Effects of internal luminescence and internal optics on V-oc and J(sc) of III-V solar cells

For solar cells dominated by radiative recombination, the performance can be significantly enhanced by improving the internal optics. Internally radiated photons can be directly emitted from the cell, but if confined by good internal reflectors at the front and back of the cell they can also be re-absorbed with a significant probability. This so-called photon recycling leads to an increase in the equilibrium minority carrier concentration and therefore the open-circuit voltage, Voc. In multijunction cells, the internal luminescence from a particular junction can also be coupled into a lower bandgap junction where it generates photocurrent in addition to the externally generated photocurrent, and affects the overall performance of the tandem. We demonstrate and discuss the implications of a detailed model that we have developed for real, non-idealized solar cells that calculates the external luminescent efficiency, accounting for wavelength-dependent optical properties in each layer, parasitic optical and electrical losses, multiple reflections within the cell and isotropic internal emission. The calculation leads to Voc, and we show data on high quality GaAs cells that agree with the trends in the model as the optics are systematically varied. For multijunction cells the calculation also leads to the luminescent coupling efficiency, and we show data on GaInP/GaAs tandems where the trends also agree as the coupling is systematically varied. In both cases, the effects of the optics are most prominent in cells with good material quality. The model is applicable to any solar cell for which the optical properties of each layer are well-characterized, and can be used to explore a wide phase space of design for single junction and multijunction solar cells.

High-quality region-based foreground segmentation using a spatial grid of SVM classifiers

This paper presents a novel background modeling system that uses a spatial grid of Support Vector Machines classifiers for segmenting moving objects, which is a key step in many video-based consumer applications. The system is able to adapt to a large range of dynamic background situations since no parametric model or statistical distribution are assumed. This is achieved by using a different classifier per image region that learns the specific appearance of that scene region and its variations (illumination changes, dynamic backgrounds, etc.). The proposed system has been tested with a recent public database, outperforming other state-of-the-art algorithms.

Projet d'un dictionnaire français-provençal, ou Dictionnaire de la langue d'oc, ancienne et moderne, soumis aux différentes académies des provinces méridionnales de la France, et à toutes les personnes qui voudraient seconder l'auteur par leur concours, ou l'éclairer par leurs lumières.

Cover-title.

Dictionnaire provençal-français ou dictionnaire de la langue d'Oc, ancienne et moderne, suivi d'un vocabulaire français--provençal...

Mode of access: Internet.

Short-term electricity price forecasting using wavelet and SVM techniques

In deregulated electricity market, modeling and forecasting the spot price present a number of challenges. By applying wavelet and support vector machine techniques, a new time series model for short term electricity price forecasting has been developed in this paper. The model employs both historical price and other important information, such as load capacity and weather (temperature), to forecast the price of one or more time steps ahead. The developed model has been evaluated with the actual data from Australian National Electricity Market. The simulation results demonstrated that the forecast model is capable of forecasting the electricity price with a reasonable forecasting accuracy.

Gaussian processes and SVM: Mean field results and leave-one-out

In this chapter, we elaborate on the well-known relationship between Gaussian processes (GP) and Support Vector Machines (SVM). Secondly, we present approximate solutions for two computational problems arising in GP and SVM. The first one is the calculation of the posterior mean for GP classifiers using a `naive' mean field approach. The second one is a leave-one-out estimator for the generalization error of SVM based on a linear response method. Simulation results on a benchmark dataset show similar performances for the GP mean field algorithm and the SVM algorithm. The approximate leave-one-out estimator is found to be in very good agreement with the exact leave-one-out error.

Labeling large scale social media data using budget-driven One-class SVM classification

The social media classification problems draw more and more attention in the past few years. With the rapid development of Internet and the popularity of computers, there is astronomical amount of information in the social network (social media platforms). The datasets are generally large scale and are often corrupted by noise. The presence of noise in training set has strong impact on the performance of supervised learning (classification) techniques. A budget-driven One-class SVM approach is presented in this thesis that is suitable for large scale social media data classification. Our approach is based on an existing online One-class SVM learning algorithm, referred as STOCS (Self-Tuning One-Class SVM) algorithm. To justify our choice, we first analyze the noise-resilient ability of STOCS using synthetic data. The experiments suggest that STOCS is more robust against label noise than several other existing approaches. Next, to handle big data classification problem for social media data, we introduce several budget driven features, which allow the algorithm to be trained within limited time and under limited memory requirement. Besides, the resulting algorithm can be easily adapted to changes in dynamic data with minimal computational cost. Compared with two state-of-the-art approaches, Lib-Linear and kNN, our approach is shown to be competitive with lower requirements of memory and time.