Krzysztof Cpałka

The collection of 20 scientific articles published within the Special Issue titled “Artificial Intelligence in Materials Science and Engineering” provides a comprehensive overview of contemporary materials engineering, where traditional research methods are increasingly bolstered by advanced digital tools

This study introduces a novel approach for analyzing theoretical Raman spectra, designed to facilitate spectral interpretation, particularly for complex systems such as functional mesoporous silica-based thin films. The proposed methodology relies on spectral decomposition supported by theoretical calculations, representing a step toward the development of autonomous research laboratories. The method assigns vibrational shifts to individual atoms within a molecular model and uses this information to generate partial spectra corresponding to specific atomic groupings. Unlike separate calculations for isolated components, this approach preserves the mutual interactions within the entire molecular structure, providing a more accurate representation of the vibrational environment. Decomposing the theoretical spectrum into contributions from atomic groups significantly simplifies the assignment of Raman bands to specific structural units, thereby enhancing the interpretative power of theoretical spectra and their correlation with experimental data. The method was demonstrated using real Raman spectroscopic data obtained from mesoporous SBA-15 silica thin films containing copper phosphonate groups. This work also highlights the critical role of molecular modeling and DFT calculations in Raman spectral analysis and outlines future perspectives for the use of artificial intelligence to automate and optimize the spectral interpretation process.

This paper tackles the challenge of achieving Input-to-state stable (ISS) synchronization in actuator-saturated sampled-data control (SDC) networks for Markovian jump (MJ) Lur’e networks. We investigate the impacts of actuator saturation on system performance and stability, proposing a control strategy that ensures synchronization in the presence of external disturbances. Our analysis employs a Wirtinger-based integral inequality alongside a modified free matrix-based integral inequality (MFMBII), providing a framework for examining Lur’e networks. Initially, we create an MFMBII that combines the dynamics of MJ Lur’e networks and takes into consideration time-varying delays. Second, we formulate two sufficient conditions for the SDC design that ensure mean-square ISS error of specification for the hybrid closed-loop system. We do this by combining the MFMBII method with the Lyapunov-Krasovskii functional (LKF). Through a systematic methodology, we demonstrate that the proposed method maintains bounded state responses and converges to a common trajectory at an exponential rate. The results highlight the effectiveness of integrating ISS with SDC in managing complex dynamical networks. Finally, the proposed ISS method is validated through a numerical example, confirming its efficacy.

In this paper, an intelligent approach to the Short-Term Wind Power Prediction (STWPP) problem is considered, with the use of various types of Deep Neural Networks (DNNs). The impact of the prediction time horizon length on accuracy, and the influence of temperature on prediction effectiveness have been analyzed. Three types of DNNs have been implemented and tested, including: CNN (Convolutional Neural Networks), GRU (Gated Recurrent Unit), and H-MLP (Hierarchical Multilayer Perceptron). The DNN architectures are part of the Deep Learning Prediction (DLP) framework that is applied in the Deep Learning Power Prediction System (DLPPS). The system is trained based on data that comes from a real wind farm. This is significant because the prediction results strongly depend on weather conditions in specific locations. The results obtained from the proposed system, for the real data, are presented and compared. The best result has been achieved for the GRU network. The key advantage of the system is a high effectiveness prediction using a minimal subset of parameters. The prediction of wind power in wind farms is very important as wind power capacity has shown a rapid increase, and has become a promising source of renewable energies.