For machine learning algorithms, fine-tuning hyperparameters is a computational challenge due to the large size of the problem space. An efficient strategy for adjusting hyperparameters can be established with the use of the greedy search and Swarm intelligence algorithms. The Random Search and Grid Search optimization techniques show promise and efficiency for this task. The small population of solutions used at the outset, and the costly goal functions used by these searches, can lead to slow convergence or execution time in some cases. In this research, we propose using the machine learning model known as Support Vector Machine and optimizing it using four distinct algorithms—the Ant Bee Colony Algorithm, the Genetic Algorithm, the Whale Optimization, and the Particle Swarm Optimization—to evaluate the computational cost of SVM after hyper-tuning. Computational complexity comparisons of these optimization algorithms were performed to determine the most effective strategies for hyperparameter tuning. It was found that the Genetic Algorithm had a lower temporal complexity than other algorithms.

This research paper presents novel condensed CNN architecture for the recognition of multispectral images, which has been developed to address the lack of attention paid to neural network designs for multispectral and hyperspectral photography in comparison to RGB photographs. The proposed architecture is able to recognize 10-band multispectral images and has fewer parameters than popular deep designs, such as ResNet and DenseNet, thanks to recent advancements in more efficient smaller CNNs. The proposed architecture is trained from scratch, and it outperforms a comparable network that was trained on RGB images in terms of accuracy and efficiency. The study also demonstrates the use of a Bayesian variant of CNN architecture to show that a network able to process multispectral information greatly reduces the uncertainty associated with class predictions in comparison to standard RGB images. The results of the study are demonstrated by comparing the accuracy of the network’s predictions to the images.

This article aims to explore the oscillatory characteristics of a controlled asymmetric rotor system when subjected to rub and impact forces between the rotor and stator. Four electromagnetic poles are used to control the whirling motion of the rotor system through a linear proportional‐derivative control law. The equations of motion that govern the whole system dynamics are derived including the rub and impact forces. The derived mathematical model is analyzed in two basic steps. Firstly, the obtained model is treated as a weakly nonlinear system using perturbation analysis to obtain the slow‐flow modulating equations when neglecting the rub and impact forces. Depending on the obtained slow‐flow equations, different response curves are plotted to explore the system’s periodic vibrations and determine the conditions at which the system can exhibit rub and impact force. Secondly, the whole system model including the rub and impact forces is investigated by using the bifurcation diagrams, Poincare map, frequency spectrums, and temporal oscillations. The obtained results revealed that the applied control law could mitigate the system whirling oscillations and prevent the rub and impact forces if the control gains are tuned properly. However, the system can perform period‐n, quasiperiodic, or chaotic motion depending on the shaft spinning speed if the controller fails to eliminate the contact between the rotor and stator.

This study introduces a decentralized control framework designed to improve energy efficiency in vehicle platooning by optimizing inter-vehicle gaps to minimize aerodynamic drag, a significant factor in energy consumption. The proposed framework integrates extremum seeking control (ESC), a proportional integral derivative controller with feedforward compensation (PIDFC), and an extended state observer for estimating aerodynamic drag coefficients. Through this integration, the ESC dynamically adjusts inter-vehicle gaps to minimize the drag force based on the estimated aerodynamic drag coefficients, while the PIDFC ensures precise tracking of the optimized gaps and effectively addresses disturbances arising from aerodynamic variations. The results show that the proposed ESC with PIDFC converged to the optimal distance gaps 37.14% faster than the ESC with PPC, within a steady-state error of $\pm 1\%$. Additionally, it improved the computational efficiency by 70.6%, compared to the ESC with PPC. Furthermore, it reduced the energy consumption by 15.3%, compared to the fixed-gap approach. These findings demonstrate the framework’s potential to enhance the energy efficiency, scalability, and practicality, advancing sustainable and autonomous transportation systems.

Cooperative and Autonomous Vehicle (CAV) platoons offer significant potential for improving road safety, traffic efficiency, and energy consumption, but maintaining precise inter-vehicle spacing and synchronized velocity under disturbances while ensuring string stability remains challenging. This paper presents a fully decentralized two-layer architecture for homogeneous platoons whose identical vehicle dynamics and information flow produce an inherent symmetrical system structure. Operating under a predecessor-following topology with a constant time headway policy, the upper layer generates a smooth velocity reference based on local spacing and relative-velocity errors, while the lower layer employs a two-degree-of-freedom (2-DOF) digital RST controller designed through discrete-time pole placement and sensitivity-function shaping. The 2-DOF structure enables independent tuning of tracking and disturbance-rejection dynamics and provides a computationally lightweight solution suitable for embedded automotive platforms. The paper develops a stability analysis demonstrating internal stability and $L_2$ string stability within this symmetrical closed-loop architecture. Simulations confirm string-stable behavior with attenuated spacing and velocity errors across the platoon during aggressive leader maneuvers and under input disturbances. The proposed method yields smooth control effort, fast transient recovery, and accurate spacing regulation, offering a robust and scalable control strategy for real-time longitudinal motion control in connected and automated vehicle platoons.