Connected and Autonomous Vehicles (CAVs) enable different functionalities and capabilities such as navigation and path planning, automated driving assistance, cruise control, low-carbon transportation, and independent...
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Connected and Autonomous Vehicles (CAVs) enable different functionalities and capabilities such as navigation and path planning, automated driving assistance, cruise control, low-carbon transportation, and independent decision-making in real-world scenarios. However, the increased usage of CAVs reduces the possible vulnerabilities in the Internet of Vehicles (IoVs) framework, exposing it to cyber-attacks. An Intrusion Detection System (IDS) is a method to report network attacks by Autonomous Vehicles (AVs) without authorization and encryption techniques for internal and external vehicular transmission. To alleviate this risk, the lightweight IDS system must identify attacks on vehicular systems. Deep learning (DL) approaches promising algorithms for intrusion detection in CAVs, which leverage their capability to automatically extract and learn complex patterns from intricate and vast datasets. DL-based IDS can efficiently identify anomalous activities indicative of system vulnerabilities or cyberattacks by analyzing vehicle behaviour, network traffic, and sensor data in real-time, thus ensuring the integrity and security of CAV operations. Bio-inspired optimizationalgorithms have recently been used for feature selection (FS) and hyperparameter tuning processes, commonly stimulated by physical ideologies, evolution theories, and specific characteristics of living beings, to solve optimization issues competently in different applications. Therefore, this study introduces a novel planet optimization algorithm with a Deep Ensemble Learning-based Intrusion Detection (POADEL-ID) technique for CAV networks. The presented POADEL-ID model concentrates on identifying intrusions in the CAV networks. In the POADEL-ID technique, linear scaling normalization (LSN) is utilized for the data scaling process. Besides, the high-dimensionality problem is resolved by the POA-based FS approach. Moreover, intrusion detection is performed by using an ensemble of three techniques, namely l
Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-inte...
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Nuclear reactor control is pivotal for the safe and efficient operation of nuclear power plants, facilitating the regulation of reactor reactivity. This study introduces an optimized fractional-order proportional-integral-derivative (FOPID) controller tailored for maintaining reactivity levels in nuclear power plants, particularly during load-following operations. The controller adjusts the position of control rod to regulate power output effectively. We enhance FOPID controller's performance using a modification of planet optimization algorithm (POA-M), leveraging the strengths of the Arithmetic optimizationalgorithm (AOA) to improve its exploitation capabilities. We evaluate the efficacy of POA-M-FOPID controller against traditional techniques, including POA, AOA, and Particle Swarm optimization (PSO). We assess its performance using the Egyptian Testing Research Reactor (ETRR-2) as a case study. Our results demonstrate that the POA-M-FOPID controller outperforms alternative algorithms across various control metrics, exhibiting superior resilience and efficiency. Notably, the utilization of the POA-M-FOPID controller yields remarkable improvements in reactor power performance, achieving significantly reduced settling time (25.27 sec) and maximum overshoot (0.67 %) compared to conventional FOPID controllers incorporating POA, AOA, and PSO methods. These findings underscore the effectiveness of POA-MFOPID in enhancing nuclear reactor control systems, offering potential benefits for broader nuclear power industry in terms of safety, stability, and operational efficiency.
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