Advanced Technologies Applications Center

Havana, Cuba

Advanced Technologies Applications Center

Havana, Cuba
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Ruiz M.D.,Petroleum Research Center | Bustamante I.T.,Advanced Technologies Applications Center | Dago A.,Petroleum Research Center | Hernandez N.,Advanced Technologies Applications Center | And 2 more authors.
Journal of Chemometrics | Year: 2010

The goal of this paper is the development of a multivariate calibration method for the quantitative determination of petroleum hydrocarbons in water and waste water by using FT-IR spectroscopy and PLS as a regression method to improve the results attained at the present time through the univariate standard method. In order to evaluate the performance of the regression model, four experimental responses obtained from an independent validation set prepared with spiked samples were examined: Root mean square error of prediction (RMSEP), average recovery, standard deviation, and relative standard deviation. In order to compare final results, the univariate model was developed together with the multivariate approach. The results show that the multivariate calibration method outperforms the univariate standard method. The accuracy of the results, capability of detection, and the high index of recovery obtained show that a multivariate calibration approach for the determination of petroleum hydrocarbons in water and waste water by means of IR spectroscopy can be seen as a very promising option to improve the current univariate standard method. Copyright © 2010 John Wiley & Sons, Ltd.


Rodriguez-Gonzalez A.Y.,Advanced Technologies Applications Center | Rodriguez-Gonzalez A.Y.,National Institute of Astrophysics, Optics and Electronics | Martinez-Trinidad J.F.,National Institute of Astrophysics, Optics and Electronics | Carrasco-Ochoa J.A.,National Institute of Astrophysics, Optics and Electronics | Ruiz-Shulcloper J.,Advanced Technologies Applications Center
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

In this paper, we focus on frequent pattern mining using non Boolean similarity functions. Several properties and propositions that allow pruning the search space of frequent similar patterns, are proposed. Based on these properties, an algorithm for mining frequent similar patterns using non Boolean similarity functions is also introduced. We evaluate the quality of the frequent similar patterns computed by our algorithm by means of a supervised classifier based on frequent patterns. © 2010 Springer-Verlag Berlin Heidelberg.


Rodriguez-Gonzalez A.Y.,Optics 1 | Martinez-Trinidad J.F.,Optics 1 | Carrasco-Ochoa J.A.,Optics 1 | Ruiz-Shulcloper J.,Advanced Technologies Applications Center
Expert Systems with Applications | Year: 2013

Most of the current algorithms for mining association rules assume that two object subdescriptions are similar when they are exactly equal, but in many real world problems some other similarity functions are used. Commonly these algorithms are divided in two steps: Frequent pattern mining and generation of interesting association rules from frequent patterns. In this work, two algorithms for mining frequent similar patterns using similarity functions different from the equality are proposed. Additionally, the GenRules Algorithm is adapted to generate interesting association rules from frequent similar patterns. Experimental results show that our algorithms are more effective and obtain better quality patterns than the existing ones. © 2013 Elsevier Ltd. All rights reserved.

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