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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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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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Deep Semantic and Attentive Network for Unsupervised Video Summarization

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ACM Transactions on Multimedia Computing, Communications and Applications 2022年第2期18卷 1–21页

作者： Zhong, Sheng-Hua Lin, Jingxu Lu, Jianglin Fares, Ahmed Ren, Tongwei College of Computer Science and Software Engineering Shenzhen University Shenzhen518060 China Department of Electrical Engineering Computer Systems Engineering Program Faculty of Engineering at Shoubra Benha University Cairo Egypt State Key Laboratory for Novel Software Technology Nanjing University Nanjing210023 China

With the rapid growth of video data, video summarization is a promising approach to shorten a lengthy video into a compact version. Although supervised summarization approaches have achieved state-of-the-art performance, they require frame-level annotated labels. Such an annotation process is time-consuming and tedious. In this article, we propose a novel deep summarization framework named Deep Semantic and Attentive Network for Video Summarization (DSAVS) that can select the most semantically representative summary by minimizing the distance between video representation and text representation without any frame-level labels. Another challenge associated with video summarization tasks mainly originates from the difficulty of considering temporal information over a long time. Long Short-Term Memory (LSTM) performs well for temporal dependencies modeling but does not work well with long video clips. Therefore, we introduce a self-attention mechanism into our summarization framework to capture the long-range temporal dependencies among the frames. Extensive experiments on two popular benchmark datasets, i.e., SumMe and TVSum, show that our proposed framework outperforms other state-of-the-art unsupervised approaches and even most supervised methods. © 2022 Association for Computing Machinery.

关键词： Semantics

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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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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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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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Robust In-Vehicle Diagnostic-Based Intrusion Detection System

Robust In-Vehicle Diagnostic-Based Intrusion Detection Syste...

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2024 International Symposium on Networks, computers and Communications, ISNCC 2024

作者： Shafee, Ahmed Awaad, Tasneem A. Moro, Ahmed Adams State University Department of Engineering and Computer Science AlamosaCO United States Ain Shams University Faculty of Engineering Department of Computer and Systems Engineering Cairo Egypt Siemens Eda Cairo Egypt Siemens Digital Industries Software Integrated Electrical Systems Segment Cairo Egypt

ISBN: (纸本)9798350364910

Cybersecurity has become a significant concern for automotive manufacturers as modern cars increasingly incorporate electronic components. Electronic Control Units (ECUs) have evolved to become the central control units for critical car functions such as engines and brakes, experiencing rapid technological advancements. However, this swift progression in ECU technology has also made them prime targets for cyber attacks. This vulnerability has spurred researchers to focus on securing ECUs. Numerous studies have proposed intrusion detection systems (IDS) to protect against attacks on ECUs in vehicles. Yet, these IDSs are not impenetrable;attackers can exploit them by launching evasion attacks, which can trigger numerous false positive alarms. Such false alarms can be disruptive and potentially hazardous for drivers. Additionally, attackers can evade IDSs from detecting malicious data that can cause harm to the vehicle. Accordingly, in this paper, we propose a novel training framework to train a robust in-vehicle IDS that can encounter evasion attacks. Our methodology is based on implementing two rounds of mimic learning technique for training Random Forest (RF) based IDS. RF has been chosen to incorporate the randomness of the RF architecture to enhance the robustness of the model. Additionally, in each round of the two rounds of the mimic learning technique, an RF model with a different architecture is chosen to improve the resilience of the model against evasion attacks without affecting its accuracy. Our Experimental results have shown the effectiveness of our framework against evasion attacks. © 2024 IEEE.

关键词： Robustness (control systems)

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Dynamic and Private Recommendation System

Dynamic and Private Recommendation System

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2023 International Conference on Digital Applications, Transformation and Economy, ICDATE 2023

作者： Lo, Gee Yen Tan, Terence Peng Lian Curtin University Malaysia Computer Systems and Networking Miri Malaysia Curtin University Malaysia Department of Electrical & Computer Engineering Miri Malaysia

ISBN: (纸本)9798350310689

The amount of data that we retrieve and use every day is increasing rapidly. As data naturally exist in different data domains, the number of data domains that we retrieve and use every day has also increased as a result. As different recommendation systems are needed for different data domains, more and more recommendation systems will need to be built. Furthermore, users will lose their privacy as many recommendation systems collect information about their users. This paper aims to solve the aforementioned issues by creating a recommendation system that will work with any data domain and upholds the privacy of its users. The Dynamic Implementation Of Privacy Based Recommendation System Application is created in this paper. The objective of this thesis is achieved as the Dynamic Implementation Of Privacy Based Recommendation System Application developed in this paper upholds the privacy of its users as it only stores users' data in their device's local database and the application has the ability to adapt to different data domains. © 2023 IEEE.

关键词： Recommender systems

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