This paper investigates whether unsupervised machine learning techniques can identify meaningful and interpretable driving style clusters among Formula 1 (F1) drivers, using telemetry and lap-time data extracted via the FastF1 Python library applied to the data of the 2023 F1 Season. Four clustering algorithms are implemented and compared: K-Means, Hierarchical Agglomerative Clustering, Density-Based Spatial Clustering of Applications with Noise (DBSCAN), and Gaussian Mixture Models (GMM). A suite of performance features is engineered from raw race telemetry, including relative lap time, lap-time consistency (measured via the Median Absolute Deviation), tyre degradation slope, overtaking efficiency, and intra-team relative sector differentials. Car performance confounders are partially mitigated by computing intra-team relative metrics throughout the entire feature set. A Variance Inflation Factor (VIF) analysis is applied to identify and remove highly collinear features prior to clustering. Principal Component Analysis (PCA) is employed for dimensionality reduction and visualization. Results suggest that between 3 and 5 statistically meaningful driver clusters emerge, broadly characterized as elite performers, aggressive racers, precision drivers, and solid midfield operators. K-Means with k = 4 and Ward-linkage Hierarchical Clustering yield the most internally consistent groupings, outperforming DBSCAN and GMM on silhouette and Davies-Bouldin scores. A supplementary supervised validation experiment confirms that the engineered feature set captures genuinely driver-discriminative signal, with a Random Forest classifier achieving 78.3% accuracy in predicting driver identity under 5-fold cross-validation. Unsupervised learning can surface interpretable driving style profiles from Formula 1 data, though the explanatory power of these clusters is constrained by data limitations including the small per season sample size, incomplete telemetry coverage and the dominance of car performance over driver skill in raw lap-time metrics.
In this context, the objective of the Gantry Crane Scheduling Problem (GCSP) is to develop a schedule that minimizes the completion times of the RTGCs in the container storage areas. The makespan based on a set of container loading or unloading operations. This heuristic is able to find slot schedules with indicated capacity that are efficient for small instances, their performance decreases as the size increases. In this framework, a Variable Neighborhood Search (VNS) was developed for the GCSP which contains a local search architecture to retain efficient solutions. Computational evaluations show that VNS is able to significantly outperform the results obtained by CPLEX.
In the current security environment, where the dependence on computer systems is increasing, and the technological field is constantly changing, the threats and vulnerabilities typology to networks is also growing, so a key task is to ensure the network’s security access. To address these challenges, we propose an efficient hybrid network authentication mechanism that combines and integrates actual access control components based on cryptography and biometrics. These elements play a vital role in the field of information security and aim to resolve the shortcomings of conventional authentication methods and enhance the security level of sensitive data, especially in the government and military domains. In this paper, we present a mechanism that comprises biometric fingerprint recognition and card authentication based on Arduino modules with the Paillier homomorphic encryption algorithm, a reliable solution that can facilitate secure access to computer systems and networks and minimize the risk of unauthorized access. A statistical assessment is performed using several parameters such as histogram analysis, information entropy, Mean Square Error (MSE), peak signal-to-noise ratio (PSNR), correlation coefficient, and average encryption time to verify the efficiency and robustness of the encryption algorithm.
A controller design with integration of Sliding Mode Control (SMC) and Model Predictive Control (MPC) has been proposed here for path-tracking and attitude control of multirotors. Sliding mode control has been used for tracking desired attitude and altitude under modeling errors and external disturbances. Model predictive control has also been designed to track the desired path in the horizontal plane of motion, which has been constrained to have feasible values of reference roll and pitch angles. In addition, some considerations have been made on the control design integration and thrust force constraints. This technique has been examined by tracking two different desired trajectories and checking the integral of the absolute error. It is shown that the proposed technique has outstanding performance under the external disturbances and control saturation.
This study investigated the absorption coefficient of a perforated plate under different design variables and discussed variable optimization. First, a perforated plate sample was printed using stereolithography (SLA) 3D printing, and the absorption coefficient of the sample at a 1/3 octave band was measured using an impedance tube. The design variables included the perforated plate's thickness, perforation rate, and aperture size. We used the Taguchi method for analysis to obtain the optimal combination of variables. The results showed that the perforation rate strongly affected the absorption coefficient in the frequency range of 500 to 6300 Hz. Additionally, the Taguchi method was used to analyze the experimental data because it could quickly find the factors with high influence and the estimated value of the optimal forecast combination.
The current dashboard system is limited in communication capability. This study proposed a digital twin dashboard system framework. To realize the digital-twin-based dashboard system, for the field application of this research laboratory, a simple contract net-based communication protocol is designed to realize a dashboard system with communication capabilities. The digital twin-based dashboard system is successfully implemented in a flexible GPU card assembly line. Enabling each digital twin module system to interact and operate flexibly to demonstrate the relationship between the digital twins and the physical modules.
