Rehabilitaci��n de Molduras Huecas de Piedra Artificial

En las d��cadas de 1930, 1940 y 1950 se utiliz�� con cierta profusi��n en las fachadas espa��olas la soluci��n de molduras horizontales resueltas con elementos huecos prefabricados de piedra artificial. Con el paso del tiempo, dichas molduras han sufrido procesos de desprendimiento debido a la entrada de agua por el tablero superior, con la consiguiente corrosi��n y rotura de los alambres de anclaje, por lo que requieren una reparaci��n. Se describe un caso de rehabilitaci��n mediante reanclado desde el exterior de las diferentes molduras de una fachada de un edificio singular en Madrid, con varillas roscadas de acero inoxidable y resina epoxi de adherencia, as�� como la introducci��n de juntas de dilataci��n con el objeto de reducir las variaciones dimensionales del conjunto.

Aplicaci��n de t��cnicas de inteligencia artificial a la predicci��n de contaminantes atmosf��ricos

Abstract Air pollution is a big threat and a phenomenon that has a specific impact on human health, in addition, changes that occur in the chemical composition of the atmosphere can change the weather and cause acid rain or ozone destruction. Those are phenomena of global importance. The World Health Organization (WHO) considerates air pollution as one of the most important global priorities. Salamanca, Gto., Mexico has been ranked as one of the most polluted cities in this country. The industry of the area led to a major economic development and rapid population growth in the second half of the twentieth century. The impact in the air quality is important and significant efforts have been made to measure the concentrations of pollutants. The main pollution sources are locally based plants in the chemical and power generation sectors. The registered concerning pollutants are Sulphur Dioxide (SO2) and particles on the order of ���10 micrometers or less (PM10). The prediction in the concentration of those pollutants can be a powerful tool in order to take preventive measures such as the reduction of emissions and alerting the affected population. In this PhD thesis we propose a model to predict concentrations of pollutants SO2 and PM10 for each monitoring booth in the Atmospheric Monitoring Network Salamanca (REDMAS - for its spanish acronym). The proposed models consider the use of meteorological variables as factors influencing the concentration of pollutants. The information used along this work is the current real data from REDMAS. In the proposed model, Artificial Neural Networks (ANN) combined with clustering algorithms are used. The type of ANN used is the Multilayer Perceptron with a hidden layer, using separate structures for the prediction of each pollutant. The meteorological variables used for prediction were: Wind Direction (WD), wind speed (WS), Temperature (T) and relative humidity (RH). Clustering algorithms, K-means and Fuzzy C-means, are used to find relationships between air pollutants and weather variables under consideration, which are added as input of the RNA. Those relationships provide information to the ANN in order to obtain the prediction of the pollutants. The results of the model proposed in this work are compared with the results of a multivariate linear regression and multilayer perceptron neural network. The evaluation of the prediction is calculated with the mean absolute error, the root mean square error, the correlation coefficient and the index of agreement. The results show the importance of meteorological variables in the prediction of the concentration of the pollutants SO2 and PM10 in the city of Salamanca, Gto., Mexico. The results show that the proposed model perform better than multivariate linear regression and multilayer perceptron neural network. The models implemented for each monitoring booth have the ability to make predictions of air quality that can be used in a system of real-time forecasting and human health impact analysis. Among the main results of the development of this thesis we can cite: A model based on artificial neural network combined with clustering algorithms for prediction with a hour ahead of the concentration of each pollutant (SO2 and PM10) is proposed. A different model was designed for each pollutant and for each of the three monitoring booths of the REDMAS. A model to predict the average of pollutant concentration in the next 24 hours of pollutants SO2 and PM10 is proposed, based on artificial neural network combined with clustering algorithms. Model was designed for each booth of the REDMAS and each pollutant separately. Resumen La contaminaci��n atmosf��rica es una amenaza aguda, constituye un fen��meno que tiene particular incidencia sobre la salud del hombre. Los cambios que se producen en la composici��n qu��mica de la atm��sfera pueden cambiar el clima, producir lluvia ��cida o destruir el ozono, fen��menos todos ellos de una gran importancia global. La Organizaci��n Mundial de la Salud (OMS) considera la contaminaci��n atmosf��rica como una de las m��s importantes prioridades mundiales. Salamanca, Gto., M��xico; ha sido catalogada como una de las ciudades m��s contaminadas en este pa��s. La industria de la zona propici�� un importante desarrollo econ��mico y un crecimiento acelerado de la poblaci��n en la segunda mitad del siglo XX. Las afectaciones en el aire son graves y se han hecho importantes esfuerzos por medir las concentraciones de los contaminantes. Las principales fuentes de contaminaci��n son fuentes fijas como industrias qu��micas y de generaci��n el��ctrica. Los contaminantes que se han registrado como preocupantes son el Bi��xido de Azufre (SO2) y las Part��culas Menores a 10 micr��metros (PM10). La predicci��n de las concentraciones de estos contaminantes puede ser una potente herramienta que permita tomar medidas preventivas como reducci��n de emisiones a la atm��sfera y alertar a la poblaci��n afectada. En la presente tesis doctoral se propone un modelo de predicci��n de concentraci ��n de los contaminantes m��s cr��ticos SO2 y PM10 para cada caseta de monitorizaci��n de la Red de Monitorizaci��n Atmosf��rica de Salamanca (REDMAS). Los modelos propuestos plantean el uso de las variables meteorol ��gicas como factores que influyen en la concentraci��n de los contaminantes. La informaci��n utilizada durante el desarrollo de este trabajo corresponde a datos reales obtenidos de la REDMAS. En el Modelo Propuesto (MP) se aplican Redes Neuronales Artificiales (RNA) combinadas con algoritmos de agrupamiento. La RNA utilizada es el Perceptr��n Multicapa con una capa oculta, utilizando estructuras independientes para la predicci��n de cada contaminante. Las variables meteorol��gicas disponibles para realizar la predicci��n fueron: Direcci��n de Viento (DV), Velocidad de Viento (VV), Temperatura (T) y Humedad Relativa (HR). Los algoritmos de agrupamiento K-means y Fuzzy C-means son utilizados para encontrar relaciones existentes entre los contaminantes atmosf��ricos en estudio y las variables meteorol��gicas. Dichas relaciones aportan informaci��n a las RNA para obtener la predicci��n de los contaminantes, la cual es agregada como entrada de las RNA. Los resultados del modelo propuesto en este trabajo son comparados con los resultados de una Regresi��n Lineal Multivariable (RLM) y un Perceptr��n Multicapa (MLP). La evaluaci��n de la predicci��n se realiza con el Error Medio Absoluto, la Ra��z del Error Cuadr��tico Medio, el coeficiente de correlaci��n y el ��ndice de acuerdo. Los resultados obtenidos muestran la importancia de las variables meteorol��gicas en la predicci��n de la concentraci��n de los contaminantes SO2 y PM10 en la ciudad de Salamanca, Gto., M��xico. Los resultados muestran que el MP predice mejor la concentraci��n de los contaminantes SO2 y PM10 que los modelos RLM y MLP. Los modelos implementados para cada caseta de monitorizaci ��n tienen la capacidad para realizar predicciones de calidad del aire, estos modelos pueden ser implementados en un sistema que permita realizar la predicci��n en tiempo real y analizar el impacto en la salud de la poblaci��n. Entre los principales resultados obtenidos del desarrollo de esta tesis podemos citar: Se propone un modelo basado en una red neuronal artificial combinado con algoritmos de agrupamiento para la predicci��n con una hora de anticipaci ��n de la concentraci��n de cada contaminante (SO2 y PM10). Se dise���� un modelo diferente para cada contaminante y para cada una de las tres casetas de monitorizaci��n de la REDMAS. Se propone un modelo de predicci��n del promedio de la concentraci��n de las pr��ximas 24 horas de los contaminantes SO2 y PM10, basado en una red neuronal artificial combinado con algoritmos de agrupamiento. Se dise���� un modelo para cada caseta de monitorizaci��n de la REDMAS y para cada contaminante por separado.

Artificial Neural Nets with Interaction of Afferents

The aim is to obtain computationally more powerful, neuro physiologically founded, arti���cial neurons and neural nets. Arti���cial Neural Nets (ANN) of the Perceptron type evolved from the original proposal by McCulloch an Pitts classical paper [1]. Essentially, they keep the computing structure of a linear machine followed by a non linear operation. The McCulloch-Pitts formal neuron (which was never considered by the author���s to be models of real neurons) consists of the simplest case of a linear computation of the inputs followed by a threshold. Networks of one layer cannot compute anylogical function of the inputs, but only those which are linearly separable. Thus, the simple exclusive OR (contrast detector) function of two inputs requires two layers of formal neurons

Dendritic-Like Reliable Computation in Artificial Neurons

Dendritic computation is a term that has been in neuro physiological research for a long time [1]. It is still controversial and far for been clari���ed within the concepts of both computation and neurophysiology [2], [3]. In any case, it hasnot been integrated neither in a formal computational scheme or structure, nor into formulations of arti���cial neural nets. Our objective here is to formulate a type of distributed computation that resembles dendritic trees, in such a way that it shows the advantages of neural network distributed computation, mostly the reliability that is shown under the existence of holes (scotomas) in the computing net, without ?blind spots?.

Influencia de la radiaci��n UV en las propiedades mec��nicas y en el comportamiento en fractura de un polimero artificial bioinspirado en la seda de ara��a

En el presente trabajo se estudia la influencia de la radiaci��n UV sobre las propiedades mec��nicas y las superficies de fractura de un pol��mero artificial bioinspirado en la seda de ara��a. Las fibras de seda de ara��a constituyen un material enormemente atractivo ya que su elevada resistencia y deformabilidad lo convierten en el material con mayor trabajo hasta rotura de los conocidos hasta el momento. Adem��s se ha encontrado que posee una elevada biocompatibilidad y un comportamiento biodegradable. Debido a estas excelentes propiedades se han dedicado importantes esfuerzos a intentar producir fibras inspiradas en la seda de ara��a. Fruto de estos esfuerzos es el pol��mero artificial estudiado en este trabajo. Dicho pol��mero presenta una secuencia de amino��cidos inspirada en la spidro��na 1, que es una de las dos prote��nas que conforman la seda de ara��a natural. Uno de los factores m��s perjudiciales para los pol��meros es la radiaci��n ultravioleta (UV), de presencia ubicua en aplicaciones al aire libre, ya que puede provocar la modificaci��n de sus enlaces covalentes y, como consecuencia, modificar sus propiedades mec��nicas. Para evaluar el efecto de la radiaci��n UV sobre el material bioinspirado se ha estudiado el comportamiento a tracci��n simple de fibras sometidas a diferentes tiempos de irradiaci��n con luz UV de longitud de onda de 254 nm. Se ha observado que la radiaci��n UV de 254 nm modifica considerablemente las propiedades mec��nicas de este material a tiempos de exposici��n elevados (a partir de 3 d��as de irradiaci��n). Adem��s se ha estudiado el comportamiento a fractura de este material cuando es irradiado con luz UV. Se ha observado que a medida que aumenta el tiempo de irradiaci��n las superficies de fractura comienzan a ser cada vez m��s planas, obteni��ndose un aspecto extremadamente especular para muestras irradiadas durante 16 d��as

Miner��a de Datos usando Metaplasticidad Artificial en la Rehabilitaci��n Cognitiva de Pacientes con Da��o Cerebral

El prop��sito principal de esta investigaci��n es la aplicaci��n de la Metaplasticidad Artificial en un Perceptr��n Multicapa (AMMLP) como una herramienta de miner��a de datos para la predicci��n y extracci��n expl��cita de conocimiento del proceso de rehabilitaci��n cognitiva en pacientes con da��o cerebral adquirido. Los resultados obtenidos por el AMMLP junto con el posterior an��lisis de la base de datos ayudar��an a los terapeutas a conocer las caracter��sticas de los pacientes que mejoran y los programas de rehabilitaci��n que han seguido. Esto incrementar��a el conocimiento del proceso de rehabilitaci��n y facilitar��a la elaboraci��n de hip��tesis terap��uticas permitiendo la optimizaci��n y personalizaci��n de las terapias. La evaluaci��n del AMMLP se ha realizado con datos proporcionados por el Institut Guttmann. Los resultados del AMMLP fueron comparados con los obtenidos con una red neuronal de retropropagaci��n y con ��rboles de decisi��n. La exactitud en la predicci��n obtenida por el AMMLP en la subfunci��n cognitiva memoria verbal-visual fue de 90.71 %, resultado muy superior a los obtenidos por los dem��s algoritmos.

Improvement for detection of microcalcifications through clustering algorithms and artificial neural networks

A new method for detecting microcalcifications in regions of interest (ROIs) extracted from digitized mammograms is proposed. The top-hat transform is a technique based on mathematical morphology operations and, in this paper, is used to perform contrast enhancement of the mi-crocalcifications. To improve microcalcification detection, a novel image sub-segmentation approach based on the possibilistic fuzzy c-means algorithm is used. From the original ROIs, window-based features, such as the mean and standard deviation, were extracted; these features were used as an input vector in a classifier. The classifier is based on an artificial neural network to identify patterns belonging to microcalcifications and healthy tissue. Our results show that the proposed method is a good alternative for automatically detecting microcalcifications, because this stage is an important part of early breast cancer detection

Aerodynamic Optimization of High-Speed Trains Nose using a Genetic Algorithm and Artificial Neural Network

An aerodynamic optimization of the train aerodynamic characteristics in term of front wind action sensitivity is carried out in this paper. In particular, a genetic algorithm (GA) is used to perform a shape optimization study of a high-speed train nose. The nose is parametrically defined via B��zier Curves, including a wider range of geometries in the design space as possible optimal solutions. Using a GA, the main disadvantage to deal with is the large number of evaluations need before finding such optimal. Here it is proposed the use of metamodels to replace Navier-Stokes solver. Among all the posibilities, Rsponse Surface Models and Artificial Neural Networks (ANN) are considered. Best results of prediction and generalization are obtained with ANN and those are applied in GA code. The paper shows the feasibility of using GA in combination with ANN for this problem, and solutions achieved are included.

Image sub-segmentation by PFCM and Artificial Neural Networks to detect pore space in 2D and 3D CT soil images

The image by Computed Tomography is a non-invasive alternative for observing soil structures, mainly pore space. The pore space correspond in soil data to empty or free space in the sense that no material is present there but only fluids, the fluid transport depend of pore spaces in soil, for this reason is important identify the regions that correspond to pore zones. In this paper we present a methodology in order to detect pore space and solid soil based on the synergy of the image processing, pattern recognition and artificial intelligence. The mathematical morphology is an image processing technique used for the purpose of image enhancement. In order to find pixels groups with a similar gray level intensity, or more or less homogeneous groups, a novel image sub-segmentation based on a Possibilistic Fuzzy c-Means (PFCM) clustering algorithm was used. The Artificial Neural Networks (ANNs) are very efficient for demanding large scale and generic pattern recognition applications for this reason finally a classifier based on artificial neural network is applied in order to classify soil images in two classes, pore space and solid soil respectively.

On the Biological Plausibility of Artificial Metaplasticity

The training algorithm studied in this paper is inspired by the biological metaplasticity property of neurons. Tested on different multidisciplinary applications, it achieves a more efficient training and improves Artificial Neural Network Performance. The algorithm has been recently proposed for Artificial Neural Networks in general, although for the purpose of discussing its biological plausibility, a Multilayer Perceptron has been used. During the training phase, the artificial metaplasticity multilayer perceptron could be considered a new probabilistic version of the presynaptic rule, as during the training phase the algorithm assigns higher values for updating the weights in the less probable activations than in the ones with higher probability

A Prediction Model to Diabetes using Artificial Metaplasticity

Diabetes is the most common disease nowadays in all populations and in all age groups. Different techniques of artificial intelligence has been applied to diabetes problem. This research proposed the artificial metaplasticity on multilayer perceptron (AMMLP) as prediction model for prediction of diabetes. The Pima Indians diabetes was used to test the proposed model AMMLP. The results obtained by AMMLP were compared with other algorithms, recently proposed by other researchers, that were applied to the same database. The best result obtained so far with the AMMLP algorithm is 89.93%

Artificial cognitive control system based on the shared circuits model of sociocognitive capacities. A first approach

sharedcircuitmodels is presented in this work. The sharedcircuitsmodelapproach of sociocognitivecapacities recently proposed by Hurley in The sharedcircuitsmodel (SCM): how control, mirroring, and simulation can enable imitation, deliberation, and mindreading. Behavioral and Brain Sciences 31(1) (2008) 1���22 is enriched and improved in this work. A five-layer computational architecture for designing artificialcognitivecontrolsystems is proposed on the basis of a modified sharedcircuitsmodel for emulating sociocognitive experiences such as imitation, deliberation, and mindreading. In order to show the enormous potential of this approach, a simplified implementation is applied to a case study. An artificialcognitivecontrolsystem is applied for controlling force in a manufacturing process that demonstrates the suitability of the suggested approach