Heart rate differences between symptomatic and asymptomatic Brugada syndrome patients at night

Peer ReviewedPostprint (author's final draft

Editorial: Special issue ISA 2023

Funding for open access charge: Universidade da Coruña/CISUG.[Abstract] ISA 2023 is a significant forum for presenting the development and applications of innovative techniques in closely related areas. The exchange of ideas between scientists and technicians from both academic and business sectors is essential to facilitate the development of systems that meet the demands of today’s society. Technology transfer in this field remains a challenge, so such contributions are mainly considered in this symposium. The ISA Special Session features discussions and publications on developing innovative techniques for complex problems. This Special Issue includes 11 papers selected from extended contributions presented at the Special Session on Intelligent Systems Applications (ISA) under the framework of the 20th International Symposium on Distributed Computing and Artificial Intelligence 2023 (DCAI 2023), held in Guimaraes, Portugal, 12–14 July 2023, and organized by LASI and Centro Algoritmi of the University of Minho (Portugal)

Detection of DoS Attacks in an IoT Environment with MQTT Protocol Based on Intelligent Binary Classifiers

[Abstract] The present work deals with the problem of detecting Denial of Service attacks in an IoT environment. To achieve this goal, a dataset registered in an MQTT protocol network is used, applying dimension reduction techniques combined with classification algorithms. The final classifiers presents successful results.Xunta de Galicia; ED431G 2019/0

A hybrid intelligent model to predict the hydrogen concentration in the producer gas from a downdraft gasifier

[Abstract] This research work presents an artificial intelligence approach to predicting the hydrogen concentration in the producer gas from biomass gasification. An experimental gasification plant consisting of an air-blown downdraft fixed-bed gasifier fueled with exhausted olive pomace pellets and a producer gas conditioning unit was used to collect the whole dataset. During an extensive experimental campaign, the producer gas volumetric composition was measured and recorded with a portable syngas analyzer at a constant time step of 10 seconds. The resulting dataset comprises nearly 75 hours of plant operation in total. A hybrid intelligent model was developed with the aim of performing fault detection in measuring the hydrogen concentration in the producer gas and still provide reliable values in the event of malfunction. The best performing hybrid model comprises six local internal submodels that combine artificial neural networks and support vector machines for regression. The results are remarkably satisfactory, with a mean absolute prediction error of only 0.134% by volume. Accordingly, the developed model could be used as a virtual sensor to support or even avoid the need for a real sensor that is specific for measuring the hydrogen concentration in the producer gas.Junta de Andalucía; 1381442Xunta de Galicia; ED431G 2019/01Ministerio de Universidades; FPU19/0093

Model-based analysis of the autonomic response to head-up tilt testing in Brugada syndrome

The etiology of Brugada syndrome (BS) is complex and multifactorial, making risk stratification in this population a major challenge. Since changes in the autonomic modulation of these patients are commonly related to arrhythmic events, we analyze in this work whether the response to head-up tilt (HUT) testing on this population may provide useful, complementary information for risk stratification. In order to perform this analysis, a coupled physiological model integrating the cardiac electrical activity, the cardiovascular system and the baroreceptors reflex control of the autonomic function, in response to HUT is proposed. A sensitivity analysis was performed, based on a screening method, evidencing the influence of cardiovascular parameters on blood pressure and of baroreflex regulation on heart rate. The most sensitive parameters have been identified on a set of 20 subjects (8 controls and 12 BS patients), so as to assess subject-specific model parameters. According to the results, controls showed an increased sympathetic modulation after tilting, as well as a reduced left ventricular contractility was observed in symptomatic, with respect to asymptomatic BS patients. These results provide new insights regarding the autonomic mechanisms regulating the cardiovascular system in BS which might be used as a complementary source of information, along with classical electrophysiological parameters, for BS risk stratification.Peer ReviewedPostprint (author's final draft

Gaining deep knowledge of Android malware families through dimensionality reduction techniques

[Abstract] This research proposes the analysis and subsequent characterisation of Android malware families by means of low dimensional visualisations using dimensional reduction techniques. The well-known Malgenome data set, coming from the Android Malware Genome Project, has been thoroughly analysed through the following six dimensionality reduction techniques: Principal Component Analysis, Maximum Likelihood Hebbian Learning, Cooperative Maximum Likelihood Hebbian Learning, Curvilinear Component Analysis, Isomap and Self Organizing Map. Results obtained enable a clear visual analysis of the structure of this high-dimensionality data set, letting us gain deep knowledge about the nature of such Android malware families. Interesting conclusions are obtained from the real-life data set under analysis

A Hybrid Intelligent System to Forecast Solar Energy Production

Manuscrito aceptado[Abstarct]: There is wide acknowledgement that solar energy is a promising and renewable source of electricity. However, complementary sources are sometimes required, due to its limited capacity, in order to satisfy user demand. A Hybrid Intelligent System (HIS) is proposed in this paper to optimize the range of possible solar energy and power grid combinations. It is designed to predict the energy generated by any given solar thermal system. To do so, the novel HIS is based on local models that implement both supervised learning (artificial neural networks) and unsupervised learning (clustering). These techniques are combined and applied to a realworld installation located in Spain. Alternative models are compared and validated in this case study with data from a whole year. With an optimum parameter fit, the proposed system managed to calculate the solar energy produced by the panel with an error that was lower than 10-4 in 86% of cases

Hybrid intelligent system for a synchronous rectifier converter control and soft switching ensurement

[Abastract]: This research implements an intelligent control strategy in a synchronous rectifier buck converter to assure that the converter operates in soft-switching mode. The converter is analysed and the two different switching modes are presented: Hard-switching and Soft-Switching. Afterwards, an intelligent model is implemented with the aim of identifying and classifying the switching mode of the power converter. The model implementation is based on classification methods through intelligent algorithms that differentiate between the two modes of operation. Satisfactory results have been obtained with the implemented classification method, achieving high accuracy and allowing the implementation of the model into the control strategy of the converter; assuring that the converter operates in the desired operating mode: Soft-Switching mode

Multi-GPU Development of a Neural Networks Based Reconstructor for Adaptive Optics

[Abstract] Aberrations introduced by the atmospheric turbulence in large telescopes are compensated using adaptive optics systems, where the use of deformable mirrors and multiple sensors relies on complex control systems. Recently, the development of larger scales of telescopes as the E-ELT or TMT has created a computational challenge due to the increasing complexity of the new adaptive optics systems. The Complex Atmospheric Reconstructor based on Machine Learning (CARMEN) is an algorithm based on artificial neural networks, designed to compensate the atmospheric turbulence. During recent years, the use of GPUs has been proved to be a great solution to speed up the learning process of neural networks, and different frameworks have been created to ease their development. The implementation of CARMEN in different Multi-GPU frameworks is presented in this paper, along with its development in a language originally developed for GPU, like CUDA. This implementation offers the best response for all the presented cases, although its advantage of using more than one GPU occurs only in large networks.The authors appreciate support from the Spanish Economics and Competitiveness Ministry, through Grant AYA2014-57648-P, and the Government of the Principality of Asturias (Consejería de Economía y Empleo), through Grant FC-15-GRUPIN14-017.Gobierno del Principado de Asturias; FC-15-GRUPIN14-01