Appliance Identification in NILM Applications by means of a Convolutional Auto-Encoder

n energy efficiency applications, Non-Intrusive Load Monitoring techniques (NILM) are typically used to deduce which electrical loads are being used in a building at a given time. The identification of household appliances, in particular manually operated ones, is relevant information that can also be applied to infer the routines of tenants in Active and Assisted Living environments (AAL). These tools and applications are becoming increasingly interesting, especially in Western countries, where the ageing population is putting a strain on public social and health services. In this context, this work aims to classify the on/off events of the devices considered in the BLUED database. For this purpose, an architecture is presented, consisting of a Convolutional Auto-Encoder (CAE) followed by a classifier neural network. The CAE is used to implement a dimensionality reduction process after the encoder. Input data are formatted as images, created with extracted sections of the high-frequency electric current signal captured around the switching events. It is noteworthy that this dimensionality reduction also allows a decrease in the computational load of the classifier. Regarding the CAE functionality, the reconstruction error reaches a value of 1.579 · 10−3, whereas in the validation stage a weighted average classification F1-score of 87 % is obtained for the whole architecture

Comparison of Neural Networks for High-Sampling Rate NILM Scenario

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.The common objective of techniques employed to identify the use of household appliances is related to energy efficiency and the reduction of energy consumption. In addition, through load monitoring it is possible to assess the degree of independence of tenants with minimal invasion of privacy and thus develop sustainable health systems capable of providing the required services remotely. Both approaches should initially deal with the load identification stage. For that purpose, this work presents three different solutions that take the events of the electrical current signal acquired at high frequency and process them for classification by using two different topologies of Artificial Neural Networks (ANN). The data of interest used as input for the ANN in the proposals are the normalized signal captured around the events, the images created by dividing that signal into sections and organizing them in a matrix, and the images coming from the Short Time Fourier Transform (STFT) of the signal around the event. The dataset BLUED is used to carry out the validation of the proposal, where some of the proposed architectures obtain an F1 score above 90% for more than fifteen devices under classification.Universidad de AlcaláAgencia Estatal de Investigació

Evaluating Human Activity and Usage Patterns of Appliances with Smart Meters

2022 IEEE International Symposium on Medical Measurements and Applications (MeMeA), 22-24 June 2022, Messina, Italy.Population ageing is becoming a key issue for most western countries, due to the challenges that it poses to the sustainability of future healthcare systems. In this context, many proposals and development are emerging trying to enhance the independent living of elderly and cognitive impaired people at their own homes. For that purpose, the massive deployment of smart meter at houses and buildings, initially focused on improving the energy management, has become a useful tool to provide the society with a variety of services and applications that can be employed for independent living. This work proposes the use of a commercial smart meter that delivers the disaggregated consumption per appliance every hour. This device has been installed on a test house during a training period of two months, in order to infer the behavior routines in the usage of the microwave. After the training, every new day can be compared to the obtained usage pattern of that appliance, in order to launch a notification when the day routine significantly differs. Similarly, since the use of the microwave is related to cooking, activities such as breakfast, lunch or dinner, may also be monitored and/or compared to a trained pattern. The proposal has been validated preliminary with experimental data coming from the aforementioned household.Agencia Estatal de InvestigaciónUniversidad de Alcal

Direct X-Ray detection of the spin Hall effect in CuBi

The spin Hall effect and the inverse spin Hall effect are important spin-charge conversion mechanisms. The direct spin Hall effect induces a surface spin accumulation from a transverse charge current due to spin-orbit coupling even in nonmagnetic conductors. However, most detection schemes involve additional interfaces, leading to large scattering in reported data. Here we perform interface-free x-ray spectroscopy measurements at the Cu L3,2 absorption edges of highly Bi-doped Cu (Cu95Bi5). The detected x-ray magnetic circular dichroism signal corresponds to an induced magnetic moment of (2.2 ± 0.5) × 10-12 μB A-1 cm2 per Cu atom averaged over the probing depth, which is of the same order of magnitude as found for Pt measured by magneto-optics. The results highlight the importance of interface-free measurements to assess material parameters and the potential of CuBi for spin-charge conversion application

A comprehensive survey on reinforcement-learning-based computation offloading techniques in Edge Computing Systems

Producción CientíficaIn recent years, the number of embedded computing devices connected to the Internet has exponentially increased. At the same time, new applications are becoming more complex and computationally demanding, which can be a problem for devices, especially when they are battery powered. In this context, the concepts of computation offloading and edge computing, which allow applications to be fully or partially offloaded and executed on servers close to the devices in the network, have arisen and received increasing attention. Then, the design of algorithms to make the decision of which applications or tasks should be offloaded, and where to execute them, is crucial. One of the options that has been gaining momentum lately is the use of Reinforcement Learning (RL) and, in particular, Deep Reinforcement Learning (DRL), which enables learning optimal or near-optimal offloading policies adapted to each particular scenario. Although the use of RL techniques to solve the computation offloading problem in edge systems has been covered by some surveys, it has been done in a limited way. For example, some surveys have analysed the use of RL to solve various networking problems, with computation offloading being one of them, but not the primary focus. Other surveys, on the other hand, have reviewed techniques to solve the computation offloading problem, being RL just one of the approaches considered. To the best of our knowledge, this is the first survey that specifically focuses on the use of RL and DRL techniques for computation offloading in edge computing system. We present a comprehensive and detailed survey, where we analyse and classify the research papers in terms of use cases, network and edge computing architectures, objectives, RL algorithms, decision-making approaches, and time-varying characteristics considered in the analysed scenarios. In particular, we include a series of tables to help researchers identify relevant papers based on specific features, and analyse which scenarios and techniques are most frequently considered in the literature. Finally, this survey identifies a number of research challenges, future directions and areas for further study.Consejería de Educación de la Junta de Castilla y León y FEDER (VA231P20)Ministerio de Ciencia e Innovación y Agencia Estatal de Investigación (Proyecto PID2020-112675RB-C42, PID2021-124463OBI00 y RED2018-102585-T, financiados por MCIN/AEI/10.13039/501100011033

Minecraft para diseños HDL: flujo de síntesis de Verilog para circuitos de redstone

Memoria ID-076. Ayudas de la Universidad de Salamanca para la innovación docente, curso 2019-2020.[ES]El objetivo de este proyecto es crear un espacio virtual mediante la utilización de tecnología Minecraft que permita al estudiante aumentar el contenido académico relacionado con los conocimientos impartidos en las distintas ramas de las asignaturas de electrónica-física de la Universidad de Salamanca

Impact of Biological Agents on Postsurgical Complications in Inflammatory Bowel Disease : A Multicentre Study of Geteccu

Background: The impact of biologics on the risk of postoperative complications (PC) in inflammatory bowel disease (IBD) is still an ongoing debate. This lack of evidence is more relevant for ustekinumab and vedolizumab. Aims: To evaluate the impact of biologics on the risk of PC. Methods: A retrospective study was performed in 37 centres. Patients treated with biologics within 12 weeks before surgery were considered "exposed". The impact of the exposure on the risk of 30-day PC and the risk of infections was assessed by logistic regression and propensity score-matched analysis. Results: A total of 1535 surgeries were performed on 1370 patients. Of them, 711 surgeries were conducted in the exposed cohort (584 anti-TNF, 58 vedolizumab and 69 ustekinumab). In the multivariate analysis, male gender (OR: 1.5; 95% CI: 1.2-2.0), urgent surgery (OR: 1.6; 95% CI: 1.2-2.2), laparotomy approach (OR: 1.5; 95% CI: 1.1-1.9) and severe anaemia (OR: 1.8; 95% CI: 1.3-2.6) had higher risk of PC, while academic hospitals had significantly lower risk. Exposure to biologics (either anti-TNF, vedolizumab or ustekinumab) did not increase the risk of PC (OR: 1.2; 95% CI: 0.97-1.58), although it could be a risk factor for postoperative infections (OR 1.5; 95% CI: 1.03-2.27). Conclusions: Preoperative administration of biologics does not seem to be a risk factor for overall PC, although it may be so for postoperative infections

La ciudad como experiencia de conocimiento: Nuevas metodologías de aprendizaje, iniciación de la investigación y extensión universitaria a partir de la historia urbana

Depto. de GeografíaDepto. de Historia Moderna y ContemporáneaDepto. de Historia del ArteFac. de Geografía e HistoriaFALSEsubmitte

Diseño de infografías científicas en el aula a través de herramientas web 3.0 y recursos en abierto

El proyecto que se presenta pretende instruir a estudiantes y profesores en las competencias necesarias para el diseño y elaboración de infografías científicas de contenidos académicos, utilizando para ello herramientas de la web 3.0 disponibles en abierto. Exponer ideas científicas mediante la elaboración de infografías es una realidad que ha llegado ya a todos los hogares gracias a los medios de comunicación: al exponer noticias en papel o en web, los redactores ya no recurren únicamente al texto con imágenes, sino que se valen de elementos infográficos explicativos que ayudan a que el público comprenda mejor la noticia en su dimensión más técnica. Aplicar la metodología de realización de infografías científicas a la práctica docente en Humanidades y Ciencias Sociales (Historia del Arte, Historia Moderna, Educación, Psicología, Bellas Artes y Documentación) se presenta no solo como una estrategia útil para que los estudiantes sinteticen las claves de determinados temas, sino también como una herramienta interesante para dotar de competencias de difusión científica a los alumnos, facilitando así su inserción laboral. Asimismo, esta proyecto ha facilitado la conformación de un equipo innovador profundamente interdisciplinar (Historia, Historia del Arte, Psicología, Tecnologías y Bellas Artes) e interinstitucional (UCM, UNED y URJC) con un enorme potencial de cara a futuras propuestas de innovación docente

Healthcare workers hospitalized due to COVID-19 have no higher risk of death than general population. Data from the Spanish SEMI-COVID-19 Registry

Aim To determine whether healthcare workers (HCW) hospitalized in Spain due to COVID-19 have a worse prognosis than non-healthcare workers (NHCW). Methods Observational cohort study based on the SEMI-COVID-19 Registry, a nationwide registry that collects sociodemographic, clinical, laboratory, and treatment data on patients hospitalised with COVID-19 in Spain. Patients aged 20-65 years were selected. A multivariate logistic regression model was performed to identify factors associated with mortality. Results As of 22 May 2020, 4393 patients were included, of whom 419 (9.5%) were HCW. Median (interquartile range) age of HCW was 52 (15) years and 62.4% were women. Prevalence of comorbidities and severe radiological findings upon admission were less frequent in HCW. There were no difference in need of respiratory support and admission to intensive care unit, but occurrence of sepsis and in-hospital mortality was lower in HCW (1.7% vs. 3.9%; p = 0.024 and 0.7% vs. 4.8%; p<0.001 respectively). Age, male sex and comorbidity, were independently associated with higher in-hospital mortality and healthcare working with lower mortality (OR 0.211, 95%CI 0.067-0.667, p = 0.008). 30-days survival was higher in HCW (0.968 vs. 0.851 p<0.001). Conclusions Hospitalized COVID-19 HCW had fewer comorbidities and a better prognosis than NHCW. Our results suggest that professional exposure to COVID-19 in HCW does not carry more clinical severity nor mortality