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4th International Conference on Advanced electrical and Energy Systems, AEES 2023

作者： Aly, Rabab Hamed M. Hussein, Aziza I. The Higher Institute for Management and Information Technology Minya Egypt Faculty of Computer Science Nahda University Computer Science Department Beni Suef Egypt College of Engineering Effat University Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350342659

Each photovoltaic (PV) system demonstrates a specific point on the current-voltage (IV) curve characteristics where the power generated reaches its maximum value which is referred to as the maximum power point (MPP). The maximum power point tracking (MPPT) system is designed to sample the output of solar cells and adjust the load in order to achieve maximum power output under various environmental conditions. In this paper, a design of a boost converter with MPPT based on a PID controller is proposed. The goal is to optimize the extraction of the highest attainable power from photovoltaic systems. The extracted power is subsequently directed to the load through a boost converter, which elevates the voltage to the necessary level. The PID controller is calibrated by Neural Gas Network (NGN) and utilized to achieve a consistent output voltage irrespective of fluctuations in the power source or the connected load. Moreover, an NGN technique is applied to generate the optimal PID parameters before using the PID. The comprehensive system design depicted has been simulated using MATLAB Simulink to verify the functionality of the system. © 2023 IEEE.

关键词： Controllers

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Safe Satellite Electronics Design Utilizing COTS Components, FDIR Techniques, LCL Protections, and Thorough Qualifications 37

Safe Satellite Electronics Design Utilizing COTS Components,...

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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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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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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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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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Can Copyright Be Reduced to Privacy? 5

Can Copyright Be Reduced to Privacy?

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5th Symposium on Foundations of Responsible Computing, FORC 2024

作者： Elkin-Koren, Niva Hacohen, Uri Livni, Roi Moran, Shay Faculty of Law Tel Aviv University Israel School of Electrical Engineering Tel Aviv University Israel Departments of Mathematics and Computer Science Technion Haifa Israel

ISBN: (纸本)9783959773195

There is a growing concern that generative AI models will generate outputs closely resembling the copyrighted materials for which they are trained. This worry has intensified as the quality and complexity of generative models have immensely improved, and the availability of extensive datasets containing copyrighted material has expanded. Researchers are actively exploring strategies to mitigate the risk of generating infringing samples, with a recent line of work suggesting to employ techniques such as differential privacy and other forms of algorithmic stability to provide guarantees on the lack of infringing copying. In this work, we examine whether such algorithmic stability techniques are suitable to ensure the responsible use of generative models without inadvertently violating copyright laws. We argue that while these techniques aim to verify the presence of identifiable information in datasets, thus being privacy-oriented, copyright law aims to promote the use of original works for the benefit of society as a whole, provided that no unlicensed use of protected expression occurred. These fundamental differences between privacy and copyright must not be overlooked. In particular, we demonstrate that while algorithmic stability may be perceived as a practical tool to detect copying, such copying does not necessarily constitute copyright infringement. Therefore, if adopted as a standard for detecting an establishing copyright infringement, algorithmic stability may undermine the intended objectives of copyright law. © Niva Elkin-Koren, Uri Hacohen, Roi Livni, and Shay Moran.

关键词： Copyrights

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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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SQAT-LD: SPeech Quality Assessment Transformer Utilizing Listener Dependent Modeling for Zero-Shot Out-of-Domain MOS Prediction

SQAT-LD: SPeech Quality Assessment Transformer Utilizing Lis...

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2023 IEEE Automatic Speech Recognition and Understanding Workshop, ASRU 2023

作者： Shen, Kailai Yan, Diqun Dong, Li Ren, Ying Wu, Xiaoxun Hu, Jing Ningbo University Faculty of Electrical Engineering and Computer Science China Zhejiang Key Laboratory of Mobile Network Application Technology China

ISBN: (纸本)9798350306897

In this paper, we propose the speech quality assessment transformer utilizing listener dependent modeling (SQAT-LD) mean opinion score (MOS) prediction system, which was submitted to the 2023 VoiceMOS Challenge. The system is based on a combination of self-supervised learning (SSL) models and listener-dependent modeling. Due to this challenge's emphasis on real-world and challenging zero-shot out-of-domain MOS prediction in three different voice evaluation scenarios, we specifically designed a two-branch module to predict scores and weights for each frame, aiming to achieve better generalization. In the challenge, our system achieved fourth place in Track 1a, second place in Track 1b and first place in Track 2. Additionally, we conducted an ablation study to investigate the effectiveness of our proposed method. © 2023 IEEE.

关键词： Forecasting

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Ensemble Learning and Event-Based Feature Mining for Li-Ion Battery State Prediction 22

Ensemble Learning and Event-Based Feature Mining for Li-Ion ...

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22nd International Learning and Technology Conference, L and T 2025

作者： Qaisar, Saeed Mian Subasi, Abdulhamit Effat University Electrical and Computer Engineering Department Jeddah21478 Saudi Arabia Institute of Biomedicine Faculty of Medicine University of Turku Turku20520 Finland College of Engineering Effat University Department of Computer Science Jeddah21478 Saudi Arabia

ISBN: (纸本)9798331532215

Contemporary power solutions are revolutionizing the existing trend in residential and transportation services. Mobile realizations, in particular, are advancing based on innovative mobile power supply solutions. Batteries are essential to several key applications, such as electric vehicles (EVs), satellites, and mobile phones. Additionally, batteries are elementary parts of the energy storage for distributed renewable microgrids. The are just a few applications based on the batteries-based power supply. Li-ion batteries are frequently used because of their exceptional qualities, which include endurance, high power density, and compact size. Besides these notable benefits the Li-Ion batteries are expensive. To effectively compensate for this high initial cost, the "battery management systems"(BMSs) are used. These solutions are effective in maximizing the Li-Ion battery life and also guarantee a safer operation. Based on the advantages of modular design, the contemporary Li-Ion batteries can be comprised of thousands of individual cells. It results in complex and sophisticated BMSs and can render a notable overhead in terms of power consumption. The focus of this research is on enhancing the existing Li-Ion BMSs by incorporating novel event-based feature mining and machine/ensemble learning techniques. The event-based feature extractor mines pertinent information and promises significant real-time compression. In order to predict the capacity of Li-Ion cells, the produced feature set is then processed utilizing robust machine/ensemble learning algorithms, and the performance of considered regressors is compared. A real Li-Ion battery dataset is used to demonstrate the applicability. For the case of a bagging-based ensemble regressor, the method obtained a correlation coefficient of 0.9996. © 2025 IEEE.

关键词： Lithium-ion batteries

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