Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT

SABER, Amr Mausad; Behiry, Mohamed H.; AMIN, Mohamed

doi:10.24846/v31i2y202208

Cited by 4 publications

(2 citation statements)

References 25 publications

(32 reference statements)

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“…Also, the rest of the parameters in table 2 change online such as the gear ratio parameter. Where 𝐾 𝑝 , 𝐾 𝑖 , 𝑘 𝑑 are proportional, integral and differential gains respectively [16]. In this paper the COVID-19 optimization technique will be used to obtain the optimal values of PID controller parameters.…”

Section: System Modelingmentioning

confidence: 99%

A COVID-19 Based on Fractional Order Integral-Tilt Derivative Controller for Nonlinear Servomechanism Model

Shamseldin,

Salah,

Mohamed

et al. 2023

International Journal of Electrical Engineering and Computer Science

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In this research, a new one-axis servomechanism investigation is presented, taking into account parameter fluctuation and system uncertainty. Additionally, a novel method for very effective TID control was created to accurately monitor a chosen profile. A comparative study between the suggested control method and the well-known controllers (PID and Nonlinear PID) is also included. The COVID-19 optimization technique was used to discover the best control parameters. Through the online simulation, the servomechanism system's settings were modified at random within a predetermined range. As nonlinearity resources (friction, backlash, environmental influences), these parameters fluctuate and contribute to system uncertainty. It had been completed and looked at to compare the linear and nonlinear models. The results demonstrate that the suggested controller is capable of tracking the number of operational points with high accuracy, a short rising time, and little overrun.

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Section: System Modelingmentioning

confidence: 99%

A COVID-19 Based on Fractional Order Integral-Tilt Derivative Controller for Nonlinear Servomechanism Model

Shamseldin,

Salah,

Mohamed

et al. 2023

International Journal of Electrical Engineering and Computer Science

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“…Section 3 knowledge and background which consists of four parts as follows: part 1 explains types of Cyber Attacks such as Malware Phishing, Man in the middle of the attack, SQL injection, and DNS tunnelling; part 2 includes few instances of cyberattacks within 2022 as Theft of Crypto.com, Breach of data at the Red Cross, and Cash app data breach. part three discusses significance of Cybersecurity, while fourth part contains the types of Cyber Security such Cloud security, Mobile security, Security with Zero Trust, Network security, Application security, IoT [40,41], and End-point security.…”

Section: Introductionmentioning

confidence: 99%

Cyberattack Detection in Wireless Sensor Networks using a Hybrid Feature Reduction Technique with AI and Machine Learning Methods

Behiry

Aly

2023

Preprint

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Security of a Wireless Sensor Network (WSN) is crucial for preventing data sharing from intruders. This paper makes a suggestion for a machine learning-based intelligent hybrid model and AI for identifying cyberattacks. The security of a Wireless Sensor Network (WSN) guards against malevolent hackers cyberattacks on data, networks, and computers. The qualities that are most closely associated to the selected attack categories are also identified using a feature reduction algorithm (SVD and PCA) and machine learning methods. In order to reduce/extract features and rank them, this paper suggests using the K-means clustering model enhanced information gain (KMC-IG). A Synthetic Minority Excessively Technique is also being introduced. Intrusion prevention systems and network traffic categorization are the eventual important stage. The study evaluates the accuracy, precision, recall, and F-measure of a proposed deep learning-based feed-forward neural network algorithm for intrusion detection and classification. Three important datasets, namely NSL-KDD, UNSW-NB 15, and CICIDS 2017, are considered, and the proposed algorithm's performance is assessed for each dataset under two scenarios: full features and reduced features. The study also compares the results of the proposed DLFFNN-KMC-IG with benchmark machine learning approaches. After dimensional reduction and balancing, the proposed algorithm achieves high accuracy, precision, recall, and F-measure for all three datasets. Specifically, for the NSL-KDD dataset in the reduced feature set, the algorithm achieves 99.7% accuracy, 99.8% precision, 97.8% recall, and 98.8% F-measure. Similarly, for the CICIDS2017 dataset, the algorithm achieves 99.8% accuracy, 98.7% precision, 97.7% recall, and 98.7% F-measure. Finally, for the UNSW-NB15 dataset, the algorithm achieves 99.1% accuracy, 98.7% precision, 98.4% recall, and 99.6% F-measure.

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Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

Saravanan,

Suresh,

Pazhanimuthu

et al. 2024

e-Prime - Advances in Electrical Engineering, Electronics and E

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Real-Time Optimization for an AVR System Using Enhanced Harris Hawk and IIoT

Cited by 4 publications

References 25 publications

A COVID-19 Based on Fractional Order Integral-Tilt Derivative Controller for Nonlinear Servomechanism Model

A COVID-19 Based on Fractional Order Integral-Tilt Derivative Controller for Nonlinear Servomechanism Model

Cyberattack Detection in Wireless Sensor Networks using a Hybrid Feature Reduction Technique with AI and Machine Learning Methods

Artificial rabbits optimization algorithm based tuning of PID controller parameters for improving voltage profile in AVR system using IoT

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