Guilherme Barreto | CNPq - Academia.edu (original) (raw)

Papers by Guilherme Barreto

In this work the characterization of the Nile Tilapia viscera was performed. To do this, the oil ... more In this work the characterization of the Nile Tilapia viscera was performed. To do this, the oil (lipids) and the dreg (protein) were extracted. Acidity parameters were evaluated in the presence and absence of biliary juice. The other byproduct analyzed, dreg (protein), was evaluated in terms of its potential use as a raw material in the production of biofertilizers. The evaluation showed a high moisture value for the dreg, approximately 95%, a protein mean of 2.34% and an ash percentage of 1.0%. The C/N ratio was assessed at 22:1. In the evaluation of the biliary juice, the percentage increase of the acidity in the oil showed significant changes ranging from 5% to 10%, around 8.72% to 16.92% of the reference value, represented in this case by the extracted oil in the absence of the bile. When the percentage increase reached between 20% and 40%, the oil presents the greatest variations in its final acidity, 28.92% and 51.94%, respectively. Thus, this study is significant in assistin...

Reject option is a technique used to improve classifier’s reliability in decision support systems... more Reject option is a technique used to improve classifier’s reliability in decision support systems. It consists in withholding the automatic classification of an item, if the decision is considered not sufficiently reliable. The rejected item is then handled by a different classifier or by a human expert. The vast majority of the works on this issue has been concerned with the developement of reject option mechanisms to be used by supervised learning architectures (e.g., MLP, LVQ or SVM). In this paper, however, we aim at proposing alternatives to this view which are based on the Self-Organizing Map (SOM), originally an unsupervised learning scheme, but that has also been successfully used in the design of prototype-based classifiers. The basic hypothesis we defend is that it is possible to design SOM-based classifiers endowed with reject option mechanisms whose performances are comparable to or better than those achieved by standard supervised classifiers. For this purpose, we carri...

System identification comprises a number of linear and nonlinear tools for black-box modeling of ... more System identification comprises a number of linear and nonlinear tools for black-box modeling of dynamical systems, with applications in several areas of engineering, control, biology and economy. However, the usual Gaussian noise assumption is not always satisfied, specially if data is corrupted by impulsive noise or outliers. Bearing this in mind, the present paper aims at evaluating how Gaussian Process (GP) models perform in system identification tasks in the presence of outliers. More specifically, we compare the performances of two existing robust GP-based regression models in experiments involving five benchmarking datasets with controlled outlier inclusion. The results indicate that, although still sensitive in some degree to the presence of outliers, the robust models are indeed able to achieve lower prediction errors in corrupted scenarios when compared to conventional GP-based approach. Keywords— Robust system identification, Gaussian process, Approximate Bayesian inferen...

In this paper, we introduce a design methodology for prototype-based classifiers, more specifical... more In this paper, we introduce a design methodology for prototype-based classifiers, more specifically the well-known LVQ family, aiming at improving their accuracy in fault detection/classification tasks. A laboratory testbed is constructed to generate the datasets which are comprised of short-circuit faults of different impedance levels, in addition to samples of the normal functioning of the motor. The generated data samples are difficult to classify as normal or faulty ones, especially if the faults are of high impedance (usually misinterpreted as non-faulty samples). Aiming at reducing misclassification, we use K-means and cluster validation techniques for finding an adequate number of labeled prototypes and their correct initialization for the efficient design of LVQ classifiers. By means of comprehensive computer simulations, we compare the performances of several LVQ classifiers in the aforementioned engineering application, showing that the proposed methodology eventually lead...

Resumo. In this paper we evaluate the performances of randomized pattern classifiers in the task ... more Resumo. In this paper we evaluate the performances of randomized pattern classifiers in the task of EEG-based epileptic seizures detection. Our goal is to investigate if these new class of machine learning methods actually outperform powerful nonlinear classifiers, such as the MLP and SVM, in complex pattern recognition tasks. The rationale for the current work comes from the observation that the recent wave of applications involving randomized classifiers tend to report only positive reports, in which these networks always achieve equivalent or better performances than non-randomized nonlinear classifiers. A comprehensive performance evaluation is carried out, with the results strongly corroborate our hypothesis that randomized classifiers usually do not perform better than well trained standard nonlinear classifiers. Additionally, the performances of randomized classifiers are more dependent on the feature extraction method than non-randomized ones.

Advances in Computational Intelligence

In this paper, randomized single-hidden layer feedforward networks (SLFNs) are extended to handle... more In this paper, randomized single-hidden layer feedforward networks (SLFNs) are extended to handle outliers sequentially in online system identification tasks involving large-scale datasets. Starting from the description of the original batch learning algorithms of the evaluated randomized SLFNs, we discuss how these neural architectures can be easily adapted to cope with sequential data by means of the famed least mean squares (LMS). In addition, a robust variant of this rule, known as the least mean M-estimate (LMM) rule, is used to cope with outliers. Comprehensive performance comparison on benchmarking datasets are carried out in order to assess the validity of the proposed methodology.

Neural Computing and Applications

Data from real-world regression problems are quite often contaminated with outliers. In order to ... more Data from real-world regression problems are quite often contaminated with outliers. In order to efficiently handle such undesirable samples, robust parameter estimation methods have been incorporated into randomized neural network (RNN) models, usually replacing the ordinary least squares (OLS) method. Despite recent successful applications to outlier-contaminated scenarios, significant issues remain unaddressed in the design of reliable outlier-robust RNN models for regression tasks. For example, the number of hidden neurons impacts directly on the norm of the estimated output weights, since the OLS method will rely on an ill-conditioned hidden-layer output matrix. Another design concern involves the high sensitivity of RNNs to the randomization of the hidden layer weights, an issue that can be suitably handled, e.g., by intrinsic plasticity techniques. Bearing these concerns in mind, we describe several ideas introduced in previous works concerning the design of RNN models that are both robust to outliers and numerically stable. A comprehensive evaluation of their performances is carried out across several benchmarking regression datasets taking into account accuracy, weight norms, and training time as figures of merit.

Energies

The permanent magnet (PM) spherical motor has been subject to growing interest from the scientifi... more The permanent magnet (PM) spherical motor has been subject to growing interest from the scientific community due to its potential for applications in distinct areas, particularly in robotics, prosthetics, satellite control, sensors or camera systems. Motivated by this movement, the current work presents all the steps for the efficient design and construction of a spherical motor model, using compound deposition technology with the aid of a 3D printer. Furthermore, we report comprehensive studies on the accuracy of the positioning system of the proposed motor using only three stator coils, which jointly act to move the rotor axis toward any point in the hemisphere. Unmodeled nonlinear phenomena, such as friction, impair accurate positioning of the motor actuator, but this is solved by means of a visual servo control system, which allows the user to collect input–output data to train an artificial neural network model. Details on the construction of the proposed motor are reported, in...

Anais do Simpósio Brasileiro de Banco de Dados (SBBD)

A epilepsia é um distúrbio neurológico caracterizado por uma perturbação elétrica anormal no cére... more A epilepsia é um distúrbio neurológico caracterizado por uma perturbação elétrica anormal no cérebro, causando convulsões recorrentes. O exame mais utilizado no diagnóstico da epilepsia é o eletroencefalograma (EEG), onde a atividade elétrica cerebral de um paciente é mensurada e analisada visualmente. Contudo, identificar os padrões epilépticos no sinal de EEG através de inspeção visual é uma tarefa demorada e exaustiva para profissionais da área. Assim, o desenvolvimento de algoritmos que possam identificar esses padrões de forma automática, auxiliando o diagnóstico médico, tornou-se um importante desafio. Neste trabalho, propomos três modelos de classificação, baseados em detecção de anomalias. Os resultados obtidos demonstram alto desempenho e robustez a ruídos em relação resultados encontrados na literatura.

Anais do 14º Simpósio Brasileiro de Automação Inteligente

Journal of Intelligent & Robotic Systems

This paper relies on the resource-oriented architecture (ROA) to propose a middleware that shares... more This paper relies on the resource-oriented architecture (ROA) to propose a middleware that shares resources (sensors, actuators and services) of one or more robots through the TCP/IP network, providing greater efficiency in the development of software applications for robotics. The proposed middleware consists of a set of web services that provides access to representational state of resources through simple and high-level interfaces to implement a software architecture for autonomous robots. The benefits of the proposed approach are manifold: i) full abstraction of complexity and heterogeneity of robotic devices through web services and uniform interfaces, ii) scalability and independence of the operating system and programming language, iii) secure control of resources for local or remote applications through the TCP/IP network, iv) the adoption of the Resource Description Framework (RDF), XML language and HTTP protocol, and v) dynamic configuration of the connections between services at runtime. The middleware was developed using the Linux operating system (Ubuntu), with some applications built as proofs of concept for the Android operating system. The architecture specification and the open source implementation of the proposed middleware are detailed in this article, as well as applications for robot remote control via wireless networks, voice command functionality, and obstacle detection and avoidance.

Proceedings XIII Brazilian Congress on Computational Inteligence

Anais do XV Encontro Nacional de Inteligência Artificial e Computacional (ENIAC 2018)

In this paper, the classical polynomial model for wind turbines power curve estimation is revisit... more In this paper, the classical polynomial model for wind turbines power curve estimation is revisited aiming at an automatic and parsimonious design. In this regard, using genetic algorithms we introduce a methodoloy for estimating a suitable order for the polynomial as well its relevant terms. The proposed methodology is compared with the state of the art in estimating the power curve of wind turbines, such as logistic models (with 4 and 5 parameters), artificial neural networks and weighted polynomial regression. We also show that the proposed approach performs better than the standard LASSO approach for building regularized sparse models. The results indicate that the proposed methodology consistently outperforms all the evaluated alternative methods.

Nonlinear Dynamics

The kernel recursive least squares (KRLS), a nonlinear counterpart of the famed RLS algorithm, pe... more The kernel recursive least squares (KRLS), a nonlinear counterpart of the famed RLS algorithm, performs linear regression in a high-dimensional feature space induced by a Mercer kernel. Despite the growing interest in the KRLS for nonlinear signal processing, the presence of outliers in the estimation data causes the resulting predictor’s performance to deteriorate considerably. Bearing this in mind, we introduce an approach to amalgamate the kernel-based learning framework that gives rise to the KRLS algorithm with the robust regression framework of M-estimators with the aim of building an outlier-robust variant for the KRLS. Initially, we develop the theoretical aspects of the proposed algorithm and then analyze its behavior in nonlinear system identification problems using synthetic and real-world datasets (including a large-scale one) contaminated with outliers. The obtained results indicate that the robust variant of the KRLS algorithm consistently outperforms the state-of-the-art in robust adaptive filtering algorithms, as the amount of outliers in the data increases.