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2023 International Conference on Smart Applications, Communications and Networking, SmartNets 2023

作者： Da Costa Rocha, Jesse De Souza, Marcela Alves Cardoso, Evelin Helena Silva Vijaykumar, Nandamudi De Araujo, Jasmine Priscyla Leite Frances, Renato Lisboa Graduate Program in Electrical Engineering Belém Para Brazil Information and Communication Technology Center Marabá Para Brazil Department of Computer Science Capitão Poço Para Brazil Coordination of Applied Research and Technological Development São Paulo Brazil

ISBN: (纸本)9798350302523

This article proposes an intelligent platform for monitoring students' steps on their way to school until they leave the school to their homes. This platform can identify students and notify those responsible and competent authorities in various situations of school life, such as: entering and leaving the school bus, entering and leaving school, entering the school cafeteria, etc. The first application aims to control access to the school bus through facial recognition. In the tests carried out, the recognition system achieved excellent results in all metrics. © 2023 IEEE.

关键词： Students

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PBF-AMP-Onto: an ontology for powder bed fusion additive manufacturing processes 1

PBF-AMP-Onto: an ontology for powder bed fusion additive man...

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1st International Workshop on Semantic materials science: Harnessing the Power of Semantic Web Technologies in materials science, SeMatS 2024

作者： Pour, Mina Abd Nikooie Tarafder, Prithwish Wiberg, Anton Li, Huanyu Moverare, Johan Lambrix, Patrick Department of Computer and Information Science Linköping University Linköping Sweden Swedish e-Science Research Centre Linköping University Linköping Sweden Department of Management and Engineering Linköping University Linköping Sweden Wallenberg Initiative Materials Science for Sustainability Linköping University Linköping Sweden Wallenberg AI Autonomous Systems and Software Program Linköping University Linköping Sweden

Additive manufacturing is an innovative production approach aimed at creating products that traditional techniques cannot produce with the desired quality and requirements. Throughout the additive manufacturing process, data is either used (such as materials properties, printer characteristics and settings) or generated (such as monitoring data during printing, slicing strategies setting parameters). However, managing such data with complex relationships remains a significant challenge in both research and industry in the additive manufacturing field. To address this issue, we developed a modular ontology that can be used as the basis for a framework that supports decision-making systems, facilitate semanticsaware data management, and enhance the understanding and optimization of additive manufacturing processes. In this paper we focus on one of the state-of-the-art additive manufacturing approaches, i.e., powder bed fusion. To show the use and the feasibility of our approach, we created a knowledge graph for an actual additive manufacturing experiment based on our ontology, and show how queries relevant to domain experts can be answered using this knowledge graph. © 2024 CEUR-WS. All rights reserved.

关键词： Ontology

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Surface engineering Towards Boosted Dielectric Properties for High-Temperature Polymers Under electrical Extremes

Surface Engineering Towards Boosted Dielectric Properties fo...

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Conference on electrical Insulation and Dielectric Phenomena, (CEIDP)

作者： Jierui Zhou Marina Dabaghian Anna Marie LaChance Jing Hao Wesley Zhong Luyi Sun Yang Cao Department of Electrical and Computer Engineering University of Connecticut Storrs CT USA Electrical Insulation Research Center Institute of Materials Science University of Connecticut Storrs CT USA Department of Chemical and Biomolecular Engineering University of Connecticut Storrs CT USA Polymer Program Institute of Materials Science University of Connecticut Storrs CT USA

Polymer-thin films are critical dielectric materials for capacitive energy storage in power and electronic systems. The rapid development of wide bandgap semiconductors urgently calls for polymers that can attain high energy density and high charge-discharge efficiency at elevated temperatures. However, high-temperature polymers usually suffer from compromised dielectric breakdown strength and soaring conduction loss due to the intrinsic constraint between their bandgaps and thermal stability. In this work, we propose a facile and high throughput interface engineering technique utilizing nanocoatings formed by montmorillonite (MMT) nanoclays, which can serve as an out-of-the-box solution to bypass this constraint. The highly ordered MMT-based nanocoatings with layered structure can significantly block the excessive charge injection at high electric fields and dissipate the charge carriers along the in-plane direction. The MMT-coated PI films exhibit improved breakdown strength, suppressed conduction loss, and boosted charging-discharging efficiency. This work holds the promise of improving the dielectric properties of polymers through surface engineering, enabling their application at concurrent electrical and thermal extremes.

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Reinforcement Learning-Based Fault-Tolerant Control of Uncertain Strict-Feedback Nonlinear Systems With Intermittent Actuator Faults

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IEEE Transactions on Neural Networks and Learning Systems 2025年第6期36卷 10464-10478页

作者： Qinmin Yang Huaying Li Zhengwei Ruan Bo Fan Shuzhi Sam Ge State Key Laboratory of Industrial Control Technology College of Control Science and Engineering Zhejiang University Hangzhou China Hangzhou Huawei Communication Technology Company Ltd. Hangzhou China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China Department of Electrical and Computer Engineering Institute for Functional Intelligent Materials National University of Singapore Queenstown Singapore

In this work, a novel reinforcement learning-based adaptive fault-tolerant control (FTC) scheme with actuator redundancy is presented for a nonlinear strict-feedback system with nonlinear dynamics and uncertainties. A learning-based switching function technique is established to steer different groups of actuators automatically and successively to mitigate the impact of faulty actuators by observing a switching performance index. The optimal tracking control problem (OTCP) of strict-feedback nonlinear systems is transformed into an equivalent optimal regulation problem of each affine subsystem via adaptive feedforward controllers. Subsequently, the designed objective functions associated with Hamilton–Jacobi–Bellman (HJB) estimate errors caused by neural network (NN) approximations can be minimized by the reinforcement learning algorithm without value or policy iterations. It is proved that the tracking objective can be achieved and all signals in the closed-loop system can be guaranteed to be bounded, as long as the minimum time interval between two successive failures is bounded. Theoretical results are verified by simulations.

关键词： Actuators Switches Fault tolerant systems Control systems Optimal control Redundancy Nonlinear dynamical systems Adaptive control Regulation Industrial control

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The effect of boron incorporation on leakage and wake-up in ferroelectric Al1-xScxN

arXiv

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arXiv 2025年

作者： Gremmel, Maike Savant, Chandrashekhar Prakash Bhattacharya, Debaditya Schönweger, Georg Jena, Debdeep Fichtner, Simon Institute of Material Science Kiel University Germany Department of Materials Science and Engineering Cornell University United States School of Electrical and Computer Engineering Cornell University United States Germany Kavli Institute Cornell for Nanoscale Science Cornell University United States

This study explores the influence of boron incorporation on the structural and electrical properties of ferroelectric Aluminum Scandium Nitride (Al1-xScxN) thin films, focusing on leakage currents, wake-up effects, and imprint behavior. Al1-xScxN films were incorporated with varying boron concentrations and analyzed under different deposition conditions to determine their structural integrity and ferroelectric performance. Key findings include a reduction in leakage currents, non-trivial alterations in bandgap energy as well as an increasing coercive fields with increasing boron content. Films with 6-13 at.% boron exhibited N-polar growth, while those with 16 at.% boron showed mixed polarity after deposition, which affected their ferroelectric response during the initial switching cycles - as did the addition of boron itself compared to pure Al1-xScxN . With increasing boron content, wake-up became gradually more pronounced and was strongest for pure Al1-xBxN . © 2025, CC BY-NC-ND.

关键词： Ferroelectricity

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A Novel Approach to Identify the Anomaly Detection in Electricity usage based on Machine Learning Algorithms and Big Data 3

A Novel Approach to Identify the Anomaly Detection in Electr...

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3rd International Conference on Smart Electronics and Communication, ICOSEC 2022

作者： Panneerselvam, M. Neela, K. Rajeshwari, R. Vengadapathiraj, M. Sobitha, S. Mohanavel, V. Sona College of Technology Department of Electrical and Electronics Engineering Salem India Sri Sairam Engineering College Department of Information Technology Chennai India S.A Engineering College Department of Master of Computer Application Chennai India Rajalakshmi Instittue of Technology Department of Electronics and Communication Engineering Chennai India Saveetha School of Engineering Simats Department of Computer Science and Engineering Chennai India Centre for Materials Engineering and Regenerative Medicine Bharath Institute of Higher Education and Research Tamil Nadu Chennai600073 India Chandigarh University Department of Mechanical Engineering Punjab Mohali140413 India

ISBN: (数字)9781665497640

ISBN: (纸本)9781665497640

Comprehension mistakes and frauds may both be detected via the analysis of advanced metering information. By means of on-site examinations or even other techniques, the energy suppliers will be made aware of any suspect usage patterns by this study. The analysis of usage patterns using Machine Learning (ML) techniques may reveal errors, measuring interruptions from assaults, or fine motor control with utility companies. Another of the basic instances of an anomaly detection is fraud detection since it is difficult to categories transactional or utilization information. Additionally, scams vary in type, making it difficult to consistently learn from them. This research study presents an unsupervised ML method to identify out-of-the-ordinary qualities in time series, set a threshold for the proportion of out-of-the-ordinary readings towards the overall measurements, and then classify the moving average as questionable or not. Initially, two different methods are provided to find the differences in time-series based data streaming: Spectral Residual-Convolutional Neural Network and anomaly trained model based on martingales. The properly established information is then subjected to Fisher Linear Discriminant analysis and Two-Class Boosted Decision Tree applications. © 2022 IEEE.

关键词： Large dataset

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Optical vortices enabled by structural vortices

arXiv

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arXiv 2023年

作者： Liu, Yuanfeng Zhou, Le Guo, Mengfan Xu, Zongqi Ma, Jing Wen, Yongzheng Litchinitser, Natalia M. Shen, Yang Sun, Jingbo Zhou, Ji School of Materials Science and Engineering Tsinghua University Beijing100084 China Department of Electrical and Computer Engineering Duke University DurhamNC27708 United States

The structural symmetry of solids plays an important role in defining their linear and nonlinear optical properties. The quest for versatile, cost-effective, large-scale, and defect-free approaches and materials platforms for tailoring structural and optical properties on demand has been underway for decades. We experimentally demonstrate a bottom-up self-assembly-based organic engineered material comprised of synthesized molecules with large dipole moments that are crystallized into a spherulite structure. The molecules align in an azimuthal direction, resulting in a vortex polarity with spontaneously broken symmetry leading to strong optical anisotropy and nonlinear optical responses. These unique polarization properties of the judiciously designed organic spherulite combined with the symmetry of structured optical beams enable a plethora of new linear and nonlinear light-matter interactions, including the generation of optical vortex beams with complex spin states and on-demand topological charges at the fundamental, doubled, and tripled frequencies. The results of this work are likely to enable numerous applications in areas such as high-dimensional quantum information processing, with large capacity and high security. The demonstrated spherulite crystals facilitate stand-alone micro-scale devices that rely on the unique micro-scale spontaneous vortex polarity that is likely to enable future applications for high-dimensional quantum information processing, spatiotemporal optical vortices, and a novel platform for optical manipulation and trapping. Copyright © 2023, The Authors. All rights reserved.

关键词： Vortex flow

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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13th International electrical engineering Congress, iEECON 2025

作者： Nantapanich, Nutyada Mahatthanajatuphat, Pisit Wongsin, Norakamon Lee, Wilaiporn Rajamangala University of Technology Thanyaburi Faculty of Engineering Department of Electronics and Telecommunication Engineering Pathum Thani Thailand Yothinburana School Regular Program in Mathematics-Science Bangkok Thailand King Mongkut's University of Technology North Bangkok Faculty of Engineering Department of Electrical and Computer Engineering Bangkok Thailand

ISBN: (纸本)9798331543952

The article introduces a two-dimensional polynomial regression model for the predictive analysis of glucose concentration in a fractal microwave sensor NP model, utilizing frequency and transmission coefficient differences to examine their correlation with glucose concentration levels. The sensor employed in this investigation is fabricated on an economical FR-4 substrate to minimize costs. The sensor structure is developed with the fractal structure reduction method established by the NP model to operate at a frequency of roughly 2.97 GHz. The CST Studio software is utilized to analyze the findings from the simulation of the suggested sensor structure. Furthermore, the proposed sensor is integrated into a test apparatus comprising a sensor platform and a liquid tube constructed from PPA material. The liquid under examination is a glucose and water mixture at varying concentrations. It was analyzed using a VNA instrument to evaluate its frequency response and the magnitude of the alteration in the transmission coefficient. The values are utilized to create a two-dimensional polynomial regression model. The experiments demonstrated that the proposed sensor model possesses an Adjusted R² value of 0.99499 and an average error of approximately 3.57% for glucose concentration analysis throughout the range of 5% to 30%, with sensitivities of 0.00879 dB/% for insertion loss and 0.092 MHz/% for frequency variation, facilitating accurate glucose concentration prediction analysis. © 2025 IEEE.

关键词： Polynomial regression

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Heart Failure: Machine Learning Prediction Within a 5-Year Framework 24

Heart Failure: Machine Learning Prediction Within a 5-Year F...

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24th IEEE International Conference on Bioinformatics and Bioengineering, BIBE 2024

作者： Tsarapatsani, Konstantina-Helen Tsakanikas, Vasilios D. Sakellarios, Antonis Trampisch, Hans J. Karapintzou, Efterpi Rudolf, Henrik Matsopoulos, George K. Fotiadis, Dimitrios I. Biomedical Research Institute Foundation for Research and Technology-Hellas Ioannina Greece Ruhr University Bochum Department of Medical Informatics Biometry and Epidemiology Bochum Germany University of Ioannina Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Greece Institute for Biostatistics and Informatics in Medicine and Ageing Research University Medical Center Rostock Germany School of Electrical and Computer Engineering National Technical University of Athens Athens Greece University of Ioannina and Biomedical Research Institute-FORTH Unit of Medical Technology and Intelligent Information Systems Dept. of Materials Science and Engineering Ioannina Greece

ISBN: (纸本)9798331518622

Heart failure (HF) is a complex syndrome that is affected by many factors and causes. It is crucial to early recognize the disease subtypes and the unidentified clinical pathways that give rise to it. Machine learning (ML) is the tool that assist to deal with these challenges and improve the prediction of HF. In this work, the HF risk prediction was implemented by employed ML classifiers, such as Random Forest (RF), Extreme Grading Boosting (XGBoost) and Light Gradient-Boosting Machine (LGBM). We utilized the data from the German epidemiological trial on ankle brachial index - getABI cohort, which includes 6,454 patients. The performance of classifiers was estimated by Accuracy (ACC), Sensitivity, Specificity and the area under the receiver operating characteristic curve (AUC) in mean values for each ML classifier. The results were also interpreted using the Explainable artificial intelligence (XAI) approach, the Shapley Additive exPlanations (SHAP) values. Our work reveals that LGBM classifier predict the HF risk within 5 years follow-up in general population with 68 % accuracy. Moreover, the N-terminal pro-B-type natriuretic peptide (NT-proBNP) was identified as the most important feature for HF risk prediction. © 2024 IEEE.

关键词： Risk perception

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2D Polynomial Regression Modeling for Predictive Analysis of Glucose Concentration in NP-Model Fractal Microwave Sensor Using Frequency and Transmission Coefficient Disparities

2D Polynomial Regression Modeling for Predictive Analysis of...

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International electrical engineering Congress (iEECON)

作者： Nutyada Nantapanich Pisit Mahatthanajatuphat Norakamon Wongsin Wilaiporn Lee Department of Electronics and Telecommunication Engineering Faculty of Engineering Rajamangala University of Technology Thanyaburi Pathum Thani Thailand Regular Program in Mathematics-Science Yothinburana School Bangkok Thailand Department of Electrical and Computer Engineering Faculty of Engineering King Mongkut's University of Technology North Bangkok Bangkok Thailand

ISBN: (数字)9798331543952

ISBN: (纸本)9798331543969

关键词： Analytical models Adaptation models Correlation Liquids Predictive models Microwave sensors Polynomials Fractals Glucose Predictive analytics

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