Análise do uso de livros-texto digitais abertos no contexto da Educação Superior na América Latina

Entre as muitas barreiras para o acesso e permanência na Educação Superior nos países em desenvolvimento e subdesenvolvidos, como é o caso da totalidade dos países da América Latina, encontra-se o fator financeiro: ainda que não haja taxas de matrículas e mensalidades a serem pagas, há uma série de custos colaterais que nem sempre são considerados. Um destes custos é o dos materiais didáticos, em especial os livros-texto das disciplinas, de forma que iniciativas que possibilitem alternativas para os livros tradicionais devem ser consideradas. Neste sentido, o presente trabalho mostra os resultados da utilização de 25 livros-texto abertos gratuitos, criados de maneira colaborativa por autores de nove países Latino-americanas, disponibilizados de forma digital, no âmbito do Projeto LATIn. Um conjunto de 186 professores e 1.835 alunos espalhados em nove Universidades utilizou e avaliou as potencialidades e fragilidades deste modelo, e os resultados desta análise são apresentados neste artigo

FP-AK-QIEAR-R in protein folding application

There are many Evolutionary Algorithms which main features are: population, evolutionary operations (crossover, mate, mutation and others). Most of them are based on randomness and follow a criteria using fitness like selector. The proposal uses probability density function according to best of initial population to sample new population and save better individuals iteratively. Then using centroid criteria sample for every dimension and get better individuals. It had good results with benchmark functions. A real application was performed with experiments in protein folding and it showed good results. © 2016 IEEE.Trabajo de investigació

Multiprocessor task scheduling on heterogeneous environments by a Hybrid Chemical Reactions Optimization

This work presents the modeling and implementation of a Hybrid Chemical Reactions Optimization that uses CRO operators to find good solutions, and heuristics to initialize the first population and mapping tasks in processors to solve the Multiprocessor task scheduling problem, taking into account the heterogeneity of the multiprocessor system. © 2016 IEEE.Trabajo de investigació

Parameters analysis of QIEA-R in convergence quality

QIEA-R (Quantum Inspired Evolutionary Algorithm with Real Codification) was proposed for solving numerical problems obtaining better results when compared with traditional EAs, DE and PSO algorithms. It is inspired on the concept of quantum superposition in order to reduce the number of evaluations. QIEA-R has two important steps: initialization of the quantum population and updating of the quantum population. This paper analyzes these two steps and parameters related: Size of classical population, number of iterations, over some benchmark functions using statistical measurements to evaluate their importance and effect in convergence quality. The results shows the importance of quantum population size and update frequency. © 2016 IEEE.Trabajo de investigació

Controlling oil production in smart wells by MPC strategy with reinforcement learning

This work presents the modeling and development of a methodology based on Model Predictive Control - MPC that uses a machine learning model, based on Reinforcement Learning, as the method for searching the optimal control policy, and a neural network as a proxy, for modeling the nonlinear plant. The neural network model was developed to predict the following variables: average pressure of the reservoir, the daily production of oil, gas, water and water cut in the production well, for three consecutive values, to perform the predictive control. This model is applied as a strategy to control the oil production in an oil reservoir with existing producer and injector wells. The experiments were carried out on a synthetic oil reservoir model that consists in a reservoir with three layers with different permeability and one producer well and one injector well, both completed in the three layers. There are three valves located into the injector well, one for each completion, which are the handling variables of the model. The oil production of the producer well is the controlled variable. The experiments performed have considered various set points and also the impact of disturbances on the production well. The obtained results indicate that the proposed model is capable of controlling oil production even with disturbances in the producing well, for different reference values for oil production and supporting some features of the petroleum reservoir systems such as: strong non-linearity, long delay in the system response, and multivariate characteristic. Copyright 2010, Society of Petroleum Engineers.Trabajo de investigació

Toward a more Generalized Quantum-Inspired Evolutionary Algorithm for Combinatorial Optimization Problems

In this paper, a generalization of the original Quantum-Inspired Evolutionary Algorithm (QIEA): the Generalized Quantum-Inspired Evolutionary Algorithm (GQIEA) is proposed. Like QIEA, GQIEA is also based on the quantum computing principle of superposition of states, but extending it not only to be used for binary values {0, 1}, but for any finite set of values {1,?, n}. GQIEA, as any other quantum inspired evolutionary algorithm, defines an own quantum individual, an evaluation function and population operators. As in QIEA, GQIEA also defines a generalized Q-gate operator, which is a variation operator to drive the individuals toward better solutions. To demonstrate its effectiveness and applicability, the proposal will be applied to the Knapsack Problem (KP), a classic combinatorial optimization problem. Results show that GQIEA has a good performance even with a small population. © 2015 IEEE.Trabajo de investigació

An approach to real-coded quantum inspired evolutionary algorithm using particles filter

This work proposes, implements and evaluates the FP-QIEA-R model as a new quantum inspired evolutionary algorithm based on the concept of quantum superposition that allows the optimization process to be carried on with a smaller number of evaluations. This model is based on a QIEA-R, but instead of just using quantum individuals based on uniform probability density functions, where the update consists on change the width and mean of each pdf; this proposal uses a combined mechanism inspired in particle filter and multilinear regression, re-sampling and relative frequency with the QIEA-R to estimate the probability density functions in a better way. To evaluate this proposal, some experiments under benchmark functions are presented. The obtained statistics from the outcomes show the improved performance of this proposal optimizing numerical problems. © 2015 IEEE.Trabajo de investigació

Dynamic and recursive oil-reservoir proxy using Elman neural networks

In this work, a reservoir simulation approximation model (proxy) based on recurrent artificial neural networks is proposed. This model is intended to obtain rates of oil, gas and water production at time t+1 from the respective production rates, average pressure and water cut at t time and the well operation points to be applied in t + 1. Also, this model is able to follow the dynamics of the reservoir system applying online learning from real production observed values. Also, this model allows perform fast and accurate production forecasting for several steps using a recursive mechanism. This model will be inserted into an oil-production control tool to find the optimal operation conditions within a forecast horizon. The obtained outcomes over the approximation tests indicate the methodology is adequate to perform production forecasts. © 2016 IEEE.Trabajo de investigació

OVMMSOM: A Variation of MMSOM and VMSOM as a Clusterization Technique

In this paper the Optimized Vector and Marginal Median Self-Organizing Map (OVMMSOM) was proposed as a new method of train Self-Organizing Maps (SOM). This variant is based on order statistics, Marginal Median SOM (MMSOM) and Vector Median SOM (VMSOM). This training model combines MMSOM and VMSOM defining their particular importance through a ? participation index. To demonstrate the effectiveness of the proposal, images from the COIL100 data set was clusterized and the Compose Density between and within clusters (CDbw) validity index was used. The performed experiments show that the proposed model outperforms standard SOM network trained in batch and even results from MMSOM and VMSOM by separately. © 2015 IEEE.Trabajo de investigació

Time-series prediction with BEMCA approach: Application to short rainfall series

This paper presents a new method to forecast short rainfall time-series. The new framework is by means of Bayesian enhanced modified combined approach (BEMCA) using permutation and relative entropy with Bayesian inference. The aim at the proposed filter is focused on short datasets consisting of at least 36 samples. The structure of the artificial neural networks (ANNs) change according to data model selected, such as the Bayesian approach can be combined with the entropic information of the series. Then computational results are assessed on time series competition and rainfall series, afterwards they are compared with ANN nonlinear approaches proposed in recent work and naïve linear technique such us ARMA. To show a better performance of BEMCA filter, results are analyzed in their forecast horizons by SMAPE and RMSE indices. BEMCA filter shows an increase of accuracy in 3-6 prediction horizon analyzing the dynamic behavior of chaotic series for short series predictions. © 2017 IEEE.Trabajo de investigació