A Framework for Simulating Home Control Networks

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Smart Management Consumption in Renewable Energy Fed Ecosystems

Advances in embedded electronic systems, the development of new communication protocols, and the application of artificial intelligence paradigms have enabled the improvement of current automation systems of energy management. Embedded devices integrate different sensors with connectivity, computing resources, and reduced cost. Communication and cloud services increase their performance; however, there are limitations in the implementation of these technologies. If the cloud is used as the main source of services and resources, overload problems will occur. There are no models that facilitate the complete integration and interoperability in the facilities already created. This article proposes a model for the integration of smart energy management systems in new and already created facilities, using local embedded devices, Internet of Things communication protocols and services based on artificial intelligence paradigms. All services are distributed in the new smart grid network using edge and fog computing techniques. The model proposes an architecture both to be used as support for the development of smart services and for energy management control systems adapted to the installation: a group of buildings and/or houses that shares energy management and energy generation. Machine learning to predict consumption and energy generation, electric load classification, energy distribution control, and predictive maintenance are the main utilities integrated. As an experimental case, a facility that incorporates wind and solar generation is used for development and testing. Smart grid facilities, designed with artificial intelligence algorithms, implemented with Internet of Things protocols, and embedded control devices facilitate the development, cost reduction, and the integration of new services. In this work, a method to design, develop, and install smart services in self-consumption facilities is proposed. New smart services with reduced costs are installed and tested, confirming the advantages of the proposed model.This research was funded by the Industrial Computers and Computer Networks program (Informatica Industrial y redes de Computadores (I2RC)) (2018/2019) funded by the University of Alicante, Wak9 Holding BV company under the eo-TICCproject, and the Valencian Innovation Agency under scientific innovation unit (UCIE Ars Innovatio) of the University of Alicante at https://web.ua.es/es/ars-innovatio/unidad-cientifica-de-innovacion-ars-innovatio.html

Intelligent Power Management System Using Hybrid Renewable Energy Resources and Decision Tree Approach

Optimal power usage and consumption require continuous monitoring, forecasting electric energy consumption and renewable generation. To facilitate integration of renewable energies and optimize their resources, new communication and data processing technologies are used in new projects. This article shows the works and results obtained in the eoTICC project. The objective is to design and develop an intelligent energy manager using the Archimedes wind turbine and a solar generation system, both integrated in industrial and residential power facilities. Solutions based on Artificial Intelligence paradigms and Internet of Things protocols allow automatic decision making to optimize energy management. In a facility, the energy demand and weather forecasts can be known by an intelligent energy manager. With these conditions, the energy manager can develop rules based on decision trees to automate control actions aimed at optimizing the use of energy. This article shows the architecture of IoT infrastructure and the first rules designed in the project. The result obtained provides improvements in the use of renewable energy in current facilities that do not use this type of intelligent management. The improvements allow to use the energy at the time of generation, avoiding unnecessary storage.This research was supported by Industrial Computers and Computer Networks program (I2RC) (2017/2018) funded by the University of Alicante, Wak9 Holding BV company under eo-TICC project and the Valencian Innovation Agency under scientific innovation unit (UCIE Ars Innovatio) of the University of Alicante

Gait Analysis Using Computer Vision Based on Cloud Platform and Mobile Device

Frailty and senility are syndromes that affect elderly people. The ageing process involves a decay of cognitive and motor functions which often produce an impact on the quality of life of elderly people. Some studies have linked this deterioration of cognitive and motor function to gait patterns. Thus, gait analysis can be a powerful tool to assess frailty and senility syndromes. In this paper, we propose a vision-based gait analysis approach performed on a smartphone with cloud computing assistance. Gait sequences recorded by a smartphone camera are processed by the smartphone itself to obtain spatiotemporal features. These features are uploaded onto the cloud in order to analyse and compare them to a stored database to render a diagnostic. The feature extraction method presented can work with both frontal and sagittal gait sequences although the sagittal view provides a better classification since an accuracy of 95% can be obtained.This research is part of the FRASE MINECO project (TIN2013-47152-C3-2-R) funded by the Ministry of Economy and Competitiveness of Spain

Modelado y simulación de la distribución de energía eléctrica en sistemas genéricos consistentes en diversas fuentes y múltiples modos de transmisión. Optimización del uso de las fuentes con criterios de sostenibilidad

Esta investigación aborda el problema de la distribución eléctrica en sistemas genéricos compuestos por varias fuentes de energía (hidroeléctrica, eólica, solar, etc.), múltiples medios de transmisión (líneas de transmisión, medios de almacenamiento, convertidores, etc.) y varios centros de consumo (edificios, viviendas, electrodomésticos, portátiles, teléfonos móviles, etc.), concretamente, haciendo uso de criterios sostenibles que aprovechen las bondades de las fuentes de energía renovables. La aportación global de este trabajo es la generalidad del modelo formal de sistema eléctrico, basado en sistemas multiagente, y las estrategias de gestión parametrizadas, que permiten obtener sistemas de gestión de la distribución acordes a un problema concreto. En consecuencia, esta investigación abre una línea metodológica e instrumental para la obtención de soluciones al problema de la distribución eléctrica a todos los niveles, desde las redes de alta energía, hasta el microabastecimiento que requieren los dispositivos "wearable", desde las sencillas redes con fuente única de suministro, hasta las configuraciones híbridas multifrecuenciales e incluso multimedia (coexistencia de transporte de energía con datos e información de control)

Sistema de climatización por suelo radiante coalimentado por energías renovables

Se presenta el sistema de climatización basado en suelo radiante coalimentado por fuentes de energía renovables, que es capaz de proporcionar calor o frío. Se ha modelado una instalación genérica, que ha sido particularizada e integrada en una vivienda-laboratorio de unos 54m2. El sistema está compuesto por placas termo-solares, calentador eléctrico, bomba de calor aire-agua, acumuladores, circuito de suelo radiante y fancoil. El sistema de control está basado en un autómata que integra sensores y actuadores de diversas tecnologías que permiten monitorizar el sistema, visualizar y gestionar la instalación de forma remota y realizar un control minimizando el gasto energético. El sistema de control es reactivo en tiempo real por lo que cada vez que sucede algún cambio en el entorno se desencadenas las acciones oportunas.Este trabajo ha sido realizado gracias al convenio de colaboración, INSTALACIONESRIBERA2-09, con la empresa Instalaciones Ribera S.L. que ha sido financiado por el Instituto de la Mediana y Pequeña Industria Valenciana

A vision based proposal for classification of normal and abnormal gait using RGB camera

Human gait is mainly related to the foot and leg movements but, obviously, the entire motor system of the human body is involved. We hypothesise that movement parameters such as dynamic balance, movement harmony of each body element (arms, head, thorax…) could enable us to finely characterise gait singularities to pinpoint potential diseases or abnormalities in advance. Since this paper deals with the preliminary problem pertaining to the classification of normal and abnormal gait, our study will revolve around the lower part of the body. Our proposal presents a functional specification of gait in which only observational kinematic aspects are discussed. The resultant specification will confidently be open enough to be applied to a variety of gait analysis problems encountered in areas connected to rehabilitation, sports, children’s motor skills, and so on. To carry out our functional specification, we develop an extraction system through which we analyse image sequences to identify gait features. Our prototype not only readily lets us determine the dynamic parameters (heel strike, toe off, stride length and time) and some skeleton joints but also satisfactorily supplies us with a proper distinction between normal and abnormal gait. We have performed experiments on a dataset of 30 samples.This research is part of the FRASE MINECO project (TIN2013-47152-C3-2-R) funded by the Ministry of Economy and Competitiveness of Spain

Gait-A Database: dataset of subjects walking

Gait-A Database is a dataset of subjects walking over an 8 m hall. It contains both normal walking patterns along with some abnormal walking patterns feigned by the subjects. The dataset contains two folders, normal and abnormal, each containing the respective set of walking patterns. In each folder there are the different walking samples recorded. Each sample contains a silhouette folder containing the silhouette extracted (using Mean of Gaussians background subtraction technique) corresponding to each frame of the sequence