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Puigjaner L.,Polytechnic University of Catalonia | Munoz E.,Research Center En Matematicas Ac | Capon-Garcia E.,ETH Zurich
Advances in Intelligent Systems and Computing | Year: 2017

The advancement of science in the past century gave rise to a number of revolutionary discoveries that deeply affected the way of life of our society. A brief description of today's state of the art software in Process Systems Engineering: meta-models providing an integrated/unified decision support framework at all levels (strategic, tactic, operational) to the industry is presented in this second part. Additionally, knowledge as base of future developments and the efforts that CAPE approach has envisaged in order to develop systems thinking and systems problem solving are also introduced. Finally, it is also presented the research project called Batch Process Ontology Framework based on ISA standards, which has evolved along different fields of CAPE, regarding optimization methodologies, batch process, monitoring & control, scheduling, supply chain design, life cycle assessment, mathematical programming, modeling, and operations research. © Springer International Publishing AG 2017.

Munoz E.,Research Center En Matematicas Ac | Capon-Garcia E.,ETH Zurich | Lainez J.M.,Purdue University | Espuna A.,Polytechnic University of Catalonia | Puigjaner L.,Polytechnic University of Catalonia
Chemical Engineering Research and Design | Year: 2013

Enterprises are highly complex systems in which one or more organizations share a definite mission, goals and objectives to offer a product or service. In this study, an ontological framework is built as a mechanism for exchanging information and knowledge models for multiple applications and effective integration between hierarchical levels. The potential of the general semantic framework that is developed is demonstrated using a case study concerning the enterprise supply chain network design-planning problem. © 2013 The Institution of Chemical Engineers.

Munoz E.,Research Center En Matematicas Ac | Capon-Garcia E.,ETH Zurich | Lainez-Aguirre J.M.,Purdue University | Espuna A.,Polytechnic University of Catalonia | Puigjaner L.,Polytechnic University of Catalonia
Computers and Chemical Engineering | Year: 2015

The integration of planning and scheduling decisions in rigorous mathematical models usually results in large scale problems. In order to tackle the problem complexity, decomposition techniques based on duality and information flows between a master and a set of subproblems are widely applied. In this sense, ontologies improve information sharing and communication in enterprises and can even represent holistic mathematical models facilitating the use of analytic tools and providing higher flexibility for model building. In this work, we exploit this ontologies' capability to address the optimal integration of planning and scheduling using a Lagrangian decomposition approach. Scheduling/planning sub-problems are created for each facility/supply chain entity and their dual solution information is shared by means of the ontological framework. Two case studies based on a STN representation of supply chain planning and scheduling models are presented to emphasize the advantages and limitations of the proposed approach. © 2014 Elsevier Ltd.

Alba A.,Autonomous University of San Luis Potosi | Arce-Santana E.,Autonomous University of San Luis Potosi | Rivera M.,Research Center En Matematicas Ac
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2010

Motion estimation is one of the most important tasks in computer vision. One popular technique for computing dense motion fields consists in defining a large enough set of candidate motion vectors, and assigning one of such vectors to each pixel, so that a given cost function is minimized. In this work we propose a novel method for finding a small set of adequate candidates, making the minimization process computationally more efficient. Based on this method, we present algorithms for the estimation of dense optical flow using two minimization approaches: one based on a classic block-matching procedure, and another one based on entropy-controlled quadratic Markov measure fields which allow one to obtain smooth motion fields. Finally, we present the results obtained from the application of these algorithms to examples taken from the Middlebury database. © 2010 Springer-Verlag.

Rivera M.,Research Center En Matematicas Ac | Dalmau O.,Research Center En Matematicas Ac | Mio W.,Florida State University | Ramirez-Manzanares A.,University of Guanajuato
Computer Journal | Year: 2012

We present a novel framework for image segmentation based on the maximum likelihood estimator. A common hypothesis for explaining the differences among image regions is that they are generated by sampling different likelihood functions called models. In this work, we construct on last hypothesis and, additionally, we assume that such samples come from independent and identically distributed random variables. Thus, the probability (likelihood) that a particular model generates the observed value (at a given pixel) is estimated by computing the likelihood of the sample composed with the surrounding pixels. This simple approach allows us to propose efficient segmentation methods able to deal with textured images. Our approach is naturally extended for combining different features. Experiments in interactive image segmentation, automatic stereo analysis and denoising of brain water diffusion multi-tensor fields demonstrate the capabilities of our approach. © 2011 The Author. Published by Oxford University Press on behalf of The British Computer Society. All rights reserved.

Hernandez-Lopez F.J.,Research Center En Matematicas Ac | Rivera M.,Research Center En Matematicas Ac
Proceedings of Special Session - 9th Mexican International Conference on Artificial Intelligence: Advances in Artificial Intelligence and Applications, MICAI 2010 | Year: 2010

We present a method for foreground-background video segmentation in real-time that may be used in applications as, for instance, Background Substitution, Analysis of Surveillance Cameras, Highway Cars Detection and so on. Our approach implements a probabilistic segmentation based on the binary Quadratic Markov Measure Fields models (QMMFs). That framework regularizes the likelihood of each pixel to belong to each one of the models (foreground and background). Then our proposal consists of a model for the likelihood that takes into account: an estimation of the static background, motion of the foreground, illumination changes and casted shadows. In order to fulfill the real-time requirement we implement a parallel version of our algorithm in CUDA using a NVIDIA GPU. © 2010 IEEE.

Ramirez-Manzanares A.,University of Guanajuato | Rivera M.,Research Center En Matematicas Ac | Kornprobst P.,French Institute for Research in Computer Science and Automation | Lauze F.,Copenhagen University
Journal of Mathematical Imaging and Vision | Year: 2011

Motion estimation in sequences with transparencies is an important problem in robotics and medical imaging applications. In this work we propose a variational approach for estimating multi-valued velocity fields in transparent sequences. Starting from existing local motion estimators, we derive a variational model for integrating in space and time such a local information in order to obtain a robust estimation of the multi-valued velocity field. With this approach, we can indeed estimate multi-valued velocity fields which are not necessarily piecewise constant on a layer-each layer can evolve according to a non-parametric optical flow. We show how our approach outperforms existing methods; and we illustrate its capabilities on challenging experiments on both synthetic and real sequences. © 2011 Springer Science+Business Media, LLC.

Daza M.L.,Research Center En Matematicas Ac | Capistran M.A.,Research Center En Matematicas Ac | Christen J.A.,Research Center En Matematicas Ac | Guadarrama L.,Research Center En Matematicas Ac
Mathematical Methods in the Applied Sciences | Year: 2016

We pose a Bayesian formulation of the inverse problem associated to recovering both the support and the refractive index of a convex obstacle given measurements of near-field scattered waves. Aiming at sampling efficiently from the arising posterior distribution using Markov Chain Monte Carlo, we construct a sampler (probability transition kernel) that is invariant under affine transformations of space. A point cloud method is used to approximate the scatterer support. We show that affine invariant sampling can successfully address the presence of multiple scales in inverse scattering in inhomogeneous media. © 2016 John Wiley & Sons, Ltd.

Hernandez-Lopez F.J.,Research Center En Matematicas Ac | Rivera M.,Research Center En Matematicas Ac
Machine Vision and Applications | Year: 2014

We present a method for foreground/background video segmentation (change detection) in real-time that can be used, in applications such as background subtraction or analysis of surveillance cameras. Our approach implements a probabilistic segmentation based on the Quadratic Markov Measure Field models. This framework regularizes the likelihood of each pixel belonging to each one of the classes (background or foreground). We propose a new likelihood that takes into account two cases: the first one is when the background is static and the foreground might be static or moving (Static Background Subtraction), the second one is when the background is unstable and the foreground is moving (Unstable Background Subtraction). Moreover, our likelihood is robust to illumination changes, cast shadows and camouflage situations. We implement a parallel version of our algorithm in CUDA using a NVIDIA Graphics Processing Unit in order to fulfill real-time execution requirements. © 2013 Springer-Verlag Berlin Heidelberg.

PubMed | Research Center En Matematicas Ac, Global Alliance for Improved Nutrition GAIN and University of Santa María in Ecuador
Type: Journal Article | Journal: Maternal and child health journal | Year: 2016

To determine the association between breastfeeding practices, diet and physical activity and maternal postpartum weight.This was a secondary data analysis of a randomized community trial on beneficiaries of the Programa de Desarrollo Humano Oportunidades, recently renamed Prospera (n = 314 pregnant women), without any diseases that could affect body weight. Generalized estimating equations were used to determine the association between postpartum weight change and changes in diet, physical activity and type of breastfeeding.The mean postpartum weight change from the first to the third month was 0.6 2.2 kg. Women who breastfed exclusively for 3 months had a 4.1 (SE = 1.9) kg weight reduction in comparison with women who did not provide exclusive breastfeeding or who discontinued breastfeeding before 3 months (p = 0.04). There was no association between postpartum weight change and physical activity (p = 0.24) or energy intake (p = 0.06).Exclusive breastfeeding was associated with maternal postpartum weight reduction. These results reinforce the World Health Organization recommendation of exclusive breastfeeding during the first 6 months of life in order to reduce the risk of weight retention or weight gain in postpartum women. It has been well established that exclusive breastfeeding is beneficial for both infants and mothers, but promoting breastfeeding as a strategy to promote postpartum weight loss is of paramount importance, especially in countries like Mexico where excessive weight in women of reproductive age is a public health problem.

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