This work deals with the design of a Fuzzy Logic Control (FLC) based Energy Management System (EMS) for smoothing the grid power profile of a grid-connected electro-thermal microgrid. The case study aims to design an Energy Management System (EMS) to reduce the impact on the grid power when renewable energy sources are incorporated to pre-existing grid-connected household appliances. The scenario considers a residential microgrid comprising photovoltaic and wind generators, flat-plate collectors, electric and thermal loads and electrical and thermal energy storage systems and assumes that neither renewable generation nor the electrical and thermal load demands are controllable. The EMS is built through two low-complexity FLC blocks of only 25 rules each. The first one is in charge of smoothing the power profile exchanged with the grid, whereas the second FLC block drives the power of the Electrical Water Heater (EWH). The EMS uses the forecast of the electrical and thermal power balance between generation and consumption to predict the microgrid behavior, for each 15-minute interval, over the next 12 hours. Simulations results, using real one-year measured data show that the proposed EMS design achieves 11.4% reduction of the maximum power absorbed from the grid and an outstanding reduction of the grid power profile ramp-rates when compared with other state-of-the-art studies.

Healthcare buildings exhibit a different electrical load predictability depending on their size and nature. Large hospitals behave similarly to small cities, whereas primary care centers are expected to have different consumption dynamics. In this work, we jointly analyze the electrical load predictability of a large hospital and that of its associated primary care center. An unsupervised load forecasting scheme using combined classic methods of principal component analysis (PCA) and autoregressive (AR) modeling, as well as a supervised scheme using orthonormal partial least squares (OPLS), are proposed. Both methods reduce the dimensionality of the data to create an efficient and low-complexity data representation and eliminate noise subspaces. Because the former method tended to underestimate the load and the latter tended to overestimate it in the large hospital, we also propose a convex combination of both to further reduce the forecasting error. The analysis of data from 7 years in the hospital and 3 years in the primary care center shows that the proposed low-complexity dynamic models are flexible enough to predict both types of consumption at practical accuracy levels.

This paper presents a study in order to identify the value range of the main parameters within carrier sense multiple access (CSMA) defined in IEEE 802.15.4 that guarantees a satisfactory wireless sensor network (WSN) performance for a volcano monitoring application. Moreover, this study performs the comparison among several test-beds in outdoor scenarios with the purpose of distinguishing the optimal number of nodes for each gateway according the main constraints given by an existing sensor network for real-time (RT) volcano monitoring system such as sampling time, packet loss, and delay. We used a mathematical model that works with Markovian techniques and involves some parameters of CSMA mechanism within the model, such as the minimum value of the backoff exponent (BEmin), the contention window length (W), and the number of slots (L). We obtained the approximate values of these parameters by the interpolation of the normalized throughput curves from the deployment, and thus, we could obtain a mathematical model with the specifications required for the RT volcano monitoring. After validating the model with test-bed outdoor deployments we found that BEmin, W and L are key factors for determining the performance of a WSN, these parameters guarantee the range in which the WSN works according to the constraints imposed for this particular volcano monitoring application.

Atrial fibrillation (AF) remains a significant health challenge as the number of patients increases with the aging of the population worldwide. AF detection continues to be a significant challenge, particularly in the long term, to improve the quality of life of people suffering from the disease and reduce treatment costs. In recent decades, machine learning and deep learning have provided promising results in classifying heart conditions with moderate interpretability. We hypothesized that the complexity of AF could be handled in low-dimensional latent spaces in terms of non-Gaussian manifolds and that feature selection techniques on an audio-inspired feature set could improve the detection and interpretability of the AF phenomenon. Therefore, we used long-term monitoring databases in which noise-reduction filters preprocessed the signals and extracted their RR-value sequences. From these datasets, several audio spectral characteristics were calculated and used as input vectors for different types of manifold estimators. We also used the least absolute shrinkage and selection operator (LASSO) regression, a feature selection technique. Our experiments showed that these spectral representations of AF segments can yield better-defined low-dimensional embedding manifolds and acceptable intrinsic separability of AF from sinus rhythm. Moreover, including supervised stages and LASSO regression improved the overall performance of this proposal. The performance obtained in classifying AF signals versus normal ones reaches a precision of 91.2%, a recall of 95.5%, and an accuracy of 94.5%. These results pave the way for manifold learning and low-dimensional latent spaces in AF applications, and they support the advantage of using machine learning methods in different AF problems.

Micro-earthquakes are frequently associated with volcanic activity and are vital indicators of volcanic processes. These minor seismic events occur within or near volcanic systems, yielding valuable insights into subsurface activities. Geologists meticulously record and analyze these events to monitor volcanoes and forecast eruptions. While recent years have seen several studies proposing automated detection and classification systems of seismic events, approaches based on Manifold Learning techniques could be beneficial in terms of information interpretability and transfer learning to other Machine Learning tasks. This paper presents a novel approach employing audio features and psychoacoustic scales to represent micro-earthquakes at Cotopaxi and Llaima Volcanoes, and these representations are then transformed into low-dimensional latent spaces. We implemented a multi-class classification system for events generated by these volcanoes, incorporating feature selection techniques based on audio-inspired features. This approach enhances the detection of volcanic phenomena triggering eruptions and improves interpretability. Our results indicated high accuracy, with rates of 94.44% for Llaima Volcano and 95.45% for Cotopaxi Volcano when utilizing mutual information to select the most relevant features. Spectral Roll-off Point and Spectral Flux dominate in classifying events for both volcanoes. These findings suggest that low-dimensional latent spaces, particularly when utilizing spectral features, can be a promising foundation for developing transfer learning schemes in general, and new multi-class classification systems in particular, for detecting volcanic micro-earthquakes.

In this paper we present an outdoor smart parking prototype using wireless sensor networks as a possible solution to solve the problem of parking, through an application that present the information in order to find free parking spaces in real time, this system can help to improve the quality of life in smart cities, because the time for finding a free parking space could be reduced, as well as help to reduce pollution and to improve vehicular traffic. The use of a wireless sensor networks allow for easy installation outdoor, the system could use a solar panel to charge the batteries and being independent of an external power source wired or constantly replacing batteries. The vacancies spaces are presented to users through a smart device. The main results show a reduction of 50% of time to obtain a free parking space, a reduction in fuel consumption, and an estimated reduction of vehicle pollution. Finally, we determine for our campus university the fuel saved were 2,71 galons/year/vehicle.