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3rd IEEE International Conference on Smart Technologies for Power, Energy and control, STPEC 2023

作者： Wazir, Awadh Ba Alhussainy, Abdullah A. Alobaidi, Abdulraheem H. Altaf, Aatif Kumar, Anil Kumar, Mahendra Alghamdi, Sultan King Abdulaziz University Faculty of Engineering Department of Electrical and Computer Engineering Jeddah Saudi Arabia King Abdulaziz University Center of Research Excellence in Renewable Energy and Power Systems Jeddah Saudi Arabia Dr. B R Ambedkar National Institute of Technology Jalandhar Instrumentation and Control Engineering Department Punjab Jalandhar India

ISBN: (纸本)9798350304732

This paper introduces a robust controller designed for load frequency control in a dual-area integrated power grid. Here, the suggested controller is a blend of a fuzzy logic controller with an integral controller. The parameters of the integral controller and frequency bias settings are optimized to enhance the dynamic response of the proposed controller. To attain this, three distinct optimization algorithms are employed, i.e., genetic algorithm, simulated annealing, and pattern search. A new objective function using settling time, settling-max, and settling-min with a suitable weight factor is used to improve the parameters estimation process. A comparative analysis is performed using the proposed approach and classical-I controller under two cases considering several load disturbance scenarios. Furthermore, the suggested hybrid controller is evaluated and compared with similar models in the literature. As expected, the proposed controller exhibits superior performance. © 2023 IEEE.

关键词： Fuzzy logic controller integral controller load frequency control optimization technique parameter estimation

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Investigating Optimal Tracking Intervals for Solar Tracking systems to Enhance Efficiency and Performance

Investigating Optimal Tracking Intervals for Solar Tracking ...

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2024 IEEE International Conference on Artificial Intelligence and Green Energy, ICAIGE 2024

作者： Dahli, Khadidja Ilinca, Adrian Benallal, Abdellah Cheggaga, Nawel University of Blida 1 Electrical and Remote Control Systems Laboratory LabSET Faculty of Technology Blida Algeria Ecole de Technologie Superieure Department of Mechanical Engineering MontrealQC Canada University of Quebec at Rimouski Wind Energy Laboratory RimouskiQC Canada

ISBN: (纸本)9798350389838

Conventional solar trackers continuously adjust solar panels to optimize energy production, a process that can consume significant energy, potentially surpassing the energy gained. This research examines the most effective tracking interval for solar tracking systems to mitigate this issue. Through testing with intervals ranging from 5 minutes to 5 hours, it was determined that setting tracking intervals no shorter than 1 hour and no longer than 2 hours and 30 minutes is recommended to achieve a balance between energy efficiency and production. Additionally, this study suggests that elevation and azimuth angles should not be tracked using the same time periods;instead, a 2-hour interval for elevation tracking and a 3-hour interval for azimuth angle tracking are advised for optimal performance. The proposed discrete tracking method consumes less than 0.15% of the energy produced, thereby enhancing energy efficiency, reducing unnecessary energy consumption, and improving the overall reliability and lifespan of solar tracking systems. This approach has the potential to significantly impact the future of solar power by enhancing the efficiency and reliability of solar tracking systems, thereby increasing the viability and appeal of solar energy as a primary renewable resource. © 2024 IEEE.

关键词： discrete tracking energy efficiency renewable resource solar power solar trackers

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Testing of SLAM methods in the Matlab environment 19

Testing of SLAM methods in the Matlab environment

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19th IEEE World Symposium on Applied Machine Intelligence and Informatics, SAMI 2021

作者： Mihalik, Michal Hrubos, Marian Janota, Ales University of Zilina Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Zilina Slovakia University of Faculty of Electrical Engineering and Information Technology Department of Control and Information Systems Zilina Slovakia

ISBN: (纸本)9781728180533

This work is focused on establishing TCP/IP connection between SICK LD-OEM 1000 laser scanner and PC. Space measurements will be made using this connection. Then we analyze the data and process it. The data is processed by the Monte Carlo Simultaneous localization and mapping algorithm, which will be used to compile a map of the scanned space. This work is based on MATLAB software environment. © 2021 IEEE.

关键词： MATLAB

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Robust controller Design for a Boiler-Turbine Unit 31

Robust Controller Design for a Boiler-Turbine Unit

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31st International Conference on Cybernetics and Informatics, K and I 2022

作者： Kozakova, Alena Capkova, Romana Puleva, Teofana Slovak University of Technology in Bratislava Faculty of Electrical Engineering and Information Technology Bratislava Slovakia Technical University of Sofia Department of Systems and Control Sofia Bulgaria

ISBN: (数字)9781665487757

ISBN: (纸本)9781665487757

The paper deals with modelling and robust controller design for a boiler-turbine unit in the turbo power plant Maritsa Iztok-2. Based on a simplified first principles nonlinear simulation model and related linearized model in form of a 2x2 transfer matrix with a known structure of individual entries, the resulting plant model is obtained as a set of transfer matrices identified from simulation experiments on the nonlinear model in selected operating points. A robust controller with a decentralized structure is designed using the unified robust decentralized controller design method, the core of which is the equivalent subsystems method (ESM). The controller design results are verified by simulation on the linearized as well as the nonlinear models. © 2022 IEEE.

关键词： Robust control

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Optimizing Machine Learning Algorithms to Enhance Blood Donation for Liver Patients: A Case Study 7

Optimizing Machine Learning Algorithms to Enhance Blood Dona...

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7th IET Smart Cities Symposium, SCS 2023

作者： Mijwil, Maad M. El-Kenawy, El-Sayed M. Bala, Indu Aljanabi, Mohammad Abotaleb, Mostafa Ibrahim, Abdelhameed Computer Techniques Engineering Department Baghdad College of Economic Sciences University Baghdad Iraq Department of Communications and Electronics Delta Higher Institute of Engineering and Technology Egypt School of Electrical and Electronics Engineering Lovely Professional University Punjab India Department of Computer Aliraqia University Baghdad Iraq Department of System Programming South Ural State University Chelyabinsk Russia Computer Engineering and Control Systems Department Faculty of Engineering Mansoura University Mansoura Egypt

ISBN: (纸本)9781839539831

Blood is vital for transporting oxygen, nutrients, and hormones to all body parts as it circulates through arteries and veins. It removes carbon dioxide, regulates body temperature, and maintains the body's immune system. Individuals constantly need blood and its derivatives to save their lives and improve their health through medical treatments and surgical operations. Liver diseases are one of the diseases that affects the health of individuals and requires blood to continue living. These diseases cause significant damage to people's health, and early diagnosis plays a crucial role in saving lives. In this paper, machine learning algorithms (support vector machine and random forest) are involved in detecting liver diseases and determining whether donors are suitable to donate blood from blood values. This paper is applied research that found that the performance measures of the random forest algorithm achieved excellent performance in identifying suitable people to donate blood. © The Institution of engineering & Technology 2023.

关键词： Support vector machines

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Towards Optimal Spatio-Temporal Decomposition of control-Related Sum-of-Squares Programs

arXiv

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arXiv 2024年

作者： Cibulka, Vit Korda, Milan Haniš, Tomaš Department of Control Engineering Faculty of Electrical Engineering Czech Technical University Prague Czech Republic CNRS Laboratory for Analysis and Architecture of Systems Toulouse France

This paper presents a method for calculating the Region of Attraction (ROA) of nonlinear dynamical systems, both with and without control. The ROA is determined by solving a hierarchy of semidefinite programs (SDPs) defined on a splitting of the time and state space. Previous works demonstrated that this splitting could significantly enhance approximation accuracy, although the improvement was highly dependent on the ad-hoc selection of split locations. In this work, we eliminate the need for this ad-hoc selection by introducing an optimization-based method that performs the splits through conic differentiation of the underlying semidefinite programming problem. We provide the differentiability conditions for the split ROA problem, prove the absence of a duality gap, and demonstrate the effectiveness of our method through numerical examples. © 2024, CC BY.

关键词： Nonlinear dynamical systems

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Mathematical Modeling of the Novel Coronavirus Pandemic in Iran: A Model With Vaccination 8

Mathematical Modeling of the Novel Coronavirus Pandemic in I...

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8th International Conference on control, Instrumentation and Automation, ICCIA 2022

作者： Shadi, Reza Fakharian, Ahmad Khaloozadeh, Hamid Qazvin Branch Islamic Azad University Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Iran K.N. Toosi University of Technology Department of Systems and Control Engineering Tehran Iran

ISBN: (纸本)9781665495691

The novel coronavirus (COVID-19) is a major health threat caused by a virus called Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2). COVID-19 can cause acute respiratory illness, which is deadly in some people. Epidemio-logically, vaccination is the safest and most effective approach to gaining immunity and fighting against viral diseases. In this paper, the extended compartmental SEIR model, considering vaccination as a pharmaceutical intervention, to assess the behavior of the COVID-19 outbreak in Iran is proposed. Initially, some mathematical analysis of the model, including positivity, boundedness, the basic reproduction number, and herd immunity is investigated. In order to validate the proposed model, the biological parameters of the model are estimated based on real-confirmed cases in Iran at a specified interval of time. Finally, to examine the concept of flattening the curve of the disease outbreak, the impacts of physical distancing and vaccination on mitigating the burden of the epidemic are compared graphically. © 2022 IEEE.

关键词： Vaccines

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Transmission Dynamics and Optimal control Strategy to Mitigate the Spread of Novel Coronavirus: The Case of Iran 30

Transmission Dynamics and Optimal Control Strategy to Mitiga...

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30th International Conference on electrical engineering, ICEE 2022

作者： Shadi, Reza Fakharian, Ahmad Khaloozadeh, Hamid Islamic Azad University Faculty of Electrical Biomedical and Mechatronics Engineering Qazvin Branch Qazvin Iran K.N. Toosi University of Technology Department of Systems and Control Engineering Tehran Iran

ISBN: (纸本)9781665480871

The outbreak of the novel coronavirus (COVID-19) is currently considered a great challenge to the health of human society. In this study, since the COVID-19 vaccines are currently being developed, a vaccine allocation as an effective pharmaceutical strategy to immunize people against disease is being considered. To this end, a new extended SIR-type model including vaccination compartment and reinfection transmission to predict disease behavior in Iran has been formulated. The mathematical analysis of the model is investigated to verify that the proposed model is well-posed epidemiologically. The biological parameters are evaluated via a nonlinear least-square fitting approach. Finally, to investigate the impact of preventive pharmaceutical measures on flattening the curve of the COVID-19 incidence in Iran, the optimal control strategy is applied. The results of numerical simulations and the optimal control analysis illustrate that the combined implementation of time-dependent measures has a drastic impact on disease burden reduction. © 2022 IEEE.

关键词： COVID-19

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Next Generation Memristor Reservoir Computing

Next Generation Memristor Reservoir Computing

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Metrology for Extended Reality, Artificial Intelligence and Neural engineering (MetroXRAINE), IEEE International Conference on

作者： K. Nikiruy T. Ivanov M. Ziegler D. Rossetti F. Corinto A. Ascoli R. Tetzlaff A. S. Demirkol N. Schmitt Department of Electrical Engineering and Information Technology Micro-and Nanoelectronic Systems TU Ilmenau Ilmenau Germany Department of Electronics and Telecommunications (DET) Politecnico di Torino Turin Italy Faculty of Electrical and Computer Engineering Institute of Circuits and Systems TU Dresden Dresden Germany

ISBN: (数字)9798350378009

ISBN: (纸本)9798350378016

The nonlinear transformation used in reservoir computing can be effectively replaced by nonlinear vector autoregression (NVAR) for data prediction. In such a method, also known as next generation reservoir computing (NGRC), the input signal consists of a linear part, including several previous data points, and their nonlinear combinations. Here we show that the application of this method to a network with memristive weights (memristors) can be used to predict signals, depending on the nature of the nonlinear functions and the number of memristors. The network allows an accurate prediction of chaotic time series of Mackey-Glass and Duffing oscillators.

关键词： Performance evaluation Accuracy Time series analysis Memristors Reservoir computing Neural engineering Ordinary differential equations Vectors Next generation networking Oscillators

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Intelligent Estimation of Non-Invasive Blood Pressure From the Electrocardiogram Signal (ECG)

Intelligent Estimation of Non-Invasive Blood Pressure From t...

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electrical engineering and Automatic control (ICEEAC), International Conference on

作者： Siham Moussaoui Hocine Chebi Sidali Fellag Yasmina Fadila Didouche Department of Electrical Engineering Systems Systems and Telecommunications Engineering Laboratory Boumerdes University Algeria Faculty of Electrical Engineering Laboratory Intelligent Control and Electrical Power System (ICEPS) Djillali Liabes University of Sidi Bel Abbes Sidi Bel Abbes Algeria Department of Chemistry Faculty of Sciences Boumerdes University Algeria

ISBN: (数字)9798350349740

ISBN: (纸本)9798350349757

Continuous real-time blood pressure monitoring offers remarkable potential for the detection of cardiovascular disease. In fact, detection from an inflatable cuff makes the patient uncomfortable as a consequence of the inflation pressure of the cuff on the arm, which causes failures in detecting the results or errors in interpreting the systolic blood pressure values and diastolic blood pressure. In this contribution, we present an automatic system to predict the blood pressure signal based on the complexity analysis of the ECG signal, where the features extracted are signal mobility, signal complexity, fractal dimension, entropy, and autocorrelation with the neural network approach (ANN). Our technique is mainly tested on the MIMIC-III databases available to the public on physionet to have the ECG and ABP signals for feature extraction. The results provided argue in favor of our method in terms of efficiency and precision. Indeed, the proposed approach offers the possibility of detecting arterial pressure such as systolic arterial pressure and diastolic arterial pressure in real time

关键词： Deep learning Training Estimation Artificial neural networks Electrocardiography Feature extraction Blood pressure

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