A Fast Algorithm For Sparse Multichannel Blind Deconvolution

Coordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)We have addressed blind deconvolution in a multichannel framework. Recently, a robust solution to this problem based on a Bayesian approach called sparse multichannel blind deconvolution (SMBD) was proposed in the literature with interesting results. However, its computational complexity can be high. We have proposed a fast algorithm based on the minimum entropy deconvolution, which is considerably less expensive. We designed the deconvolution filter to minimize a normalized version of the hybrid l(1)/l(2)-norm loss function. This is in contrast to the SMBD, in which the hybrid l(1)/l(2)-norm function is used as a regularization term to directly determine the deconvolved signal. Results with synthetic data determined that the performance of the obtained deconvolution filter was similar to the one obtained in a supervised framework. Similar results were also obtained in a real marine data set for both techniques.811V7V16CAPESCNPqPetrobrasCoordenação de Aperfeiçoamento de Pessoal de Nível Superior (CAPES)Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Debiasing Machine Learning Models by Using Weakly Supervised Learning

We tackle the problem of bias mitigation of algorithmic decisions in a setting where both the output of the algorithm and the sensitive variable are continuous. Most of prior work deals with discrete sensitive variables, meaning that the biases are measured for subgroups of persons defined by a label, leaving out important algorithmic bias cases, where the sensitive variable is continuous. Typical examples are unfair decisions made with respect to the age or the financial status. In our work, we then propose a bias mitigation strategy for continuous sensitive variables, based on the notion of endogeneity which comes from the field of econometrics. In addition to solve this new problem, our bias mitigation strategy is a weakly supervised learning method which requires that a small portion of the data can be measured in a fair manner. It is model agnostic, in the sense that it does not make any hypothesis on the prediction model. It also makes use of a reasonably large amount of input observations and their corresponding predictions. Only a small fraction of the true output predictions should be known. This therefore limits the need for expert interventions. Results obtained on synthetic data show the effectiveness of our approach for examples as close as possible to real-life applications in econometrics.Comment: 30 pages, 25 figure

Previsão de carga multinodal utilizando redes neurais de regressão generalizada

Neste trabalho, dá-se ênfase à previsão de carga multinodal, também conhecida como previsão de carga por barramento. Para realizar esta demanda, há necessidade de dispor de uma técnica que proporcione a precisão desejada, seja confiável e de baixo tempo de processamento. O conhecimento prévio das cargas locais é de extrema importância para o planejamento e operação dos sistemas de energia elétrica. Para realizar a previsão de carga multinodal foram empregadas duas metodologias, uma que prevê as cargas individualmente e outra que utiliza as previsões dos fatores de participação e a previsão de carga global. O principal objetivo deste trabalho é elaborar um modelo de previsor de carga de curto prazo, genérico e que pode ser aplicado na previsão de carga multinodal. Para tanto, utilizou-se redes neurais de regressão generalizada (GRNN), cujas entradas são compostas de variáveis exógenas globais e de cargas locais, sem a necessidade da inclusão de variáveis exógenas locais. Ainda, projetou-se uma nova arquitetura de rede neural artificial, baseada na GRNN, além de propor um procedimento para a redução do número de entradas da GRNN e um filtro para o pré-processamento do banco de dados de treinamento. Os dados, para testar as metodologias e as redes neurais artificiais, são referentes a um subsistema de distribuição de energia elétrica da Nova Zelândia composto por nove subestaçõesIn this work, it is emphasized the multi-nodal load forecast, also known as bus load forecast. To perform this demand, there it is necessary a technique that is precise, trustable and has a short-time processing. The previous knowledge of the local loads is of extreme importance to the planning and operation of the electrical power and energy systems. To perform the multi-nodal load forecast is employed two different methodologies, one that forecast the loads individually and another that uses the participation factors forecasts and the global load forecast. The main objective of this work is to elaborate a generic model of a short-term load forecaster, which can be applied to the multi-nodal load forecast. For this, it was used general regression neural networks (GRNN), with inputs based on external global factors and local loads, without the need of external local factors. Still, it was developed a new architecture of an artificial neural network based on a GRNN and proposed a procedure to reduce the number of input variables of the GRNN and a filter for preprocessing the training data. The dataset, to test the methodologies and the artificial neural networks, refers to a New Zealand electrical distribution subsystem composed of nine substationsConselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Blind deconvolution and blind source separation of sparse signals and its applications in seismic reflection

Orientador: João Marcos Travassos RomanoTese (doutorado) - Universidade Estadual de Campinas, Faculdade de Engenharia Elétrica e de ComputaçãoResumo: Os problemas de desconvolução cega e separação cega de fontes são atraentes tanto pelas aplicações que lhes permeiam quanto pelos seus aspectos teóricos, sendo que a característica não supervisionada traz diversos desafios para a área de processamento digital de sinais. O presente trabalho aborda ambos os problemas, sendo que suas principais contribuições dizem respeito a métodos que exploram as propriedades de esparsidade dos sinais envolvidos. Nesse contexto, formulam-se teoremas que estabelecem condições suficientes, sobre os sinais envolvidos, capazes de garantir a desconvolução e a separação cega de sinais esparsos; e, ainda, critérios e algoritmos para a desconvolução e separação cega de sinais esparsos. Por fim, os métodos são aplicados em problemas de grande relevância no processamento de dados sísmicos de reflexão, tais como a desconvolução sísmica, a atenuação de reflexões múltiplas de superfície e a atenuação do ruído de ground-rollAbstract: The blind deconvolution and blind source separation problems are of great relevance due to their interesting theoretical and practical aspects. Moreover, their unsupervised characteristic constitutes a real challenge in digital signal processing. The present work deals with both problems and the major contributions are performed in the context of sparse signals. For that, theorems are stated in order to provide sufficient conditions over the involved signals to ensure the blind deconvolution and the blind source separation of sparse signals. In addition, based on the stated theorems, criteria and algorithms are proposed for both problems. Finally, these methods are applied in relevant seismic reflection problems: seismic deconvolution, surface-related multiple attenuation, and the attenuation of the ground-roll noiseDoutoradoTelecomunicações e TelemáticaDoutor em Engenharia Elétrica142714/2011-97362-13-7CNPQCAPE

Short-Term Multinodal Load Forecasting Using a Modified General Regression Neural Network

Multinodal load forecasting deals with the loads of several interest nodes in an electrical network system, which is also known as bus load forecasting. To perform this demand, a technique that is precise, reliable, and has short-time processing is necessary. This paper uses two methodologies for short-term multinodal load forecasting. The first individually forecasts the local loads and the second forecasts the global load and individually forecasts the load participation factors to estimate the local loads. For the forecasts, a modified general regression neural network and a procedure to automatically reduce the number of inputs of the artificial neural networks are proposed. To design the forecasters, the previous study of the local loads was not necessary, thus reducing the complexity of the multinodal load forecasting. Tests were carried out by using a New Zealand distribution subsystem and the results obtained were found to be compatible with those available in the specialized literature.Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq

Seismic Wave Separation by Means of Robust Principal Component Analysis

Publication in the conference proceedings of EUSIPCO, Bucharest, Romania, 201

Error Entropy Criterion in Echo State Network Training

Abstract. Echo state networks offer a promising possibility for an effective use of recurrent structures as the presence of feedback is accompanied with a relatively simple training process. However, such simplicity, which is obtained through the use of an adaptive linear readout that minimizes the mean-squared error, limits the capability of exploring the statistical information of the involved signals. In this work, we apply an informationtheoretic learning framework, based on the error entropy criterion, to the ESN training, in order to improve the performance of the neural model, whose advantages are analyzed in the context of supervised channel equalization problem.