Center for Research in Mathematics
Center for Research in Mathematics
Marmolejo J.A.,Anahuac University |
Velasco J.,Center for Research in Mathematics |
Selley H.J.,Anahuac University
PLoS ONE | Year: 2017
This paper presents an adaptive random search approach to address a short term generation scheduling with network constraints, which determines the startup and shutdown schedules of thermal units over a given planning horizon. In this model, we consider the transmission network through capacity limits and line losses. The mathematical model is stated in the form of a Mixed Integer Non Linear Problem with binary variables. The proposed heuristic is a population-based method that generates a set of new potential solutions via a random search strategy. The random search is based on the Markov Chain Monte Carlo method. The main key of the proposed method is that the noise level of the random search is adaptively controlled in order to exploring and exploiting the entire search space. In order to improve the solutions, we consider coupling a local search into random search process. Several test systems are presented to evaluate the performance of the proposed heuristic. We use a commercial optimizer to compare the quality of the solutions provided by the proposed method. The solution of the proposed algorithm showed a significant reduction in computational effort with respect to the full-scale outer approximation commercial solver. Numerical results show the potential and robustness of our approach. © 2017 Marmolejo et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
Fernandez T.,National Autonomous University of Mexico |
Harmony T.,National Autonomous University of Mexico |
Mendoza O.,Center for Research in Mathematics |
Lopez-Alanis P.,National Autonomous University of Mexico |
And 3 more authors.
Brain and Cognition | Year: 2012
Learning disabilities (LD) are one of the most frequent problems for elementary school-aged children. In this paper, event-related EEG oscillations to semantically related and unrelated pairs of words were studied in a group of 18 children with LD not otherwise specified (LD-NOS) and in 16 children with normal academic achievement. We propose that EEG oscillations may be different in LD NOS children versus normal control children that may explain some of the deficits observed in the LD-NOS group. The EEGs were recorded using the 10/20 system. EEG segments were edited by visual inspection 1000. ms before and after the stimulus, and only correct responses were considered in the analysis. Time-frequency (1-50. Hz) topographic maps were obtained for the increases and decreases of power after the event with respect to the pre-stimulus average values. Significant differences between groups were observed in the behavioral responses. LD-NOS children show less number of correct responses and more omissions and false alarms than the control group. The event-induced EEG responses showed significant differences between groups. The control group showed greater power increases in the frequencies 1-6. Hz than the LD-NOS group from 300 to 700. ms. These differences were mainly observed in frontal regions, both to related and non-related words. This was interpreted as a deficit in attention, both to internal and external events, deficits in activation of working memory and deficits in encoding and memory retrieval in the LD-NOS children. Differences between groups were also observed in the suppression of alpha and beta rhythms in the occipital regions to related words in frequencies between 8 and 17. Hz from 450 to 750. ms. LD-NOS children showed shorter durations of the decreases in power than the control group. These results suggest also deficits in attention and memory retrieval. It may be concluded that LD-NOS children showed physiological differences from normal children that may explain their cognitive deficiencies. © 2012 Elsevier Inc.
Iracheta-Cortez R.,Center for Research in Mathematics |
Flores-Guzman N.,Center for Research in Mathematics
2016 IEEE 36th Central American and Panama Convention, CONCAPAN 2016 | Year: 2017
This paper describes the steps for carrying out automated Hardware-In-the-Loop tests to protective relays with the real-time power system simulator RTDS. The main features with such tests are the performance verification of new protective relays, before their commissioning within the electrical substations, and the improvement of the power system reliability. A brief description of the software, hardware and applications of the RTDS simulator is made. A digital simulation model of a power network is proposed, as a benchmark case, to perform the Hardware-In-the-Loop tests with the distance relay SEL-421. Finally, a report is made to analyze the performance of the distance relay. © 2016 IEEE.
Marroquin J.L.,Center for Research in Mathematics |
Biscay R.J.,University of Valparaíso |
Ruiz-Correa S.,Center for Research in Mathematics |
Alba A.,Cuerpo Academico de Analisis y Procesamiento de Senales |
And 2 more authors.
NeuroImage | Year: 2011
A new method for detecting activations in random fields, which may be useful for addressing the issue of multiple comparisons in neuroimaging, is presented. This method is based on some constructs of mathematical morphology - specifically, morphological erosions and dilations - that enable the detection of active regions in random fields possessing moderate activation levels and relatively large spatial extension, which may not be detected by the standard methods that control the family-wise error rate. The method presented here permits an appropriate control of the false positive errors, without having to adjust any threshold values, other than the significance level. The method is easily adapted to permutation-based procedures (with the usual restrictions), and therefore does not require strong assumptions about the distribution and spatio-temporal correlation structure of the data. Some examples of applications to synthetic data, including realistic fMRI simulations, as well as to real fMRI and electroencephalographic data are presented, illustrating the power of the presented technique. Comparisons with other methods that combine voxel intensity and cluster size, as well as some extensions of the method presented here based on their basic ideas are presented as well. © 2011 Elsevier Inc.
Rivera M.,Center for Research in Mathematics |
Dalmau O.,Center for Research in Mathematics
IEEE Transactions on Image Processing | Year: 2012
We present a framework for image segmentation based on quadratic programming, i.e., by minimization of a quadratic regularized energy linearly constrained. In particular, we present a new variational derivation of the quadratic Markov measure field (QMMF) models, which can be understood as a procedure for regularizing model preferences (memberships or likelihoods). We also present efficient optimization algorithms. In the QMMFs, the uncertainty in the computed regularized probability measure field is controlled by penalizing Gini's coefficient, and hence, it affects the convexity of the quadratic programming problem. The convex case is reduced to the solution of a positive definite linear system, and for that case, an efficient Gauss-Seidel (GS) scheme is presented. On the other hand, we present an efficient projected GS with subspace minimization for optimizing the nonconvex case. We demonstrate the proposal capabilities by experiments and numerical comparisons with interactive two-class segmentation, as well as the simultaneous estimation of segmentation and (parametric and nonparametric) generative models. We present extensions to the original formulation for including color and texture clues, as well as imprecise user scribbles in an interactive framework. © 2011 IEEE.
Salinas-Gutierrez R.,Center for Research in Mathematics |
Hernandez-Aguirre A.,Center for Research in Mathematics |
Villa-Diharce E.R.,Center for Research in Mathematics
Proceedings of the 12th Annual Genetic and Evolutionary Computation Conference, GECCO '10 | Year: 2010
A new Estimation of Distribution Algorithm is presented. The proposed algorithm, called D-vine EDA, uses a graphical model which is based on pair copula decomposition. By means of copula functions it is possible to model the dependence structure in a joint distribution with marginals of different type. Thus, this paper introduces the D-vine EDA and performs experiments and statistical tests to assess the best algorithm. The set of experiments shows the potential of the D-vine EDA. Copyright 2010 ACM.
Serrano Rubio J.P.,Center for Research in Mathematics |
Hernandez Aguirre A.,Center for Research in Mathematics |
Herrera Guzman R.,Center for Research in Mathematics
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013
This paper presents the implementation of a Multilayer Perceptron (MLP) using a new higher order neuron whose decision region is generated by a conic section (circle, ellipse, parabola, hyperbola). We call it the hyper-conic neuron. The conic neuron is defined for the conformal space where it can freely work and take advantage of all the rules of Geometric (Clifford) Algebra. The proposed neuron is a non-linear associator that estimates distances from vectors (points) to decision regions. The computational model of the conic neuron is based on the geometric product (an outer product plus an inner product) of geometric algebra in conformal space. The Particle Swarm Optimization (PSO) algorithm is used to find the values of the weights that properly define some MLP for a given classification problem. The performance is presented with a classical benchmark used in neural computing. © 2013 Springer-Verlag.
Hasimoto-Beltran R.,Center for Research in Mathematics
Proceedings - 3rd International Conference on Multimedia Information Networking and Security, MINES 2011 | Year: 2011
We present a new LUT design consisting of a non-iterative plaintext transformation and a high dimensionally (K-map) populated dynamic Look-Up Table with random access that outperforms the security and speed of previous LUT based schemes. Experimental analysis of the proposed scheme reveals excellent statistical properties, sensitivity to plaintext/system-key changes, robustness to differential and chosen plaintext attack, and high performance for real-time multimedia communications. © 2011 IEEE.
Debruyne M.,University of Antwerp |
Hubert M.,Catholic University of Leuven |
Van Horebeek J.,Center for Research in Mathematics
Computational Statistics and Data Analysis | Year: 2010
Kernel Principal Component Analysis extends linear PCA from a Euclidean space to any reproducing kernel Hilbert space. Robustness issues for Kernel PCA are studied. The sensitivity of Kernel PCA to individual observations is characterized by calculating the influence function. A robust Kernel PCA method is proposed by incorporating kernels in the Spherical PCA algorithm. Using the scores from Spherical Kernel PCA, a graphical diagnostic is proposed to detect points that are influential for ordinary Kernel PCA. © 2009 Elsevier B.V. All rights reserved.
Hasimoto-Beltran R.,Center for Research in Mathematics
International Journal of Bifurcation and Chaos | Year: 2013
In this work, we present a new chaos-based cryptosystem scheme consisting of a noniterative plaintext transformation and a high dimensionally (K-map) populated dynamic Look-Up Table (LUT) with random access that outperforms the security and speed of previous LUT-based chaotic encryption schemes. Experimental analysis of the proposed scheme reveals excellent statistical properties, naturally extended permanent cycle, and high performance for real-time multimedia communications. Our scheme is one order of magnitude faster than the fastest LUT-based approach in the literature and robust to differential and chosen plaintext attacks. © 2013 World Scientific Publishing Company.