Designing the input vector to ANN-based models for short-term load forecast in electricity distribution systems

The present trend to electricity market restructuring increases the need for reliable short-term load forecast (STLF) algorithms, in order to assist electric utilities in activities such as planning, operating and controlling electric energy systems. Methodologies such as artificial neural networks (ANN) have been widely used in the next hour load forecast horizon with satisfactory results. However, this type of approach has had some shortcomings. Usually, the input vector (IV) is defined in a arbitrary way, mainly based on experience, on engineering judgment criteria and on concern about the ANN dimension, always taking into consideration the apparent correlations within the available endogenous and exogenous data. In this paper, a proposal is made of an approach to define the IV composition, with the main focus on reducing the influence of trial-and-error and common sense judgments, which usually are not based on sufficient evidence of comparative advantages over previous alternatives. The proposal includes the assessment of the strictly necessary instances of the endogenous variable, both from the point of view of the contiguous values prior to the forecast to be made, and of the past values representing the trend of consumption at homologous time intervals of the past. It also assesses the influence of exogenous variables, again limiting their presence at the IV to the indispensable minimum. A comparison is made with two alternative IV structures previously proposed in the literature, also applied to the distribution sector. The paper is supported by a real case study at the distribution sector.http://www.sciencedirect.com/science/article/B6V2T-4MCW9X5-1/1/00590212b5295357d45465c710d645a

Instrumented Footwear and Machine Learning for Gait Analysis and Training

Gait analysis allows clinicians and researchers to quantitatively characterize the kinematics and kinetics of human movement. Devices that quantify gait can be either portable, such as instrumented shoes, or non-portable, such as motion capture systems and instrumented walkways. There is a tradeoff between these two classes of systems in terms of portability and accuracy. However, recent computer advances allow for the collection of meaningful data outside of the clinical setting. In this work, we present the DeepSole system combined with the different neural network models. This system is a fully capable to characterize the gait of the individuals and provide vibratory feedback to the wearer. Thanks to the flexible construction and its wireless capabilities, it can be comfortably worn by wide arrange of people, both able-bodied and people with pathologies that affect their gait. It can be used for characterization, training, and as an abstract sensor to measure human gait in real-time. Three neural network models were designed and implemented to map the sensors embedded in the DeepSole system to gait characteristics and events. The first one is a recurrent neural network that classifies the gait into the correct gait phase of the wearer. This model was validated with data from healthy young adults and children with Cerebral Palsy. Furthermore, this model was implemented in real-time to provide vibratory feedback to healthy young adults to create temporal asymmetry on the dominant side during regular walking. During the experiment, the subjects who walked had an increased stance time on both sides, but the dominant side was affected more. The second model is encoder-decoder recurrent neural network that maps the sensors into current gait cycle percentage. This model is useful to provide continuous feedback that is synchronized to the gait. This model was implemented in real-time to provide vibratory feedback to six muscle groups used during regular walking. The effects of the vibration were analyzed. It was found that depending on the feedback, the subjects changed their spatial and temporal gait parameters. The third model uses all the sensors in the instrumented footwear to identify a motor phenomenon called freezing of gait in patients with Parkinson’s Disease. This phenomenon is characterized by transient periods, usually lasting for several seconds, in which attempted ambulation is halted. The model has better performance than the state-of-the-art and does not require any pre-processing. The DeepSole system when used in conjunction with the presented models is able to characterize and provide feedback in a wide range of scenarios. The system is portable, comfortable, and can accommodate a wide range of populations who can benefit from this wearable technology

Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis

En esta tesis, se propone el uso de la técnica de Doppler transcraneal (DTC) para monitorizar la actividad cerebral durante la exposición a entornos virtuales (EV) y así poder analizar los correlatos cerebrales del sentido de presencia. Las hipótesis de partida son las siguientes: 1) DTC se podrá utilizar fácilmente en combinación con sistemas de realidad virtual. 2) Los datos de velocidad de flujo sanguíneo medidos por DTC se podrán utilizar para analizar cambios de actividad cerebral durante la exposición a EV. 3) Habrá diferencias en la velocidad del flujo sanguíneo asociadas a distintos niveles de presencia. 4) Habrá correlación entre el grado de presencia medido por cuestionarios y parámetros de la velocidad de flujo sanguíneo. 5) Cada componente de la experiencia virtual tendrá una influencia en las variaciones de velocidad observadas. Para analizar las hipótesis planteadas, se realizaron cuatro experimentos distintos, en los que se analizó la velocidad del flujo sanguíneo durante: 1) distintas condiciones de navegación, 2) distintas condiciones de inmersión, 3) una tarea de percepción visual y 4) tareas motoras para manejo de un joystick. Durante la tesis, se han propuesto distintas técnicas de procesado de señal basadas en análisis espectral y en la obtención parámetros no lineales de la señal, que no habían sido utilizadas previamente en experimentos psicofisiológicos con DTC. Se ha observado que existe un incremento en la velocidad del flujo sanguíneo durante la exposición a un EV, el cual puede deberse a distintos factores que intervienen en la experiencia: tareas de interacción visuoespacial, tareas de atención, la creación y ejecución de un plan motor, cambios emocionales Los análisis han mostrado que existen correlaciones significativas entre la velocidad media de flujo sanguíneo en las arterias cerebrales medias durante la exposición al EV y respuestas a los cuestionarios de presencia utilizados.Rey Solaz, B. (2010). Contributions to the Development of Objective Techniques for Presence Measurement in Virtual Environments by means of Brain Activity Analysis [Tesis doctoral no publicada]. Universitat Politècnica de València. https://doi.org/10.4995/Thesis/10251/8505Palanci

Investigación en matemáticas, economía y ciencias sociales

El resultado de este libro que reune inquietudes académicas en torno a temas tan estudiados como los que están alrededor del maíz, del frijol o del café; y tan contemporáneos como las aplicaciones concretas de las ciencias ya citadas, al estudio de la adopción del comercio electrónico en empresas del sector agroindustrial o, el caso de la generación de biogas o energía eléctrica por medio de biodigestores. Al editar este texto e incorporarlo a la bibliografía de los temas de referencia, se enriquecen opciones de consulta para los estudiosos de esos temas en general; pero también para interesados en aspectos tan específicos como la cadena de suministro del mercado hortofrutícola en Texcoco