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6th IEEE Novel Intelligent and Leading Emerging Sciences Conference, NILES 2024

作者： Mohamed, Noor El-Deen M. El Zoghby, Helmy M. Abdelhakam, Mostafa M. Elmesalawy, Mahmoud M. Helwan University Faculty of Engineering Computer and Systems Engineering Department Cairo Egypt Helwan University Department of Electrical Power and Machines Engineering Cairo Egypt Helwan University Faculty of Engineering Electronics and Communications Engineering Department Cairo Egypt

ISBN: (纸本)9798350378511

Renewable hybrid energy systems are crucial for ensuring energy sustainability. This work presents an advanced control and management system for green hydrogen production, leveraging artificial intelligence (AI) and Internet of Things (IoT) technologies. The system optimizes renewable energy utilization, improves hydrogen production efficiency, and ensures a reliable energy supply to meet varying demands. AI algorithms are used, including Random Forest, Artificial Neural Networks (ANN) and Long Short-Term Memory (LSTM) networks to forecast photovoltaic (PV) energy production and load consumption. These models are integrated into a centralized control system that dynamically adjusts energy distribution between immediate consumption and hydrogen production. Using data from the Rye microgrid, the system's effectiveness is demonstrated in enhancing decision-making for energy management in small-scale islanded operations. © 2024 IEEE.

关键词： Clean energy

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Multi-channel CNN to classify Nepali COVID-19 related tweets using hybrid features

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Journal of Ambient Intelligence and Humanized Computing 2024年第3期15卷 2047-2056页

作者： Sitaula, Chiranjibi Shahi, Tej Bahadur Department of Electrical and Computer Systems Engineering Monash University Wellington Rd ClaytonVIC3800 Australia Earth Observation and AI Research Group Department of Infrastructure Engineering The University of Melbourne ParkvilleVIC3010 Australia School of Engineering and Technology Central Queensland University Rockhampton Australia Central Department of Computer Science and Information Technology Tribhuvan University TU Rd Kathmandu Kirtipur44618 Nepal

Because of the current COVID-19 pandemic’s increasing fears among people, it has triggered several health complications such as depression and anxiety. Such complications have not only affected developed countries but also developing countries such as Nepal. These complications can be understood from peoples’ tweets and comments posted online after their felicitous analysis and sentiment classification. Nevertheless, owing to the limited number of tokens and words in each tweet, it is crucial to capture multiple pieces of information associated with them for better understanding. In this study, we, first, represent each tweet by combining both syntactic and semantic information, called hybrid features. The syntactic information is generated from the bag-of-words method, whereas the semantic information is generated from the combination of the fastText-based (ft) and domain-specific (ds) methods. Second, we design a novel multi-channel CNN (MCNN) model, which ensembles the multiple CNNs, to capture multi-scale information for better classification. Last, we evaluate the efficacy of both the proposed feature extraction method and the MCNN model classifying tweets into three sentiment classes (positive, neutral, and negative) on the NepCOV19Tweets dataset, which is the only public COVID-19 tweets dataset in the Nepali language. The evaluation results show that the proposed hybrid features outperform individual feature extraction methods with the highest classification accuracy of 69.7% and the MCNN model outperforms the existing methods with the highest classification accuracy of 71.3% during classification. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023.

关键词： COVID-19

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Random suppression of ECG artifacts using a NLSRLMMN-based cascaded noise canceller 58

Random suppression of ECG artifacts using a NLSRLMMN-based c...

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58th Asilomar Conference on Signals, systems and computers, ACSSC 2024

作者： Faiz, Mohammed Mujahid Ulla Zerguine, Azzedine Kale, Izzet Presidency University Department of Electrical and Computer Engineering Karnataka Bengaluru560064 India Interdisciplinary Research Center for Communication Systems and Sensing Kfupm Electrical Engineering Department Dhahran31261 Saudi Arabia Department of Computer Science and Engineering LondonW1W 6UW United Kingdom

ISBN: (纸本)9798350354058

Normally, the electrocardiogram (ECG) signal artifacts are removed in a particular sequence starting with baseline wander removal at the beginning. In this work, a methodology has been proposed using a novel algorithm called the Normalized Leaky Sign Regressor Least Mean Mixed Norm (NLSRLMMN) algorithm to remove multiple ECG artifacts such as baseline wander, motion artifacts, muscle artifacts, and power line interference randomly without following a particular sequence. Through rigorous simulations we have shown that if we remove multiple ECG artifacts randomly using our proposed approach then an improvement in signal to noise ratio and mean square error is observed as opposed to a conventional method that employs the Least Mean Square (LMS) algorithm in all the cascaded adaptive noise canceller stages. © 2024 IEEE.

关键词： Mean square error

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Control of Linear Servo Carts with Integral-Based Disturbance Rejection

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computers, Materials & Continua 2022年第10期73卷 453-463页

作者： Ibrahim M.Mehedi Abdulah Jeza Aljohani Ubaid M.Al-Saggaf Ahmed I.Iskanderani Thangam Palaniswamy Mohamed Mahmoud Mohammed J.Abdulaal Muhammad Bilal Waleed Alasmary Department of Electrical and Computer Engineering(ECE) King Abdulaziz University5521589Saudi Arabia Center of Excellence in Intelligent Engineering Systems(CEIES) King Abdulaziz University6521589Saudi Arabia Electrical and Computer Engineering TN38505United States Computer and Information Systems Umm Al-Qura UniversityMakkahSaudi Arabia

This paper describes a system designed for linear servo cart systems that employs an integral-based Linear Active Disturbance Rejection Control(ILADRC)scheme to detect and respond to *** upgrade in this control technique provides extensive immunity to uncertainties,attenuation,internal disturbances,and external sources of *** fundamental technology base of LADRC is Extended State Observer(ESO).LADRC,when combined with Integral action,becomes a hybrid control technique,namely *** tracking is based on Bode’s Ideal Transfer Function(BITF)in this proposed ILADRC *** proves to be a very robust and appropriate pole placement *** proposed LSC system has experimented with the hybrid ILADRC technique plotted the *** the results,it is evident that the proposed ILADRC scheme enhances the robustness of the LSC system with remarkable disturbance ***,the results of a linear quadratic regulator(LQR)and ILADRC schemes are comparatively *** analysis deduced the improved performance of ILADRC over the LQR control scheme.

关键词： Pole placement ADRC Active disturbance rejection robust control linear servo cart system

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A Model-Based Approach for Improving Reinforcement Learning Efficiency Leveraging Expert Observations 63

A Model-Based Approach for Improving Reinforcement Learning ...

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63rd IEEE Conference on Decision and Control, CDC 2024

作者： Ozcan, Erhan Can Giammarino, Vittorio Queeney, James Paschalidis, Ioannis Ch. Boston University Division of Systems Engineering BostonMA02215 United States CambridgeMA02139 United States Boston University Division of Systems Engineering Department of Electrical and Computer Engineering Department of Biomedical Engineering BostonMA02215 United States

ISBN: (纸本)9798350316339

This paper investigates how to incorporate expert observations (without explicit information on expert actions) into a deep reinforcement learning setting to improve sample efficiency. First, we formulate an augmented policy loss combining a maximum entropy reinforcement learning objective with a behavioral cloning loss that leverages a forward dynamics model. Then, we propose an algorithm that automatically adjusts the weights of each component in the augmented loss function. Experiments on a variety of continuous control tasks demonstrate that the proposed algorithm outperforms various benchmarks by effectively utilizing available expert observations. © 2024 IEEE.

关键词： Deep reinforcement learning

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An Investigation into the Accuracy of Blood Oxygen Saturation Measurement using Smart Watch Devices Among Pediatric Users in Saudi Arabia 3

An Investigation into the Accuracy of Blood Oxygen Saturatio...

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3rd IEEE International Biomedical Instrumentation and Technology Conference, IBITeC 2023

作者： Turki, Ahmad F. King Abdul Aziz University Center of Excellence in Intelligent Engineering Systems Electrical and Computer Engineering Department Jeddah Saudi Arabia

ISBN: (纸本)9798350302424

Advancements in technology have led to the development and increased use of biomedical wearable devices equipped with various biosensors. This study aimed to evaluate the accuracy of blood oxygen saturation measurements in Saudi pediatric users using the Apple Watch Series 6 and Samsung Galaxy Watch 4. These smartwatches, widely available and relatively affordable fitness trackers, offer the capability to measure blood oxygen saturation and facilitate health monitoring. This paper presents preliminary findings from an investigation into the accuracy of the Apple Watch and Samsung Watch in measuring blood oxygen saturation. Data was collected from a sample of one hundred Saudi pediatric users, comprising 43 females and 57 males, with an average age of 13±3.3 years and a BMI of 21.07±6.3 kg/m2. Three measurements of blood oxygen saturation were simultaneously recorded using a hospital-grade pulse oximetry machine, as well as the Apple Watch and the Samsung Watch. Statistical comparisons were conducted using a T-test with a significance level of p=0.05, and the results were visualized using Bland-Altman plots. The T-test results indicated a statistically significant difference between the oximeter readings of the smartwatches and the medical-grade oximeter. Additionally, the calculation of errors revealed discrepancies between the measurements obtained from the smartwatches and the hospital-grade oximeter. It was observed that the errors were generally lower in measurements taken from male subjects compared to female subjects. The Bland-Altman plots, considering data from both the Apple Watch and the Samsung Watch, showed moderate agreement between the smartwatches and the hospital pulse oximetry. These findings suggest that while the Apple Watch and Samsung Watch demonstrate potential for measuring blood oxygen saturation, there are discrepancies compared to the accuracy of the hospital-grade oximeter. Further research and refinement may be necessary to improve the pr

关键词： Watches

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Comparative Analysis of Intelligent Braking Controllers for Electric Vehicles

Renewable Energy and Power Quality Journal

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Renewable Energy and Power Quality Journal 2022年 20卷 55-60页

作者： Vodovozov, V. Raud, Z. Petlenkov, E. Department of Electrical Power Engineering and Mechatronics Estonia Department of Computer Systems Tallinn University of Technology Ehitajate tee 5 19086 Estonia

Contemporary society benefits multiple advantages from electric cars. However, there exist still many obstacles to common adoption of the electrically-fed vehicles related mostly to the battery-based energy sources. This analysis addresses the energy recovery in battery and hybrid electric vehicles during their gradual deceleration and emergency stop. Using the proposed classification of braking controllers, several solutions are considered related to the growth on energy saving. Here, intelligent braking systems with fuzzy logic and neural network controllers are compared and evaluated based on data collected in different institutions and by the authors themselves from real vehicles and laboratory test benches. © 2022, European Association for the Development of Renewable Energy, Environment and Power Quality (EA4EPQ). All rights reserved.

关键词： Energy conservation

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Corrections to "Reed Solomon Codes Against Adversarial Insertions and Deletions"

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IEEE Transactions on Information Theory 2025年第4期71卷 3237-3238页

作者： Con, Roni Shpilka, Amir Tamo, Itzhak Technion – Israel Institute of Technology Department of Computer Science Haifa3200003 Israel Tel Aviv University Blavatnik School of Computer Science Tel Aviv6997801 Israel Tel Aviv University Department of Electrical Engineering-Systems Tel Aviv6997801 Israel

The purpose of this note is to correct an error made by Con et al. (2023), specifically in the proof of Theorem 9. Here we correct the proof but as a consequence we get a slightly weaker result. In Theorem9, we claimed that for integers k and n such that k q RS code that can decode from n - 2k + 1 insdel errors where (Formula presented). Here we prove the following. Theorem 1: For integers n and k q RS-code, where (Formula presented) is a prime power, that can decode from n - 2k + 1 adversarial insdel errors. Note that the exponent of n is 4k - 3 whereas in Theorem 9 it is 4k - 4. For constant dimensional codes, the field size is of order O(n4k-3) , and in particular, for k = 2 the field size is of order O(n5) . © 1963-2012 IEEE.

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Direct Drive Permanent Magnet Synchronous Generator: Design, Modeling, and Control for Wind Energy Applications

Periodica polytechnica Electrical engineering and computer s...

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Periodica polytechnica electrical engineering and computer science 2024年第2期68卷 168-177页

作者： Larabi, Zina Ghedamsi, Kaci Aouzellag, Djamal Department of Electrical Engineering Faculty of Electrical Engineering and Computer Sciences University of Tizi-Ouzou P. O. B. 17 RP Tizi-Ouzou15000 Algeria Department of Electrical Systems Engineering Faculty of Technology University of Boumerdes Independence Avenue Boumerdes35000 Algeria Laboratoire de Maitrise des Energies Renouvelables Faculty of Technology University of Bejaia Road of Targa Ouzemmour Bejaia06000 Algeria

The prominent trend in wind turbine technology centers on the adoption of direct-drive permanent magnet synchronous generators (DD-PMSG), a choice driven by their capacity to deliver superior efficiency through the elimination of *** paper presents a comprehensive exploration of the design, modeling, and control aspects of a DD-PMSG intended for harnessing wind energy conversion. Initially, the geometrical design of the generator is meticulously carried out, adhering to predefined technical specifications and constraints. Subsequently, an in-depth internal modeling, focusing on the electromagnetic behavior of the designed generator, is executed using finite element analysis (FEA) through the Ansys Maxwell RMXpert software. Finally, the external modeling and control system integration of the designed generator, connected to the grid through power electronic converters, are simulated using the Matlab/Simulink software suite. The resulting findings underscore the efficacy and viability of the proposed generator for wind turbine applications, affirming its potential to enhance wind energy conversion systems. © 2024 Budapest University of Technology and Economics. All rights reserved.

关键词： Wind turbines

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A Zero-Sequence Current Analysis Approach for Rotating Machinery Fault Diagnosis of Induction Motor Drivetrain Based on Sparse Learning

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IEEE Transactions on Power Electronics 2025年第7期40卷 9800-9810页

作者： Liu, Zhao-Hua Long, Jun-Jie Wei, Hua-Liang Liu, Kan Zhang, Ying-Jie Li, Xiao-Hua Hunan University of Science and Technology School of Information and Electrical Engineering Xiangtan411201 China The University of Sheffield School of Electrical and Electronic Engineering Department of Automatic Control and Systems Engineering SheffieldS1 3JD United Kingdom Hunan University College of Mechanical and Vehicle Engineering Changsha410082 China Hunan University College of Computer Science and Electronic Engineering Changsha410082 China

Fault diagnosis of rotating machinery driven by induction motors has received increasing attention. Current diagnostic methods, which can be performed on existing inverters or current transformers of three-phase induction machines, have become a more economical and reliable alternative to vibration diagnostic methods. Existing references mainly focus on the stator current analysis of single-phase, but most single-phase current analysis methods utilize only a fraction of the total information accessible in the three-phase system. Field experience shows that zero-sequence current (ZSC) has more distinctive fault characteristics compared to single-phase current, and therefore it is better to use in mechanical fault detection and diagnostic tasks. This article proposes a novel sparse learning-based method for ZSC analysis for induction rotating motor drive fault diagnosis. First, it elaborates and compares the effectiveness of ZSC in revealing fault characteristics compared with single-phase current by analyzing the fault diagnosis mechanism of ZSC. In addition, the method proposes a correlation entropy-enhanced sparse learning method for the problem of high-frequency noise and interference in the ZSC signals, so as to enhance the learning of the features of the noise-containing signals. The diagnostic efficacy of the proposed method has been verified through experiments on two real datasets. © 1986-2012 IEEE.

关键词： Induction motors

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