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18th IEEE International Conference on Advanced Motion Control, AMC 2024

作者： Deng, Jifei Sierla, Seppo Sun, Jie Vyatkin, Valeriy Northeastern University State Key Laboratory of Rolling and Automation Shenyang China Aalto University Department of Electrical Engineering and Automation Espoo Finland Luleå University of Technology Department of Computer Science Electrical and Space Engineering Luleå Sweden

ISBN: (纸本)9798350342819

This paper proposes an intelligent dynamic modeling method for strip rolling process. Actuators of a cold rolling mill perform actions, including work roll bending, intermediate roll bending, and roll gap tilting, to regulate the product quality. Conventional first-principles modeling necessitates intricate mathematical equations derived from process analysis and action evaluation. However, the complexity of the model increases the difficulty of upgrading the model. To address this limitation, a novel approach involving a deep neural network with a Gaussian distribution layer is proposed to effectively capture the process dynamics. A negative log likelihood is established as the loss function for the model using real-world industrial data. Experiment outcomes demonstrate the efficacy of the proposed method in achieving an accurate dynamic model. © 2024 IEEE.

关键词： Deep neural networks

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Digitalization of Building electrical systems Towards Energy Efficiency: Planning and Design Insights

Digitalization of Building Electrical Systems Towards Energy...

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Renewable Energies and Smart Technologies (REST), International Conference on

作者： Edon Hajdini Agnesë Avdiu Mimoza Ibrani Department of Power Systems Faculty of Electrical and Computer Engineering (Alumni) Prishtina Republic of Kosovo Department of Telecommunications Faculty of Electrical and Computer Engineering Prishtina Republic of Kosovo

ISBN: (数字)9798350358902

ISBN: (纸本)9798350358919

The digitalization of building electrical systems is considered a transformative step towards enhancing energy efficiency. In this paper are presented the design and analysis in-sights of power digitalization in electrical systems for commercial buildings using EcoStruxure Power Design software, allowing for the simulation of electrical loads and providing the right circuit breakers and cable sections of feeders based on the planned load. To comprehensively analyze and harness the benefits of digitalization, we initially developed a conventional electrical system. Through this conventional scheme, shortcomings, and devised strategies are identified to enhance energy efficiency, preventive maintenance, proactive maintenance, as well as monitoring and remote commanding after implementation. To conduct the comparative analysis, we present a design of a digital electrical system where implementation within the project is structured across two layers: connected products and edge control. This integrated approach optimizes the operational efficiency and performance of the electrical system while leveraging the capabilities afforded by digitalization. A comparative analysis of the conventional electrical system and the digital electrical system is presented, highlighting the importance and advantages of digitalization.

关键词： Renewable energy sources Buildings Reliability engineering Energy efficiency Software Maintenance Software reliability

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Security Evaluation of Open Access Wi-Fi Networks in a Public University in Junin, Peru 30

Security Evaluation of Open Access Wi-Fi Networks in a Publi...

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30th IEEE International Conference on Electronics, electrical engineering and Computing, INTERCON 2023

作者： Aguilar, Brayan Orcada, Fabrizio Chamorro, Carlos Huamanchahua, Deyby Valcarcel-Castillo, Hector Universidad Continental Department of Systems and Computer Engineering Huancayo Peru Universidad Continental Department of Mechatronics Engineering Huancayo Peru Universidad de Ingenieria y Tecnologia - Utec Department of Electrical and Mechatronics Engineering Lima Peru

ISBN: (纸本)9798350315578

This article develops a topic of interest for users who can access the Internet and do not know the dangers of connecting to a free access network where the administrator of this is unknown, so a connection to this network would not be recommended. The central theme of this research is to demonstrate the risks of connecting to unknown access networks. The methodology used was the evaluation of how many users were being connected to the network created to monitor. The data obtained from users already connected have also been evaluated. According to the days they were connected through the networks, there is a strong demand for connected users. The conclusions show that some users are unaware of the dangers of connecting to one of these networks mentioned above and the risk of the amount of information that a person connected to the same network can obtain with the help of programs already created for its correct use. This work is a product of evaluating the dangers of connecting to an unknown network in Huancayo, Peru. © 2023 IEEE.

关键词： Wi-Fi

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Improved Feed-Forward Control of Active Power Decoupler for PV Microinverter With Non-Sinusoidal Power Decoupling Capability

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IEEE Transactions on Power Electronics 2025年

作者： Shen, Yidi Hasnain, Arafat Zakzewski, Daniel Resalayyan, Rakesh Khaligh, Alireza University of Maryland Department of Electrical and Computer Engineering Maryland Power Electronics Laboratory Institute for Systems Research College ParkMD20742 United States

In single-phase power conversion systems, there is an inherent difference between the dc-side constant and ac-side oscillating power, and power decoupling is required especially in applications like photovoltaic (PV) inverters. Active power decoupling (APD) converters are often used to balance instantaneous power while reducing the size of dc link capacitors and improving the system reliability. Conventionally, APD converters are controlled to decouple only sinusoidal power at double-line frequency. However, the control is further complicated when non-sinusoidal power is delivered due to non-linear circuit components, non-linear loads, or grid distortions. This paper proposes an improved feed-forward APD control scheme with the capability of decoupling non-sinusoidal power, which has application in single-phase systems, particularly all types of PV microinverters. The proposed feed-forward control includes the capacitor voltage calculation under arbitrary power harmonics and a dead-time compensation to increase the accuracy of duty prediction. The proposed algorithm can work as open-loop control to eliminate APD sensors and can serve as a feed-forward term in closed-loop control to improve power decoupling performance, thereby simplifying the controller design. The proposed approach is experimentally verified using a dc-parallel APD prototype, a 50 V 250 W microinverter with non-linear ac capacitor, and a diode rectifier load. © 1986-2012 IEEE.

关键词： Closed loop control systems

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Neural Network Trained Cold-plate Models for Modular Converter Exploration

Neural Network Trained Cold-plate Models for Modular Convert...

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2024 IEEE Energy Conversion Congress and Exposition, ECCE 2024

作者： Shabani, Hamed Cuzner, Robert M. Ordonez, Juan Marques, Crístofer Hood University of Wisconsin - Milwaukee Center for Sustainable Electrical Energy Systems Department of Electrical and Computer Engineering MilwaukeeWI United States FAMU-FSU College of Engineering Florida State University Center for Advanced Power Systems Department of Mechanical Engineering TallahasseeFL United States RS Rio Grande Brazil FAMU-FSU College of Engineering Center for Advanced Power Systems TallahasseeFL United States

ISBN: (纸本)9798350376067

Designing power electronics for medium voltage (MV) and applications, such as the Modular Multilevel Converter (MMC) MVac-MVdc converter for Multi-Terminal (MT) MVdc systems, requires a multidisciplinary approach to optimize a highly intricate system. One of the main challenges in this field is exploring various MMC implementations and evaluating them through appropriate metrics to determine the most effective approach. To address this challenge, this paper introduces a Virtual Prototyping Process (VPP) for Power Electronic Building Block (PEBB) based systems that leverages neural network-trained coldplate surrogate models. These coldplate surrogate model is useful in managing the complexity of a single thermal interface point connected to multiple coldplate-cooled elements within an MMC Sub-Module Drawer (SMD) to a system-level thermal management system. This approach enables the correlation of the intrinsic power capability of point of source to point of load Power Trains as a function of coolant approach and thermal management system capability to facilitate solutuion space exploration. This exploration is conducted in an evolutionary environment (within the VPP) that balances competing objectives like power density, efficiency, specific power and specific cost. The shipboard Integrated Power and Energy System (IPES) application is used as a use-case example. © 2024 IEEE.

关键词： Benchmarking

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On the Stabilization of a Class of OSL Conformable Fractional Order Nonlinear systems 11

On the Stabilization of a Class of OSL Conformable Fractiona...

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11th IEEE International Conference on systems and Control, ICSC 2023

作者： Alsharif, Ali Omar M. Jmal, A. Naifar, O. Ahmed, Ausama H. Gassara, Hamdi Makhlouf, A. Ben Electrical Engineering Department BP 1173 Sfax3038 Tunisia Elmergib University Faculty of Engineering Department of Electrical & Computer Engineering Alkhums Libya National School of Engineering of Sfax University of Sfax Laboratory of Sciences and Techniques of Automatic Control and Computer Engineering Tunisia Faculty of Sciences of Sfax Department of Mathematics Route Soukra BP 1171 Sfax3000 Tunisia

ISBN: (纸本)9798350304886

One-Sided Lipschitz (OSL) fractional order modeling is a top choice for solving the stabilization issue of nonlinear systems. Despite numerous studies on the subject, there remains a gap in understanding when it comes to fractional calculus. By providing a stabilizing strategy for a certain type of OSL fractional order nonlinear systems, this study fills the gap. A numerical example demonstrating the correctness of the suggested approach and demonstrating its efficacy for the tested class. © 2023 IEEE.

关键词： Stabilization

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RSMA-Enabled Multigroup Multicast Rate-Matching for Multibeam Satellite systems 24

RSMA-Enabled Multigroup Multicast Rate-Matching for Multibea...

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24th IEEE International Workshop on Signal Processing Advances in Wireless Communications, SPAWC 2023

作者： Lee, Juhwan Seong, Jaehyup Lee, Jungwoo Le Ruyet, Didier Yin, Longfei Clerckx, Bruno Shin, Wonjae Seoul National University Department of Electrical and Computer Engineering Korea Republic of Ajou University Department of Artificial Intelligence Convergence Network Korea Republic of Conservatoire National des Arts et Métiers Cedric Laboratory France Imperial College Department of Electrical and Electronic Engineering London United Kingdom Austria Ajou University Department of Electrical and Computer Engineering Korea Republic of

ISBN: (纸本)9781665496261

Rate-splitting multiple access (RSMA) is a novel multiple access technique that breaks messages into common and private parts to mitigate interference in a flexible manner. Motivated by its robustness, RSMA is promising in multibeam satellite systems, where a satellite broadly serves terrestrial users through a multibeam multicast transmission with a full frequency reuse among all spot beams. In the multibeam satellite system, there are heterogeneous traffic demands among the spot beams. To meet the different traffic requirements for the spot beams, we present a novel RSMA-enabled rate-matching framework that matches the traffic demand for each spot beam through partially decoding inter-beam interference. We optimize the precoding and power allocation of the private/common messages to achieve the minimization of the difference between data rates and traffic demands among all spot beams. Numerical results show that the proposed framework significantly outperforms conventional transmission schemes in terms of meeting the traffic demands for the spot beams. © 2023 IEEE.

关键词： Multicasting

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Federated Learning Model for Early Detection of Dementia Using Blood Biosamples

Federated Learning Model for Early Detection of Dementia Usi...

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2023 IEEE International Conference on Artificial Intelligence, Blockchain, and Internet of Things, AIBThings 2023

作者： Elsersy, Mohamed Sherif, Ahmed Imam, Ahmad Abdel-Aliem Khan Mamun, Mohammad M. R. Khalil, Kasem Haitham, Mohamed Higher Colleges of Technology Computer and Information Systems Dept. Abu Dhabi United Arab Emirates University of Southern Mississippi School of Computing Sciences and Computer Engineering MS United States William Carey University College of Osteopathic Medicine Mississippi MS United States Tennessee Technological University Electrical and Computer Engineering Department TN United States University of Mississippi Electrical and Computer Engineering Department MS United States Cairo Egypt

ISBN: (纸本)9798350322347

Alzheimer's disease (AD) is a serious, long-term health problem that causes much pain and loss for the person with it and their family. Its early and accurate detection might result in a substantial reduction of the disease outcomes and consequences. Blood biosamples are a simple and inexpensive technique in medical testing. This paper proposes diagnostic models for blood biosamples based on federated learning (FL) and its modifications to detect AD early. Our experiments used blood biosample data sets from the ADNI website to evaluate our models. Our performance analysis indicates that our algorithms are more accurate and achieve an accuracy of 87% for early detection. © 2023 IEEE.

关键词： Neurodegenerative diseases

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Phasmatodea Population Evolution Algorithm Based on Spiral Mechanism and Its Application to Data Clustering

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computers, Materials & Continua 2025年第4期83卷 475-496页

作者： Jeng-Shyang Pan Mengfei Zhang Shu-Chuan Chu Xingsi Xue Václav Snášel College of Computer Science and Engineering Shandong University of Science and TechnologyQingdao266590China School of Artificial Intelligence Nanjing University of Information Science and TechnologyNanjing210044China Department of Information Management Chaoyang University of TechnologyTaichung41349TaiwanChina Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of TechnologyFuzhou350118China Faculty of Electrical Engineering and Computer Science VŠB-Technical University of OstravaOstrava70833Czech Republic

Data clustering is an essential technique for analyzing complex datasets and continues to be a central research topic in data *** clustering algorithms,such as K-means,are widely used due to their simplicity and *** paper proposes a novel Spiral Mechanism-Optimized Phasmatodea Population Evolution Algorithm(SPPE)to improve clustering *** SPPE algorithm introduces several enhancements to the standard Phasmatodea Population Evolution(PPE)***,a Variable Neighborhood Search(VNS)factor is incorporated to strengthen the local search capability and foster population ***,a position update model,incorporating a spiral mechanism,is designed to improve the algorithm’s global exploration and convergence ***,a dynamic balancing factor,guided by fitness values,adjusts the search process to balance exploration and exploitation *** performance of SPPE is first validated on CEC2013 benchmark functions,where it demonstrates excellent convergence speed and superior optimization results compared to several state-of-the-art metaheuristic *** further verify its practical applicability,SPPE is combined with the K-means algorithm for data clustering and tested on seven *** results show that SPPE-K-means improves clustering accuracy,reduces dependency on initialization,and outperforms other clustering *** study highlights SPPE’s robustness and efficiency in solving both optimization and clustering challenges,making it a promising tool for complex data analysis tasks.

关键词： Phasmatodea population evolution algorithm data clustering meta-heuristic algorithm

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Machine learning algorithms for predicting the risk of chronic kidney disease in type 1 diabetes patients: a retrospective longitudinal study

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Neural Computing and Applications 2024年第26期36卷 16545-16565页

作者： Chowdhury, Md Nakib Hayat Reaz, Mamun Bin Ibne Ali, Sawal Hamid Md Crespo, María Liz Cicuttin, Andrés Ahmad, Shamim Haque, Fahmida Bakar, Ahmad Ashrif A. Razak, Mohd Ibrahim Bin Shapiai Abd Bhuiyan, Mohammad Arif Sobhan Department of Electrical Electronic and Systems Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Saidpur Cantonment Saidpur Nilphamari5310 Bangladesh Department of Electrical and Electronic Engineering Independent University Bangladesh Dhaka Bashundhara Bangladesh Department of Electrical Electronic and Systems Engineering Centre of Advanced Electronic and Communication Engineering Universiti Kebangsaan Malaysia UKM Selangor Bangi43600 Malaysia Malaysia-Japan International Institute of Technology Universiti Teknologi Malaysia Jalan Sultan Yahya Petra Kuala Lumpur54100 Malaysia Trieste34151 Italy Department of Computer Science and Engineering University of Rajshahi Rajshahi6205 Bangladesh Artificial Intelligence Resource Molecular Imaging Branch National Cancer Institute BethesdaMD United States Department of Electrical and Electronics Engineering Xiamen University Malaysia Selangor Bandar Sunsuria Sepang43900 Malaysia

Chronic kidney disease (CKD) is a significant concern for individuals with type 1 diabetes (T1D), impacting their quality of life and healthcare costs. Identifying T1D patients at greater risk of developing CKD is crucial for preventive measures. However, it is challenging due to the asymptomatic progression of CKD and limited nephrologist availability in many countries. This study explores machine learning algorithms to predict CKD risk in T1D patients using ten years of retrospective data from the Epidemiology of Diabetes Interventions and Complications clinical trial. Eleven machine learning algorithms were applied to twenty-two readily available features from T1D patients’ routine check-ups and self-assessments to develop 10-year CKD risk prediction models. In addition, we also proposed a heterogeneous ensemble model (STK) using a stacking generalization approach. The models’ performance was evaluated using different evaluation metrics and repeated stratified k-fold cross-validation. Several predictive models showed reliable performance in CKD risk prediction, with the proposed ensemble model being the best performing with an average accuracy of 0.97, specificity of 0.98, sensitivity/recall of 0.96, precision of 0.98, F1 score of 0.97, Kappa and MCC score of 0.94, AUROC of 0.99, and Precision-Recall curve of 0.99. The proposed machine learning approach could be applicable for CKD risk prediction in T1D patients to ensure the necessary precautions to overcome the risk. © The Author(s), under exclusive licence to Springer-Verlag London Ltd., part of Springer Nature 2024.

关键词： Machine learning

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