This research deal with the drift compensation problem in sensor arrays named electronic noses. The drift problem occurs in this kind of sensor when they are exposed to an analyte for long periods, which may cause that the response of the sensor varies with time. Some approaches in the literature have tackled the drift compensation problem from the point of view of signal processing algorithms to obtain high rates of accuracy independently of time. In this work, the drift problem is solved using transfer learning with the joint distribution adaptation (JDA) method, which adapts both marginal and conditional distributions between domains, and requires no labeled data in the target domain to perform a classification task with a machine learning algorithm. The developed methodology for drift compensation is validated by measuring accuracy in the classification process. Validation considers a data set that measured six volatile organic compounds during a period of three years under strongly controlled operating conditions using a series of 16 metal oxide gas (MOX) sensors. JDA and Kernel JDA are used with three different types of kernels to determine the best behavior in terms of accuracy to correct the drift in electronic noses. As a result, it can be concluded that the approach using JDA outperforms standard learners like K-Nearest Neighbor (KNN) method.

Microgrids play a crucial role in optimizing renewable energy by integrating various sources to generate and store electricity. Effective resource management within microgrids is essential for improving efficiency and reducing operational costs. This study employs bibliometric analysis to explore key trends and emerging technologies in microgrid energy management. The analysis begins with a co-occurrence network to visualize relationships between key terms and their interconnected research areas, helping to identify thematic clusters. A trend topic analysis follows, highlighting the evolution of significant themes from 2010 to 2024. A dendrogram analysis further organizes subtopics hierarchically, while a word map analysis provides an overview of dominant themes and their practical applications. Thematic map analysis categorizes keywords into distinct clusters, offering a comprehensive understanding of technology trends and connections in the field. Key findings highlight four clusters: energy management systems, smart power grid optimization, battery management systems, and electric frequency control. These clusters underscore critical research gaps and innovative approaches in optimizing microgrid performance. By identifying future research directions, this study aims to contribute to the development of sustainable and efficient energy systems.

Improvements in computing capacity have allowed computers today to execute increasingly complex tasks. One of the main benefits of these improvements is the possibility of developing machine learning algorithms, of which the fields of application are extensive and varied. However, an area in which this type of algorithms acquires an increasing relevance is structural health monitoring (SHM), where inspection strategies and guided wave-based approaches make the evaluation of the structural conditions of an aircraft, vessel or building among others possible, by detecting and classifying existing damages. The use of sensors, data acquisition systems (DAQ) and computation has also allowed these damage detection and classification tasks to be carried out automatically. Despite today’s advances, it is still necessary to continue with the development of more robust, reliable, and low-cost structural health monitoring systems. For this reason, this work contemplates three key points: (i) the configuration of a data acquisition system for signal gathering from an an active piezoelectric (PZT) sensor network; (ii) the development of a damage classification methodology based on signal processing techniques (normalization and PCA), from which the models that describe the structural conditions of the plate are built; and (iii) the use of machine learning algorithms, more specifically, three variants of the self-organizing maps called CPANN (counterpropagation artificial neural network), SKN (supervised Kohonen) and XYF (X–Y fused Kohonen). The data obtained allowed one to carry out an experimental validation of the damage classification methodology, to determine the presence of damages in two aluminum plates of different sizes, where masses were added to change the vibrational responses captured by the sensor network and a composite (CFRP) plate with real damages, such as delamination and cracks. This classification methodology allowed one to obtain excellent results by validating the usefulness of the SKN and XYF networks in damage classification tasks, showing overall accuracies of 73.75% and 72.5%, respectively, according to the cross-validation process. These percentages are higher than those obtained in comparison with other neural networks such as: kNN, discriminant analysis, classification trees, partial least square discriminant analysis, and backpropagation neural networks, when the cross-validation process was applied.

Autonomous underwater vehicle control has been a topic of research in the last decades. The challenges addressed vary depending on each research group’s interests. In this study, we focus on the Predictive Functional Control (PFC), which is a control strategy that is easy to understand, install, and tune and optimize. PFC is being developed and applied in industrial applications such as distillation, reactors, and furnaces. This document presents the first application of the Predictive Functional Control in autonomous underwater vehicles, as well as the simulation results of PFC, fuzzy, and gain scheduling controllers. Through simulations and navigation tests at sea, which successfully validate the performance of PFC strategy in motion control of autonomous underwater vehicles, PFC performance is compared to other control techniques such as fuzzy and gain scheduling control. The experimental tests presented here offer effective results concerning control objectives in high and intermediate levels of control. In high-level point stabilization and path following scenarios are proven. In the intermediate levels, the results show that position and speed behaviors are improved using the PFC controller, which offers the smoothest behavior. The simulation depicting predictive functional control was the most effective regarding constraints management and control rate change in the Guanay II underwater vehicle actuator. The industry has not embraced the development of control theories for industrial systems because of the high investment in experts required to implement each technique successfully. However, this work on the functional predictive control strategy evidences its easy implementation in several applications, making it a viable option for the industry given the short time needed to learn, implement, and operate, decreasing impact on the business and increasing immediacy.

This study presents a deep learning-based framework for forecasting energy demand in a quicklime production company, aiming to enhance operational efficiency and enable data-driven decision-making for industrial scalability. Using one year of real electricity consumption data, the methodology integrates temporal and operational variables—such as load profile, active power, shift indicators, and production-related proxies—to capture the dynamics of energy usage throughout the manufacturing process. Several neural network architectures, including Long Short-Term Memory (LSTM), Gated Recurrent Unit (GRU), and Conv1D models, were trained and compared to predict short-term power demand with 10-min resolution. Among these, the GRU model achieved the highest predictive accuracy, with a best performance of RMSE = 2.18 kW, MAE = 0.49 kW, and SMAPE = 3.64% on the test set. The resulting forecasts support cost-efficient scheduling under time-of-use tariffs and provide valuable insights for infrastructure planning, capacity management, and sustainability optimization in energy-intensive industries.

Since its introduction, the number of successful applications of the predictive functional control PFC technique has grown into the thousands. In this research, the first autonomous underwater vehicle application is presented to illustrate the technical benefits associated with the use of PFC technique. The article presents an underlying framework in control movement scenarios and results of the test performed in the Mediterranean Sea. This work present a part of results obtained on our research.