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International Scientific Conference Electronics (ET)

作者： Matyo Dinev Valentina Kukenska Petar Minev Department of Computer Systems and Technology Faculty of Electrical Engineering and Electronics Technical University of Gabrovo Gabrovo Bulgaria

The subject of this paper is sequential decomposition algorithms. They are applied to graph models of various sizes. A study of their speed and applicability was made with software applications developed for the purpose. The obtained results are presented in tabular and graphical form. Conclusions have been made about their applicability in the design automation of circuit and systems.

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Machine learning predicts long-term mortality after acute myocardial infarction using systolic time intervals and routinely collected clinical data

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Intelligent Medicine 2024年第3期4卷 170-176页

作者： Bijan Roudini Boshra Khajehpiri Hamid Abrishami Moghaddam Mohamad Forouzanfar Department of Systems Engineering School of Advanced TechnologyUniversity of QuebecMontrealCanada Machine Vision and Medical Image Processing(MVMIP)Laboratory Faculty of Electrical and Computer EngineeringK.N.Toosi University of TechnologyTehranIran Research Center of the Montreal University Institute of Geriatrics MontrealCanada

Background Precise estimation of current and future comorbidities of patients with cardiovascular disease is an important factor in prioritizing continuous physiological monitoring and new *** learning(ML)models have shown satisfactory performance in short-term mortality prediction in patients with heart disease,whereas their utility in long-term predictions is *** study aimed to investigate the performance of tree-based ML models on long-term mortality prediction and effect of two recently introduced biomarkers on long-term *** This study used publicly available data from the Collaboration Center of Health Information Appli-cation at the Ministry of Health and Welfare,Taiwan,*** collected data were from patients admitted to the cardiac care unit for acute myocardial infarction(AMI)between November 2003 and September *** collected and analyzed mortality data up to December *** records were used to gather demo-graphic and clinical data,including age,gender,body mass index,percutaneous coronary intervention status,and comorbidities such as hypertension,dyslipidemia,ST-segment elevation myocardial infarction,and non-ST-segment elevation myocardial *** the data,collected from 139 patients with AMI,from medical and demographic records as well as two recently introduced biomarkers,brachial pre-ejection period(bPEP)and brachial ejection time(bET),we investigated the performance of advanced ensemble tree-based ML algorithms(random forest,AdaBoost,and XGBoost)to predict all-cause mortality within 14 years.A nested cross-validation was performed to evaluate and compare the performance of our developed models precisely with that of the conventional logistic regression(LR)as the baseline *** The developed ML models achieved significantly better performance compared to the baseline LR(C-Statistic,0.80 for random forest,0.79 for AdaBoost,and 0.78 for XGBoost,vs.0.77 for LR)(PRF<0.001,PAdaBoost<0.001,a

关键词： Electrocardiogram Predication Mortality prediction Machine learning

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Modelling and control of a multi-product pipeline transportation process

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International Journal of Information Technology (Singapore) 2025年 1-11页

作者： Jiménez-Cabas, Javier Piñeres-Espitia, Gabriel Rodelo-Serrano, Esteban Pamucar, Dragan Ramirez-Leon, Hermes Butt, Shariq Aziz Naz, Sumera Simic, Vladimir Computer Science and Electronic Department Universidad de La Costa Barranquilla Colombia Departamento de Ingeniería Mecánica Universidad Simón Bolívar Barranquilla Venezuela Department of Operations Research and Statistics Faculty of Organizational Sciences University of Belgrade Belgrade Serbia Department of Industrial Engineering & Management Yuan Ze University Taoyuan Taiwan Department of Mechanics and Mathematics Western Caspian University Baku Azerbaijan Faculty of Transport and Traffic Engineering University of Belgrade Vojvode Stepe 305 Belgrade 11010 Serbia Department of Computer Science and Engineering College of Informatics Korea University Seoul 02841 South Korea School of Systems and Technology Department of Computer Science University of Management and Technology Lahore Pakistan Department of Mathematics Division of Science and Technology University of Education Lahore Pakistan

One of the most critical processes in the petroleum industry is transporting crude oil and its derivatives. Usually, it is done, due to easiness and economic aspects, through pipelines;several products, such as gasoline, kerosene, naphtha, and liquefied petroleum gas, among others, are moved in batch processes to both distribution and storage centers or transport ships. Since these transport systems transport different products, many works have focused on developing optimization strategies for scheduling these pipelines. That is, strategies that seek to optimize the use of the pipeline by establishing at what time points certain products must be transported. These strategies would be located at the planning level in the automation pyramid. In this work, on the other hand, different control strategies are proposed for a pipeline, but at the operation level. These strategies would be located at the control level, below the planning strategies in the automation pyramid. A pipeline of three pipe sections is modeled to develop the proposed control strategies, and the mathematical model is validated with actual data. The results show that, first, the proposed model approximates the actual behavior of the pipeline quite well and, second, that the combination of PID and feedforward control is an excellent alternative to implement control strategies for the operation of liquid transportation systems through pipelines. © Bharati Vidyapeeth's Institute of computer Applications and Management 2025.

关键词： control strategies liquid transportation modeling of system

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Intelligent Slime Mould Optimization with Deep Learning Enabled Traffic Prediction in Smart Cities

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computers, Materials & Continua 2022年第12期73卷 6563-6577页

作者： Manar Ahmed Hamza Hadeel Alsolai Jaber S.Alzahrani Mohammad Alamgeer Mohamed Mahmoud Sayed Abu Sarwar Zamani Ishfaq Yaseen Abdelwahed Motwakel Department of Computer and Self Development Preparatory Year DeanshipPrince Sattam bin Abdulaziz UniversityAlKharjSaudi Arabia Department of Information Systems College of Computer and Information SciencesPrincess Nourah bint Abdulrahman UniversityP.O.Box 84428Riyadh11671Saudi Arabia Department of Industrial Engineering College of Engineering at AlqunfudahUmm Al-Qura UniversitySaudi Arabia Department of Information Systems College of Science&Art at MahayilKing Khalid UniversitySaudi Arabia Department of Computer Science and Bioinformatics Singhania UniversityPacheri BariDistrict JhnujhunuRajasthanIndia Department of Architectural Engineering Faculty of Engineering and TechnologyFuture University in EgyptNew Cairo11835Egypt

Intelligent Transportation System(ITS)is one of the revolutionary technologies in smart cities that helps in reducing traffic congestion and enhancing traffic *** the help of big data and communication technologies,ITS offers real-time investigation and highly-effective traffic *** Flow Prediction(TFP)is a vital element in smart city management and is used to forecast the upcoming traffic conditions on transportation network based on past *** Network(NN)and Machine Learning(ML)models are widely utilized in resolving real-time issues since these methods are capable of dealing with adaptive data over a period of *** Learning(DL)is a kind of ML technique which yields effective performance on data classification and prediction *** this motivation,the current study introduces a novel Slime Mould Optimization(SMO)model with Bidirectional Gated Recurrent Unit(BiGRU)model for Traffic Prediction(SMOBGRU-TP)in smart ***,data preprocessing is performed to normalize the input data in the range of[0,1]using minmax normalization ***,BiGRUmodel is employed for effective forecasting of traffic in smart ***,the novelty of the work lies in using SMO algorithm to effectively adjust the hyperparameters of BiGRU *** proposed SMOBGRU-TP model was experimentally validated and the simulation results established the model’s superior performance in terms of prediction compared to existing techniques.

关键词： Smart cities traffic flow prediction slime mould optimization algorithm deep learning intelligent models

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Latency Optimization in LEO Satellite Communications with Hybrid Beam Pattern and Interference Control

arXiv

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arXiv 2024年

作者： Zhang, Qianqian Hu, Ye Jung, Minchae Department of Electrical and Computer Engineering Rowan University NJ United States Department of Industrial and Systems Engineering University of Miami FL United States Department of Electronics and Information Engineering Sejong University Seoul Korea Republic of

The rapid advancement of low Earth orbit (LEO) satellite communication systems has significantly enhanced global connectivity, offering high-capacity, low-latency services crucial for next-generation applications. However, the dense configuration of LEO constellations poses challenges in resource allocation optimization and interference management, complicating coexistence with other communication systems. To address these limitations, this paper proposes a novel framework for optimizing the beam scheduling and resource allocation in multi-beam LEO systems. To satisfy the uneven terrestrial traffic demand, a hybrid beam pattern is employed to enhance the downlink quality of service and minimize the transmission latency from LEO satellites to ground user terminals. Additionally, a dynamic co-channel interference (CCI) control mechanism is developed to mitigate inter-beam interference within the LEO constellation and limit cross-system interference affecting protected users from other networks. The problem of user-beam-frequency allocation with power optimization is formulated as a mixed-integer dynamic programming model and solved using a low-complexity neural network-based graph generation algorithm. Simulation results show that the proposed approach outperforms the baseline methods of full frequency reuse and single-channel transmission, and highlights the potential for further performance improvement with multi-user transmissions. © 2024, CC BY.

关键词： Frequency allocation

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Decentralized and Uncoordinated Learning of Stable Matchings: A Game-Theoretic Approach

arXiv

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arXiv 2024年

作者： Etesami, S. Rasoul Srikant, R. Department of Industrial and Systems Engineering Department of Electrical and Computer Engineering Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States Department of Electrical and Computer Engineering and Coordinated Science Laboratory University of Illinois Urbana-Champaign UrbanaIL61801 United States

We consider the problem of learning stable matchings with unknown preferences in a decentralized and uncoordinated manner, where "decentralized" means that players make decisions individually without the influence of a central platform, and "uncoordinated" means that players do not need to synchronize their decisions using pre-specified rules. First, we provide a game formulation for this problem with known preferences, where the set of pure Nash equilibria (NE) coincides with the set of stable matchings, and mixed NE can be rounded to a stable matching. Then, we show that for hierarchical markets, applying the exponential weight (EXP) learning algorithm to the stable matching game achieves logarithmic regret in a fully decentralized and uncoordinated fashion. Moreover, we show that EXP converges locally and exponentially fast to a stable matching in general markets. We also introduce another decentralized and uncoordinated learning algorithm that globally converges to a stable matching with arbitrarily high probability. Finally, we provide stronger feedback conditions under which it is possible to drive the market faster toward an approximate stable matching. Our proposed game-theoretic framework bridges the discrete problem of learning stable matchings with the problem of learning NE in continuous-action games. Copyright © 2024, The Authors. All rights reserved.

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A New Approach for Long-Term Demand Forecasting

SSRN

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SSRN 2024年

作者： Paulos, José Pedro Azevedo, Filipe Fidalgo, José Nuno INESC TEC - Institute for Systems and Computer Engineering Technology and Science Porto Portugal The Department of Electrical Engineering ISEP IPP - School of Engineering Polytechnic Institute of Porto Portugal The Department of Electrical Engineering and Computers of the Faculty of Engineering of the University of Porto Portugal INESC TEC Porto Portugal

The methodology adopted in this article is based on the principle that load diagrams to be forecasted have two distinct components: shape and magnitude. The present work was focused on the shape component, which was essentially defined by historic data and calendar information. The implemented approach was applied to five European countries using four years for training and one year for testing. The goal was to forecast the hourly demand diagram for an entire future year. The forecasting models were based on three forecasting techniques: General Regression Neural Networks (GRNN), Multiple Linear Regression (MLR), and Artificial Neural Networks (ANN). The performance analysis showed a mean absolute percentage error roughly between 2% and 6%, depending on the country and year being forecasted. Investigating the effect of temperature on predictions was the aim of a second study, which allows us to define uncertainty margins around the forecasted demand curve. This outcome is an essential element for power system planning. © 2024, The Authors. All rights reserved.

关键词： Multiple linear regression

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Machine Learning-Based Multivariate Classification of Cardiac Autonomic Neuropathy Via Imbalanced ECG Recordings

Machine Learning-Based Multivariate Classification of Cardia...

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Advances in Science and engineering Technology International Conferences (ASET)

作者： Muaz Al Radi Abdelfatah Ahmad Abdulrahman Ahmad Abderrahmene Boudiaf Nouf Alkaabi Maher Malouf Herbert Jelinek Department of Electrical Engineering and Computer Science Khalifa University Abu Dhabi UAE Department of Industrial And Systems Engineering Khalifa University Abu Dhabi UAE Department of Biomedical Engineering Khalifa University Abu Dhabi UAE

ISBN: (数字)9798350344134

ISBN: (纸本)9798350344141

Cardiac Autonomic Neuropathy (CAN) is a frequent complication of diabetes that presents a diagnostic challenge in clinical settings. Ewing tests, comprising five assessments of blood pressure and heart rate, have traditionally been used to categorize the severity of CAN. This paper investigates the application of a machine learning (ML) approach that leverages patient demographic information and electrocardiogram (ECG) recordings to automatically classify CAN dysfunction without any of the Ewing tests. Additionally, the ML model being proposed addresses the challenges posed by imbalanced datasets both at the data level, through the utilization of synthetic minority over-sampling technique (SMOTE), and at the algorithmic level, by employing the focal loss function. The effectiveness of the proposed techniques is reflected in the ROC curve achieving an area under curve (AUC) value of 92%. Moreover, the model is able to produce 80% accurate prediction of the development of CAN. However, for enhancing system's classification performance, two Ewing tests are supported as additional features. A higher performance is achieved as indicated by accuracy and F1-score values of 88% and 88%, respectively.

关键词： Measurement Accuracy Prevention and mitigation Machine learning Artificial neural networks Electrocardiography Predictive models Prediction algorithms Recording Classification algorithms

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Integrating Sustainability in engineering with a Double Degree Program Case Study 65

Integrating Sustainability in Engineering with a Double Degr...

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65th IEEE Annual International Scientific Conference on Power and electrical engineering of Riga Technical University, RTUCON 2024

作者： Arhun, Shchasiana Hnatov, Andrii Kunicina, Nadezhda Trunova, Iryna Ulianets, Olha Daleka, Vasyl Kharkiv National Automobile and Highway University Automobile Faculty Vehicle Electronics Department Kharkiv Ukraine Institute of Industrial Electronics and Electrical Engineering Riga Technical University Faculty of Computer Science Information Technology and Energy Riga Latvia O. M. Beketov National University of Urban Economy in Kharkiv Department of electric transport Kharkiv Ukraine

ISBN: (纸本)9798350365771

This article presents an innovative analysis of the development and implementation of double degree programs in higher education, focusing on the emerging fields of electric vehicles and automotive electronics. The study examines the 'Electric Vehicles and Automotive Electronics' educational program, highlighting the critical skills and competencies that students acquire to meet the evolving demands of the modern job market. An algorithm for establishing educational partnerships has been developed, leading to the proposal of a pioneering double degree program between Kharkiv National Automobile and Highway University (KhNAHU) and Riga Technical University (RTU), supported by the Erasmus+ DIGITRANS project. The results show that such collaborative educational frameworks significantly enrich engineering education, giving students a competitive edge in the global workforce. This research provides valuable insights for higher education and international educational initiatives, emphasizing a scalable model for developing interdisciplinary and international programs. © 2024 IEEE.

关键词： Students

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A Practical computer Architecture Education with RISC-V and TL-Verilog

A Practical Computer Architecture Education with RISC-V and ...

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International Scientific Conference Electronics (ET)

作者： Petar Minev Valentina Kukenska Ilian Varbov Matyo Dinev Department of Computer Systems and Technologies Faculty of Electrical Engineering and Electronics Technical University of Gabrovo Gabrovo Bulgaria

The report presents a hands-on learning approach that can be implemented in the computer Architectures labs. A model of a pipelined microarchitecture RISC-V processor core developed using the high-level hardware description language TL-Verilog is described. Basic steps for implementation and visualization of the model are proposed. The necessity of its application in the educational process of students with different training profiles is substantiated.

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