Object identification by using orthonormal circus functions from the trace transform

In this paper we present an efficient way to both compute and extract salient information from trace transform signatures to perform object identification tasks. We also present a feature selection analysis of the classical trace-transform functionals, which reveals that most of them retrieve redundant information causing misleading similarity measurements. In order to overcome this problem, we propose a set of functionals based on Laguerre polynomials that return orthonormal signatures between these functionals. In this way, each signature provides salient and non-correlated information that contributes to the description of an image object. The proposed functionals were tested considering a vehicle identification problem, outperforming the classical trace transform functionals in terms of computational complexity and identification rate

Weather Radar Estimations Feeding an Artificial Neural Network Model Weather Radar Estimations Feeding an Artificial Neural Network Model

The application of ANNs (Artifi cial Neural Networks) has been studied by many researchers in modelling rainfall runoff processes. However, the work so far has been focused on the rainfall data from traditional raingauges. Weather radar is a modern technology which could provide high resolution rainfall in time and space. In this study, a comparison in rainfall runoff modelling between the raingauge and weather radar has been carried out. The data were collected from Brue catchment in Southwest of England, with 49 raingauges covering 136 km2 and two C-band weather radars. This raingauge network is extremely dense (for research purposes) and does not represent the usual raingauge density in operational flood forecasting systems. The ANN models were set up with both lumped and spatial rainfall input. The results showed that raingauge data outperformed radar data in all the events tested, regardless of the lumped and spatial input. La aplicación de Redes Neuronales Artificiales (RNA) en el modelado de lluvia-flujo ha sido estudiada ampliamente. Sin embargo, hasta ahora se han utilizado datos provenientes de pluviómetros tradicionales. Los radares meteorológicos son una tecnología moderna que puede proveer datos de lluvia de alta resolución en tiempo y espacio. Este es un trabajo de comparación en el modelado lluvia-flujo entre pluviómetros y radares meteorológicos. Los datos provienen de la cuenca del río Brue en el suroeste de Inglaterra, con 49 pluviómetros cubriendo 136 km2 y dos radares meteorológicos en la banda C. Esta red de pluviómetros es extremadamente densa (para investigación) y no representa la densidad usual en sistemas de predicción de inundaciones. Los modelos de RNA fueron implementados con datos de entrada de lluvia tanto espaciados como no distribuidos. Los resultados muestran que los datos de los pluviómetros fueron mejores que los datos de los radares en todos los eventos probados.</p

Two-Feeder Dynamic Voltage Restorer for Application in Custom Power Parks

The custom power park (CPP) is a concept that has been developed for the purpose of dealing with power quality problems. The CPP is powered by two independent feeders and consists of different types of custom power devices for compensation (dynamic voltage restorer, DVR) and reconfiguration (static transfer switch, STS). Normally, the loads are fed by the preferred feeder (PF) through the STS. When a fault occurs in the PF, the STS performs a reconfiguration of the feeders and feeds the load with an alternate feeder. In this paper, a two-input DVR based on the matrix converter is proposed to provide stable voltage to the load. Each of the DVR inputs is connected to a different feeder, and the PF powers the load. Thus, the DVR has an operational behavior like the STS when reconfiguring the energy transfer of the two feeders and injects it into the PF to guarantee a stable voltage in the load. The control algorithm in the DVR is performed in the dq reference frame to have a unit power factor in feeders and load. The modulation schemes are based on space vector modulation and modified carrier based pulse width modulation. The efficiency and multifunctionality of the proposed DVR is corroborated by simulations on the MATLAB/Simulink

A Two-Grid Interline Dynamic Voltage Restorer Based on Two Three-Phase Input Matrix Converters

Energy quality problems can cause diverse failures of sensitive equipment. Dynamic voltage restorers (DVRs) are devices that have been proposed to protect sensitive loads from voltage sag effects. However, the compensation capacity of DVRs is limited by the amount of energy stored in the restorer. One way to overcome this limitation is to use the interline DVR (IDVR) structure in which two or more DVRs connected to different feeders share a common DC-link. This paper proposes a new IDVR topology based on two three-phase input matrix converters (TTI-MC) without a capacitor in the dc-link. The TTI-MC integrates the power from two feeders and synthesizes the dc-link voltage. The inverters take the dc-link voltage and generate the appropriate compensation voltages to keep the load voltages stable. Each one of the inverters has its own modulation algorithm which are synchronized with the TTI-MC control. Inverter control is carried out in the reference frame dq and a modified space vector pulse width modulation (MSV-PWM) technique is used. TTI-MC control is performed in the dq reference frame with proportional-integral controllers and the modulation is based on the carrier-based pulse width modulation (MCB-PWM) technique. The proposed Two Input Interline DVR (TI-IDVR) extends its compensation range and has multifunctional capabilities. The efficiency of the proposed TI-IDVR is corroborated by simulations on MATLAB/Simulink