A semantic autonomous video surveillance system for dense camera networks in smart cities

Producción CientíficaThis paper presents a proposal of an intelligent video surveillance system able to detect and identify abnormal and alarming situations by analyzing object movement. The system is designed to minimize video processing and transmission, thus allowing a large number of cameras to be deployed on the system, and therefore making it suitable for its usage as an integrated safety and security solution in Smart Cities. Alarm detection is performed on the basis of parameters of the moving objects and their trajectories, and is performed using semantic reasoning and ontologies. This means that the system employs a high-level conceptual language easy to understand for human operators, capable of raising enriched alarms with descriptions of what is happening on the image, and to automate reactions to them such as alerting the appropriate emergency services using the Smart City safety network

Application of mixed reality to ultrasound-guided femoral arterial cannulation during real-time practice in cardiac interventions

Producción CientíficaMixed reality opens interesting possibilities as it allows physicians to interact with both, the real physical and the virtual computer-generated environment and objects, in a powerful way. A mixed reality system, based in the HoloLens 2 glasses, has been developed to assist cardiologists in a quite complex interventional procedure: the ultrasound-guided femoral arterial cannulations, during real-time practice in interventional cardiology. The system is divided into two modules, the transmitter module, responsible for sending medical images to HoloLens 2 glasses, and the receiver module, hosted in the HoloLens 2, which renders those medical images, allowing the practitioner to watch and manage them in a 3D environment. The system has been successfully used, between November 2021 and August 2022, in up to 9 interventions by 2 different practitioners, in a large public hospital in central Spain. The practitioners using the system confirmed it as easy to use, reliable, real-time, reachable, and cost-effective, allowing a reduction of operating times, a better control of typical errors associated to the interventional procedure, and opening the possibility to use the medical imagery produced in ubiquitous e-learning. These strengths and opportunities were only nuanced by the risk of potential medical complications emerging from system malfunction or operator errors when using the system (e.g., unexpected momentary lag). In summary, the proposed system can be taken as a realistic proof of concept of how mixed reality technologies can support practitioners when performing interventional and surgical procedures during real-time daily practice.Junta de Castilla y León - Gerencia Regional de Salud (SACyL) (grant number GRS 2275/A/2020)Instituto de Salud Carlos III (grant number DTS21/00158)Publicación en abierto financiada por el Consorcio de Bibliotecas Universitarias de Castilla y León (BUCLE), con cargo al Programa Operativo 2014ES16RFOP009 FEDER 2014-2020 DE CASTILLA Y LEÓN, Actuación:20007-CL - Apoyo Consorcio BUCL

Temperature and relative humidity estimation and prediction in the tobacco drying process using artificial neural networks

Producción CientíficaThis paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.Centro para el Desarrollo Tecnológico Industrial (CDTI), proyecto "Mejora de la competitividad del sector del tabaco en Extremadura: nuevos procesos y productos" (under project IDI-20100986)Junta de Castilla y León, financiado por el Plan Regional de Proyectos de Investigación (proyecto VA034A10-2

An intelligent surveillance platform for large metropolitan areas with dense sensor deployment

Producción CientíficaThis paper presents an intelligent surveillance platform based on the usage of large numbers of inexpensive sensors designed and developed inside the European Eureka Celtic project HuSIMS. With the aim of maximizing the number of deployable units while keeping monetary and resource/bandwidth costs at a minimum, the surveillance platform is based on the usage of inexpensive visual sensors which apply efficient motion detection and tracking algorithms to transform the video signal in a set of motion parameters. In order to automate the analysis of the myriad of data streams generated by the visual sensors, the platform’s control center includes an alarm detection engine which comprises three components applying three different Artificial Intelligence strategies in parallel. These strategies are generic, domain-independent approaches which are able to operate in several domains (traffic surveillance, vandalism prevention, perimeter security, etc.). The architecture is completed with a versatile communication network which facilitates data collection from the visual sensors and alarm and video stream distribution towards the emergency teams. The resulting surveillance system is extremely suitable for its deployment in metropolitan areas, smart cities, and large facilities, mainly because cheap visual sensors and autonomous alarm detection facilitate dense sensor network deployments for wide and detailed coveraMinisterio de Industria, Turismo y Comercio and the Fondo de Desarrollo Regional (FEDER) and the Israeli Chief Scientist Research Grant 43660 inside the European Eureka Celtic project HuSIMS (TSI-020400-2010-102)

A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants

[EN] Recent technological advances in the power generation and information technologies areas are helping to change the modern electricity supply system in order to comply with higher energy efficiency and sustainability standards. Smart grids are an emerging trend that introduce intelligence in the power grid to optimize resource usage. In order for this intelligence to be effective, it is necessary to retrieve enough information about the grid operation together with other context data such as environmental variables, and intelligently modify the behavior of the network elements accordingly. This article presents a multi-agent system model for virtual power plants, a new power plant concept in which generation no longer occurs in big installations, but is the result of the cooperation of smaller and more intelligent elements. The proposed model is not only focused on the management of the different elements, but includes a set of agents embedded with artificial neural networks for collaborative forecasting of disaggregated energy demand of domestic end users, the results of which are also shown in this article.We would like to express our thanks to the coordinators of the project OptimaGrid for the information provided on MAS-based micro-grids, and the creators of a MAS INGENIAS methodology. This article has been partially funded by the project SociAAL (Social Ambient Assisted Living), supported by Spanish Ministry for Economy and Competitiveness, with grant TIN2011-28335-C02-01, by the Programa de Creacion y Consolidacion de Grupos de Investigacion UCM-Banco Santander for the group number 921354 (GRASIA group).Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro, B.; Sanchez-Esguevillas, A.; Lloret, J.; Chinarro, D.... (2013). A Multi-Agent System Architecture for Smart Grid Management and Forecasting of Energy Demand in Virtual Power Plants. IEEE Communications Magazine. 51(1):106-113. https://doi.org/10.1109/MCOM.2013.6400446S10611351

Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks

Electricity is indispensable and of strategic importance to national economies. Consequently, electric utilities make an effort to balance power generation and demand in order to offer a good service at a competitive price. For this purpose, these utilities need electric load forecasts to be as accurate as possible. However, electric load depends on many factors (day of the week, month of the year, etc.), which makes load forecasting quite a complex process requiring something other than statistical methods. This study presents an electric load forecast architectural model based on an Artificial Neural Network (ANN) that performs Short-Term Load Forecasting (STLF). In this study, we present the excellent results obtained, and highlight the simplicity of the proposed model. Load forecasting was performed in a geographic location of the size of a potential microgrid, as microgrids appear to be the future of electric power supply.Hernández, L.; Baladrón Zorita, C.; Aguiar Pérez, JM.; Carro Martínez, B.; Sanchez-Esguevillas, A.; Lloret, J. (2013). Short-Term Load Forecasting for Microgrids Based on Artificial Neural Networks. Energies. 6(3):1385-1408. doi:10.3390/en6031385S1385140863Booklets European Comission. Your Guide to the Lisbon Treaty 2009http://ec.europa.eu/publications/booklets/others/84/en.pdfHernandez, L., Baladron, C., Aguiar, J. M., Carro, B., Sanchez-Esguevillas, A., Lloret, J., … Cook, D. (2013). A multi-agent system architecture for smart grid management and forecasting of energy demand in virtual power plants. 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A novel approach to short-term load forecasting using fuzzy neural networks. IEEE Transactions on Power Systems, 13(2), 480-492. doi:10.1109/59.667372Elman, J. L. (1990). Finding Structure in Time. Cognitive Science, 14(2), 179-211. doi:10.1207/s15516709cog1402_1Elman, J. L. (1991). Distributed representations, simple recurrent networks, and grammatical structure. Machine Learning, 7(2-3), 195-225. doi:10.1007/bf00114844Kohonen, T. (1990). The self-organizing map. Proceedings of the IEEE, 78(9), 1464-1480. doi:10.1109/5.58325Baladrón, C., Aguiar, J. M., Calavia, L., Carro, B., Sánchez-Esguevillas, A., & Hernández, L. (2012). Performance Study of the Application of Artificial Neural Networks to the Completion and Prediction of Data Retrieved by Underwater Sensors. Sensors, 12(2), 1468-1481. doi:10.3390/s120201468Martínez-Martínez, V., Baladrón, C., Gomez-Gil, J., Ruiz-Ruiz, G., Navas-Gracia, L. M., Aguiar, J. M., & Carro, B. (2012). Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks. Sensors, 12(10), 14004-14021. doi:10.3390/s121014004Park, D. C., El-Sharkawi, M. A., Marks, R. J., Atlas, L. E., & Damborg, M. J. (1991). Electric load forecasting using an artificial neural network. IEEE Transactions on Power Systems, 6(2), 442-449. doi:10.1109/59.76685Ho, K. L., Hsu, Y.-Y., & Yang, C.-C. (1992). Short term load forecasting using a multilayer neural network with an adaptive learning algorithm. IEEE Transactions on Power Systems, 7(1), 141-149. doi:10.1109/59.141697Drezga, I., & Rahman, S. (1998). Input variable selection for ANN-based short-term load forecasting. IEEE Transactions on Power Systems, 13(4), 1238-1244. doi:10.1109/59.736244Drezga, I., & Rahman, S. (1999). Short-term load forecasting with local ANN predictors. IEEE Transactions on Power Systems, 14(3), 844-850. doi:10.1109/59.780894Lee, K. Y., Cha, Y. T., & Park, J. H. (1992). 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IEEE Transactions on Power Systems, 13(4), 1386-1391. doi:10.1109/59.736281Senjyu, T., Mandal, P., Uezato, K., & Funabashi, T. (2005). Next Day Load Curve Forecasting Using Hybrid Correction Method. IEEE Transactions on Power Systems, 20(1), 102-109. doi:10.1109/tpwrs.2004.831256Hsu, Y.-Y., & Yang, C.-C. (1991). Design of artificial neural networks for short-term load forecasting. Part 2: Multilayer feedforward networks for peak load and valley load forecasting. IEE Proceedings C Generation, Transmission and Distribution, 138(5), 414. doi:10.1049/ip-c.1991.0052Rejc, M., & Pantos, M. (2011). Short-Term Transmission-Loss Forecast for the Slovenian Transmission Power System Based on a Fuzzy-Logic Decision Approach. IEEE Transactions on Power Systems, 26(3), 1511-1521. doi:10.1109/tpwrs.2010.2096829Wang, Y., Xia, Q., & Kang, C. (2011). Secondary Forecasting Based on Deviation Analysis for Short-Term Load Forecasting. 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Laboratorio Transmedia Núm. 1. Cielo interior de neones rajados por un gigante. Relatos de ciencia ficción

El primer número de la colección Laboratorio Transmedia (ISSN: 2794-0861) contiene siete relatos de ciencia ficción escritos durante el curso 2021/2022 por estudiantes de la Facultad de Ciencias de la Información

Encuesta sobre el Defensor del Pueblo

Esta encuesta, a la que responden Catedráticos de Derecho Constitucional y Administrativo, así como ex-Defensores del Pueblo, se centra en la figura del Defensor del Pueblo en España, y, más concretamente, en cuestiones tales como la valoración que merece la labor desarrollada por el Defensor del Pueblo desde su creación, la necesidad de coordinación entre los Defensores del Pueblo de los diferentes niveles de organización territorial, su ámbito de actuación, y la posibilidades de mejorar los mecanismos de control de la administración

SELNET clinical practice guidelines for bone sarcoma

Bone sarcoma are infrequent diseases, representing < 0.2% of all adult neoplasms. A multidisciplinary management within reference centers for sarcoma, with discussion of the diagnostic and therapeutic strategies within an expert multidisciplinary tumour board, is essential for these patients, given its heterogeneity and low frequency. This approach leads to an improvement in patient's outcome, as demonstrated in several studies. The Sarcoma European Latin-American Network (SELNET), aims to improve clinical outcome in sarcoma care, with a special focus in Latin-American countries. These Clinical Practice Guidelines (CPG) have been developed and agreed by a multidisciplinary expert group (including medical and radiation oncologist, surgical oncologist, orthopaedic surgeons, radiologist, pathologist, molecular biologist and representatives of patients advocacy groups) of the SELNET consortium, and are conceived to provide the standard approach to diagnosis, treatment and follow-up of bone sarcoma patients in the Latin-American context