Integrated visual vocabulary in latent Dirichlet allocation-based scene classification for IKONOS image

Scene classification based on latent Dirichlet allocation (LDA) is a more general modeling method known as a bag of visual words, in which the construction of a visual vocabulary is a crucial quantization process to ensure success of the classification. A framework is developed using the following new aspects: Gaussian mixture clustering for the quantization process, the use of an integrated visual vocabulary (IVV), which is built as the union of all centroids obtained from the separate quantization process of each class, and the usage of some features, including edge orientation histogram, CIELab color moments, and gray-level co-occurrence matrix (GLCM). The experiments are conducted on IKONOS images with six semantic classes (tree, grassland, residential, commercial/industrial, road, and water). The results show that the use of an IVV increases the overall accuracy (OA) by 11 to 12% and 6% when it is implemented on the selected and all features, respectively. The selected features of CIELab color moments and GLCM provide a better OA than the implementation over CIELab color moment or GLCM as individuals. The latter increases the OA by only ∼2 to 3%. Moreover, the results show that the OA of LDA outperforms the OA of C4.5 and naive Bayes tree by ∼20%. © 2014 Society of Photo-Optical Instrumentation Engineers (SPIE) [DOI: 10.1117/1.JRS.8.083690]

Sparse density estimation on the multinomial manifold

A new sparse kernel density estimator is introduced based on the minimum integrated square error criterion for the finite mixture model. Since the constraint on the mixing coefficients of the finite mixture model is on the multinomial manifold, we use the well-known Riemannian trust-region (RTR) algorithm for solving this problem. The first- and second-order Riemannian geometry of the multinomial manifold are derived and utilized in the RTR algorithm. Numerical examples are employed to demonstrate that the proposed approach is effective in constructing sparse kernel density estimators with an accuracy competitive with those of existing kernel density estimators.

The error of representation: basic understanding

Representation error arises from the inability of the forecast model to accurately simulate the climatology of the truth. We present a rigorous framework for understanding this kind of error of representation. This framework shows that the lack of an inverse in the relationship between the true climatology (true attractor) and the forecast climatology (forecast attractor) leads to the error of representation. A new gain matrix for the data assimilation problem is derived that illustrates the proper approaches one may take to perform Bayesian data assimilation when the observations are of states on one attractor but the forecast model resides on another. This new data assimilation algorithm is the optimal scheme for the situation where the distributions on the true attractor and the forecast attractors are separately Gaussian and there exists a linear map between them. The results of this theory are illustrated in a simple Gaussian multivariate model.

Analysis and clustering of residential customers energy behavioral demand using smart meter data

Clustering methods are increasingly being applied to residential smart meter data, providing a number of important opportunities for distribution network operators (DNOs) to manage and plan the low voltage networks. Clustering has a number of potential advantages for DNOs including, identifying suitable candidates for demand response and improving energy profile modelling. However, due to the high stochasticity and irregularity of household level demand, detailed analytics are required to define appropriate attributes to cluster. In this paper we present in-depth analysis of customer smart meter data to better understand peak demand and major sources of variability in their behaviour. We find four key time periods in which the data should be analysed and use this to form relevant attributes for our clustering. We present a finite mixture model based clustering where we discover 10 distinct behaviour groups describing customers based on their demand and their variability. Finally, using an existing bootstrapping technique we show that the clustering is reliable. To the authors knowledge this is the first time in the power systems literature that the sample robustness of the clustering has been tested.

Using laboratory and field measurements to constrain a single habit shortwave optical parameterization for cirrus

A single habit parameterization for the shortwave optical properties of cirrus is presented. The parameterization utilizes a hollow particle geometry, with stepped internal cavities as identified in laboratory and field studies. This particular habit was chosen as both experimental and theoretical results show that the particle exhibits lower asymmetry parameters when compared to solid crystals of the same aspect ratio. The aspect ratio of the particle was varied as a function of maximum dimension, D, in order to adhere to the same physical relationships assumed in the microphysical scheme in a configuration of the Met Office atmosphere-only global model, concerning particle mass, size and effective density. Single scattering properties were then computed using T-Matrix, Ray Tracing with Diffraction on Facets (RTDF) and Ray Tracing (RT) for small, medium, and large size parameters respectively. The scattering properties were integrated over 28 particle size distributions as used in the microphysical scheme. The fits were then parameterized as simple functions of Ice Water Content (IWC) for 6 shortwave bands. The parameterization was implemented into the GA6 configuration of the Met Office Unified Model along with the current operational long-wave parameterization. The GA6 configuration is used to simulate the annual twenty-year short-wave (SW) fluxes at top-of-atmosphere (TOA) and also the temperature and humidity structure of the atmosphere. The parameterization presented here is compared against the current operational model and a more recent habit mixture model.

Inferential Implications of Over-Parametrization: A Case Study in Incomplete Categorical Data

P>In the context of either Bayesian or classical sensitivity analyses of over-parametrized models for incomplete categorical data, it is well known that prior-dependence on posterior inferences of nonidentifiable parameters or that too parsimonious over-parametrized models may lead to erroneous conclusions. Nevertheless, some authors either pay no attention to which parameters are nonidentifiable or do not appropriately account for possible prior-dependence. We review the literature on this topic and consider simple examples to emphasize that in both inferential frameworks, the subjective components can influence results in nontrivial ways, irrespectively of the sample size. Specifically, we show that prior distributions commonly regarded as slightly informative or noninformative may actually be too informative for nonidentifiable parameters, and that the choice of over-parametrized models may drastically impact the results, suggesting that a careful examination of their effects should be considered before drawing conclusions.Resume Que ce soit dans un cadre Bayesien ou classique, il est bien connu que la surparametrisation, dans les modeles pour donnees categorielles incompletes, peut conduire a des conclusions erronees. Cependant, certains auteurs persistent a negliger les problemes lies a la presence de parametres non identifies. Nous passons en revue la litterature dans ce domaine, et considerons quelques exemples surparametres simples dans lesquels les elements subjectifs influencent de facon non negligeable les resultats, independamment de la taille des echantillons. Plus precisement, nous montrons comment des a priori consideres comme peu ou non-informatifs peuvent se reveler extremement informatifs en ce qui concerne les parametres non identifies, et que le recours a des modeles surparametres peut avoir sur les conclusions finales un impact considerable. Ceci suggere un examen tres attentif de l`impact potentiel des a priori.

Nonstationary feature extraction techniques for automatic classification of impact acoustic signals

Condition monitoring of wooden railway sleepers applications are generallycarried out by visual inspection and if necessary some impact acoustic examination iscarried out intuitively by skilled personnel. In this work, a pattern recognition solutionhas been proposed to automate the process for the achievement of robust results. Thestudy presents a comparison of several pattern recognition techniques together withvarious nonstationary feature extraction techniques for classification of impactacoustic emissions. Pattern classifiers such as multilayer perceptron, learning cectorquantization and gaussian mixture models, are combined with nonstationary featureextraction techniques such as Short Time Fourier Transform, Continuous WaveletTransform, Discrete Wavelet Transform and Wigner-Ville Distribution. Due to thepresence of several different feature extraction and classification technqies, datafusion has been investigated. Data fusion in the current case has mainly beeninvestigated on two levels, feature level and classifier level respectively. Fusion at thefeature level demonstrated best results with an overall accuracy of 82% whencompared to the human operator.

Bayesian analysis of extreme events with threshold estimation

The aim of this paper is to analyze extremal events using Generalized Pareto Distributions (GPD), considering explicitly the uncertainty about the threshold. Current practice empirically determines this quantity and proceeds by estimating the GPD parameters based on data beyond it, discarding all the information available be10w the threshold. We introduce a mixture model that combines a parametric form for the center and a GPD for the tail of the distributions and uses all observations for inference about the unknown parameters from both distributions, the threshold inc1uded. Prior distribution for the parameters are indirectly obtained through experts quantiles elicitation. Posterior inference is available through Markov Chain Monte Carlo (MCMC) methods. Simulations are carried out in order to analyze the performance of our proposed mode1 under a wide range of scenarios. Those scenarios approximate realistic situations found in the literature. We also apply the proposed model to a real dataset, Nasdaq 100, an index of the financiai market that presents many extreme events. Important issues such as predictive analysis and model selection are considered along with possible modeling extensions.

Ensaios sobre política monetária

Esta tese é composta de três ensaios a respeito de política monetária. O primeiro ensaio aborda o canal em que as crises financeiras aumentam a ineficiência alocativa nos países emergentes. O segundo ensaio trata do grau de não-neutralidade da moeda no Brasil de acordo com o modelo de Golosov e Lucas (2007). O terceiro ensaio estima a inclinação da hazard function da precifi cação para o Brasil pela metodologia de Finite Mixture Model.

On the determination of epsilon during discriminative GMM training

Discriminative training of Gaussian Mixture Models (GMMs) for speech or speaker recognition purposes is usually based on the gradient descent method, in which the iteration step-size, ε, uses to be defined experimentally. In this letter, we derive an equation to adaptively determine ε, by showing that the second-order Newton-Raphson iterative method to find roots of equations is equivalent to the gradient descent algorithm. © 2010 IEEE.

Regressão Simplex aplicada a delineamentos de mistura e utilização do Algoritmo Boosting

In the composition of this work are present two parts. The first part contains the theory used. The second part contains the two articles. The first article examines two models of the class of generalized linear models for analyzing a mixture experiment, which studied the effect of different diets consist of fat, carbohydrate, and fiber on tumor expression in mammary glands of female rats, given by the ratio mice that had tumor expression in a particular diet. Mixture experiments are characterized by having the effect of collinearity and smaller sample size. In this sense, assuming normality for the answer to be maximized or minimized may be inadequate. Given this fact, the main characteristics of logistic regression and simplex models are addressed. The models were compared by the criteria of selection of models AIC, BIC and ICOMP, simulated envelope charts for residuals of adjusted models, odds ratios graphics and their respective confidence intervals for each mixture component. It was concluded that first article that the simplex regression model showed better quality of fit and narrowest confidence intervals for odds ratio. The second article presents the model Boosted Simplex Regression, the boosting version of the simplex regression model, as an alternative to increase the precision of confidence intervals for the odds ratio for each mixture component. For this, we used the Monte Carlo method for the construction of confidence intervals. Moreover, it is presented in an innovative way the envelope simulated chart for residuals of the adjusted model via boosting algorithm. It was concluded that the Boosted Simplex Regression model was adjusted successfully and confidence intervals for the odds ratio were accurate and lightly more precise than the its maximum likelihood version.

Quantificação e avaliação de abertura no dossel em áreas de concessões florestais: Mamuru-Arapiuns-PA

As florestas tropicais da Amazônia historicamente foram alvo de práticas pouco sustentáveis de uso da terra, restando-lhes as cicatrizes de degradação advinda da exploração madeireira predatória, do uso indiscriminado do fogo, das altas taxas de desmatamento e de outras atividades que interferem nas ações de conservação da biodiversidade desta floresta. A atuação do Estado neste cenário é necessária através de políticas que incentivem formas de uso mais sustentáveis, como é o caso das concessões florestais que buscam através do manejo florestal, contribuir para a conservação dos recursos naturais e da manutenção da biodiversidade. A geração de produtos como o Índice de Vegetação por Diferença Normalizada, Modelo Linear de Mistura Espectral e Fração de Abertura de Dossel foram realizados no intuito de criar elementos de interpretação e análise da variável abertura de dossel. Esta pesquisa teve como área de estudo a Unidade de Manejo Florestal I no Conjunto de Glebas Mamuru-Arapiuns, região oeste do estado do Pará; onde foram quantificados e avaliados a abertura de dossel nessa área de concessão florestal, através de imagens multiespectrais e fotos hemisféricas, com vistas a analisar a degradação e a qualidade do manejo executado nesta área. Os resultados obtidos mostraram que é possível estabelecer um processo de monitoramento com o uso dos sensores e técnicas aplicados, uma vez que os dados de MLME, em especial a imagem-fração solo apresentaram forte relação de covariância com os dados obtidos em campo através de fotos hemisféricas, permitindo considera-lo como uma boa ferramenta de alerta para as ações de monitoramentos das florestas amazônicas. Desta forma é possível tornar a gestão florestal mais acessível tanto ao poder público, quanto a entidades não governamentais ou privadas visando fiscalizar as ações de exploração florestal e agregar as populações que vivem nestas áreas tanto oportunidades de renda quanto a conservação florestal.

Accuracy of genomic predictions in Bos indicus (Nellore) cattle

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Emprego de imageamento passivo na análise da variabilidade espacial da turbidez no espelho d'água do Lago Paranoá, Distrito Federal

The water has an important role in human society, especially in Brazil. Its uses are multiple, including supply, energy production, recreation and others. The National Policy for Water Resources (Law No 9.433/97) states in its articles the importance of water use in accordance to their multiple uses, prioritizing the supply for humans and animals. In this approach, it is important to consider the physical and chemical quality of water to meet these demands, scope of the legal framework applied to the Brazilian water bodies according to their main uses, in order to guarantee the water quality compatible with the most demanding uses and to reduce the costs of pollution control through ongoing preventive actions. Among the various parameters that seek to analyze the physical and chemical quality of water it is intended to understand the spatial distribution of turbidity in the lake's surface, since the variation of the components that alter this parameter can be detected by means of passive remote sensing. The application of the Linear spectral mixture model allowed, satisfactorily, the identification of turbidity spatial distribution patterns in the lake.

Electronic energy transfer processes and charge carrier transport in pi-conjugated polymers

Conjugated polymers have attracted tremendous academical and industrial research interest over the past decades due to the appealing advantages that organic / polymeric materials offer for electronic applications and devices such as organic light emitting diodes (OLED), organic field effect transistors (OFET), organic solar cells (OSC), photodiodes and plastic lasers. The optimization of organic materials for applications in optoelectronic devices requires detailed knowledge of their photophysical properties, for instance energy levels of excited singlet and triplet states, excited state decay mechanisms and charge carrier mobilities. In the present work a variety of different conjugated (co)polymers, mainly polyspirobifluorene- and polyfluorene-type materials, was investigated using time-resolved photoluminescence spectroscopy in the picosecond to second time domain to study their elementary photophysical properties and to get a deeper insight into structure-property relationships. The experiments cover fluorescence spectroscopy using Streak Camera techniques as well as time-delayed gated detection techniques for the investigation of delayed fluorescence and phosphorescence. All measurements were performed on the solid state, i.e. thin polymer films and on diluted solutions. Starting from the elementary photophysical properties of conjugated polymers the experiments were extended to studies of singlet and triplet energy transfer processes in polymer blends, polymer-triplet emitter blends and copolymers. The phenomenon of photonenergy upconversion was investigated in blue light-emitting polymer matrices doped with metallated porphyrin derivatives supposing an bimolecular annihilation upconversion mechanism which could be experimentally verified on a series of copolymers. This mechanism allows for more efficient photonenergy upconversion than previously reported for polyfluorene derivatives. In addition to the above described spectroscopical experiments, amplified spontaneous emission (ASE) in thin film polymer waveguides was studied employing a fully-arylated poly(indenofluorene) as the gain medium. It was found that the material exhibits a very low threshold value for amplification of blue light combined with an excellent oxidative stability, which makes it interesting as active material for organic solid state lasers. Apart from spectroscopical experiments, transient photocurrent measurements on conjugated polymers were performed as well to elucidate the charge carrier mobility in the solid state, which is an important material parameter for device applications. A modified time-of-flight (TOF) technique using a charge carrier generation layer allowed to study hole transport in a series of spirobifluorene copolymers to unravel the structure-mobility relationship by comparison with the homopolymer. Not only the charge carrier mobility could be determined for the series of polymers but also field- and temperature-dependent measurements analyzed in the framework of the Gaussian disorder model showed that results coincide very well with the predictions of the model. Thus, the validity of the disorder concept for charge carrier transport in amorphous glassy materials could be verified for the investigated series of copolymers.