Automatic Circle Detection on Images Based on an Evolutionary Algorithm That Reduces the Number of Function Evaluations

This paper presents an algorithm for the automatic detection of circular shapes from complicated and noisy images with no consideration of the conventional Hough transform principles. The proposed algorithm is based on a newly developed evolutionary algorithm called the Adaptive Population with Reduced Evaluations (APRE). Our proposed algorithm reduces the number of function evaluations through the use of two mechanisms: (1) adapting dynamically the size of the population and (2) incorporating a fitness calculation strategy, which decides whether the calculation or estimation of the new generated individuals is feasible. As a result, the approach can substantially reduce the number of function evaluations, yet preserving the good search capabilities of an evolutionary approach. Experimental results over several synthetic and natural images, with a varying range of complexity, validate the efficiency of the proposed technique with regard to accuracy, speed, and robustness

Varianza condicional de medias móviles no-lineales.

We present a new heteroskedastic conditional variance model using NonLinear Moving Average as the basis for this specification [NLMACH(q)]. The typical problem of this class of models-i.e., noninvertibility—is solved by means of an intuitive parametric restriction; this allows us to use Maximum Likelihood as the estimation procedure. The statistical properties of the new model are both simple and attractive for empirical purposes in finance: a natural fat-tailed distribution stands out. The Autocorrelation Function of the squared process allows us for identification of the number of lags to be included in the new specification. In addition, we present several Monte Carlo experiments where the properties of the model using finite samples are exhibited. Finally, an empirical application using exchange rates and capital market bonds is shown.Conditionally Heteroskedastic Models, NLMACH(q), Volatility, Fat-tailed Distributions

Multithreshold Segmentation by Using an Algorithm Based on the Behavior of Locust Swarms

As an alternative to classical techniques, the problem of image segmentation has also been handled through evolutionary methods. Recently, several algorithms based on evolutionary principles have been successfully applied to image segmentation with interesting performances. However, most of them maintain two important limitations: (1) they frequently obtain suboptimal results (misclassifications) as a consequence of an inappropriate balance between exploration and exploitation in their search strategies; (2) the number of classes is fixed and known in advance. This paper presents an algorithm for the automatic selection of pixel classes for image segmentation. The proposed method combines a novel evolutionary method with the definition of a new objective function that appropriately evaluates the segmentation quality with respect to the number of classes. The new evolutionary algorithm, called Locust Search (LS), is based on the behavior of swarms of locusts. Different to the most of existent evolutionary algorithms, it explicitly avoids the concentration of individuals in the best positions, avoiding critical flaws such as the premature convergence to suboptimal solutions and the limited exploration-exploitation balance. Experimental tests over several benchmark functions and images validate the efficiency of the proposed technique with regard to accuracy and robustness

Computational Intelligence in Image Processing

[No abstract available

Multi-class Gaussian Process Classification with Noisy Inputs

It is a common practice in the machine learning community to assume that the observed data are noise-free in the input attributes. Nevertheless, scenarios with input noise are common in real problems, as measurements are never perfectly accurate. If this input noise is not taken into account, a supervised machine learning method is expected to perform sub-optimally. In this paper, we focus on multi-class classification problems and use Gaussian processes (GPs) as the underlying classifier. Motivated by a data set coming from the astrophysics domain, we hypothesize that the observed data may contain noise in the inputs. Therefore, we devise several multi-class GP classifiers that can account for input noise. Such classifiers can be efficiently trained using variational inference to approximate the posterior distribution of the latent variables of the model. Moreover, in some situations, the amount of noise can be known before-hand. If this is the case, it can be readily introduced in the proposed methods. This prior information is expected to lead to better performance results. We have evaluated the proposed methods by carrying out several experiments, involving synthetic and real data. These include several data sets from the UCI repository, the MNIST data set and a data set coming from astrophysics. The results obtained show that, although the classification error is similar across methods, the predictive distribution of the proposed methods is better, in terms of the test log-likelihood, than the predictive distribution of a classifier based on GPs that ignores input noiseWe would like to thank M. A. Sanchez-Conde, J. Coronado and V. Gammaldi for pointing our attention to the data set that motivated this work, as well as for the discussions concerning the data extraction. We thank as well E. Fernandez-Martınez, A. Suarez and C. M. Alaız-Gudin for useful discussions and feedback about the work. BZ especially acknowledges the hospitality of the Machine Learning group of UAM during the development of this project. BZ is supported by the Programa Atraccion de Talento de la Comunidad de Madrid under grant n. 2017-T2/TIC-5455, from the Spanish MINECO’s “Centro de Excelencia Severo Ochoa” Programme via grant SEV-2016-0597, and from the Comunidad de Madrid project SI1-PJI-2019-00294, of which BZ is the P.I. The authors gratefully acknowledge the use of the facilities of Centro de Computacion Cientıfica (CCC) at Universidad Autonoma de Madrid. The authors also acknowledge financial support from Spanish Plan Nacional I+D+i, grants TIN2016-76406-P. Finally, the authors acknowledge financial support from PID2019-106827GB-I00/AEI/10.13039/50110001103

Evaluación de un modelo de envejecimiento de baterías de litio aplicadas a sistemas aislados de energías renovables

La problemática generada por el crecimiento de la demanda energética centra la atención de la sociedad en la necesidad de un cambio de paradigma en torno a la energía que consumimos y como afecta a nuestro entorno. Es necesaria una transición energética racional que promueva la utilización de energías renovables (eólica, fotovoltaica, termo solar…etc.) y que impulse el compromiso de la sociedad ante el objetivo de obtener una energía no contaminante, fiable y universal. Este importante desafío está compuesto por muchos factores. Uno de los más importantes es el almacenamiento energético. Para ello en este trabajo se evaluará el comportamiento de un tipo de batería (ión litio). Se espera por tanto que este proyecto permita la mejora en el desarrollo de este tipo de tecnología. Para enfocar este objetivo se reproducirá el modelo de Astaneh et al., 2018 [1] publicado en International Journal of Electrical Power & Energy Systems en el que se plantea la formulación matemática del comportamiento de una batería ión-litio en diferentes instalaciones fotovoltaicas aisladas y se analizará su comportamiento. El objetivo es realizar un estudio de la vida útil de un banco de baterías ión-litio en diferentes escenarios y con diferentes elementos que aporte un punto de vista esclarecedor sobre su comportamiento. Para ello se enumerarán los diferentes elementos que componen la instalación fotovoltaica realizando una explicación sobre su funcionamiento y detallando los diferentes tipos que podemos encontrar. Se mostrará y se desarrollará una breve explicación del modelo matemático en el que se basa este trabajo para entender su funcionamiento. Esto se realizará con el apoyo de diagramas y ecuaciones que faciliten su comprensión. A continuación se mostrará la validación del modelo en cuestión con respecto al trabajo de Astaneh et al., 2018 [2]. Se presentarán diferentes simulaciones para distintos casos en diferentes localizaciones y con diferentes configuraciones en la instalación. Se evaluarán las llamadas “variables en observación” y su impacto en la longevidad de las baterías para cada uno de los escenarios presentados. Para finalizar, una vez obtenidos los resultados de las diferentes simulaciones, se detallarán las conclusiones oportunas que nos indiquen las situaciones más favorables para dotar de una mayor vida útil este tipo de baterías

Is the real effective exchange rate biased against the PPP hypothesis?

We show that the use of the real effective exchange rate to test for purchasing power parity, as in Astorga (2012) and other studies, introduces a bias against finding evidence of PPP. The bias is illustrated using unit root tests applied to bilateral real rates

Appendix for the PPP hypothesis and structural breaks: the case of Mexico

This appendix presents an extended explanation for our finding of mean reversion of the real exchange rate to a shifting mean using monthly data for Mexico, 1969-2010. Because such shifts coincide with trade liberalization in Mexico, we conclude that changes in the tradable/nontradable goods composition of the price index used in the empirical estimations caused the mean shifts