superconductivity Transition temperature Complex oxides Interfaces Electron microscopy

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The Impact of Varying Batch Sizes on the Deep Neural Network Algorithms Performance 8

The Impact of Varying Batch Sizes on the Deep Neural Network...

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8th International Conference on electrical, Telecommunication and computer engineering, ELTICOM 2024

作者： Muharakah, Naemah Sihombing, Poltak Efendi, Syahril Fahmi, Fahmi Universitas Sumatera Utara Faculty of Computer Science and Information Technology Medan20155 Indonesia Universitas Sumatera Utara Department of Electrical Engineering Medan20155 Indonesia

ISBN: (纸本)9798331531249

Batch size is one of the determining factors in the quantity of sample data used in one iterative model training in deep learning. One of the key factors influencing the effectiveness of hyperparameters and model training results. Group size is very important in selection because the right group size will influence the effectiveness and performance of the training. Choosing a batch size that is too small or large can affect accuracy and performance. Deep Neural Network (DNN) algorithm is a form of artificial neural network consisting of several deep layers. There are several batch sizes used in data processing. Optimal performance for batch size is obtained when using a batch size of 64, as it yields the highest average AUC value of 0.815. Furthermore, the optimal configuration was achieved with a batch size of 64 and 6 hidden layers, utilizing a combination of Leaky-ReLV and Sigmoid activation functions, resulting in an AUC value of 0.83. © 2024 IEEE.

关键词： Activation Function Area Under the Curve Batchsizes Deep Neural Network Hidden Layer

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RoRankNet: Modeling Ordinal Progression in Alzheimer's Disease with Plackett-Luce Ranking and Hard Instance Learning

RoRankNet: Modeling Ordinal Progression in Alzheimer's Disea...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yu, Minghang Gao, Linlin Wu, Dan Wang, Jianhua Faculty of Electrical Engineering and Computer Science Ningbo University Ningbo China College of Biomedical Engineering and Instrument Science Zhejiang University Zhejiang China The First Affiliated Hospital Xiamen University Department of Radiology Xiamen China

ISBN: (纸本)9798350386226

Alzheimer's disease (AD) is a chronic neurodegenerative disorder characterized by progression from normal control (NC) to mild cognitive impairment (MCI), and ultimately to AD. These stages reflect ordinal deterioration of specific brain tissues. Timely and accurately identifying these stages is crucial for effective treatment. While existing multi-classification methods using structural magnetic resonance imaging show promise, they often overlook the inherently ordinal progressive relations. Addressing this, our study introduces RoRankNet, a robust ranking network designed to model the ordinal progression of AD. Specifically, we propose a distance-based Plackett-Luce ranking module to effectively represent the distance relations of these stages, enhancing the alignment between extracted features and disease progression. Additionally, we present a hard instance rectification module to identify and correct challenging instances, enhancing classification robustness. Our extensive experiments demonstrate RoRankNet's superiority in classifying NC, MCI, and AD. Notably, our approach not only preserves the ordinal nature in feature space but also excels in localizing discriminative biomarkers essential for accurate stage prediction in clinical practice. Source codes will be publicly available soon. © 2024 IEEE.

关键词： Neurodegenerative diseases

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Integrated Microfluidic Rapid Response and Highly Sensitive Electrochemical Aptasensor for Simultaneous Detection of Aflatoxin B1 and Deoxynivalenol

Integrated Microfluidic Rapid Response and Highly Sensitive ...

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IEEE International Conference on Micro Electro Mechanical Systems

作者： Jinlei Wu Qinghui Jin Ping Yang Wanlei Gao The Faculty of Electrical Engineering and Computer Science Ningbo University China

ISBN: (数字)9798331508890

ISBN: (纸本)9798331508906

A novel electrochemical aptasensor was designed for the simultaneous detection of aflatoxin B1 (AFB1) and deoxynivalenol (DON) using dual-working microelectrodes and PDMS-based microfluidic channels. The system provides high sensitivity, with detection limits of 7.74 pg/mL for AFB1 and 0.172 ng/mL for DON across broad ranges (0.03 − 300 ng/mL for AFB1, 0.3 − 3000 ng/mL for DON). Results can be obtained within 20 minutes, and the platform exhibits excellent reproducibility, selectivity, and stability. Validation with corn extract highlights its potential for on-site food safety monitoring.

关键词： Nanoparticles Micromechanical devices Electric potential Microelectrodes Sensitivity Food safety Reproducibility of results Sensors Microfluidics Monitoring

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Solving (Max) 3-SAT via Quadratic Unconstrained Binary Optimization 1

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23rd International Conference on Computational science, ICCS 2023

作者： Nüßlein, Jonas Zielinski, Sebastian Gabor, Thomas Linnhoff-Popien, Claudia Feld, Sebastian Institute for Informatics LMU Munich Munich Germany Faculty of Electrical Engineering Mathematics and Computer Science TU Delft Delft Netherlands

ISBN: (数字)9783031360305

ISBN: (纸本)9783031360299

We introduce a novel approach to translate arbitrary 3-sat instances to Quadratic Unconstrained Binary Optimization (qubo) as they are used by quantum annealing (QA) or the quantum approximate optimization algorithm (QAOA). Our approach requires fewer couplings and fewer physical qubits than the current state-of-the-art, which results in higher solution quality. We verified the practical applicability of the approach by testing it on a D-Wave quantum annealer. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2023.

关键词： Qubits

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Multimodal Gait Analysis Acquisition System: Challenges and Lessons Learned

Multimodal Gait Analysis Acquisition System: Challenges and ...

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2023 IEEE Symposium Series on Computational Intelligence, SSCI 2023

作者： Ruiz, Karla Michelle Marquez Cervantes, Pilar Pineda Villa, Carlos Martinez-Villasenor, Lourdes Ponce, Hiram Barrera-Animas, Ari Y. Universidad Panamericana Cdmx Faculty of Enginnering Mexico Massachusetts Institute of Technology Electrical Engineering and Computer Science Boston United States

ISBN: (纸本)9781665430654

Nowadays, gait data analysis has become an ex-tremely valuable tool that, without much knowledge, provides significant support in various areas, especially in medicine. This type of analysis not only contributes to generating accurate diagnoses but also plays a fundamental role in physical rehabilitation processes. To harness the potential of this analysis, a multimodal system is being developed with the purpose of enhancing the storage, analysis, and synchronization of multiple modules. The imperative arises from certain constraints within prevailing devices and methods, which stem from their intricate and delicate nature. Therefore, the aim of the study involves creating an implementation that prioritizes flexibility, lightness, and autonomy, all with the ultimate aim of attaining complete self-sufficiency in future advancements. © 2023 IEEE.

关键词： autonomy gait medicine multimodal system

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